diff --git a/spaces/101-5/gpt4free/g4f/.v1/gpt4free/quora/__init__.py b/spaces/101-5/gpt4free/g4f/.v1/gpt4free/quora/__init__.py
deleted file mode 100644
index 5d9e80c19e255137edeedbcc5a7a361ae600e1f9..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/.v1/gpt4free/quora/__init__.py
+++ /dev/null
@@ -1,478 +0,0 @@
-import json
-from datetime import datetime
-from hashlib import md5
-from json import dumps
-from pathlib import Path
-from random import choice, choices, randint
-from re import search, findall
-from string import ascii_letters, digits
-from typing import Optional, Union, List, Any, Generator
-from urllib.parse import unquote
-
-import selenium.webdriver.support.expected_conditions as EC
-from fake_useragent import UserAgent
-from pydantic import BaseModel
-from pypasser import reCaptchaV3
-from requests import Session
-from selenium.webdriver import Firefox, Chrome, FirefoxOptions, ChromeOptions
-from selenium.webdriver.common.by import By
-from selenium.webdriver.support.wait import WebDriverWait
-from tls_client import Session as TLS
-
-from .api import Client as PoeClient
-from .mail import Emailnator
-
-SELENIUM_WEB_DRIVER_ERROR_MSG = b'''The error message you are receiving is due to the `geckodriver` executable not 
-being found in your system\'s PATH. To resolve this issue, you need to download the geckodriver and add its location 
-to your system\'s PATH.\n\nHere are the steps to resolve the issue:\n\n1. Download the geckodriver for your platform 
-(Windows, macOS, or Linux) from the following link: https://github.com/mozilla/geckodriver/releases\n\n2. Extract the 
-downloaded archive and locate the geckodriver executable.\n\n3. Add the geckodriver executable to your system\'s 
-PATH.\n\nFor macOS and Linux:\n\n- Open a terminal window.\n- Move the geckodriver executable to a directory that is 
-already in your PATH, or create a new directory and add it to your PATH:\n\n```bash\n# Example: Move geckodriver to 
-/usr/local/bin\nmv /path/to/your/geckodriver /usr/local/bin\n```\n\n- If you created a new directory, add it to your 
-PATH:\n\n```bash\n# Example: Add a new directory to PATH\nexport PATH=$PATH:/path/to/your/directory\n```\n\nFor 
-Windows:\n\n- Right-click on "My Computer" or "This PC" and select "Properties".\n- Click on "Advanced system 
-settings".\n- Click on the "Environment Variables" button.\n- In the "System variables" section, find the "Path" 
-variable, select it, and click "Edit".\n- Click "New" and add the path to the directory containing the geckodriver 
-executable.\n\nAfter adding the geckodriver to your PATH, restart your terminal or command prompt and try running 
-your script again. The error should be resolved.'''
-
-# from twocaptcha import TwoCaptcha
-# solver = TwoCaptcha('72747bf24a9d89b4dcc1b24875efd358')
-
-MODELS = {
-    'Sage': 'capybara',
-    'GPT-4': 'beaver',
-    'Claude+': 'a2_2',
-    'Claude-instant': 'a2',
-    'ChatGPT': 'chinchilla',
-    'Dragonfly': 'nutria',
-    'NeevaAI': 'hutia',
-}
-
-
-def extract_formkey(html):
-    script_regex = r'<script>if\(.+\)throw new Error;(.+)</script>'
-    script_text = search(script_regex, html).group(1)
-    key_regex = r'var .="([0-9a-f]+)",'
-    key_text = search(key_regex, script_text).group(1)
-    cipher_regex = r'.\[(\d+)\]=.\[(\d+)\]'
-    cipher_pairs = findall(cipher_regex, script_text)
-
-    formkey_list = [''] * len(cipher_pairs)
-    for pair in cipher_pairs:
-        formkey_index, key_index = map(int, pair)
-        formkey_list[formkey_index] = key_text[key_index]
-    formkey = ''.join(formkey_list)
-
-    return formkey
-
-
-class Choice(BaseModel):
-    text: str
-    index: int
-    logprobs: Any
-    finish_reason: str
-
-
-class Usage(BaseModel):
-    prompt_tokens: int
-    completion_tokens: int
-    total_tokens: int
-
-
-class PoeResponse(BaseModel):
-    id: int
-    object: str
-    created: int
-    model: str
-    choices: List[Choice]
-    usage: Usage
-    text: str
-
-
-class ModelResponse:
-    def __init__(self, json_response: dict) -> None:
-        self.id = json_response['data']['poeBotCreate']['bot']['id']
-        self.name = json_response['data']['poeBotCreate']['bot']['displayName']
-        self.limit = json_response['data']['poeBotCreate']['bot']['messageLimit']['dailyLimit']
-        self.deleted = json_response['data']['poeBotCreate']['bot']['deletionState']
-
-
-class Model:
-    @staticmethod
-    def create(
-        token: str,
-        model: str = 'gpt-3.5-turbo',  # claude-instant
-        system_prompt: str = 'You are ChatGPT a large language model. Answer as consisely as possible',
-        description: str = 'gpt-3.5 language model',
-        handle: str = None,
-    ) -> ModelResponse:
-        if not handle:
-            handle = f'gptx{randint(1111111, 9999999)}'
-
-        client = Session()
-        client.cookies['p-b'] = token
-
-        formkey = extract_formkey(client.get('https://poe.com').text)
-        settings = client.get('https://poe.com/api/settings').json()
-
-        client.headers = {
-            'host': 'poe.com',
-            'origin': 'https://poe.com',
-            'referer': 'https://poe.com/',
-            'poe-formkey': formkey,
-            'poe-tchannel': settings['tchannelData']['channel'],
-            'user-agent': UserAgent().random,
-            'connection': 'keep-alive',
-            'sec-ch-ua': '"Chromium";v="112", "Google Chrome";v="112", "Not:A-Brand";v="99"',
-            'sec-ch-ua-mobile': '?0',
-            'sec-ch-ua-platform': '"macOS"',
-            'content-type': 'application/json',
-            'sec-fetch-site': 'same-origin',
-            'sec-fetch-mode': 'cors',
-            'sec-fetch-dest': 'empty',
-            'accept': '*/*',
-            'accept-encoding': 'gzip, deflate, br',
-            'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8',
-        }
-
-        payload = dumps(
-            separators=(',', ':'),
-            obj={
-                'queryName': 'CreateBotMain_poeBotCreate_Mutation',
-                'variables': {
-                    'model': MODELS[model],
-                    'handle': handle,
-                    'prompt': system_prompt,
-                    'isPromptPublic': True,
-                    'introduction': '',
-                    'description': description,
-                    'profilePictureUrl': 'https://qph.fs.quoracdn.net/main-qimg-24e0b480dcd946e1cc6728802c5128b6',
-                    'apiUrl': None,
-                    'apiKey': ''.join(choices(ascii_letters + digits, k=32)),
-                    'isApiBot': False,
-                    'hasLinkification': False,
-                    'hasMarkdownRendering': False,
-                    'hasSuggestedReplies': False,
-                    'isPrivateBot': False,
-                },
-                'query': 'mutation CreateBotMain_poeBotCreate_Mutation(\n  $model: String!\n  $handle: String!\n  $prompt: String!\n  $isPromptPublic: Boolean!\n  $introduction: String!\n  $description: String!\n  $profilePictureUrl: String\n  $apiUrl: String\n  $apiKey: String\n  $isApiBot: Boolean\n  $hasLinkification: Boolean\n  $hasMarkdownRendering: Boolean\n  $hasSuggestedReplies: Boolean\n  $isPrivateBot: Boolean\n) {\n  poeBotCreate(model: $model, handle: $handle, promptPlaintext: $prompt, isPromptPublic: $isPromptPublic, introduction: $introduction, description: $description, profilePicture: $profilePictureUrl, apiUrl: $apiUrl, apiKey: $apiKey, isApiBot: $isApiBot, hasLinkification: $hasLinkification, hasMarkdownRendering: $hasMarkdownRendering, hasSuggestedReplies: $hasSuggestedReplies, isPrivateBot: $isPrivateBot) {\n    status\n    bot {\n      id\n      ...BotHeader_bot\n    }\n  }\n}\n\nfragment BotHeader_bot on Bot {\n  displayName\n  messageLimit {\n    dailyLimit\n  }\n  ...BotImage_bot\n  ...BotLink_bot\n  ...IdAnnotation_node\n  ...botHelpers_useViewerCanAccessPrivateBot\n  ...botHelpers_useDeletion_bot\n}\n\nfragment BotImage_bot on Bot {\n  displayName\n  ...botHelpers_useDeletion_bot\n  ...BotImage_useProfileImage_bot\n}\n\nfragment BotImage_useProfileImage_bot on Bot {\n  image {\n    __typename\n    ... on LocalBotImage {\n      localName\n    }\n    ... on UrlBotImage {\n      url\n    }\n  }\n  ...botHelpers_useDeletion_bot\n}\n\nfragment BotLink_bot on Bot {\n  displayName\n}\n\nfragment IdAnnotation_node on Node {\n  __isNode: __typename\n  id\n}\n\nfragment botHelpers_useDeletion_bot on Bot {\n  deletionState\n}\n\nfragment botHelpers_useViewerCanAccessPrivateBot on Bot {\n  isPrivateBot\n  viewerIsCreator\n}\n',
-            },
-        )
-
-        base_string = payload + client.headers['poe-formkey'] + 'WpuLMiXEKKE98j56k'
-        client.headers['poe-tag-id'] = md5(base_string.encode()).hexdigest()
-
-        response = client.post('https://poe.com/api/gql_POST', data=payload)
-
-        if 'success' not in response.text:
-            raise Exception(
-                '''
-                Bot creation Failed
-                !! Important !!
-                Bot creation was not enabled on this account
-                please use: quora.Account.create with enable_bot_creation set to True
-            '''
-            )
-
-        return ModelResponse(response.json())
-
-
-class Account:
-    @staticmethod
-    def create(
-        proxy: Optional[str] = None,
-        logging: bool = False,
-        enable_bot_creation: bool = False,
-    ):
-        client = TLS(client_identifier='chrome110')
-        client.proxies = {'http': f'http://{proxy}', 'https': f'http://{proxy}'} if proxy else {}
-
-        mail_client = Emailnator()
-        mail_address = mail_client.get_mail()
-
-        if logging:
-            print('email', mail_address)
-
-        client.headers = {
-            'authority': 'poe.com',
-            'accept': '*/*',
-            'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
-            'content-type': 'application/json',
-            'origin': 'https://poe.com',
-            'poe-tag-id': 'null',
-            'referer': 'https://poe.com/login',
-            'sec-ch-ua': '"Chromium";v="112", "Google Chrome";v="112", "Not:A-Brand";v="99"',
-            'sec-ch-ua-mobile': '?0',
-            'sec-ch-ua-platform': '"macOS"',
-            'sec-fetch-dest': 'empty',
-            'sec-fetch-mode': 'cors',
-            'sec-fetch-site': 'same-origin',
-            'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
-            'poe-formkey': extract_formkey(client.get('https://poe.com/login').text),
-            'poe-tchannel': client.get('https://poe.com/api/settings').json()['tchannelData']['channel'],
-        }
-
-        token = reCaptchaV3(
-            'https://www.recaptcha.net/recaptcha/enterprise/anchor?ar=1&k=6LflhEElAAAAAI_ewVwRWI9hsyV4mbZnYAslSvlG&co=aHR0cHM6Ly9wb2UuY29tOjQ0Mw..&hl=en&v=4PnKmGB9wRHh1i04o7YUICeI&size=invisible&cb=bi6ivxoskyal'
-        )
-        # token = solver.recaptcha(sitekey='6LflhEElAAAAAI_ewVwRWI9hsyV4mbZnYAslSvlG',
-        #     url        = 'https://poe.com/login?redirect_url=%2F',
-        #     version    = 'v3',
-        #     enterprise = 1,
-        #     invisible  = 1,
-        #     action     = 'login',)['code']
-
-        payload = dumps(
-            separators=(',', ':'),
-            obj={
-                'queryName': 'MainSignupLoginSection_sendVerificationCodeMutation_Mutation',
-                'variables': {
-                    'emailAddress': mail_address,
-                    'phoneNumber': None,
-                    'recaptchaToken': token,
-                },
-                'query': 'mutation MainSignupLoginSection_sendVerificationCodeMutation_Mutation(\n  $emailAddress: String\n  $phoneNumber: String\n  $recaptchaToken: String\n) {\n  sendVerificationCode(verificationReason: login, emailAddress: $emailAddress, phoneNumber: $phoneNumber, recaptchaToken: $recaptchaToken) {\n    status\n    errorMessage\n  }\n}\n',
-            },
-        )
-
-        base_string = payload + client.headers['poe-formkey'] + 'WpuLMiXEKKE98j56k'
-        client.headers['poe-tag-id'] = md5(base_string.encode()).hexdigest()
-
-        print(dumps(client.headers, indent=4))
-
-        response = client.post('https://poe.com/api/gql_POST', data=payload)
-
-        if 'automated_request_detected' in response.text:
-            print('please try using a proxy / wait for fix')
-
-        if 'Bad Request' in response.text:
-            if logging:
-                print('bad request, retrying...', response.json())
-            quit()
-
-        if logging:
-            print('send_code', response.json())
-
-        mail_content = mail_client.get_message()
-        mail_token = findall(r';">(\d{6,7})</div>', mail_content)[0]
-
-        if logging:
-            print('code', mail_token)
-
-        payload = dumps(
-            separators=(',', ':'),
-            obj={
-                'queryName': 'SignupOrLoginWithCodeSection_signupWithVerificationCodeMutation_Mutation',
-                'variables': {
-                    'verificationCode': str(mail_token),
-                    'emailAddress': mail_address,
-                    'phoneNumber': None,
-                },
-                'query': 'mutation SignupOrLoginWithCodeSection_signupWithVerificationCodeMutation_Mutation(\n  $verificationCode: String!\n  $emailAddress: String\n  $phoneNumber: String\n) {\n  signupWithVerificationCode(verificationCode: $verificationCode, emailAddress: $emailAddress, phoneNumber: $phoneNumber) {\n    status\n    errorMessage\n  }\n}\n',
-            },
-        )
-
-        base_string = payload + client.headers['poe-formkey'] + 'WpuLMiXEKKE98j56k'
-        client.headers['poe-tag-id'] = md5(base_string.encode()).hexdigest()
-
-        response = client.post('https://poe.com/api/gql_POST', data=payload)
-        if logging:
-            print('verify_code', response.json())
-
-    def get(self):
-        cookies = open(Path(__file__).resolve().parent / 'cookies.txt', 'r').read().splitlines()
-        return choice(cookies)
-
-    @staticmethod
-    def delete(token: str, proxy: Optional[str] = None):
-        client = PoeClient(token, proxy=proxy)
-        client.delete_account()
-
-
-class StreamingCompletion:
-    @staticmethod
-    def create(
-        model: str = 'gpt-4',
-        custom_model: bool = None,
-        prompt: str = 'hello world',
-        token: str = '',
-        proxy: Optional[str] = None,
-    ) -> Generator[PoeResponse, None, None]:
-        _model = MODELS[model] if not custom_model else custom_model
-
-        proxies = {'http': 'http://' + proxy, 'https': 'http://' + proxy} if proxy else False
-        client = PoeClient(token)
-        client.proxy = proxies
-
-        for chunk in client.send_message(_model, prompt):
-            yield PoeResponse(
-                **{
-                    'id': chunk['messageId'],
-                    'object': 'text_completion',
-                    'created': chunk['creationTime'],
-                    'model': _model,
-                    'text': chunk['text_new'],
-                    'choices': [
-                        {
-                            'text': chunk['text_new'],
-                            'index': 0,
-                            'logprobs': None,
-                            'finish_reason': 'stop',
-                        }
-                    ],
-                    'usage': {
-                        'prompt_tokens': len(prompt),
-                        'completion_tokens': len(chunk['text_new']),
-                        'total_tokens': len(prompt) + len(chunk['text_new']),
-                    },
-                }
-            )
-
-
-class Completion:
-    @staticmethod
-    def create(
-        model: str = 'gpt-4',
-        custom_model: str = None,
-        prompt: str = 'hello world',
-        token: str = '',
-        proxy: Optional[str] = None,
-    ) -> PoeResponse:
-        _model = MODELS[model] if not custom_model else custom_model
-
-        proxies = {'http': 'http://' + proxy, 'https': 'http://' + proxy} if proxy else False
-        client = PoeClient(token)
-        client.proxy = proxies
-
-        chunk = None
-        for response in client.send_message(_model, prompt):
-            chunk = response
-
-        return PoeResponse(
-            **{
-                'id': chunk['messageId'],
-                'object': 'text_completion',
-                'created': chunk['creationTime'],
-                'model': _model,
-                'text': chunk['text'],
-                'choices': [
-                    {
-                        'text': chunk['text'],
-                        'index': 0,
-                        'logprobs': None,
-                        'finish_reason': 'stop',
-                    }
-                ],
-                'usage': {
-                    'prompt_tokens': len(prompt),
-                    'completion_tokens': len(chunk['text']),
-                    'total_tokens': len(prompt) + len(chunk['text']),
-                },
-            }
-        )
-
-
-class Poe:
-    def __init__(
-        self,
-        model: str = 'ChatGPT',
-        driver: str = 'firefox',
-        download_driver: bool = False,
-        driver_path: Optional[str] = None,
-        cookie_path: str = './quora/cookie.json',
-    ):
-        # validating the model
-        if model and model not in MODELS:
-            raise RuntimeError('Sorry, the model you provided does not exist. Please check and try again.')
-        self.model = MODELS[model]
-        self.cookie_path = cookie_path
-        self.cookie = self.__load_cookie(driver, driver_path=driver_path)
-        self.client = PoeClient(self.cookie)
-
-    def __load_cookie(self, driver: str, driver_path: Optional[str] = None) -> str:
-        if (cookie_file := Path(self.cookie_path)).exists():
-            with cookie_file.open() as fp:
-                cookie = json.load(fp)
-                if datetime.fromtimestamp(cookie['expiry']) < datetime.now():
-                    cookie = self.__register_and_get_cookie(driver, driver_path=driver_path)
-                else:
-                    print('Loading the cookie from file')
-        else:
-            cookie = self.__register_and_get_cookie(driver, driver_path=driver_path)
-
-        return unquote(cookie['value'])
-
-    def __register_and_get_cookie(self, driver: str, driver_path: Optional[str] = None) -> dict:
-        mail_client = Emailnator()
-        mail_address = mail_client.get_mail()
-
-        driver = self.__resolve_driver(driver, driver_path=driver_path)
-        driver.get("https://www.poe.com")
-
-        # clicking use email button
-        driver.find_element(By.XPATH, '//button[contains(text(), "Use email")]').click()
-
-        email = WebDriverWait(driver, 30).until(EC.presence_of_element_located((By.XPATH, '//input[@type="email"]')))
-        email.send_keys(mail_address)
-        driver.find_element(By.XPATH, '//button[text()="Go"]').click()
-
-        code = findall(r';">(\d{6,7})</div>', mail_client.get_message())[0]
-        print(code)
-
-        verification_code = WebDriverWait(driver, 30).until(
-            EC.presence_of_element_located((By.XPATH, '//input[@placeholder="Code"]'))
-        )
-        verification_code.send_keys(code)
-        verify_button = EC.presence_of_element_located((By.XPATH, '//button[text()="Verify"]'))
-        login_button = EC.presence_of_element_located((By.XPATH, '//button[text()="Log In"]'))
-
-        WebDriverWait(driver, 30).until(EC.any_of(verify_button, login_button)).click()
-
-        cookie = driver.get_cookie('p-b')
-
-        with open(self.cookie_path, 'w') as fw:
-            json.dump(cookie, fw)
-
-        driver.close()
-        return cookie
-
-    @staticmethod
-    def __resolve_driver(driver: str, driver_path: Optional[str] = None) -> Union[Firefox, Chrome]:
-        options = FirefoxOptions() if driver == 'firefox' else ChromeOptions()
-        options.add_argument('-headless')
-
-        if driver_path:
-            options.binary_location = driver_path
-        try:
-            return Firefox(options=options) if driver == 'firefox' else Chrome(options=options)
-        except Exception:
-            raise Exception(SELENIUM_WEB_DRIVER_ERROR_MSG)
-
-    def chat(self, message: str, model: Optional[str] = None) -> str:
-        if model and model not in MODELS:
-            raise RuntimeError('Sorry, the model you provided does not exist. Please check and try again.')
-        model = MODELS[model] if model else self.model
-        response = None
-        for chunk in self.client.send_message(model, message):
-            response = chunk['text']
-        return response
-
-    def create_bot(self, name: str, /, prompt: str = '', base_model: str = 'ChatGPT', description: str = '') -> None:
-        if base_model not in MODELS:
-            raise RuntimeError('Sorry, the base_model you provided does not exist. Please check and try again.')
-
-        response = self.client.create_bot(
-            handle=name,
-            prompt=prompt,
-            base_model=MODELS[base_model],
-            description=description,
-        )
-        print(f'Successfully created bot with name: {response["bot"]["displayName"]}')
-
-    def list_bots(self) -> list:
-        return list(self.client.bot_names.values())
-
-    def delete_account(self) -> None:
-        self.client.delete_account()
diff --git a/spaces/123Kumar/vits-uma-genshin-honkai123/text/cleaners.py b/spaces/123Kumar/vits-uma-genshin-honkai123/text/cleaners.py
deleted file mode 100644
index d26581deb399609163518054718ad80ecca5d934..0000000000000000000000000000000000000000
--- a/spaces/123Kumar/vits-uma-genshin-honkai123/text/cleaners.py
+++ /dev/null
@@ -1,475 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-import re
-from unidecode import unidecode
-import pyopenjtalk
-from jamo import h2j, j2hcj
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba, cn2an
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('mrs', 'misess'),
-  ('mr', 'mister'),
-  ('dr', 'doctor'),
-  ('st', 'saint'),
-  ('co', 'company'),
-  ('jr', 'junior'),
-  ('maj', 'major'),
-  ('gen', 'general'),
-  ('drs', 'doctors'),
-  ('rev', 'reverend'),
-  ('lt', 'lieutenant'),
-  ('hon', 'honorable'),
-  ('sgt', 'sergeant'),
-  ('capt', 'captain'),
-  ('esq', 'esquire'),
-  ('ltd', 'limited'),
-  ('col', 'colonel'),
-  ('ft', 'fort'),
-]]
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
-  ('ㄳ', 'ㄱㅅ'),
-  ('ㄵ', 'ㄴㅈ'),
-  ('ㄶ', 'ㄴㅎ'),
-  ('ㄺ', 'ㄹㄱ'),
-  ('ㄻ', 'ㄹㅁ'),
-  ('ㄼ', 'ㄹㅂ'),
-  ('ㄽ', 'ㄹㅅ'),
-  ('ㄾ', 'ㄹㅌ'),
-  ('ㄿ', 'ㄹㅍ'),
-  ('ㅀ', 'ㄹㅎ'),
-  ('ㅄ', 'ㅂㅅ'),
-  ('ㅘ', 'ㅗㅏ'),
-  ('ㅙ', 'ㅗㅐ'),
-  ('ㅚ', 'ㅗㅣ'),
-  ('ㅝ', 'ㅜㅓ'),
-  ('ㅞ', 'ㅜㅔ'),
-  ('ㅟ', 'ㅜㅣ'),
-  ('ㅢ', 'ㅡㅣ'),
-  ('ㅑ', 'ㅣㅏ'),
-  ('ㅒ', 'ㅣㅐ'),
-  ('ㅕ', 'ㅣㅓ'),
-  ('ㅖ', 'ㅣㅔ'),
-  ('ㅛ', 'ㅣㅗ'),
-  ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('a', '에이'),
-  ('b', '비'),
-  ('c', '시'),
-  ('d', '디'),
-  ('e', '이'),
-  ('f', '에프'),
-  ('g', '지'),
-  ('h', '에이치'),
-  ('i', '아이'),
-  ('j', '제이'),
-  ('k', '케이'),
-  ('l', '엘'),
-  ('m', '엠'),
-  ('n', '엔'),
-  ('o', '오'),
-  ('p', '피'),
-  ('q', '큐'),
-  ('r', '아르'),
-  ('s', '에스'),
-  ('t', '티'),
-  ('u', '유'),
-  ('v', '브이'),
-  ('w', '더블유'),
-  ('x', '엑스'),
-  ('y', '와이'),
-  ('z', '제트')
-]]
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('a', 'ㄟˉ'),
-  ('b', 'ㄅㄧˋ'),
-  ('c', 'ㄙㄧˉ'),
-  ('d', 'ㄉㄧˋ'),
-  ('e', 'ㄧˋ'),
-  ('f', 'ㄝˊㄈㄨˋ'),
-  ('g', 'ㄐㄧˋ'),
-  ('h', 'ㄝˇㄑㄩˋ'),
-  ('i', 'ㄞˋ'),
-  ('j', 'ㄐㄟˋ'),
-  ('k', 'ㄎㄟˋ'),
-  ('l', 'ㄝˊㄛˋ'),
-  ('m', 'ㄝˊㄇㄨˋ'),
-  ('n', 'ㄣˉ'),
-  ('o', 'ㄡˉ'),
-  ('p', 'ㄆㄧˉ'),
-  ('q', 'ㄎㄧㄡˉ'),
-  ('r', 'ㄚˋ'),
-  ('s', 'ㄝˊㄙˋ'),
-  ('t', 'ㄊㄧˋ'),
-  ('u', 'ㄧㄡˉ'),
-  ('v', 'ㄨㄧˉ'),
-  ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
-  ('x', 'ㄝˉㄎㄨˋㄙˋ'),
-  ('y', 'ㄨㄞˋ'),
-  ('z', 'ㄗㄟˋ')
-]]
-
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('ㄅㄛ', 'p⁼wo'),
-  ('ㄆㄛ', 'pʰwo'),
-  ('ㄇㄛ', 'mwo'),
-  ('ㄈㄛ', 'fwo'),
-  ('ㄅ', 'p⁼'),
-  ('ㄆ', 'pʰ'),
-  ('ㄇ', 'm'),
-  ('ㄈ', 'f'),
-  ('ㄉ', 't⁼'),
-  ('ㄊ', 'tʰ'),
-  ('ㄋ', 'n'),
-  ('ㄌ', 'l'),
-  ('ㄍ', 'k⁼'),
-  ('ㄎ', 'kʰ'),
-  ('ㄏ', 'h'),
-  ('ㄐ', 'ʧ⁼'),
-  ('ㄑ', 'ʧʰ'),
-  ('ㄒ', 'ʃ'),
-  ('ㄓ', 'ʦ`⁼'),
-  ('ㄔ', 'ʦ`ʰ'),
-  ('ㄕ', 's`'),
-  ('ㄖ', 'ɹ`'),
-  ('ㄗ', 'ʦ⁼'),
-  ('ㄘ', 'ʦʰ'),
-  ('ㄙ', 's'),
-  ('ㄚ', 'a'),
-  ('ㄛ', 'o'),
-  ('ㄜ', 'ə'),
-  ('ㄝ', 'e'),
-  ('ㄞ', 'ai'),
-  ('ㄟ', 'ei'),
-  ('ㄠ', 'au'),
-  ('ㄡ', 'ou'),
-  ('ㄧㄢ', 'yeNN'),
-  ('ㄢ', 'aNN'),
-  ('ㄧㄣ', 'iNN'),
-  ('ㄣ', 'əNN'),
-  ('ㄤ', 'aNg'),
-  ('ㄧㄥ', 'iNg'),
-  ('ㄨㄥ', 'uNg'),
-  ('ㄩㄥ', 'yuNg'),
-  ('ㄥ', 'əNg'),
-  ('ㄦ', 'əɻ'),
-  ('ㄧ', 'i'),
-  ('ㄨ', 'u'),
-  ('ㄩ', 'ɥ'),
-  ('ˉ', '→'),
-  ('ˊ', '↑'),
-  ('ˇ', '↓↑'),
-  ('ˋ', '↓'),
-  ('˙', ''),
-  ('，', ','),
-  ('。', '.'),
-  ('！', '!'),
-  ('？', '?'),
-  ('—', '-')
-]]
-
-
-def expand_abbreviations(text):
-  for regex, replacement in _abbreviations:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def lowercase(text):
-  return text.lower()
-
-
-def collapse_whitespace(text):
-  return re.sub(_whitespace_re, ' ', text)
-
-
-def convert_to_ascii(text):
-  return unidecode(text)
-
-
-def japanese_to_romaji_with_accent(text):
-  '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-  sentences = re.split(_japanese_marks, text)
-  marks = re.findall(_japanese_marks, text)
-  text = ''
-  for i, sentence in enumerate(sentences):
-    if re.match(_japanese_characters, sentence):
-      if text!='':
-        text+=' '
-      labels = pyopenjtalk.extract_fullcontext(sentence)
-      for n, label in enumerate(labels):
-        phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-        if phoneme not in ['sil','pau']:
-          text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q')
-        else:
-          continue
-        n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-        a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-        a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-        a3 = int(re.search(r"\+(\d+)/", label).group(1))
-        if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']:
-          a2_next=-1
-        else:
-          a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-        # Accent phrase boundary
-        if a3 == 1 and a2_next == 1:
-          text += ' '
-        # Falling
-        elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras:
-          text += '↓'
-        # Rising
-        elif a2 == 1 and a2_next == 2:
-          text += '↑'
-    if i<len(marks):
-      text += unidecode(marks[i]).replace(' ','')
-  return text
-
-
-def latin_to_hangul(text):
-  for regex, replacement in _latin_to_hangul:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def divide_hangul(text):
-  for regex, replacement in _hangul_divided:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def hangul_number(num, sino=True):
-  '''Reference https://github.com/Kyubyong/g2pK'''
-  num = re.sub(',', '', num)
-
-  if num == '0':
-      return '영'
-  if not sino and num == '20':
-      return '스무'
-
-  digits = '123456789'
-  names = '일이삼사오육칠팔구'
-  digit2name = {d: n for d, n in zip(digits, names)}
-  
-  modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
-  decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
-  digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
-  digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
-
-  spelledout = []
-  for i, digit in enumerate(num):
-    i = len(num) - i - 1
-    if sino:
-      if i == 0:
-        name = digit2name.get(digit, '')
-      elif i == 1:
-        name = digit2name.get(digit, '') + '십'
-        name = name.replace('일십', '십')
-    else:
-      if i == 0:
-        name = digit2mod.get(digit, '')
-      elif i == 1:
-        name = digit2dec.get(digit, '')
-    if digit == '0':
-      if i % 4 == 0:
-        last_three = spelledout[-min(3, len(spelledout)):]
-        if ''.join(last_three) == '':
-          spelledout.append('')
-          continue
-      else:
-        spelledout.append('')
-        continue
-    if i == 2:
-      name = digit2name.get(digit, '') + '백'
-      name = name.replace('일백', '백')
-    elif i == 3:
-      name = digit2name.get(digit, '') + '천'
-      name = name.replace('일천', '천')
-    elif i == 4:
-      name = digit2name.get(digit, '') + '만'
-      name = name.replace('일만', '만')
-    elif i == 5:
-      name = digit2name.get(digit, '') + '십'
-      name = name.replace('일십', '십')
-    elif i == 6:
-      name = digit2name.get(digit, '') + '백'
-      name = name.replace('일백', '백')
-    elif i == 7:
-      name = digit2name.get(digit, '') + '천'
-      name = name.replace('일천', '천')
-    elif i == 8:
-      name = digit2name.get(digit, '') + '억'
-    elif i == 9:
-      name = digit2name.get(digit, '') + '십'
-    elif i == 10:
-      name = digit2name.get(digit, '') + '백'
-    elif i == 11:
-      name = digit2name.get(digit, '') + '천'
-    elif i == 12:
-      name = digit2name.get(digit, '') + '조'
-    elif i == 13:
-      name = digit2name.get(digit, '') + '십'
-    elif i == 14:
-      name = digit2name.get(digit, '') + '백'
-    elif i == 15:
-      name = digit2name.get(digit, '') + '천'
-    spelledout.append(name)
-  return ''.join(elem for elem in spelledout)
-
-
-def number_to_hangul(text):
-  '''Reference https://github.com/Kyubyong/g2pK'''
-  tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
-  for token in tokens:
-    num, classifier = token
-    if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
-      spelledout = hangul_number(num, sino=False)
-    else:
-      spelledout = hangul_number(num, sino=True)
-    text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
-  # digit by digit for remaining digits
-  digits = '0123456789'
-  names = '영일이삼사오육칠팔구'
-  for d, n in zip(digits, names):
-    text = text.replace(d, n)
-  return text
-
-
-def number_to_chinese(text):
-  numbers = re.findall(r'\d+(?:\.?\d+)?', text)
-  for number in numbers:
-    text = text.replace(number, cn2an.an2cn(number),1)
-  return text
-
-
-def chinese_to_bopomofo(text):
-  text=text.replace('、','，').replace('；','，').replace('：','，')
-  words=jieba.lcut(text,cut_all=False)
-  text=''
-  for word in words:
-    bopomofos=lazy_pinyin(word,BOPOMOFO)
-    if not re.search('[\u4e00-\u9fff]',word):
-      text+=word
-      continue
-    for i in range(len(bopomofos)):
-      if re.match('[\u3105-\u3129]',bopomofos[i][-1]):
-        bopomofos[i]+='ˉ'
-    if text!='':
-      text+=' '
-    text+=''.join(bopomofos)
-  return text
-
-
-def latin_to_bopomofo(text):
-  for regex, replacement in _latin_to_bopomofo:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def bopomofo_to_romaji(text):
-  for regex, replacement in _bopomofo_to_romaji:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def basic_cleaners(text):
-  '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def transliteration_cleaners(text):
-  '''Pipeline for non-English text that transliterates to ASCII.'''
-  text = convert_to_ascii(text)
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def japanese_cleaners(text):
-  text=japanese_to_romaji_with_accent(text)
-  if re.match('[A-Za-z]',text[-1]):
-    text += '.'
-  return text
-
-
-def japanese_cleaners2(text):
-  return japanese_cleaners(text).replace('ts','ʦ').replace('...','…')
-
-
-def korean_cleaners(text):
-  '''Pipeline for Korean text'''
-  text = latin_to_hangul(text)
-  text = number_to_hangul(text)
-  text = j2hcj(h2j(text))
-  text = divide_hangul(text)
-  if re.match('[\u3131-\u3163]',text[-1]):
-    text += '.'
-  return text
-
-
-def chinese_cleaners(text):
-  '''Pipeline for Chinese text'''
-  text=number_to_chinese(text)
-  text=chinese_to_bopomofo(text)
-  text=latin_to_bopomofo(text)
-  if re.match('[ˉˊˇˋ˙]',text[-1]):
-    text += '。'
-  return text
-
-
-def zh_ja_mixture_cleaners(text):
-  chinese_texts=re.findall(r'\[ZH\].*?\[ZH\]',text)
-  japanese_texts=re.findall(r'\[JA\].*?\[JA\]',text)
-  for chinese_text in chinese_texts:
-    cleaned_text=number_to_chinese(chinese_text[4:-4])
-    cleaned_text=chinese_to_bopomofo(cleaned_text)
-    cleaned_text=latin_to_bopomofo(cleaned_text)
-    cleaned_text=bopomofo_to_romaji(cleaned_text)
-    cleaned_text=re.sub('i[aoe]',lambda x:'y'+x.group(0)[1:],cleaned_text)
-    cleaned_text=re.sub('u[aoəe]',lambda x:'w'+x.group(0)[1:],cleaned_text)
-    cleaned_text=re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ`'+x.group(2),cleaned_text).replace('ɻ','ɹ`')
-    cleaned_text=re.sub('([ʦs][⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ'+x.group(2),cleaned_text)
-    text = text.replace(chinese_text,cleaned_text+' ',1)
-  for japanese_text in japanese_texts:
-    cleaned_text=japanese_to_romaji_with_accent(japanese_text[4:-4]).replace('ts','ʦ').replace('u','ɯ').replace('...','…')
-    text = text.replace(japanese_text,cleaned_text+' ',1)
-  text=text[:-1]
-  if re.match('[A-Za-zɯɹəɥ→↓↑]',text[-1]):
-    text += '.'
-  return text
\ No newline at end of file
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index 06a2fa2ad76064274584edb6d5e27f90f6a596ee..0000000000000000000000000000000000000000
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-<p>To use Baka Loader 1.4, you need to download it from the internet and install it on your PC. The installation process is simple and fast, and it does not require any special skills or knowledge. Once you have installed Baka Loader 1.4, you can run it by double-clicking on the baka.loader.exe file in your program folder.</p>
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-<h2>What are the advantages and disadvantages of Baka Loader 1.4?</h2>
-<p>Baka Loader 1.4 has some advantages and disadvantages that you should consider before using it.</p>
-<p></p>
-<p>Some of the advantages of Baka Loader 1.4 are:</p>
-<ul>
-<li>It is free and easy to use.</li>
-<li>It can convert DIB files to other formats quickly and easily.</li>
-<li>It can enhance your graphics by using shaders and effects.</li>
-<li>It has a large collection of shaders and effects that you can download from the internet or use from your local folder.</li>
-<li>It supports multiple languages, such as English, Chinese, Japanese, etc.</li>
-</ul>
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-<li>It may not be compatible with some Windows versions or devices.</li>
-<li>It may not be updated regularly or have technical support.</li>
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-<li>It may not be safe or secure to download from some sources or websites.</li>
-<li>It may not be able to convert some DIB files or other graphics files due to their size or format.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Baka Loader 1.4 is a software application that can help you convert DIB files to other formats and enhance your graphics by using shaders and effects. It is free and easy to use, but it may also have some drawbacks that you should be aware of before using it.</p>
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-<h2>How to download and install Baka Loader 1.4?</h2>
-<p>If you want to download and install Baka Loader 1.4 on your PC, you need to follow these steps:</p>
-<ol>
-<li>Go to this link: <a href="http://jenovaswitness.guildwork.com/forum/threads/57716dcc002aa807a2e819e5-baka-loader-1-4">http://jenovaswitness.guildwork.com/forum/threads/57716dcc002aa807a2e819e5-baka-loader-1-4</a> and click on the download button.</li>
-<li>Wait for the download to finish and then open the downloaded file.</li>
-<li>Follow the instructions on the screen to install Baka Loader 1.4 on your PC.</li>
-<li>Choose the destination folder where you want to install Baka Loader 1.4 and click on Next.</li>
-<li>Wait for the installation to complete and then click on Finish.</li>
-<li>You can now run Baka Loader 1.4 by double-clicking on the baka.loader.exe file in your program folder.</li>
-</ol>
-<h2>What are some alternatives to Baka Loader 1.4?</h2>
-<p>Baka Loader 1.4 is not the only tool that can help you convert DIB files and use shaders and effects. There are some other alternatives that you can try if you are not satisfied with Baka Loader 1.4 or if you want to compare different options. Here are some of them:</p>
-<ul>
-<li><a href="https://www.xnview.com/en/xnconvert/">XnConvert</a>: This is a powerful and free image converter that supports over 500 formats, including DIB files. You can also apply various filters and effects to your images with this tool.</li>
-<li><a href="https://www.irfanview.com/">IrfanView</a>: This is a fast and compact image viewer and editor that can also convert DIB files to other formats. You can also use plugins to add more features and functions to this tool.</li>
-<li><a href="https://www.gimp.org/">GIMP</a>: This is a free and open source image editor that can handle DIB files and many other formats. You can also use various tools and plugins to enhance your graphics with this tool.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Baka Loader 1.4 is a software application that can help you convert DIB files to other formats and enhance your graphics by using shaders and effects. It is free and easy to use, but it may also have some drawbacks that you should be aware of before using it.</p>
-<p>If you want to try Baka Loader 1.4 for yourself, you can download it from this link: <a href="http://jenovaswitness.guildwork.com/forum/threads/57716dcc002aa807a2e819e5-baka-loader-1-4">http://jenovaswitness.guildwork.com/forum/threads/57716dcc002aa807a2e819e5-baka-loader-1-4</a></p>
-<p>If you want to learn more about DIB files, shaders, effects, or other graphics topics, you can check out these links:</p>
-<ul>
-<li><a href="https://en.wikipedia.org/wiki/Device-independent_bitmap">Device-independent bitmap - Wikipedia</a></li>
-<li><a href="https://www.lifewire.com/shaders-in-computer-graphics-958215">Shaders in Computer Graphics - Lifewire</a></li>
-<li><a href="https://www.makeuseof.com/tag/effects-in-computer-graphics/">Effects in Computer Graphics - MakeUseOf</a></li>
-</ul>
-<h2>Conclusion</h2>
-<p>Baka Loader 1.4 is a software application that can help you convert DIB files to other formats and enhance your graphics by using shaders and effects. It is free and easy to use, but it may also have some drawbacks that you should be aware of before using it.</p>
-<p>If you want to try Baka Loader 1.4 for yourself, you can download it from this link: <a href="http://jenovaswitness.guildwork.com/forum/threads/57716dcc002aa807a2e819e5-baka-loader-1-4">http://jenovaswitness.guildwork.com/forum/threads/57716dcc002aa807a2e819e5-baka-loader-1-4</a></p>
-<p>If you want to learn more about DIB files, shaders, effects, or other graphics topics, you can check out these links:</p>
-<ul>
-<li><a href="https://en.wikipedia.org/wiki/Device-independent_bitmap">Device-independent bitmap - Wikipedia</a></li>
-<li><a href="https://www.lifewire.com/shaders-in-computer-graphics-958215">Shaders in Computer Graphics - Lifewire</a></li>
-<li><a href="https://www.makeuseof.com/tag/effects-in-computer-graphics/">Effects in Computer Graphics - MakeUseOf</a></li>
-</ul>
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Contoh Surat Undangan Peneguhan Sidi.md b/spaces/1gistliPinn/ChatGPT4/Examples/Contoh Surat Undangan Peneguhan Sidi.md
deleted file mode 100644
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--- a/spaces/1gistliPinn/ChatGPT4/Examples/Contoh Surat Undangan Peneguhan Sidi.md	
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Download Game Need For Speed Undercover FREE.md b/spaces/1gistliPinn/ChatGPT4/Examples/Download Game Need For Speed Undercover FREE.md
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Fisika Universitas Jilid 1 Sears Zemansky Pdf 14l ((BETTER)).md b/spaces/1gistliPinn/ChatGPT4/Examples/Fisika Universitas Jilid 1 Sears Zemansky Pdf 14l ((BETTER)).md
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index 9d266b6ab9c4522cd1941dc77fd7ec517e2f49c9..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Fisika Universitas Jilid 1 Sears Zemansky Pdf 14l ((BETTER)).md	
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/Lo-Que-Varguitas-No-Dijo-Libro-Pdf-11-Fixed.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/Lo-Que-Varguitas-No-Dijo-Libro-Pdf-11-Fixed.md
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-## Lo Que Varguitas No Dijo Libro Pdf 11
-
-
-
- 
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- ![Lo Que Varguitas No Dijo Libro Pdf 11 Fixed](https://3.bp.blogspot.com/-vqEreKNgg-k/TW8ErNQBy9I/AAAAAAAAA_4/kTPfrtr6c58/s1600/03.jpg)
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-# Lo que Varguitas no dijo: el libro que revela la verdadera historia de amor entre Julia Urquidi y Mario Vargas Llosa
-
-  
-
-Lo que Varguitas no dijo es una obra autobiográfica de Julia Urquidi Illanes publicada en 1983, que se enfoca en el tiempo que vivió una relación con el escritor Mario Vargas Llosa. Se casaron en mayo de 1955, cuando Vargas Llosa tenía 19 años y ella 29, después de enfrentar diferentes problemas por el hecho de que Julia era hermana de la tía política de Vargas Llosa y la diferencia de edades que existía. El libro tiene relevancia porque narra los años que Urquidi vivió ayudando y apoyando a Vargas Llosa a que se convirtiera en escritor exitoso, según la autora. El matrimonio sobrevivió diferentes crisis, como los celos de Julia y la infidelidad de Mario, hasta que en 1964, por medio de una carta, Vargas Llosa le confiesa a ella su amor por su prima Patricia Llosa Urquidi (y sobrina de Julia) y sus intenciones de casarse con ella. Urquidi decide escribir este libro en respuesta a La tía Julia y el escribidor escrito por Vargas Llosa.
-
-  
-
-En este artículo, te contamos más detalles sobre este libro que muestra la otra cara de la historia de amor entre Julia Urquidi y Mario Vargas Llosa, que inspiró una de las novelas más famosas del Premio Nobel de Literatura.
-
-  
-
-## ¿Quién fue Julia Urquidi?
-
-  
-
-Julia Urquidi Illanes nació en Cochabamba, Bolivia, el 30 de marzo de 1926. Era hija de un diplomático boliviano y una dama peruana. Estudió en el Colegio Americano de La Paz y luego se trasladó a Lima, donde trabajó como secretaria en la embajada boliviana. Allí conoció a Mario Vargas Llosa, quien era sobrino político de su hermana Olga. Se enamoraron y se casaron en 1955, pese a la oposición familiar y social. Julia apoyó a Mario en sus estudios universitarios y en sus primeros pasos como escritor. Lo acompañó a París, donde vivieron entre 1959 y 1963. Sin embargo, su relación se deterioró por las infidelidades de Mario y la diferencia de caracteres. En 1964, se separaron y luego se divorciaron. Julia regresó a Lima y trabajó como productora de televisión. En 1983, publicó Lo que Varguitas no dijo, donde cuenta su versión de los hechos. Murió en Lima el 10 de marzo de 2010.
-
-  
-
-## ¿Qué dice el libro Lo que Varguitas no dijo?
-
-  
-
-El libro Lo que Varguitas no dijo es un testimonio personal de Julia Urquidi sobre su matrimonio con Mario Vargas Llosa. En él, relata cómo se conocieron, cómo se enamoraron, cómo se casaron, cómo vivieron en París y cómo se separaron. También describe los momentos felices y difíciles que compartieron, así como las personalidades y los sueños de ambos. El libro tiene un tono íntimo y emotivo, pero también crítico y reivindicativo. Julia busca mostrar su papel como esposa, compañera y colaboradora de Mario, así como defender su dignidad frente a las mentiras y las injurias que sufrió por parte de él y de su familia. El libro también es una respuesta a La tía Julia y el escribidor, la novela que Mario Vargas Llosa escribió en 1977, donde narra su historia de amor con Julia bajo el nombre ficticio de Marito y la tía Julia. En esta novela, Mario presenta a Julia como una mujer mayor, frívola y manipuladora, que sed
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Blue Hai Pani - Pani MP3 Download  Listen to Yo Yo Honey Singh Sandeep Kapoor and Soniya Sharma.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Blue Hai Pani - Pani MP3 Download  Listen to Yo Yo Honey Singh Sandeep Kapoor and Soniya Sharma.md
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-<li>The song has a catchy tune and an upbeat tempo that makes it easy to dance and sing along. The song also has a catchy chorus that repeats the words "blue hai pani pani" several times, making it easy to remember.</li>
-<li>The song has fun and playful lyrics that appeal to the young generation. The song also uses some English words and phrases, such as "sunny sunny", "baby doll", and "party all night", that make it more relatable and trendy.</li>
-<li>The song has a colorful and vibrant video that showcases the scenic locations of Australia, where the movie was shot. The video also features the actors and singers performing some cool dance moves and having fun with each other.</li>
-<li>The song has received many awards and accolades for its popularity and quality. The song won the Best Music Director award for Yo Yo Honey Singh at the 2015 Filmfare Awards. The song also won the Most Entertaining Song award at the 2015 BIG Star Entertainment Awards. The song also topped many charts and playlists, such as the Mirchi Top 20, iTunes India, Gaana.com, etc.</li>
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- <p>With so many reasons to love this song, it is no wonder that blue hai pani pani mp3 is one of the most downloaded songs in India.</p>
- <h2>How to Download Blue Hai Pani Pani MP3?</h2>
- <h3>Legal and Safe Options</h3>
- <p>If you want to download blue hai pani pani mp3, you should always opt for legal and safe options. This means that you should avoid using any pirated or illegal websites or apps that offer free downloads of songs without permission from the artists or the producers. Downloading songs from such sources can have many negative consequences, such as:</p>
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-<li>You may violate the intellectual property rights of the artists or the producers, who have invested their time, money, and effort in creating the song. This may result in legal action or penalties against you.</li>
-<li>You may expose your device to malware or viruses that can harm your data or privacy. Some illegal websites or apps may contain malicious software that can infect your device or steal your personal information.</li>
-<li>You may deprive the artists or the producers of their rightful income or recognition. By downloading songs from illegal sources, you are not supporting the artists or the producers who deserve to be paid for their work. This may affect their ability to create more quality music in the future.</li>
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- <p>Therefore, you should always use legal and safe options to download blue hai pani pani mp3. There are many platforms where you can download blue hai pani pani mp3 legally and safely, such as JioSaavn, YouTube Music, Spotify, Amazon Music, etc. These platforms offer high-quality downloads of songs at reasonable prices or subscriptions. They also respect the intellectual property rights of the artists or the producers and support them financially or otherwise.</p>
- <h3>Comparison of Different Platforms</h3>
- <p>To help you choose the best platform to download blue hai pani pani mp3 legally and safely, we have prepared a table that compares some of the most popular platforms where blue hai pani pani mp3 can be downloaded legally and safely: JioSaavn, YouTube Music, Spotify, and Amazon Music. The table includes information such as price, quality, availability, features, etc.</p>
- <table>
-<tr>
-<th>Platform</th>
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-<td>JioSaavn</td>
-<td>Free with ads or Rs. 99 per month for premium</td>
-<td>Up to 320 kbps</td>
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-<td>Unlimited downloads, offline listening, ad-free music, exclusive content, podcasts, radio, lyrics, etc.</td>
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-<td>YouTube Music</td>
-<td>Free with ads or Rs. 99 per month for premium</td>
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-<td>Free with ads or Rs. 119 per month for premium</td>
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-<td>Free for Prime members or Rs. 129 per month for non-Prime members</td>
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- <p>As you can see from the table, each platform has its own advantages and disadvantages. You can choose the one that suits your preferences and budget. However, if you ask us for our recommendation, we would suggest you to use JioSaavn to download blue hai pani pani mp3 legally and safely. Here's why:</p>
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- <p>JioSaavn is one of the best platforms to download blue hai pani pani mp3 legally and safely. It offers high-quality downloads of songs at a reasonable price or subscription. It also respects the intellectual property rights of the artists or the producers and supports them financially or otherwise. Moreover, it has some exclusive features that make it stand out from the rest of the platforms.</p>
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-<li>Create an account on JioSaavn using your email address or phone number. You can also sign in with your Facebook or Google account.</li>
-<li>Search for blue hai pani pani on the search bar or browse through the categories or playlists.</li>
-<li>Select the song from the results and click on the download icon on the bottom right corner of the screen.</li>
-<li>Choose the quality and location of the download. You can choose between low (64 kbps), medium (160 kbps), or high (320 kbps) quality. You can also choose the folder where you want to save the song on your device.</li>
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- <p>That's it! You have successfully downloaded blue hai pani pani mp3 from JioSaavn legally and safely. Now you can enjoy listening to this amazing song and have fun with your friends.</p>
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- <p>In this article, we have guided you on how to download blue hai pani pani mp3 legally and safely. We have also told you more about this amazing song and why it is so popular among music lovers.</p>
- <p>We hope you have found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you.</p>
- <p>If you liked this article, please share it with your friends and family who might also be interested in downloading blue hai pani pani mp3 legally and safely. And don't forget to subscribe to our newsletter for more tips and tricks on how to enjoy music online.</p>
- <p>Thank you for reading this article and happy listening!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about blue hai pani pani mp3:</p>
- <ol>
-<li><b>When was blue hai pani pani released?</b><br>The song was released on December 9, [user](# 2014. The song was released on December 9, 2014, as part of the movie soundtrack. The song was composed by Yo Yo Honey Singh and written by him and Lil Golu.</li>
-<li><b>Who wrote blue hai pani pani?</b><br>The song was written by Yo Yo Honey Singh and Lil Golu. Yo Yo Honey Singh is a rapper, singer, composer, and producer who has created many hit songs in Bollywood and Punjabi music. Lil Golu is a rapper and lyricist who has collaborated with Yo Yo Honey Singh on several songs.</li>
-<li><b>What does blue hai pani pani mean?</b><br>The title of the song literally means "blue water water", which is a reference to the color of the sky and the sea. The song also uses some metaphors and similes to describe the feelings of the singers, such as "tujhe lagta hai tu chaand hai sitaara" (you think you are the moon and the star) and "teri aankhon ka ye paani sunny sunny sunny" (the water in your eyes is sunny sunny sunny). The song is about having fun and enjoying life with your friends.</li>
-<li><b>How many views does blue hai pani pani have?</b><br>The official video of blue hai pani pani has over 500 million views on YouTube as of June 2023. The video features the actors and singers performing some cool dance moves and having fun with each other in Australia. The video is one of the most watched videos on YouTube in India.</li>
-<li><b>Can I use blue hai pani pani as a ringtone?</b><br>Yes, you can use blue hai pani pani as a ringtone on your phone. You can either download the song from a legal and safe platform and set it as your ringtone, or you can use a ringtone maker app to create a custom ringtone from the song. However, you should always respect the intellectual property rights of the artists or the producers and not share or distribute the ringtone without their permission.</li>
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-<ul>
-<li><b>How to improve your shooting skills:</b> Shooting is one of the most important skills in basketball, and NBA 2K14 v1.14 for Android has a realistic shooting system that requires timing and accuracy. To improve your shooting skills, you need to practice a lot and learn how to use the shot meter, which shows you when to release the ball for a perfect shot. You can also adjust the difficulty level and the camera angle to suit your preference.</li>
-<li><b>How to unlock more players and teams:</b> NBA 2K14 v1.14 for Android has a huge roster of players and teams that you can play with, but not all of them are available from the start. To unlock more players and teams, you need to play through the multiseason mode and earn points that you can use to buy them from the store. You can also unlock some players and teams by completing certain achievements or using cheats and mods.</li>
-<li><b>How to use cheats and mods:</b> Cheats and mods are ways to modify or enhance the game according to your liking. For example, you can use cheats to get unlimited money, stamina, or skill points, or you can use mods to change the appearance of players, courts, or jerseys. However, using cheats and mods may affect the performance of the game or cause some errors, so use them at your own risk. You can find some cheats and mods from this website, but we are not affiliated with them or endorse them in any way.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>NBA 2K14 v1.14 for Android is an amazing basketball game that will give you hours of entertainment and excitement. It has realistic graphics, sound effects, and gameplay that will make you feel like you are on the court with your favorite players and teams. It also has various modes and features that will keep you engaged and challenged.</p>
-<p>If you want to download NBA 2K14 v1.14 for Android, you need to follow some steps that we have explained in this article. You also need to meet some requirements that we have listed in this article. And if you want to improve your game and have more fun, you can use some tips and tricks that we have shared in this article.</p>
-<p>We hope that this article has helped you learn how to download NBA 2K14 v1.14 for Android and enjoy it on your device have enough free space on your device before downloading and installing the game.</li>
-<li><b>Q3: Can you play NBA 2K14 v1.14 for Android offline?</b></li>
-<li><b>A3:</b> Yes, you can play NBA 2K14 v1.14 for Android offline, but you will not be able to access some features that require an internet connection, such as the online multiplayer mode, the leaderboards, or the store. You will also need to activate the game online once before playing it offline.</li>
-<li><b>Q4: Can you play NBA 2K14 v1.14 for Android with your friends?</b></li>
-<li><b>A4:</b> Yes, you can play NBA 2K14 v1.14 for Android with your friends, either online or locally. To play online, you need to have an internet connection and a Google Play Games account. You can then invite your friends to join your game or join their game from the multiplayer menu. To play locally, you need to have a Wi-Fi connection and a Bluetooth connection. You can then create or join a game from the local multiplayer menu.</li>
-<li><b>Q5: What are the best teams to play with in NBA 2K14 v1.14 for Android?</b></li>
-<li><b>A5:</b> This is a subjective question that depends on your personal preference and style of play. However, some of the teams that are generally considered to be the best in NBA 2K14 v1.14 for Android are the Miami Heat, the Los Angeles Lakers, the Brooklyn Nets, the Golden State Warriors, and the Chicago Bulls. These teams have some of the best players and ratings in the game and can dominate any opponent.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/FIFA Mobile APK Mod - Snrsz Para ve Altn Hilesi Nasl Yaplr?.md b/spaces/1phancelerku/anime-remove-background/FIFA Mobile APK Mod - Snrsz Para ve Altn Hilesi Nasl Yaplr?.md
deleted file mode 100644
index d8f694e59fdc9b0b036318d4efd8c0537040c517..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/FIFA Mobile APK Mod - Snrsz Para ve Altn Hilesi Nasl Yaplr?.md	
+++ /dev/null
@@ -1,132 +0,0 @@
-<br />
-<h1>FIFA Mobile para hilesi apk: How to get unlimited coins and gems in FIFA Mobile</h1>
- <p>If you are a fan of soccer games, you have probably heard of FIFA Mobile, the mobile version of the popular FIFA franchise by EA Sports. FIFA Mobile is a free-to-play game that lets you build your ultimate team of soccer stars, compete in various modes and events, and experience realistic soccer simulation on your device. But as with most free-to-play games, FIFA Mobile also has a currency system that limits your progress and enjoyment. Coins and gems are the main currencies in FIFA Mobile, and you need them to buy players, packs, upgrades, and more. However, earning coins and gems can be slow and tedious, especially if you want to get the best players and items in the game.</p>
- <p>That's why some players resort to using cheat tools like para hilesi apk, which claims to give you unlimited coins and gems in FIFA Mobile. But what is para hilesi apk, how does it work, and is it safe to use? In this article, we will answer these questions and more, as well as provide you with a step-by-step guide on how to download, install, and use para hilesi apk on your device. Read on to find out more.</p>
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- <h2>What is FIFA Mobile and why is it so popular?</h2>
- <p>FIFA Mobile is a soccer game developed by EA Sports for iOS and Android devices. It is based on the FIFA series of games, which are known for their realistic graphics, gameplay, and licenses. FIFA Mobile features over 15,000 authentic soccer players from over 600 teams across 30+ leagues, including the Premier League, La Liga, Bundesliga, Serie A, Ligue 1, MLS, and more. You can also play with national teams from the FIFA World Cup 2022™ mode, which lets you replay the official tournament brackets with any of the 32 qualified nations.</p>
- <h3>FIFA Mobile features and gameplay</h3>
- <p>FIFA Mobile has several features that make it an immersive and engaging soccer game for mobile devices. Some of these features are:</p>
- <ul>
-<li>A brand new engine with all new graphics and gameplay</li>
-<li>New player likeness, custom run styles and celebrations</li>
-<li>Chemistry system that boosts your team performance based on player relationships</li>
-<li>VIP Program with special privileges for loyal players</li>
-<li>Advanced Passing system that lets you open up space and create more attacking opportunities</li>
-<li>Live events that correspond with the real-world tournaments throughout the soccer season</li>
-<li>Icons and Heroes that let you build a team of soccer legends from different eras</li>
-</ul>
- <p>The gameplay of FIFA Mobile is simple and intuitive. You can control your players using a virtual joystick on the left side of the screen, and use buttons on the right side to sprint, skill, pass, shoot, tackle, or switch players. You can also use swipe gestures to aim your shots or passes more precisely. The game also has an auto-play option that lets the AI control your players for you.</p>
- <h3>FIFA Mobile modes and events</h3>
- <p>FIFA Mobile has several modes and events that let you compete against other players or the AI in different scenarios. Some of these modes and events are:</p>
- <ul>
-<li>Attack Mode: A turn-based mode where you and your opponent take turns to score goals within a set time limit - Head to Head: A real-time mode where you and your opponent play a full 90-minute match with 11v11 gameplay - Season: A mode where you play a series of matches against teams from different leagues and divisions - Campaign: A mode where you complete various challenges and objectives to earn rewards and unlock new players - Events: Special modes that are based on real-world soccer tournaments, such as the UEFA Champions League, the FIFA World Cup, the Copa America, and more - Squad Building Challenges: A mode where you create a team with specific requirements and earn rewards for completing them - Team of the Week: A mode where you can play against the best players of the week from different leagues and earn their cards</li>
-</ul>
- <p>FIFA Mobile also has a social aspect, where you can join a league with other players and chat, compete, and cooperate with them. You can also participate in league tournaments, league vs league matches, and league survival events.</p>
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- <h2>What is para hilesi apk and how does it work?</h2>
- <p>Para hilesi apk is a cheat tool that claims to give you unlimited coins and gems in FIFA Mobile. It is an application that you can download and install on your device, and use it to modify the game data and resources. Para hilesi apk is not an official product of EA Sports or FIFA Mobile, and it is not endorsed or supported by them. It is a third-party tool that is created by unknown developers who may have malicious intentions.</p>
- <h3>Para hilesi apk features and benefits</h3>
- <p>Para hilesi apk promises to give you several benefits that can enhance your FIFA Mobile experience. Some of these benefits are:</p>
- <ul>
-<li>Unlimited coins and gems: You can get as many coins and gems as you want, without spending any real money or time. You can use them to buy players, packs, upgrades, and more. <li>Unlimited stamina: You can play as many matches as you want, without waiting for your stamina to refill. <li>Unlimited energy: You can participate in as many events as you want, without worrying about running out of energy. <li>Unlimited VIP points: You can access the VIP Program and enjoy its perks, such as exclusive players, packs, rewards, and more. <li>No ads: You can play the game without any interruptions or distractions from ads.</li>
-</ul>
- <h3>Para hilesi apk risks and drawbacks</h3>
- <p>However, para hilesi apk also comes with several risks and drawbacks that can ruin your FIFA Mobile experience. Some of these risks and drawbacks are:</p>
- <ul>
-<li>Ban risk: Using para hilesi apk is against the terms of service of FIFA Mobile, and it can be detected by the game's anti-cheat system. If you are caught using para hilesi apk, you may face consequences such as account suspension or deletion, loss of progress and items, or legal action. <li>Virus risk: Downloading para hilesi apk from unknown sources can expose your device to viruses, malware, spyware, or other harmful software. These can damage your device, steal your personal information, or compromise your security. <li>Compatibility risk: Para hilesi apk may not work properly with the latest version of FIFA Mobile, or with different devices or operating systems. It may cause errors, glitches, crashes, or performance issues that can affect your gameplay. <li>Quality risk: Using para hilesi apk may reduce the quality of your gameplay, as it may make the game too easy or boring. It may also take away the fun and challenge of earning coins and gems legitimately, or competing with other players fairly.</li>
-</ul>
- <h2>How to download and install para hilesi apk on your device?</h2>
- <p>If you still want to try para hilesi apk despite its risks and drawbacks, you will need to follow some steps to download and install it on your device. However, we do not recommend doing so, as it may harm your device or your account. Use para hilesi apk at your own risk.</p>
- <h3>Step-by-step guide for Android users</h3>
- <p>If you are using an Android device, here are the steps to download and install para hilesi apk:</p>
- <ol>
-<li>Go to the settings of your device and enable the option to install apps from unknown sources.</li>
-<li>Go to a website that offers para hilesi apk download link. Make sure it is a reliable and trustworthy source.</li>
-<li>Click on the download button and wait for the file to be downloaded on your device.</li>
-<li>Locate the file in your device's file manager and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and grant the necessary permissions to para hilesi apk.</ <li>Once the installation is complete, you can launch para hilesi apk from your device's app drawer or home screen.</li>
-</ol>
- <h3>Step-by-step guide for iOS users</h3>
- <p>If you are using an iOS device, here are the steps to download and install para hilesi apk:</p>
- <ol>
-<li>Go to the settings of your device and trust the profile of para hilesi apk. You may need to enter your device's passcode to do so.</li>
-<li>Go to a website that offers para hilesi apk download link. Make sure it is a reliable and trustworthy source.</li>
-<li>Click on the download button and wait for the file to be downloaded on your device.</li>
-<li>Locate the file in your device's file manager and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and grant the necessary permissions to para hilesi apk.</li>
-<li>Once the installation is complete, you can launch para hilesi apk from your device's app drawer or home screen.</li>
-</ol>
- <h2>How to use para hilesi apk to get unlimited coins and gems in FIFA Mobile?</h2>
- <p>After you have downloaded and installed para hilesi apk on your device, you can use it to get unlimited coins and gems in FIFA Mobile. Here are some tips and tricks for using para hilesi apk effectively:</p>
- <ul>
-<li>Make sure you have a stable internet connection and enough storage space on your device.</li>
-<li>Make sure you have the latest version of FIFA Mobile installed on your device.</li>
-<li>Make sure you have a backup of your FIFA Mobile account and data, in case something goes wrong or you get banned.</li>
-<li>Launch para hilesi apk and enter your FIFA Mobile username or email address.</li>
-<li>Select the amount of coins and gems you want to generate. You can also choose other options such as stamina, energy, VIP points, or no ads.</li>
-<li>Click on the generate button and wait for the process to complete. You may need to verify that you are not a robot by completing a captcha or a survey.</li>
-<li>Once the process is done, you can close para hilesi apk and open FIFA Mobile. You should see your coins and gems added to your account.</li>
-</ul>
- <h3>Alternatives to para hilesi apk</h3>
- <p>If you are looking for alternatives to para hilesi apk, there are some other ways to get coins and gems in FIFA Mobile without cheating. Some of these ways are:</p>
- <ul>
-<li>Playing matches and events: You can earn coins and gems by playing different modes and events in FIFA Mobile, such as Attack Mode, Head to Head, Season, Campaign, Events, Squad Building Challenges, Team of the Week, etc. You can also get bonus coins and gems by completing daily and weekly objectives, achievements, milestones, etc. <li>Buying packs and offers: You can buy coins and gems with real money by purchasing packs and offers in FIFA Mobile. There are different types of packs and offers available, such as player packs, icon packs, hero packs, event packs, special packs, etc. You can also get discounts and deals by checking the store regularly. <li>Selling players and items: You can sell your unwanted players and items in FIFA Mobile by using the market or the quick sell option. You can get coins by selling your players or items to other players or to the game. You can also get gems by selling some rare or special players or items. <li>Joining a league: You can join a league with other players in FIFA Mobile and benefit from their help and support. You can get coins and gems by participating in league tournaments, league vs league matches, league survival events, etc. You can also get rewards by contributing to your league's achievements.</li>
-</ul>
- <h2>Conclusion</h2>
- <h4>Summary of the main points</h4>
- <p>In this article, we have discussed FIFA Mobile para hilesi apk, a cheat tool that claims to give you unlimited coins and gems in FIFA Mobile. We have explained what FIFA Mobile is and why it is so popular, what para hilesi apk is and how it works, how to download and install para hilesi apk on your device, how to use para hilesi apk to get unlimited coins and gems in FIFA Mobile, and some alternatives to para hilesi apk. We have also highlighted some of the risks and drawbacks of using para hilesi apk, such as ban risk, virus risk, compatibility risk, quality risk, etc.</p>
- <h4>Call to action and disclaimer</h4>
- <p>If you want to try para hilesi apk for yourself, you can follow the steps we have provided above. However, we do not recommend doing so, as it may harm your device or your account. Use para hilesi apk at your own risk. We are not responsible for any damage or loss that may occur from using para hilesi apk.</p>
- <p>Alternatively, you can play FIFA Mobile the legit way and enjoy the game without cheating. You can earn coins and gems by playing matches and events, buying packs and offers, selling players and items, joining a league, etc. You can also improve your skills and strategies by learning from other players, watching tutorials, reading guides, etc. You can have fun and satisfaction by building your ultimate team of soccer stars, competing in various modes and events, and experiencing realistic soccer simulation on your device.</p>
- <p>Whatever you choose to do, we hope you have a great time playing FIFA Mobile. Thank you for reading this article.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about FIFA Mobile para hilesi apk:</p>
- <ol>
-<li>Q: Is para hilesi apk free to use? A: Yes, para hilesi apk is free to use. However, you may need to complete some verification steps before you can use it, such as completing a captcha or a survey.</li>
-<li>Q: Is para hilesi apk safe to use? A: No, para hilesi apk is not safe to use. It is a cheat tool that violates the terms of service of FIFA Mobile, and it can be detected by the game's anti-cheat system. It can also expose your device to viruses, malware, spyware, or other harmful software. It can also cause errors, glitches, crashes, or performance issues that can affect your gameplay.</li>
-<li>Q: Can I use para hilesi apk on any device or operating system? A: No, para hilesi apk may not work properly on any device or operating system. It may be incompatible with the latest version of FIFA Mobile, or with different devices or operating systems. It may also require some settings or permissions that may not be available on your device or operating system.</li>
-<li>Q: Can I use para hilesi apk with my existing FIFA Mobile account? A: Yes, you can use para hilesi apk with your existing FIFA Mobile account. However, you may risk losing your account or your progress if you are caught using para hilesi apk. You may also lose your items or rewards that you have earned legitimately in the game.</li>
-<li>Q: Can I use para hilesi apk offline? A: No, you cannot use para hilesi apk offline. You need to have a stable internet connection and enough storage space on your device to use para hilesi apk. You also need to connect to the game's servers to generate coins and gems in FIFA Mobile.</li>
-</ol></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1toTree/lora_test/ppdiffusers/models/embeddings.py b/spaces/1toTree/lora_test/ppdiffusers/models/embeddings.py
deleted file mode 100644
index a7b5c91ca515c0b6d89541d266290f0cf46e0609..0000000000000000000000000000000000000000
--- a/spaces/1toTree/lora_test/ppdiffusers/models/embeddings.py
+++ /dev/null
@@ -1,199 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# 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 paddle
-from paddle import nn
-
-
-def get_timestep_embedding(
-    timesteps: paddle.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) * paddle.arange(start=0, end=half_dim, dtype="float32")
-    exponent = exponent / (half_dim - downscale_freq_shift)
-
-    emb = paddle.exp(exponent)
-    emb = timesteps[:, None].cast("float32") * emb[None, :]
-
-    # scale embeddings
-    emb = scale * emb
-
-    # concat sine and cosine embeddings
-    emb = paddle.concat([paddle.sin(emb), paddle.cos(emb)], axis=-1)
-
-    # flip sine and cosine embeddings
-    if flip_sin_to_cos:
-        emb = paddle.concat([emb[:, half_dim:], emb[:, :half_dim]], axis=-1)
-
-    # zero pad
-    if embedding_dim % 2 == 1:
-        emb = paddle.concat(emb, paddle.zeros([emb.shape[0], 1]), axis=-1)
-    return emb
-
-
-class TimestepEmbedding(nn.Layer):
-    def __init__(self, in_channels: int, time_embed_dim: int, act_fn: str = "silu", out_dim: int = None):
-        super().__init__()
-
-        self.linear_1 = nn.Linear(in_channels, time_embed_dim)
-        self.act = None
-        if act_fn == "silu":
-            self.act = nn.Silu()
-        elif act_fn == "mish":
-            self.act = nn.Mish()
-
-        if out_dim is not None:
-            time_embed_dim_out = out_dim
-        else:
-            time_embed_dim_out = time_embed_dim
-        self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out)
-
-    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.Layer):
-    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.Layer):
-    """Gaussian Fourier embeddings for noise levels."""
-
-    def __init__(
-        self, embedding_size: int = 256, scale: float = 1.0, set_W_to_weight=True, log=True, flip_sin_to_cos=False
-    ):
-        super().__init__()
-        self.register_buffer("weight", paddle.randn((embedding_size,)) * scale)
-        self.log = log
-        self.flip_sin_to_cos = flip_sin_to_cos
-
-        if set_W_to_weight:
-            # to delete later
-            self.register_buffer("W", paddle.randn((embedding_size,)) * scale)
-
-            self.weight = self.W
-
-    def forward(self, x):
-        if self.log:
-            x = paddle.log(x.cast(self.weight.dtype))
-
-        x_proj = x[:, None] * self.weight[None, :] * 2 * np.pi
-
-        if self.flip_sin_to_cos:
-            out = paddle.concat([paddle.cos(x_proj), paddle.sin(x_proj)], axis=-1)
-        else:
-            out = paddle.concat([paddle.sin(x_proj), paddle.cos(x_proj)], axis=-1)
-        return out
-
-
-class ImagePositionalEmbeddings(nn.Layer):
-    """
-    Converts latent image classes into vector embeddings. Sums the vector embeddings with positional embeddings for the
-    height and width of the latent space.
-
-    For more details, see figure 10 of the dall-e paper: https://arxiv.org/abs/2102.12092
-
-    For VQ-diffusion:
-
-    Output vector embeddings are used as input for the transformer.
-
-    Note that the vector embeddings for the transformer are different than the vector embeddings from the VQVAE.
-
-    Args:
-        num_embed (`int`):
-            Number of embeddings for the latent pixels embeddings.
-        height (`int`):
-            Height of the latent image i.e. the number of height embeddings.
-        width (`int`):
-            Width of the latent image i.e. the number of width embeddings.
-        embed_dim (`int`):
-            Dimension of the produced vector embeddings. Used for the latent pixel, height, and width embeddings.
-    """
-
-    def __init__(
-        self,
-        num_embed: int,
-        height: int,
-        width: int,
-        embed_dim: int,
-    ):
-        super().__init__()
-
-        self.height = height
-        self.width = width
-        self.num_embed = num_embed
-        self.embed_dim = embed_dim
-
-        self.emb = nn.Embedding(self.num_embed, embed_dim)
-        self.height_emb = nn.Embedding(self.height, embed_dim)
-        self.width_emb = nn.Embedding(self.width, embed_dim)
-
-    def forward(self, index):
-        emb = self.emb(index)
-
-        height_emb = self.height_emb(paddle.arange(self.height).reshape([1, self.height]))
-
-        # 1 x H x D -> 1 x H x 1 x D
-        height_emb = height_emb.unsqueeze(2)
-
-        width_emb = self.width_emb(paddle.arange(self.width).reshape([1, self.width]))
-
-        # 1 x W x D -> 1 x 1 x W x D
-        width_emb = width_emb.unsqueeze(1)
-
-        pos_emb = height_emb + width_emb
-
-        # 1 x H x W x D -> 1 x L xD
-        pos_emb = pos_emb.reshape([1, self.height * self.width, -1])
-
-        emb = emb + pos_emb[:, : emb.shape[1], :]
-
-        return emb
diff --git a/spaces/3bdo7ss/Neutron_Chatbot/README.md b/spaces/3bdo7ss/Neutron_Chatbot/README.md
deleted file mode 100644
index c0ddca48220e3541991d2754d713c8644436b94a..0000000000000000000000000000000000000000
--- a/spaces/3bdo7ss/Neutron_Chatbot/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Neutron Chatbot
-emoji: 📊
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.3
-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/AIConsultant/MusicGen/tests/common_utils/wav_utils.py b/spaces/AIConsultant/MusicGen/tests/common_utils/wav_utils.py
deleted file mode 100644
index d3a563ee1749a58217ece55c9a08b8d93c0fc386..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/tests/common_utils/wav_utils.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 pathlib import Path
-import typing as tp
-
-import torch
-import torchaudio
-
-
-def get_white_noise(chs: int = 1, num_frames: int = 1):
-    wav = torch.randn(chs, num_frames)
-    return wav
-
-
-def get_batch_white_noise(bs: int = 1, chs: int = 1, num_frames: int = 1):
-    wav = torch.randn(bs, chs, num_frames)
-    return wav
-
-
-def save_wav(path: str, wav: torch.Tensor, sample_rate: int):
-    fp = Path(path)
-    kwargs: tp.Dict[str, tp.Any] = {}
-    if fp.suffix == '.wav':
-        kwargs['encoding'] = 'PCM_S'
-        kwargs['bits_per_sample'] = 16
-    elif fp.suffix == '.mp3':
-        kwargs['compression'] = 320
-    torchaudio.save(str(fp), wav, sample_rate, **kwargs)
diff --git a/spaces/AIFILMS/generate_human_motion/VQ-Trans/README.md b/spaces/AIFILMS/generate_human_motion/VQ-Trans/README.md
deleted file mode 100644
index 547a1d4b52a5c76f0f86c641557f99d0688c0ffd..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/generate_human_motion/VQ-Trans/README.md
+++ /dev/null
@@ -1,400 +0,0 @@
-# Motion VQ-Trans
-Pytorch implementation of paper "Generating Human Motion from Textual Descriptions with High Quality Discrete Representation"
-
-
-[[Notebook Demo]](https://colab.research.google.com/drive/1tAHlmcpKcjg_zZrqKku7AfpqdVAIFrF8?usp=sharing)
-
-
-![teaser](img/Teaser.png)
-
-If our project is helpful for your research, please consider citing : (todo)
-``` 
-@inproceedings{shen2020ransac,
-          title={RANSAC-Flow: generic two-stage image alignment},
-          author={Shen, Xi and Darmon, Fran{\c{c}}ois and Efros, Alexei A and Aubry, Mathieu},
-          booktitle={16th European Conference on Computer Vision}
-          year={2020}
-        }
-```
-
-
-## Table of Content
-* [1. Visual Results](#1-visual-results)
-* [2. Installation](#2-installation)
-* [3. Quick Start](#3-quick-start)
-* [4. Train](#4-train)
-* [5. Evaluation](#5-evaluation)
-* [6. Motion Render](#6-motion-render)
-* [7. Acknowledgement](#7-acknowledgement)
-* [8. ChangLog](#8-changlog)
-
-
-
-
-## 1. Visual Results (More results can be found in our project page (todo))
-
-![visualization](img/ALLvis.png)
-
- 
-## 2. Installation
-
-### 2.1. Environment
-
-<!-- Our model can be learnt in a **single GPU GeForce GTX 1080Ti** (12G).
-
-Install Pytorch adapted to your CUDA version : 
-
-* [Pytorch 1.2.0](https://pytorch.org/get-started/previous-versions/#linux-and-windows-1) 
-* [Torchvision 0.4.0](https://pytorch.org/get-started/previous-versions/#linux-and-windows-1)
-
-Other dependencies (tqdm, visdom, pandas, kornia, opencv-python) : 
-``` Bash
-bash requirement.sh
-``` -->
-
-Our model can be learnt in a **single GPU V100-32G**
-
-```bash
-conda env create -f environment.yml
-conda activate VQTrans
-```
-
-The code was tested on Python 3.8 and PyTorch 1.8.1.
-
-
-### 2.2. Dependencies
-
-```bash
-bash dataset/prepare/download_glove.sh
-```
-
-
-### 2.3. Datasets
-
-
-We are using two 3D human motion-language dataset: HumanML3D and KIT-ML. For both datasets, you could find the details as well as download link [[here]](https://github.com/EricGuo5513/HumanML3D).   
-
-Take HumanML3D for an example, the file directory should look like this:  
-```
-./dataset/HumanML3D/
-├── new_joint_vecs/
-├── texts/
-├── Mean.npy # same as in [HumanML3D](https://github.com/EricGuo5513/HumanML3D) 
-├── Std.npy # same as in [HumanML3D](https://github.com/EricGuo5513/HumanML3D) 
-├── train.txt
-├── val.txt
-├── test.txt
-├── train_val.txt
-└──all.txt
-```
-
-
-### 2.4. Motion & text feature extractors:
-
-We use the same extractors provided by [t2m](https://github.com/EricGuo5513/text-to-motion) to evaluate our generated motions. Please download the extractors.
-
-```bash
-bash dataset/prepare/download_extractor.sh
-```
-
-### 2.5. Pre-trained models 
-
-The pretrained model files will be stored in the 'pretrained' folder:
-```bash
-bash dataset/prepare/download_model.sh
-```
-
-<!-- Quick download : 
-
-``` Bash
-cd model/pretrained
-bash download_model.sh
-```
-
-For more details of the pre-trained models, see [here](https://github.com/XiSHEN0220/RANSAC-Flow/blob/master/model/pretrained)  -->
-
-### 2.6. Render motion (optional)
-
-If you want to render the generated motion, you need to install:
-
-```bash
-sudo sh dataset/prepare/download_smpl.sh
-conda install -c menpo osmesa
-conda install h5py
-conda install -c conda-forge shapely pyrender trimesh mapbox_earcut
-```
-
-
-
-## 3. Quick Start
-
-A quick start guide of how to use our code is available in [demo.ipynb](https://colab.research.google.com/drive/1tAHlmcpKcjg_zZrqKku7AfpqdVAIFrF8?usp=sharing)
-
-<p align="center">
-<img src="img/demo.png" width="400px" alt="demo">
-</p>
-
-
-## 4. Train
-
-Note that, for kit dataset, just need to set '--dataname kit'.
-
-### 4.1. VQ-VAE 
-
-The results are saved in the folder output_vqfinal.
-
-<details>
-<summary>
-VQ training
-</summary>
-
-```bash
-python3 train_vq.py \
---batch-size 256 \
---lr 2e-4 \
---total-iter 300000 \
---lr-scheduler 200000 \
---nb-code 512 \
---down-t 2 \
---depth 3 \
---dilation-growth-rate 3 \
---out-dir output \
---dataname t2m \
---vq-act relu \
---quantizer ema_reset \
---loss-vel 0.5 \
---recons-loss l1_smooth \
---exp-name VQVAE
-```
-
-</details>
-
-### 4.2. Motion-Transformer 
-
-The results are saved in the folder output_transformer.
-
-<details>
-<summary>
-MoTrans training
-</summary>
-
-```bash
-python3 train_t2m_trans.py  \
---exp-name VQTransformer \
---batch-size 128 \
---num-layers 9 \
---embed-dim-gpt 1024 \
---nb-code 512 \
---n-head-gpt 16 \
---block-size 51 \
---ff-rate 4 \
---drop-out-rate 0.1 \
---resume-pth output/VQVAE/net_last.pth \
---vq-name VQVAE \
---out-dir output \
---total-iter 300000 \
---lr-scheduler 150000 \
---lr 0.0001 \
---dataname t2m \
---down-t 2 \
---depth 3 \
---quantizer ema_reset \
---eval-iter 10000 \
---pkeep 0.5 \
---dilation-growth-rate 3 \
---vq-act relu
-```
-
-</details>
-
-## 5. Evaluation 
-
-### 5.1. VQ-VAE 
-<details>
-<summary>
-VQ eval
-</summary>
-
-```bash
-python3 VQ_eval.py \
---batch-size 256 \
---lr 2e-4 \
---total-iter 300000 \
---lr-scheduler 200000 \
---nb-code 512 \
---down-t 2 \
---depth 3 \
---dilation-growth-rate 3 \
---out-dir output \
---dataname t2m \
---vq-act relu \
---quantizer ema_reset \
---loss-vel 0.5 \
---recons-loss l1_smooth \
---exp-name TEST_VQVAE \
---resume-pth output/VQVAE/net_last.pth
-```
-
-</details>
-
-### 5.2. Motion-Transformer
-
-<details>
-<summary>
-MoTrans eval
-</summary>
-
-```bash
-python3 GPT_eval_multi.py  \
---exp-name TEST_VQTransformer \
---batch-size 128 \
---num-layers 9 \
---embed-dim-gpt 1024 \
---nb-code 512 \
---n-head-gpt 16 \
---block-size 51 \
---ff-rate 4 \
---drop-out-rate 0.1 \
---resume-pth output/VQVAE/net_last.pth \
---vq-name VQVAE \
---out-dir output \
---total-iter 300000 \
---lr-scheduler 150000 \
---lr 0.0001 \
---dataname t2m \
---down-t 2 \
---depth 3 \
---quantizer ema_reset \
---eval-iter 10000 \
---pkeep 0.5 \
---dilation-growth-rate 3 \
---vq-act relu \
---resume-gpt output/VQTransformer/net_best_fid.pth
-```
-
-</details>
-
-
-## 6. Motion Render 
-
-<details>
-<summary>
-Motion Render
-</summary>
-
-You should input the npy folder address and the motion names. Here is an example:
-
-```bash
-python3 render_final.py --filedir output/TEST_VQTransformer/ --motion-list 000019 005485
-```
-
-</details>
-
-### 7. Acknowledgement
-
-We appreciate helps from :  
-
-* Public code like [text-to-motion](https://github.com/EricGuo5513/text-to-motion), [TM2T](https://github.com/EricGuo5513/TM2T) etc.
-
-### 8. ChangLog
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-<!-- # VQGPT
-
-```
-# VQ during training OT
-/apdcephfs_cq2/share_1290939/jirozhang/anaconda3/envs/motionclip/bin/python3 train_251_cnn_all.py \
---batch-size 128 \
---exp-name xxxxxx \
---lr 2e-4 \
---total-iter 300000 \
---lr-scheduler 200000 \
---nb-code 512 \
---down-t 2 \
---depth 5 \
---out-dir /apdcephfs_cq2/share_1290939/jirozhang/VQCNN_HUMAN/ \
---dataname t2m \
---vq-act relu \
---quantizer ot \
---ot-temperature 1 \
---ot-eps 0.5 \
---commit 0.001 \
-```
-
-```
-# VQ251 training baseline
-/apdcephfs_cq2/share_1290939/jirozhang/anaconda3/envs/motionclip/bin/python3 train_251_cnn_all.py \
---batch-size 128 \
---exp-name VQ263_300K_512cb_down4_t2m_ema_relu_test \
---lr 2e-4 \
---total-iter 300000 \
---lr-scheduler 200000 \
---nb-code 512 \
---down-t 2 \
---depth 5 \
---out-dir /apdcephfs_cq2/share_1290939/jirozhang/VQCNN_HUMAN/ \
---dataname t2m \
---vq-act relu \
---quantizer ema \
-```
-
-
-```bash
-# gpt training + noise
-/apdcephfs_cq2/share_1290939/jirozhang/anaconda3/envs/motionclip/bin/python3 train_gpt_cnn_noise.py  \
---exp-name GPT_VQ_300K_512cb_down4_t2m_ema_relu_bs128_ws64_fid_mask1_08 \
---batch-size 128 \
---num-layers 4 \
---block-size 51 \
---n-head-gpt 8 \
---ff-rate 4 \
---drop-out-rate 0.1 \
---resume-pth output_vqhuman/VQ_300K_512cb_down4_t2m_ema_relu_bs128_ws64/net_best_fid.pth \
---vq-name VQ_300K_512cb_down4_t2m_ema_relu_bs128_ws64_fid_mask1_08 \
---total-iter 300000 \
---lr-scheduler 150000 \
---lr 0.0001 \
---if-auxloss \
---dataname t2m \
---down-t 2 \
---depth 5 \
---quantizer ema \
---eval-iter 5000 \
---pkeep 0.8
-```
-
-
-### Visualize VQ (Arch Taming) in HTML 
-
-* Generate motion. This will save generated motions in `./visual_results/vel05_taming_l1s`
-
-```
-python vis.py --dataname t2m --resume-pth /apdcephfs_cq2/share_1290939/jirozhang/VQ_t2m_bailando_relu_NoNorm_dilate3_vel05_taming_l1s/net_last.pth --visual-name vel05_taming_l1s --vis-gt --nb-vis 20
-```
-
-* Make a Webpage. Go to visual_html.py, modify the name, then run :
-
-```
-python visual_html.py
-``` -->
diff --git a/spaces/AIFILMS/generate_human_motion/pyrender/pyrender/platforms/base.py b/spaces/AIFILMS/generate_human_motion/pyrender/pyrender/platforms/base.py
deleted file mode 100644
index c9ecda906145e239737901809aa59db8d3e231c6..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/generate_human_motion/pyrender/pyrender/platforms/base.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import abc
-
-import six
-
-
-@six.add_metaclass(abc.ABCMeta)
-class Platform(object):
-    """Base class for all OpenGL platforms.
-
-    Parameters
-    ----------
-    viewport_width : int
-        The width of the main viewport, in pixels.
-    viewport_height : int
-        The height of the main viewport, in pixels
-    """
-
-    def __init__(self, viewport_width, viewport_height):
-        self.viewport_width = viewport_width
-        self.viewport_height = viewport_height
-
-    @property
-    def viewport_width(self):
-        """int : The width of the main viewport, in pixels.
-        """
-        return self._viewport_width
-
-    @viewport_width.setter
-    def viewport_width(self, value):
-        self._viewport_width = value
-
-    @property
-    def viewport_height(self):
-        """int : The height of the main viewport, in pixels.
-        """
-        return self._viewport_height
-
-    @viewport_height.setter
-    def viewport_height(self, value):
-        self._viewport_height = value
-
-    @abc.abstractmethod
-    def init_context(self):
-        """Create an OpenGL context.
-        """
-        pass
-
-    @abc.abstractmethod
-    def make_current(self):
-        """Make the OpenGL context current.
-        """
-        pass
-
-    @abc.abstractmethod
-    def make_uncurrent(self):
-        """Make the OpenGL context uncurrent.
-        """
-        pass
-
-    @abc.abstractmethod
-    def delete_context(self):
-        """Delete the OpenGL context.
-        """
-        pass
-
-    @abc.abstractmethod
-    def supports_framebuffers(self):
-        """Returns True if the method supports framebuffer rendering.
-        """
-        pass
-
-    def __del__(self):
-        try:
-            self.delete_context()
-        except Exception:
-            pass
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/htsat.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/htsat.py
deleted file mode 100644
index db96116286d307a73943886f947450215e061ba2..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/encoders/open_clap/htsat.py
+++ /dev/null
@@ -1,1022 +0,0 @@
-# Ke Chen
-# knutchen@ucsd.edu
-# HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION
-# Some layers designed on the model
-# below codes are based and referred from https://github.com/microsoft/Swin-Transformer
-# Swin Transformer for Computer Vision: https://arxiv.org/pdf/2103.14030.pdf
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from itertools import repeat
-import collections.abc
-import math
-import warnings
-
-from torch.nn.init import _calculate_fan_in_and_fan_out
-import torch.utils.checkpoint as checkpoint
-
-import random
-
-from torchlibrosa.stft import Spectrogram, LogmelFilterBank
-from torchlibrosa.augmentation import SpecAugmentation
-
-from itertools import repeat
-from .utils import do_mixup, interpolate
-
-from .feature_fusion import iAFF, AFF, DAF
-
-# from PyTorch internals
-def _ntuple(n):
-    def parse(x):
-        if isinstance(x, collections.abc.Iterable):
-            return x
-        return tuple(repeat(x, n))
-    return parse
-
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = _ntuple
-
-def drop_path(x, drop_prob: float = 0., training: bool = False):
-    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
-    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
-    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
-    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
-    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
-    'survival rate' as the argument.
-    """
-    if drop_prob == 0. or not training:
-        return x
-    keep_prob = 1 - drop_prob
-    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
-    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
-    random_tensor.floor_()  # binarize
-    output = x.div(keep_prob) * random_tensor
-    return output
-
-
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
-    """
-    def __init__(self, drop_prob=None):
-        super(DropPath, self).__init__()
-        self.drop_prob = drop_prob
-
-    def forward(self, x):
-        return drop_path(x, self.drop_prob, self.training)
-
-class PatchEmbed(nn.Module):
-    """ 2D Image to Patch Embedding
-    """
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True, patch_stride = 16,
-        enable_fusion=False, fusion_type='None'):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patch_stride = to_2tuple(patch_stride)
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patch_stride = patch_stride
-        self.grid_size = (img_size[0] // patch_stride[0], img_size[1] // patch_stride[1])
-        self.num_patches = self.grid_size[0] * self.grid_size[1]
-        self.flatten = flatten
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-        
-        padding = ((patch_size[0] - patch_stride[0]) // 2, (patch_size[1] - patch_stride[1]) // 2)
-
-        if (self.enable_fusion) and (self.fusion_type == 'channel_map'):
-            self.proj = nn.Conv2d(in_chans*4, embed_dim, kernel_size=patch_size, stride=patch_stride, padding=padding)
-        else:
-            self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_stride, padding=padding)
-        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
-
-        if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']):
-            self.mel_conv2d = nn.Conv2d(in_chans, embed_dim, kernel_size=(patch_size[0], patch_size[1]*3), stride=(patch_stride[0], patch_stride[1] * 3), padding=padding)
-            if self.fusion_type == 'daf_2d':
-                self.fusion_model = DAF()
-            elif self.fusion_type == 'aff_2d':
-                self.fusion_model = AFF(channels=embed_dim, type='2D')
-            elif self.fusion_type == 'iaff_2d':
-                self.fusion_model = iAFF(channels=embed_dim, type='2D')    
-    def forward(self, x, longer_idx = None):
-        if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']):
-            global_x = x[:,0:1,:,:]
-            
-
-            # global processing
-            B, C, H, W = global_x.shape
-            assert H == self.img_size[0] and W == self.img_size[1], \
-                f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-            global_x = self.proj(global_x)
-            TW = global_x.size(-1)
-            if len(longer_idx) > 0:
-                # local processing
-                local_x = x[longer_idx,1:,:,:].contiguous()
-                B, C, H, W = local_x.shape
-                local_x = local_x.view(B*C,1,H,W)
-                local_x = self.mel_conv2d(local_x)
-                local_x = local_x.view(B,C,local_x.size(1),local_x.size(2),local_x.size(3))
-                local_x = local_x.permute((0,2,3,1,4)).contiguous().flatten(3)
-                TB,TC,TH,_ = local_x.size()
-                if local_x.size(-1) < TW:
-                    local_x = torch.cat([local_x, torch.zeros((TB,TC,TH,TW-local_x.size(-1)), device=global_x.device)], dim=-1)
-                else:
-                    local_x = local_x[:,:,:,:TW]
-                
-                global_x[longer_idx] = self.fusion_model(global_x[longer_idx],local_x)
-            x = global_x
-        else:
-            B, C, H, W = x.shape
-            assert H == self.img_size[0] and W == self.img_size[1], \
-                f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-            x = self.proj(x)
-        
-        if self.flatten:
-            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
-        x = self.norm(x)
-        return x
-
-class Mlp(nn.Module):
-    """ MLP as used in Vision Transformer, MLP-Mixer and related networks
-    """
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-def _no_grad_trunc_normal_(tensor, mean, std, a, b):
-    # Cut & paste from PyTorch official master until it's in a few official releases - RW
-    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
-    def norm_cdf(x):
-        # Computes standard normal cumulative distribution function
-        return (1. + math.erf(x / math.sqrt(2.))) / 2.
-
-    if (mean < a - 2 * std) or (mean > b + 2 * std):
-        warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
-                      "The distribution of values may be incorrect.",
-                      stacklevel=2)
-
-    with torch.no_grad():
-        # Values are generated by using a truncated uniform distribution and
-        # then using the inverse CDF for the normal distribution.
-        # Get upper and lower cdf values
-        l = norm_cdf((a - mean) / std)
-        u = norm_cdf((b - mean) / std)
-
-        # Uniformly fill tensor with values from [l, u], then translate to
-        # [2l-1, 2u-1].
-        tensor.uniform_(2 * l - 1, 2 * u - 1)
-
-        # Use inverse cdf transform for normal distribution to get truncated
-        # standard normal
-        tensor.erfinv_()
-
-        # Transform to proper mean, std
-        tensor.mul_(std * math.sqrt(2.))
-        tensor.add_(mean)
-
-        # Clamp to ensure it's in the proper range
-        tensor.clamp_(min=a, max=b)
-        return tensor
-
-
-def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
-    # type: (Tensor, float, float, float, float) -> Tensor
-    r"""Fills the input Tensor with values drawn from a truncated
-    normal distribution. The values are effectively drawn from the
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
-    with values outside :math:`[a, b]` redrawn until they are within
-    the bounds. The method used for generating the random values works
-    best when :math:`a \leq \text{mean} \leq b`.
-    Args:
-        tensor: an n-dimensional `torch.Tensor`
-        mean: the mean of the normal distribution
-        std: the standard deviation of the normal distribution
-        a: the minimum cutoff value
-        b: the maximum cutoff value
-    Examples:
-        >>> w = torch.empty(3, 5)
-        >>> nn.init.trunc_normal_(w)
-    """
-    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
-
-
-def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
-    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
-    if mode == 'fan_in':
-        denom = fan_in
-    elif mode == 'fan_out':
-        denom = fan_out
-    elif mode == 'fan_avg':
-        denom = (fan_in + fan_out) / 2
-
-    variance = scale / denom
-
-    if distribution == "truncated_normal":
-        # constant is stddev of standard normal truncated to (-2, 2)
-        trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978)
-    elif distribution == "normal":
-        tensor.normal_(std=math.sqrt(variance))
-    elif distribution == "uniform":
-        bound = math.sqrt(3 * variance)
-        tensor.uniform_(-bound, bound)
-    else:
-        raise ValueError(f"invalid distribution {distribution}")
-
-
-def lecun_normal_(tensor):
-    variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal')
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x, attn
-
-    def extra_repr(self):
-        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
-
-
-# We use the model based on Swintransformer Block, therefore we can use the swin-transformer pretrained model
-class SwinTransformerBlock(nn.Module):
-    r""" Swin Transformer Block.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, norm_before_mlp='ln'):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        self.norm_before_mlp = norm_before_mlp
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        if self.norm_before_mlp == 'ln':
-            self.norm2 = nn.LayerNorm(dim)
-        elif self.norm_before_mlp == 'bn':
-            self.norm2 = lambda x: nn.BatchNorm1d(dim)(x.transpose(1, 2)).transpose(1, 2)
-        else:
-            raise NotImplementedError
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        if self.shift_size > 0:
-            # calculate attention mask for SW-MSA
-            H, W = self.input_resolution
-            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-            h_slices = (slice(0, -self.window_size),
-                        slice(-self.window_size, -self.shift_size),
-                        slice(-self.shift_size, None))
-            w_slices = (slice(0, -self.window_size),
-                        slice(-self.window_size, -self.shift_size),
-                        slice(-self.shift_size, None))
-            cnt = 0
-            for h in h_slices:
-                for w in w_slices:
-                    img_mask[:, h, w, :] = cnt
-                    cnt += 1
-
-            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
-    def forward(self, x):
-        # pdb.set_trace()
-        H, W = self.input_resolution
-        # print("H: ", H)
-        # print("W: ", W)
-        # pdb.set_trace()
-        B, L, C = x.shape
-        # assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows, attn = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x, attn
-
-    def extra_repr(self):
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
-               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
-
-
-class PatchMerging(nn.Module):
-    r""" Patch Merging Layer.
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-    def extra_repr(self):
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
-                 norm_before_mlp='ln'):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
-                                 num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
-                                 mlp_ratio=mlp_ratio,
-                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                 drop=drop, attn_drop=attn_drop,
-                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                 norm_layer=norm_layer, norm_before_mlp=norm_before_mlp)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x):
-        attns = []
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x, attn = blk(x)
-                if not self.training:
-                    attns.append(attn.unsqueeze(0))
-        if self.downsample is not None:
-            x = self.downsample(x)
-        if not self.training:
-            attn = torch.cat(attns, dim = 0)
-            attn = torch.mean(attn, dim = 0)
-        return x, attn
-
-    def extra_repr(self):
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-
-# The Core of HTSAT
-class HTSAT_Swin_Transformer(nn.Module):
-    r"""HTSAT based on the Swin Transformer
-    Args:
-        spec_size (int | tuple(int)): Input Spectrogram size. Default 256
-        patch_size (int | tuple(int)): Patch size. Default: 4
-        path_stride (iot | tuple(int)): Patch Stride for Frequency and Time Axis. Default: 4
-        in_chans (int): Number of input image channels. Default: 1 (mono)
-        num_classes (int): Number of classes for classification head. Default: 527
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each HTSAT-Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 8
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        config (module): The configuration Module from config.py
-    """
-
-    def __init__(self, spec_size=256, patch_size=4, patch_stride=(4,4), 
-                in_chans=1, num_classes=527,
-                 embed_dim=96, depths=[2, 2, 6, 2], num_heads=[4, 8, 16, 32],
-                 window_size=8, mlp_ratio=4., qkv_bias=True, qk_scale=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
-                 norm_layer=nn.LayerNorm, 
-                 ape=False, patch_norm=True,
-                 use_checkpoint=False, norm_before_mlp='ln', config = None, 
-                 enable_fusion = False, fusion_type = 'None', **kwargs):
-        super(HTSAT_Swin_Transformer, self).__init__()
-
-        self.config = config
-        self.spec_size = spec_size 
-        self.patch_stride = patch_stride
-        self.patch_size = patch_size
-        self.window_size = window_size
-        self.embed_dim = embed_dim
-        self.depths = depths
-        self.ape = ape
-        self.in_chans = in_chans
-        self.num_classes = num_classes
-        self.num_heads = num_heads
-        self.num_layers = len(self.depths)
-        self.num_features = int(self.embed_dim * 2 ** (self.num_layers - 1))
-        
-        self.drop_rate = drop_rate
-        self.attn_drop_rate = attn_drop_rate
-        self.drop_path_rate = drop_path_rate
-
-        self.qkv_bias = qkv_bias
-        self.qk_scale = None
-
-        self.patch_norm = patch_norm
-        self.norm_layer = norm_layer if self.patch_norm else None
-        self.norm_before_mlp = norm_before_mlp
-        self.mlp_ratio = mlp_ratio
-
-        self.use_checkpoint = use_checkpoint
-
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-
-        #  process mel-spec ; used only once
-        self.freq_ratio = self.spec_size // self.config.mel_bins
-        window = 'hann'
-        center = True
-        pad_mode = 'reflect'
-        ref = 1.0
-        amin = 1e-10
-        top_db = None
-        self.interpolate_ratio = 32     # Downsampled ratio
-        # Spectrogram extractor
-        self.spectrogram_extractor = Spectrogram(n_fft=config.window_size, hop_length=config.hop_size, 
-            win_length=config.window_size, window=window, center=center, pad_mode=pad_mode, 
-            freeze_parameters=True)
-        # Logmel feature extractor
-        self.logmel_extractor = LogmelFilterBank(sr=config.sample_rate, n_fft=config.window_size, 
-            n_mels=config.mel_bins, fmin=config.fmin, fmax=config.fmax, ref=ref, amin=amin, top_db=top_db, 
-            freeze_parameters=True)
-        # Spec augmenter
-        self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2, 
-            freq_drop_width=8, freq_stripes_num=2) # 2 2
-        self.bn0 = nn.BatchNorm2d(self.config.mel_bins)
-
-
-        # split spctrogram into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=self.spec_size, patch_size=self.patch_size, in_chans=self.in_chans, 
-            embed_dim=self.embed_dim, norm_layer=self.norm_layer, patch_stride = patch_stride,
-            enable_fusion=self.enable_fusion, fusion_type=self.fusion_type
-            )
-
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.grid_size
-        self.patches_resolution = patches_resolution
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, self.embed_dim))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=self.drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, self.drop_path_rate, sum(self.depths))]  # stochastic depth decay rule
-
-        # build layers
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = BasicLayer(dim=int(self.embed_dim * 2 ** i_layer),
-                input_resolution=(patches_resolution[0] // (2 ** i_layer),
-                                    patches_resolution[1] // (2 ** i_layer)),
-                depth=self.depths[i_layer],
-                num_heads=self.num_heads[i_layer],
-                window_size=self.window_size,
-                mlp_ratio=self.mlp_ratio,
-                qkv_bias=self.qkv_bias, qk_scale=self.qk_scale,
-                drop=self.drop_rate, attn_drop=self.attn_drop_rate,
-                drop_path=dpr[sum(self.depths[:i_layer]):sum(self.depths[:i_layer + 1])],
-                norm_layer=self.norm_layer,
-                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
-                use_checkpoint=use_checkpoint,
-                norm_before_mlp=self.norm_before_mlp)
-            self.layers.append(layer)
-
-        self.norm = self.norm_layer(self.num_features)
-        self.avgpool = nn.AdaptiveAvgPool1d(1)
-        self.maxpool = nn.AdaptiveMaxPool1d(1)
-        
-        SF = self.spec_size // (2 ** (len(self.depths) - 1)) // self.patch_stride[0] // self.freq_ratio
-        self.tscam_conv = nn.Conv2d(
-            in_channels = self.num_features,
-            out_channels = self.num_classes,
-            kernel_size = (SF,3),
-            padding = (0,1)
-        )
-        self.head = nn.Linear(num_classes, num_classes)
-
-        if (self.enable_fusion) and (self.fusion_type in ['daf_1d','aff_1d','iaff_1d']):
-            self.mel_conv1d = nn.Sequential(
-                nn.Conv1d(64, 64, kernel_size=5, stride=3, padding=2),
-                nn.BatchNorm1d(64)
-            )
-            if self.fusion_type == 'daf_1d':
-                self.fusion_model = DAF()
-            elif self.fusion_type == 'aff_1d':
-                self.fusion_model = AFF(channels=64, type='1D')
-            elif self.fusion_type == 'iaff_1d':
-                self.fusion_model = iAFF(channels=64, type='1D')
-                
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'absolute_pos_embed'}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {'relative_position_bias_table'}
-
-
-    def forward_features(self, x, longer_idx = None):
-        # A deprecated optimization for using a hierarchical output from different blocks
-
-        frames_num = x.shape[2]        
-        x = self.patch_embed(x, longer_idx = longer_idx)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-        for i, layer in enumerate(self.layers):
-            x, attn = layer(x)
-        # for x
-        x = self.norm(x)
-        B, N, C = x.shape
-        SF = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[0]
-        ST = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[1]
-        x = x.permute(0,2,1).contiguous().reshape(B, C, SF, ST)
-        B, C, F, T = x.shape
-        # group 2D CNN
-        c_freq_bin = F // self.freq_ratio
-        x = x.reshape(B, C, F // c_freq_bin, c_freq_bin, T)
-        x = x.permute(0,1,3,2,4).contiguous().reshape(B, C, c_freq_bin, -1)
-        # get latent_output
-        fine_grained_latent_output = torch.mean(x, dim = 2)
-        fine_grained_latent_output = interpolate(fine_grained_latent_output.permute(0,2,1).contiguous(), 8 * self.patch_stride[1]) 
-        
-        latent_output = self.avgpool(torch.flatten(x,2))
-        latent_output = torch.flatten(latent_output, 1)
-
-        # display the attention map, if needed
-
-        x = self.tscam_conv(x)
-        x = torch.flatten(x, 2) # B, C, T
- 
-        fpx = interpolate(torch.sigmoid(x).permute(0,2,1).contiguous(), 8 * self.patch_stride[1]) 
-            
-        x = self.avgpool(x)
-        x = torch.flatten(x, 1)
-
-        output_dict = {
-            'framewise_output': fpx, # already sigmoided
-            'clipwise_output': torch.sigmoid(x),
-            'fine_grained_embedding': fine_grained_latent_output,
-            'embedding': latent_output
-        }
-
-        return output_dict
-
-    def crop_wav(self, x, crop_size, spe_pos = None):
-        time_steps = x.shape[2]
-        tx = torch.zeros(x.shape[0], x.shape[1], crop_size, x.shape[3]).to(x.device)
-        for i in range(len(x)):
-            if spe_pos is None:
-                crop_pos = random.randint(0, time_steps - crop_size - 1)
-            else:
-                crop_pos = spe_pos
-            tx[i][0] = x[i, 0, crop_pos:crop_pos + crop_size,:]
-        return tx
-
-    # Reshape the wavform to a img size, if you want to use the pretrained swin transformer model
-    def reshape_wav2img(self, x):
-        B, C, T, F = x.shape
-        target_T = int(self.spec_size * self.freq_ratio)
-        target_F = self.spec_size // self.freq_ratio
-        assert T <= target_T and F <= target_F, "the wav size should less than or equal to the swin input size"
-        # to avoid bicubic zero error
-        if T < target_T:
-            x = nn.functional.interpolate(x, (target_T, x.shape[3]), mode="bicubic", align_corners=True)
-        if F < target_F:
-            x = nn.functional.interpolate(x, (x.shape[2], target_F), mode="bicubic", align_corners=True)
-        x = x.permute(0,1,3,2).contiguous()
-        x = x.reshape(x.shape[0], x.shape[1], x.shape[2], self.freq_ratio, x.shape[3] // self.freq_ratio)
-        # print(x.shape)
-        x = x.permute(0,1,3,2,4).contiguous()
-        x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3], x.shape[4])
-        return x
-    
-    # Repeat the wavform to a img size, if you want to use the pretrained swin transformer model
-    def repeat_wat2img(self, x, cur_pos):
-        B, C, T, F = x.shape
-        target_T = int(self.spec_size * self.freq_ratio)
-        target_F = self.spec_size // self.freq_ratio
-        assert T <= target_T and F <= target_F, "the wav size should less than or equal to the swin input size"
-        # to avoid bicubic zero error
-        if T < target_T:
-            x = nn.functional.interpolate(x, (target_T, x.shape[3]), mode="bicubic", align_corners=True)
-        if F < target_F:
-            x = nn.functional.interpolate(x, (x.shape[2], target_F), mode="bicubic", align_corners=True)  
-        x = x.permute(0,1,3,2).contiguous() # B C F T
-        x = x[:,:,:,cur_pos:cur_pos + self.spec_size]
-        x = x.repeat(repeats = (1,1,4,1))
-        return x
-
-    def forward(self, x: torch.Tensor, mixup_lambda = None, infer_mode = False, device=None):# out_feat_keys: List[str] = None):
-
-        if self.enable_fusion and x["longer"].sum() == 0:
-            # if no audio is longer than 10s, then randomly select one audio to be longer
-            x["longer"][torch.randint(0, x["longer"].shape[0], (1,))] = True
-
-        if not self.enable_fusion:
-            x = x["waveform"].to(device=device, non_blocking=True)
-            x = self.spectrogram_extractor(x)   # (batch_size, 1, time_steps, freq_bins)
-            x = self.logmel_extractor(x)    # (batch_size, 1, time_steps, mel_bins)
-            x = x.transpose(1, 3)
-            x = self.bn0(x)
-            x = x.transpose(1, 3)
-            if self.training:
-                x = self.spec_augmenter(x)
-
-            if self.training and mixup_lambda is not None:
-                x = do_mixup(x, mixup_lambda)
-                
-            x = self.reshape_wav2img(x)
-            output_dict = self.forward_features(x)
-        else:
-            longer_list = x["longer"].to(device=device, non_blocking=True)
-            x = x["mel_fusion"].to(device=device, non_blocking=True)
-            x = x.transpose(1, 3)
-            x = self.bn0(x)
-            x = x.transpose(1, 3)
-            longer_list_idx = torch.where(longer_list)[0]
-            if self.fusion_type in ['daf_1d','aff_1d','iaff_1d']:
-                new_x = x[:,0:1,:,:].clone().contiguous()
-                if len(longer_list_idx) > 0:
-                # local processing
-                    fusion_x_local = x[longer_list_idx,1:,:,:].clone().contiguous()
-                    FB,FC,FT,FF = fusion_x_local.size()
-                    fusion_x_local = fusion_x_local.view(FB * FC, FT, FF)
-                    fusion_x_local = torch.permute(fusion_x_local, (0,2,1)).contiguous()
-                    fusion_x_local = self.mel_conv1d(fusion_x_local)
-                    fusion_x_local = fusion_x_local.view(FB,FC,FF,fusion_x_local.size(-1))
-                    fusion_x_local = torch.permute(fusion_x_local, (0,2,1,3)).contiguous().flatten(2)
-                    if fusion_x_local.size(-1) < FT:
-                        fusion_x_local = torch.cat([fusion_x_local, torch.zeros((FB,FF,FT- fusion_x_local.size(-1)), device=device)], dim=-1)
-                    else:
-                        fusion_x_local = fusion_x_local[:,:,:FT]
-                    # 1D fusion
-                    new_x = new_x.squeeze(1).permute((0,2,1)).contiguous()
-                    new_x[longer_list_idx] = self.fusion_model(new_x[longer_list_idx], fusion_x_local)
-                    x = new_x.permute((0,2,1)).contiguous()[:,None,:,:]
-                else:
-                    x = new_x
-
-            elif self.fusion_type in ['daf_2d','aff_2d','iaff_2d','channel_map']:
-                x = x # no change
-
-            if self.training:
-                x = self.spec_augmenter(x)
-            if self.training and mixup_lambda is not None:
-                x = do_mixup(x, mixup_lambda)
-
-            x = self.reshape_wav2img(x)
-            output_dict = self.forward_features(x, longer_idx = longer_list_idx)
-       
-        # if infer_mode:
-        #     # in infer mode. we need to handle different length audio input
-        #     frame_num = x.shape[2]
-        #     target_T = int(self.spec_size * self.freq_ratio)
-        #     repeat_ratio = math.floor(target_T / frame_num)
-        #     x = x.repeat(repeats=(1,1,repeat_ratio,1))
-        #     x = self.reshape_wav2img(x)
-        #     output_dict = self.forward_features(x)
-        # else:
-        #     if x.shape[2] > self.freq_ratio * self.spec_size:
-        #         if self.training:
-        #             x = self.crop_wav(x, crop_size=self.freq_ratio * self.spec_size)
-        #             x = self.reshape_wav2img(x)
-        #             output_dict = self.forward_features(x)
-        #         else:
-        #             # Change: Hard code here
-        #             overlap_size = (x.shape[2] - 1) // 4
-        #             output_dicts = []
-        #             crop_size = (x.shape[2] - 1) // 2
-        #             for cur_pos in range(0, x.shape[2] - crop_size - 1, overlap_size):
-        #                 tx = self.crop_wav(x, crop_size = crop_size, spe_pos = cur_pos)
-        #                 tx = self.reshape_wav2img(tx)
-        #                 output_dicts.append(self.forward_features(tx))
-        #             clipwise_output = torch.zeros_like(output_dicts[0]["clipwise_output"]).float().to(x.device)
-        #             framewise_output = torch.zeros_like(output_dicts[0]["framewise_output"]).float().to(x.device)
-        #             for d in output_dicts:
-        #                 clipwise_output += d["clipwise_output"]
-        #                 framewise_output += d["framewise_output"]
-        #             clipwise_output  = clipwise_output / len(output_dicts)
-        #             framewise_output = framewise_output / len(output_dicts)
-        #             output_dict = {
-        #                 'framewise_output': framewise_output, 
-        #                 'clipwise_output': clipwise_output
-        #             }
-        #     else: # this part is typically used, and most easy one
-        #         x = self.reshape_wav2img(x)
-        #         output_dict = self.forward_features(x)
-        # x = self.head(x)
-
-        # We process the data in the dataloader part, in that here we only consider the input_T < fixed_T
-        
-        
-
-        return output_dict
-
-def create_htsat_model(audio_cfg, enable_fusion=False, fusion_type='None'):
-    try:
-
-        assert audio_cfg.model_name in ["tiny", "base", "large"], "model name for HTS-AT is wrong!"
-        if audio_cfg.model_name == "tiny":
-            model = HTSAT_Swin_Transformer(
-                spec_size=256,
-                patch_size=4,
-                patch_stride=(4,4),
-                num_classes=audio_cfg.class_num,
-                embed_dim=96,
-                depths=[2,2,6,2],
-                num_heads=[4,8,16,32],
-                window_size=8,
-                config = audio_cfg,
-                enable_fusion = enable_fusion,
-                fusion_type = fusion_type
-            )
-        elif audio_cfg.model_name == "base":
-            model = HTSAT_Swin_Transformer(
-                spec_size=256,
-                patch_size=4,
-                patch_stride=(4,4),
-                num_classes=audio_cfg.class_num,
-                embed_dim=128,
-                depths=[2,2,12,2],
-                num_heads=[4,8,16,32],
-                window_size=8,
-                config = audio_cfg,
-                enable_fusion = enable_fusion,
-                fusion_type = fusion_type
-            )
-        elif audio_cfg.model_name == "large":
-            model = HTSAT_Swin_Transformer(
-                spec_size=256,
-                patch_size=4,
-                patch_stride=(4,4),
-                num_classes=audio_cfg.class_num,
-                embed_dim=256,
-                depths=[2,2,12,2],
-                num_heads=[4,8,16,32],
-                window_size=8,
-                config = audio_cfg,
-                enable_fusion = enable_fusion,
-                fusion_type = fusion_type
-            )
-        
-        return model
-    except:
-        raise RuntimeError(f'Import Model for {audio_cfg.model_name} not found, or the audio cfg parameters are not enough.')
-        
\ No newline at end of file
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/transforms.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/transforms.py
deleted file mode 100644
index bdab7eb6b94ac21e950e2870b89da7bbac1f4a8e..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/losses_audio/vggishish/transforms.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import logging
-import os
-from pathlib import Path
-
-import albumentations
-import numpy as np
-import torch
-from tqdm import tqdm
-
-logger = logging.getLogger(f'main.{__name__}')
-
-
-class StandardNormalizeAudio(object):
-    '''
-        Frequency-wise normalization
-    '''
-    def __init__(self, specs_dir, train_ids_path='./data/vggsound_train.txt', cache_path='./data/'):
-        self.specs_dir = specs_dir
-        self.train_ids_path = train_ids_path
-        # making the stats filename to match the specs dir name
-        self.cache_path = os.path.join(cache_path, f'train_means_stds_{Path(specs_dir).stem}.txt')
-        logger.info('Assuming that the input stats are calculated using preprocessed spectrograms (log)')
-        self.train_stats = self.calculate_or_load_stats()
-
-    def __call__(self, item):
-        # just to generalizat the input handling. Useful for FID, IS eval and training other staff
-        if isinstance(item, dict):
-            if 'input' in item:
-                input_key = 'input'
-            elif 'image' in item:
-                input_key = 'image'
-            else:
-                raise NotImplementedError
-            item[input_key] = (item[input_key] - self.train_stats['means']) / self.train_stats['stds']
-        elif isinstance(item, torch.Tensor):
-            # broadcasts np.ndarray (80, 1) to (1, 80, 1) because item is torch.Tensor (B, 80, T)
-            item = (item - self.train_stats['means']) / self.train_stats['stds']
-        else:
-            raise NotImplementedError
-        return item
-
-    def calculate_or_load_stats(self):
-        try:
-            # (F, 2)
-            train_stats = np.loadtxt(self.cache_path)
-            means, stds = train_stats.T
-            logger.info('Trying to load train stats for Standard Normalization of inputs')
-        except OSError:
-            logger.info('Could not find the precalculated stats for Standard Normalization. Calculating...')
-            train_vid_ids = open(self.train_ids_path)
-            specs_paths = [os.path.join(self.specs_dir, f'{i.rstrip()}_mel.npy') for i in train_vid_ids]
-            means = [None] * len(specs_paths)
-            stds = [None] * len(specs_paths)
-            for i, path in enumerate(tqdm(specs_paths)):
-                spec = np.load(path)
-                means[i] = spec.mean(axis=1)
-                stds[i] = spec.std(axis=1)
-            # (F) <- (num_files, F)
-            means = np.array(means).mean(axis=0)
-            stds = np.array(stds).mean(axis=0)
-            # saving in two columns
-            np.savetxt(self.cache_path, np.vstack([means, stds]).T, fmt='%0.8f')
-        means = means.reshape(-1, 1)
-        stds = stds.reshape(-1, 1)
-        return {'means': means, 'stds': stds}
-
-class ToTensor(object):
-
-    def __call__(self, item):
-        item['input'] = torch.from_numpy(item['input']).float()
-        # if 'target' in item:
-        item['target'] = torch.tensor(item['target'])
-        return item
-
-class Crop(object):
-
-    def __init__(self, cropped_shape=None, random_crop=False):
-        self.cropped_shape = cropped_shape
-        if cropped_shape is not None:
-            mel_num, spec_len = cropped_shape
-            if random_crop:
-                self.cropper = albumentations.RandomCrop
-            else:
-                self.cropper = albumentations.CenterCrop
-            self.preprocessor = albumentations.Compose([self.cropper(mel_num, spec_len)])
-        else:
-            self.preprocessor = lambda **kwargs: kwargs
-
-    def __call__(self, item):
-        item['input'] = self.preprocessor(image=item['input'])['image']
-        return item
-
-
-if __name__ == '__main__':
-    cropper = Crop([80, 848])
-    item = {'input': torch.rand([80, 860])}
-    outputs = cropper(item)
-    print(outputs['input'].shape)
diff --git a/spaces/ASJMO/freegpt/client/css/hljs.css b/spaces/ASJMO/freegpt/client/css/hljs.css
deleted file mode 100644
index 1fcf16ba358a7c5d287b1c6e33c3afbfff38f623..0000000000000000000000000000000000000000
--- a/spaces/ASJMO/freegpt/client/css/hljs.css
+++ /dev/null
@@ -1,68 +0,0 @@
-.hljs {
-	color: #e9e9f4;
-	background: #28293629;
-	border-radius: var(--border-radius-1);
-	border: 1px solid var(--blur-border);
-	font-size: 15px;
-	word-wrap: break-word;
-	white-space: pre-wrap;
-}
-
-/* style for hljs copy */
-.hljs-copy-wrapper {
-	position: relative;
-	overflow: hidden;
-}
-
-.hljs-copy-wrapper:hover .hljs-copy-button,
-.hljs-copy-button:focus {
-	transform: translateX(0);
-}
-
-.hljs-copy-button {
-	position: absolute;
-	transform: translateX(calc(100% + 1.125em));
-	top: 1em;
-	right: 1em;
-	width: 2rem;
-	height: 2rem;
-	text-indent: -9999px;
-	color: #fff;
-	border-radius: 0.25rem;
-	border: 1px solid #ffffff22;
-	background-color: #2d2b57;
-	background-image: url('data:image/svg+xml;utf-8,<svg width="16" height="16" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M6 5C5.73478 5 5.48043 5.10536 5.29289 5.29289C5.10536 5.48043 5 5.73478 5 6V20C5 20.2652 5.10536 20.5196 5.29289 20.7071C5.48043 20.8946 5.73478 21 6 21H18C18.2652 21 18.5196 20.8946 18.7071 20.7071C18.8946 20.5196 19 20.2652 19 20V6C19 5.73478 18.8946 5.48043 18.7071 5.29289C18.5196 5.10536 18.2652 5 18 5H16C15.4477 5 15 4.55228 15 4C15 3.44772 15.4477 3 16 3H18C18.7956 3 19.5587 3.31607 20.1213 3.87868C20.6839 4.44129 21 5.20435 21 6V20C21 20.7957 20.6839 21.5587 20.1213 22.1213C19.5587 22.6839 18.7957 23 18 23H6C5.20435 23 4.44129 22.6839 3.87868 22.1213C3.31607 21.5587 3 20.7957 3 20V6C3 5.20435 3.31607 4.44129 3.87868 3.87868C4.44129 3.31607 5.20435 3 6 3H8C8.55228 3 9 3.44772 9 4C9 4.55228 8.55228 5 8 5H6Z" fill="white"/><path fill-rule="evenodd" clip-rule="evenodd" d="M7 3C7 1.89543 7.89543 1 9 1H15C16.1046 1 17 1.89543 17 3V5C17 6.10457 16.1046 7 15 7H9C7.89543 7 7 6.10457 7 5V3ZM15 3H9V5H15V3Z" fill="white"/></svg>');
-	background-repeat: no-repeat;
-	background-position: center;
-	transition: background-color 200ms ease, transform 200ms ease-out;
-}
-
-.hljs-copy-button:hover {
-	border-color: #ffffff44;
-}
-
-.hljs-copy-button:active {
-	border-color: #ffffff66;
-}
-
-.hljs-copy-button[data-copied="true"] {
-	text-indent: 0;
-	width: auto;
-	background-image: none;
-}
-
-.hljs-copy-alert {
-	clip: rect(0 0 0 0);
-	clip-path: inset(50%);
-	height: 1px;
-	overflow: hidden;
-	position: absolute;
-	white-space: nowrap;
-	width: 1px;
-}
-
-@media (prefers-reduced-motion) {
-	.hljs-copy-button {
-		transition: none;
-	}
-}
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/custom_dataset/yolov5_s-v61_syncbn_fast_1xb32-100e_cat.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/custom_dataset/yolov5_s-v61_syncbn_fast_1xb32-100e_cat.py
deleted file mode 100644
index c24cab973dbbededbd98f0d42444e689d7158695..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/custom_dataset/yolov5_s-v61_syncbn_fast_1xb32-100e_cat.py
+++ /dev/null
@@ -1,135 +0,0 @@
-_base_ = '../yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
-
-max_epochs = 100  # 训练的最大 epoch
-data_root = './data-df2/'  # 数据集目录的绝对路径
-# data_root = '/root/workspace/mmyolo/data/cat/'  # Docker 容器里面数据集目录的绝对路径
-
-# 结果保存的路径，可以省略，省略保存的文件名位于 work_dirs 下 config 同名的文件夹中
-# 如果某个 config 只是修改了部分参数，修改这个变量就可以将新的训练文件保存到其他地方
-work_dir = './work_dirs/yolov5_s_df2'
-
-# load_from 可以指定本地路径或者 URL，设置了 URL 会自动进行下载，因为上面已经下载过，我们这里设置本地路径
-# 因为本教程是在 cat 数据集上微调，故这里需要使用 `load_from` 来加载 MMYOLO 中的预训练模型，这样可以在加快收敛速度的同时保证精度
-# load_from = './work_dirs/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth'  # noqa
-
-# 根据自己的 GPU 情况，修改 batch size，YOLOv5-s 默认为 8卡 x 16bs
-train_batch_size_per_gpu = 32
-train_num_workers = 4  # 推荐使用 train_num_workers = nGPU x 4
-
-save_epoch_intervals = 2  # 每 interval 轮迭代进行一次保存一次权重
-
-# 根据自己的 GPU 情况，修改 base_lr，修改的比例是 base_lr_default * (your_bs / default_bs)
-base_lr = _base_.base_lr / 4
-
-anchors = [  # 此处已经根据数据集特点更新了 anchor，关于 anchor 的生成，后面小节会讲解
-    [(68, 69), (154, 91), (143, 162)],  # P3/8
-    [(242, 160), (189, 287), (391, 207)],  # P4/16
-    [(353, 337), (539, 341), (443, 432)]  # P5/32
-]
-
-class_name = ('short_sleeved_shirt',
-              'long_sleeved_shirt',
-              'short_sleeved_outwear',
-              'long_sleeved_outwear',
-              'vest',
-              'sling',
-              'shorts',
-              'trousers',
-              'skirt',
-              'short_sleeved_dress',
-              'long_sleeved_dress',
-              'vest_dress',
-              'sling_dress')  # 根据 class_with_id.txt 类别信息，设置 class_name
-
-num_classes = len(class_name)
-metainfo = dict(
-    classes=class_name,
-    palette=[(255,   0,   0),
-             (255, 128,   0),
-             (255, 255,   0),
-             (128, 255,   0),
-             (0, 255,   0),
-             (0, 255, 128),
-             (0, 255, 255),
-             (0, 128, 255),
-             (0,   0, 255),
-             (127,   0, 255),
-             (255,   0, 255),
-             (255,   0, 127),
-             (128, 128, 128)]  # 画图时候的颜色，随便设置即可
-)
-
-train_cfg = dict(
-    max_epochs=max_epochs,
-    val_begin=20,  # 第几个 epoch 后验证，这里设置 20 是因为前 20 个 epoch 精度不高，测试意义不大，故跳过
-    val_interval=save_epoch_intervals  # 每 val_interval 轮迭代进行一次测试评估
-    # dynamic_intervals=[(max_epochs-_base_.num_last_epochs, 1)]
-)
-
-model = dict(
-    bbox_head=dict(
-        head_module=dict(num_classes=num_classes),
-        prior_generator=dict(base_sizes=anchors),
-
-        # loss_cls 会根据 num_classes 动态调整，但是 num_classes = 1 的时候，loss_cls 恒为 0
-        loss_cls=dict(loss_weight=0.5 *
-                      (num_classes / 80 * 3 / _base_.num_det_layers))))
-
-train_dataloader = dict(
-    batch_size=train_batch_size_per_gpu,
-    num_workers=train_num_workers,
-    dataset=dict(
-        _delete_=True,
-        type='RepeatDataset',
-        # 数据量太少的话，可以使用 RepeatDataset ，在每个 epoch 内重复当前数据集 n 次，这里设置 5 是重复 5 次
-        times=2,
-        dataset=dict(
-            type=_base_.dataset_type,
-            data_root=data_root,
-            metainfo=metainfo,
-            ann_file='annotations/trainval.json',
-            data_prefix=dict(img='smaller-dataset/'),
-            filter_cfg=dict(filter_empty_gt=False, min_size=32),
-            pipeline=_base_.train_pipeline)))
-
-val_dataloader = dict(
-    dataset=dict(
-        metainfo=metainfo,
-        data_root=data_root,
-        ann_file='annotations/trainval.json',
-        data_prefix=dict(img='smaller-dataset/')))
-
-test_dataloader = val_dataloader
-
-val_evaluator = dict(ann_file=data_root + 'annotations/trainval.json')
-test_evaluator = val_evaluator
-
-optim_wrapper = dict(optimizer=dict(lr=base_lr))
-
-default_hooks = dict(
-    # 设置间隔多少个 epoch 保存模型，以及保存模型最多几个，`save_best` 是另外保存最佳模型（推荐）
-    checkpoint=dict(
-        type='CheckpointHook',
-        interval=save_epoch_intervals,
-        max_keep_ckpts=5,
-        save_best='auto'),
-    param_scheduler=dict(max_epochs=max_epochs, warmup_mim_iter=10),
-    # logger 输出的间隔
-    logger=dict(type='LoggerHook', interval=10))
-
-# custom_hooks = [
-#     dict(
-#         type="EMAHook",
-#         ema_type="ExpMomentumEMA",
-#         momentum=0.0001,
-#         update_buffers=True,
-#         strict_load=False,
-#         priority=49),
-#     dict(
-#         type="mmdet.PipelineSwitchHook",
-#         switch_epoch=max_epochs-max_epochs-_base_.num_last_epochs,
-#         switch_pipeline=_base_.train_pipeline_stage2
-#     )
-# ]
-
-visualizer = dict(vis_backends=[dict(type='LocalVisBackend'), dict(type='WandbVisBackend')])
\ No newline at end of file
diff --git a/spaces/Abhilashvj/planogram-compliance/data/scripts/get_coco.sh b/spaces/Abhilashvj/planogram-compliance/data/scripts/get_coco.sh
deleted file mode 100644
index 0d388b0a12a84c504a2b12e85e3edcac5d78530c..0000000000000000000000000000000000000000
--- a/spaces/Abhilashvj/planogram-compliance/data/scripts/get_coco.sh
+++ /dev/null
@@ -1,56 +0,0 @@
-#!/bin/bash
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Download COCO 2017 dataset http://cocodataset.org
-# Example usage: bash data/scripts/get_coco.sh
-# parent
-# ├── yolov5
-# └── datasets
-#     └── coco  ← downloads here
-
-# Arguments (optional) Usage: bash data/scripts/get_coco.sh --train --val --test --segments
-if [ "$#" -gt 0 ]; then
-  for opt in "$@"; do
-    case "${opt}" in
-    --train) train=true ;;
-    --val) val=true ;;
-    --test) test=true ;;
-    --segments) segments=true ;;
-    esac
-  done
-else
-  train=true
-  val=true
-  test=false
-  segments=false
-fi
-
-# Download/unzip labels
-d='../datasets' # unzip directory
-url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-if [ "$segments" == "true" ]; then
-  f='coco2017labels-segments.zip' # 168 MB
-else
-  f='coco2017labels.zip' # 46 MB
-fi
-echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-
-# Download/unzip images
-d='../datasets/coco/images' # unzip directory
-url=http://images.cocodataset.org/zips/
-if [ "$train" == "true" ]; then
-  f='train2017.zip' # 19G, 118k images
-  echo 'Downloading' $url$f '...'
-  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-fi
-if [ "$val" == "true" ]; then
-  f='val2017.zip' # 1G, 5k images
-  echo 'Downloading' $url$f '...'
-  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-fi
-if [ "$test" == "true" ]; then
-  f='test2017.zip' # 7G, 41k images (optional)
-  echo 'Downloading' $url$f '...'
-  curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
-fi
-wait # finish background tasks
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/deprecated/ChatgptLogin.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/deprecated/ChatgptLogin.py
deleted file mode 100644
index 07f3b914a12c2bf09bfb661cb0cf803b79299a14..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/deprecated/ChatgptLogin.py
+++ /dev/null
@@ -1,74 +0,0 @@
-from __future__ import annotations
-
-import os, re
-from aiohttp import ClientSession
-
-from ..base_provider import AsyncProvider, format_prompt
-
-
-class ChatgptLogin(AsyncProvider):
-    url                   = "https://opchatgpts.net"
-    supports_gpt_35_turbo = True
-    working               = True
-    _nonce                = None
-
-    @classmethod
-    async def create_async(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        **kwargs
-    ) -> str:
-        headers = {
-            "User-Agent"         : "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36",
-            "Accept"             : "*/*",
-            "Accept-language"    : "en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3",
-            "Origin"             : "https://opchatgpts.net",
-            "Alt-Used"           : "opchatgpts.net",
-            "Referer"            : "https://opchatgpts.net/chatgpt-free-use/",
-            "Sec-Fetch-Dest"     : "empty",
-            "Sec-Fetch-Mode"     : "cors",
-            "Sec-Fetch-Site"     : "same-origin",
-        }
-        async with ClientSession(
-            headers=headers
-        ) as session:
-            if not cls._nonce:
-                async with session.get(
-                    "https://opchatgpts.net/chatgpt-free-use/",
-                    params={"id": os.urandom(6).hex()},
-                ) as response:
-                    result = re.search(r'data-nonce="(.*?)"', await response.text())
-                if not result:
-                    raise RuntimeError("No nonce value")
-                cls._nonce = result.group(1)
-            data = {
-                "_wpnonce": cls._nonce,
-                "post_id": 28,
-                "url": "https://opchatgpts.net/chatgpt-free-use",
-                "action": "wpaicg_chat_shortcode_message",
-                "message": format_prompt(messages),
-                "bot_id": 0
-            }
-            async with session.post("https://opchatgpts.net/wp-admin/admin-ajax.php", data=data) as response:
-                response.raise_for_status()
-                data = await response.json()
-                if "data" in data:
-                    return data["data"]
-                elif "msg" in data:
-                    raise RuntimeError(data["msg"])
-                else:
-                    raise RuntimeError(f"Response: {data}")
-
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ("model", "str"),
-            ("messages", "list[dict[str, str]]"),
-            ("stream", "bool"),
-            ("temperature", "float"),
-        ]
-        param = ", ".join([": ".join(p) for p in params])
-        return f"g4f.provider.{cls.__name__} supports: ({param})"
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/agentverse/agents/simulation_agent/prisoner_dilemma.py b/spaces/AgentVerse/agentVerse/agentverse/agents/simulation_agent/prisoner_dilemma.py
deleted file mode 100644
index bf257c1688ae593b148c43afa748179633f765ce..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/agents/simulation_agent/prisoner_dilemma.py
+++ /dev/null
@@ -1,167 +0,0 @@
-from __future__ import annotations
-
-import logging
-from string import Template
-from typing import TYPE_CHECKING, List
-
-from agentverse.message import Message
-
-# from . import agent_registry
-# from .base import BaseAgent
-from agentverse.agents import agent_registry
-from agentverse.agents.base import BaseAgent
-
-if TYPE_CHECKING:
-    from agentverse.environments.base import BaseEnvironment
-
-
-class PrisonerDilemaAgent(BaseAgent):
-    def step(
-        self,
-        environment: BaseEnvironment,
-        env_description: str = "",
-    ) -> Message:
-        prompt = self._fill_prompt_template(env_description)
-
-        parsed_response = None
-        for i in range(self.max_retry):
-            try:
-                response = self.llm.generate_response(prompt)
-                parsed_response = self.output_parser.parse(self, environment, response)
-                break
-            except Exception as e:
-                logging.error(e)
-                logging.warning("Retrying...")
-                continue
-
-        if parsed_response is None:
-            logging.error(f"{self.name} failed to generate valid response.")
-
-        message = Message(
-            content=""
-            if parsed_response is None
-            else parsed_response.return_values["output"],
-            sender=self.name,
-            receiver=self.get_receiver(),
-        )
-        return message
-
-    async def astep(
-        self, environment: BaseEnvironment, env_description: str = ""
-    ) -> Message:
-        """Asynchronous version of step"""
-        prompt = self._fill_prompt_template(env_description)
-
-        parsed_response = None
-        for i in range(self.max_retry):
-            try:
-                response = await self.llm.agenerate_response(prompt)
-                parsed_response = self.output_parser.parse(self, environment, response)
-                break
-            except Exception as e:
-                logging.error(e)
-                logging.warning("Retrying...")
-                continue
-
-        if parsed_response is None:
-            logging.error(f"{self.name} failed to generate valid response.")
-
-        message = Message(
-            content=""
-            if parsed_response is None
-            else parsed_response.return_values["output"],
-            sender=self.name,
-            receiver=self.get_receiver(),
-        )
-        return message
-
-    def _fill_prompt_template(self, env_description: str = "") -> str:
-        """Fill the placeholders in the prompt template
-
-        In the conversation agent, three placeholders are supported:
-        - ${agent_name}: the name of the agent
-        - ${env_description}: the description of the environment
-        - ${role_description}: the description of the role of the agent
-        - ${chat_history}: the chat history of the agent
-        """
-        input_arguments = {
-            "agent_name": self.name,
-            "env_description": env_description,
-            "role_description": self.role_description,
-            "chat_history": self.memory.to_string(add_sender_prefix=True),
-        }
-        return Template(self.prompt_template).safe_substitute(input_arguments)
-
-    def add_message_to_memory(self, messages: List[Message]) -> None:
-        self.memory.add_message(messages)
-
-    def reset(self) -> None:
-        """Reset the agent"""
-        self.memory.reset()
-        # TODO: reset receiver
-
-
-@agent_registry.register("police")
-class PoliceAgent(PrisonerDilemaAgent):
-    interrogating_form: str
-
-    def _fill_prompt_template(self, env_description: str = "") -> str:
-        """Fill the placeholders in the prompt template
-
-        In the conversation agent, three placeholders are supported:
-        - ${agent_name}: the name of the agent
-        - ${env_description}: the description of the environment
-        - ${role_description}: the description of the role of the agent
-        - ${chat_history}: the chat history of the agent
-        """
-        input_arguments = {
-            "agent_name": self.name,
-            "env_description": env_description,
-            "role_description": self.role_description,
-            "chat_history": self.memory.to_string(add_sender_prefix=True),
-        }
-
-        role_argument = {
-            "interrogating_form": self.interrogating_form,
-        }
-
-        role_description = Template(self.role_description).safe_substitute(
-            role_argument
-        )
-        input_arguments["role_description"] = role_description
-
-        return Template(self.prompt_template).safe_substitute(input_arguments)
-
-
-@agent_registry.register("prisoner")
-class PrisonerAgent(PrisonerDilemaAgent):
-    personality: str
-    relationship_with_another: str
-
-    def _fill_prompt_template(self, env_description: str = "") -> str:
-        """Fill the placeholders in the prompt template
-
-        In the conversation agent, three placeholders are supported:
-        - ${agent_name}: the name of the agent
-        - ${env_description}: the description of the environment
-        - ${role_description}: the description of the role of the agent
-        - ${chat_history}: the chat history of the agent
-        """
-        input_arguments = {
-            "agent_name": self.name,
-            "env_description": env_description,
-            "role_description": self.role_description,
-            "chat_history": self.memory.to_string(add_sender_prefix=True),
-        }
-
-        role_argument = {
-            "personality": self.personality,
-            "relationship_with_another": self.relationship_with_another,
-        }
-
-        role_description = Template(self.role_description).safe_substitute(
-            role_argument
-        )
-        input_arguments["role_description"] = role_description
-
-        return Template(self.prompt_template).safe_substitute(input_arguments)
diff --git a/spaces/Alichuan/VITS-Umamusume-voice-synthesizer/transforms.py b/spaces/Alichuan/VITS-Umamusume-voice-synthesizer/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/Alichuan/VITS-Umamusume-voice-synthesizer/transforms.py
+++ /dev/null
@@ -1,193 +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))
-        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/Ameaou/academic-chatgpt3.1/config.py b/spaces/Ameaou/academic-chatgpt3.1/config.py
deleted file mode 100644
index 46a7dccf61b6edd94bf8484f3b80de7e8fe91628..0000000000000000000000000000000000000000
--- a/spaces/Ameaou/academic-chatgpt3.1/config.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# [step 1]>> 例如： API_KEY = "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" （此key无效）
-API_KEY = "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"    # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey1,fkxxxx-api2dkey2"
-
-# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改
-USE_PROXY = True
-if USE_PROXY:
-    # 填写格式是 [协议]://  [地址] :[端口]，填写之前不要忘记把USE_PROXY改成True，如果直接在海外服务器部署，此处不修改
-    # 例如    "socks5h://localhost:11284"
-    # [协议] 常见协议无非socks5h/http; 例如 v2**y 和 ss* 的默认本地协议是socks5h; 而cl**h 的默认本地协议是http
-    # [地址] 懂的都懂，不懂就填localhost或者127.0.0.1肯定错不了（localhost意思是代理软件安装在本机上）
-    # [端口] 在代理软件的设置里找。虽然不同的代理软件界面不一样，但端口号都应该在最显眼的位置上
-
-    # 代理网络的地址，打开你的科学上网软件查看代理的协议(socks5/http)、地址(localhost)和端口(11284)
-    proxies = {
-        #          [协议]://  [地址]  :[端口]
-        "http":  "http://127.0.0.1:7890",
-        "https": "http://127.0.0.1:7890",
-    }
-else:
-    proxies = None
-
-# [step 3]>> 多线程函数插件中，默认允许多少路线程同时访问OpenAI。Free trial users的限制是每分钟3次，Pay-as-you-go users的限制是每分钟3500次
-# 一言以蔽之：免费用户填3，OpenAI绑了信用卡的用户可以填 16 或者更高。提高限制请查询：https://platform.openai.com/docs/guides/rate-limits/overview
-DEFAULT_WORKER_NUM = 3
-
-
-# [step 4]>> 以下配置可以优化体验，但大部分场合下并不需要修改
-# 对话窗的高度
-CHATBOT_HEIGHT = 1115
-
-# 代码高亮
-CODE_HIGHLIGHT = True
-
-# 窗口布局
-LAYOUT = "LEFT-RIGHT"  # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下布局）
-
-# 发送请求到OpenAI后，等待多久判定为超时
-TIMEOUT_SECONDS = 30
-
-# 网页的端口, -1代表随机端口
-WEB_PORT = -1
-
-# 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
-MAX_RETRY = 2
-
-# OpenAI模型选择是（gpt4现在只对申请成功的人开放）
-LLM_MODEL = "gpt-3.5-turbo" # 可选 "chatglm"
-AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "api2d-gpt-3.5-turbo"]
-
-# 本地LLM模型如ChatGLM的执行方式 CPU/GPU
-LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
-
-# 设置gradio的并行线程数（不需要修改）
-CONCURRENT_COUNT = 100
-
-# 设置用户名和密码（不需要修改）（相关功能不稳定，与gradio版本和网络都相关，如果本地使用不建议加这个）
-# [("username", "password"), ("username2", "password2"), ...]
-AUTHENTICATION = []
diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/dnnlib/util.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/dnnlib/util.py
deleted file mode 100644
index 27bce0ab18a69f142db54084c0be2c014e60c20d..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/dnnlib/util.py
+++ /dev/null
@@ -1,492 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Miscellaneous utility classes and functions."""
-
-import ctypes
-import fnmatch
-import importlib
-import inspect
-import numpy as np
-import os
-import shutil
-import sys
-import types
-import io
-import pickle
-import re
-import requests
-import html
-import hashlib
-import glob
-import tempfile
-import urllib
-import urllib.request
-import uuid
-
-from distutils.util import strtobool
-from typing import Any, List, Tuple, Union
-
-
-# Util classes
-# ------------------------------------------------------------------------------------------
-
-
-class EasyDict(dict):
-    """Convenience class that behaves like a dict but allows access with the attribute syntax."""
-
-    def __getattr__(self, name: str) -> Any:
-        try:
-            return self[name]
-        except KeyError:
-            raise AttributeError(name)
-
-    def __setattr__(self, name: str, value: Any) -> None:
-        self[name] = value
-
-    def __delattr__(self, name: str) -> None:
-        del self[name]
-
-
-class Logger(object):
-    """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file."""
-
-    def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True):
-        self.file = None
-
-        if file_name is not None:
-            self.file = open(file_name, file_mode)
-
-        self.should_flush = should_flush
-        self.stdout = sys.stdout
-        self.stderr = sys.stderr
-
-        sys.stdout = self
-        sys.stderr = self
-
-    def __enter__(self) -> "Logger":
-        return self
-
-    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
-        self.close()
-
-    def write(self, text: Union[str, bytes]) -> None:
-        """Write text to stdout (and a file) and optionally flush."""
-        if isinstance(text, bytes):
-            text = text.decode()
-        if len(text) == 0:  # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
-            return
-
-        if self.file is not None:
-            self.file.write(text)
-
-        self.stdout.write(text)
-
-        if self.should_flush:
-            self.flush()
-
-    def flush(self) -> None:
-        """Flush written text to both stdout and a file, if open."""
-        if self.file is not None:
-            self.file.flush()
-
-        self.stdout.flush()
-
-    def close(self) -> None:
-        """Flush, close possible files, and remove stdout/stderr mirroring."""
-        self.flush()
-
-        # if using multiple loggers, prevent closing in wrong order
-        if sys.stdout is self:
-            sys.stdout = self.stdout
-        if sys.stderr is self:
-            sys.stderr = self.stderr
-
-        if self.file is not None:
-            self.file.close()
-            self.file = None
-
-
-# Cache directories
-# ------------------------------------------------------------------------------------------
-
-_dnnlib_cache_dir = None
-
-
-def set_cache_dir(path: str) -> None:
-    global _dnnlib_cache_dir
-    _dnnlib_cache_dir = path
-
-
-def make_cache_dir_path(*paths: str) -> str:
-    if _dnnlib_cache_dir is not None:
-        return os.path.join(_dnnlib_cache_dir, *paths)
-    if 'DNNLIB_CACHE_DIR' in os.environ:
-        return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths)
-    if 'HOME' in os.environ:
-        return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths)
-    if 'USERPROFILE' in os.environ:
-        return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths)
-    return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths)
-
-# Small util functions
-# ------------------------------------------------------------------------------------------
-
-
-def format_time(seconds: Union[int, float]) -> str:
-    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
-    s = int(np.rint(seconds))
-
-    if s < 60:
-        return "{0}s".format(s)
-    elif s < 60 * 60:
-        return "{0}m {1:02}s".format(s // 60, s % 60)
-    elif s < 24 * 60 * 60:
-        return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60)
-    else:
-        return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60)
-
-
-def ask_yes_no(question: str) -> bool:
-    """Ask the user the question until the user inputs a valid answer."""
-    while True:
-        try:
-            print("{0} [y/n]".format(question))
-            return strtobool(input().lower())
-        except ValueError:
-            pass
-
-
-def tuple_product(t: Tuple) -> Any:
-    """Calculate the product of the tuple elements."""
-    result = 1
-
-    for v in t:
-        result *= v
-
-    return result
-
-
-_str_to_ctype = {
-    "uint8": ctypes.c_ubyte,
-    "uint16": ctypes.c_uint16,
-    "uint32": ctypes.c_uint32,
-    "uint64": ctypes.c_uint64,
-    "int8": ctypes.c_byte,
-    "int16": ctypes.c_int16,
-    "int32": ctypes.c_int32,
-    "int64": ctypes.c_int64,
-    "float32": ctypes.c_float,
-    "float64": ctypes.c_double
-}
-
-
-def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
-    """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes."""
-    type_str = None
-
-    if isinstance(type_obj, str):
-        type_str = type_obj
-    elif hasattr(type_obj, "__name__"):
-        type_str = type_obj.__name__
-    elif hasattr(type_obj, "name"):
-        type_str = type_obj.name
-    else:
-        raise RuntimeError("Cannot infer type name from input")
-
-    assert type_str in _str_to_ctype.keys()
-
-    my_dtype = np.dtype(type_str)
-    my_ctype = _str_to_ctype[type_str]
-
-    assert my_dtype.itemsize == ctypes.sizeof(my_ctype)
-
-    return my_dtype, my_ctype
-
-
-def is_pickleable(obj: Any) -> bool:
-    try:
-        with io.BytesIO() as stream:
-            pickle.dump(obj, stream)
-        return True
-    except:
-        return False
-
-
-# Functionality to import modules/objects by name, and call functions by name
-# ------------------------------------------------------------------------------------------
-
-def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]:
-    """Searches for the underlying module behind the name to some python object.
-    Returns the module and the object name (original name with module part removed)."""
-
-    # allow convenience shorthands, substitute them by full names
-    obj_name = re.sub("^np.", "numpy.", obj_name)
-    obj_name = re.sub("^tf.", "tensorflow.", obj_name)
-
-    # list alternatives for (module_name, local_obj_name)
-    parts = obj_name.split(".")
-    name_pairs = [(".".join(parts[:i]), ".".join(parts[i:]))
-                  for i in range(len(parts), 0, -1)]
-
-    # try each alternative in turn
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(
-                module_name)  # may raise ImportError
-            # may raise AttributeError
-            get_obj_from_module(module, local_obj_name)
-            return module, local_obj_name
-        except:
-            pass
-
-    # maybe some of the modules themselves contain errors?
-    for module_name, _local_obj_name in name_pairs:
-        try:
-            importlib.import_module(module_name)  # may raise ImportError
-        except ImportError:
-            if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"):
-                raise
-
-    # maybe the requested attribute is missing?
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(
-                module_name)  # may raise ImportError
-            # may raise AttributeError
-            get_obj_from_module(module, local_obj_name)
-        except ImportError:
-            pass
-
-    # we are out of luck, but we have no idea why
-    raise ImportError(obj_name)
-
-
-def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
-    """Traverses the object name and returns the last (rightmost) python object."""
-    if obj_name == '':
-        return module
-    obj = module
-    for part in obj_name.split("."):
-        obj = getattr(obj, part)
-    return obj
-
-
-def get_obj_by_name(name: str) -> Any:
-    """Finds the python object with the given name."""
-    module, obj_name = get_module_from_obj_name(name)
-    return get_obj_from_module(module, obj_name)
-
-
-def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
-    """Finds the python object with the given name and calls it as a function."""
-    assert func_name is not None
-    # print('func_name: ', func_name) #'training.dataset.ImageFolderDataset'
-    func_obj = get_obj_by_name(func_name)
-    assert callable(func_obj)
-    return func_obj(*args, **kwargs)
-
-
-def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any:
-    """Finds the python class with the given name and constructs it with the given arguments."""
-    return call_func_by_name(*args, func_name=class_name, **kwargs)
-
-
-def get_module_dir_by_obj_name(obj_name: str) -> str:
-    """Get the directory path of the module containing the given object name."""
-    module, _ = get_module_from_obj_name(obj_name)
-    return os.path.dirname(inspect.getfile(module))
-
-
-def is_top_level_function(obj: Any) -> bool:
-    """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'."""
-    return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__
-
-
-def get_top_level_function_name(obj: Any) -> str:
-    """Return the fully-qualified name of a top-level function."""
-    assert is_top_level_function(obj)
-    module = obj.__module__
-    if module == '__main__':
-        module = os.path.splitext(os.path.basename(
-            sys.modules[module].__file__))[0]
-    return module + "." + obj.__name__
-
-
-# File system helpers
-# ------------------------------------------------------------------------------------------
-
-def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]:
-    """List all files recursively in a given directory while ignoring given file and directory names.
-    Returns list of tuples containing both absolute and relative paths."""
-    assert os.path.isdir(dir_path)
-    base_name = os.path.basename(os.path.normpath(dir_path))
-
-    if ignores is None:
-        ignores = []
-
-    result = []
-
-    for root, dirs, files in os.walk(dir_path, topdown=True):
-        for ignore_ in ignores:
-            dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)]
-
-            # dirs need to be edited in-place
-            for d in dirs_to_remove:
-                dirs.remove(d)
-
-            files = [f for f in files if not fnmatch.fnmatch(f, ignore_)]
-
-        absolute_paths = [os.path.join(root, f) for f in files]
-        relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths]
-
-        if add_base_to_relative:
-            relative_paths = [os.path.join(base_name, p)
-                              for p in relative_paths]
-
-        assert len(absolute_paths) == len(relative_paths)
-        result += zip(absolute_paths, relative_paths)
-
-    return result
-
-
-def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
-    """Takes in a list of tuples of (src, dst) paths and copies files.
-    Will create all necessary directories."""
-    for file in files:
-        target_dir_name = os.path.dirname(file[1])
-
-        # will create all intermediate-level directories
-        if not os.path.exists(target_dir_name):
-            os.makedirs(target_dir_name)
-
-        shutil.copyfile(file[0], file[1])
-
-
-# URL helpers
-# ------------------------------------------------------------------------------------------
-
-def is_url(obj: Any, allow_file_urls: bool = False) -> bool:
-    """Determine whether the given object is a valid URL string."""
-    if not isinstance(obj, str) or not "://" in obj:
-        return False
-    if allow_file_urls and obj.startswith('file://'):
-        return True
-    try:
-        res = requests.compat.urlparse(obj)
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-        res = requests.compat.urlparse(requests.compat.urljoin(obj, "/"))
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-    except:
-        return False
-    return True
-
-
-def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any:
-    """Download the given URL and return a binary-mode file object to access the data."""
-    assert num_attempts >= 1
-    assert not (return_filename and (not cache))
-
-    # Doesn't look like an URL scheme so interpret it as a local filename.
-    if not re.match('^[a-z]+://', url):
-        return url if return_filename else open(url, "rb")
-
-    # Handle file URLs.  This code handles unusual file:// patterns that
-    # arise on Windows:
-    #
-    # file:///c:/foo.txt
-    #
-    # which would translate to a local '/c:/foo.txt' filename that's
-    # invalid.  Drop the forward slash for such pathnames.
-    #
-    # If you touch this code path, you should test it on both Linux and
-    # Windows.
-    #
-    # Some internet resources suggest using urllib.request.url2pathname() but
-    # but that converts forward slashes to backslashes and this causes
-    # its own set of problems.
-    if url.startswith('file://'):
-        filename = urllib.parse.urlparse(url).path
-        if re.match(r'^/[a-zA-Z]:', filename):
-            filename = filename[1:]
-        return filename if return_filename else open(filename, "rb")
-
-    assert is_url(url)
-
-    # Lookup from cache.
-    if cache_dir is None:
-        cache_dir = make_cache_dir_path('downloads')
-
-    url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest()
-    if cache:
-        cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*"))
-        if len(cache_files) == 1:
-            filename = cache_files[0]
-            return filename if return_filename else open(filename, "rb")
-
-    # Download.
-    url_name = None
-    url_data = None
-    with requests.Session() as session:
-        if verbose:
-            print("Downloading %s ..." % url, end="", flush=True)
-        for attempts_left in reversed(range(num_attempts)):
-            try:
-                with session.get(url) as res:
-                    res.raise_for_status()
-                    if len(res.content) == 0:
-                        raise IOError("No data received")
-
-                    if len(res.content) < 8192:
-                        content_str = res.content.decode("utf-8")
-                        if "download_warning" in res.headers.get("Set-Cookie", ""):
-                            links = [html.unescape(link) for link in content_str.split(
-                                '"') if "export=download" in link]
-                            if len(links) == 1:
-                                url = requests.compat.urljoin(url, links[0])
-                                raise IOError("Google Drive virus checker nag")
-                        if "Google Drive - Quota exceeded" in content_str:
-                            raise IOError(
-                                "Google Drive download quota exceeded -- please try again later")
-
-                    match = re.search(
-                        r'filename="([^"]*)"', res.headers.get("Content-Disposition", ""))
-                    url_name = match[1] if match else url
-                    url_data = res.content
-                    if verbose:
-                        print(" done")
-                    break
-            except KeyboardInterrupt:
-                raise
-            except:
-                if not attempts_left:
-                    if verbose:
-                        print(" failed")
-                    raise
-                if verbose:
-                    print(".", end="", flush=True)
-
-    # Save to cache.
-    if cache:
-        safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name)
-        cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name)
-        temp_file = os.path.join(
-            cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name)
-        os.makedirs(cache_dir, exist_ok=True)
-        with open(temp_file, "wb") as f:
-            f.write(url_data)
-        os.replace(temp_file, cache_file)  # atomic
-        if return_filename:
-            return cache_file
-
-    # Return data as file object.
-    assert not return_filename
-    return io.BytesIO(url_data)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py
deleted file mode 100644
index b4c0387ca01be42a60056184d802c362a06c5139..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py
+++ /dev/null
@@ -1,532 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import warnings
-from functools import partial
-from typing import Dict, List, Optional, Union
-
-import jax
-import jax.numpy as jnp
-import numpy as np
-from flax.core.frozen_dict import FrozenDict
-from flax.jax_utils import unreplicate
-from flax.training.common_utils import shard
-from PIL import Image
-from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel
-
-from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel
-from ...schedulers import (
-    FlaxDDIMScheduler,
-    FlaxDPMSolverMultistepScheduler,
-    FlaxLMSDiscreteScheduler,
-    FlaxPNDMScheduler,
-)
-from ...utils import PIL_INTERPOLATION, logging, replace_example_docstring
-from ..pipeline_flax_utils import FlaxDiffusionPipeline
-from . import FlaxStableDiffusionPipelineOutput
-from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-# Set to True to use python for loop instead of jax.fori_loop for easier debugging
-DEBUG = False
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import jax
-        >>> import numpy as np
-        >>> import jax.numpy as jnp
-        >>> from flax.jax_utils import replicate
-        >>> from flax.training.common_utils import shard
-        >>> import requests
-        >>> from io import BytesIO
-        >>> from PIL import Image
-        >>> from diffusers import FlaxStableDiffusionImg2ImgPipeline
-
-
-        >>> def create_key(seed=0):
-        ...     return jax.random.PRNGKey(seed)
-
-
-        >>> rng = create_key(0)
-
-        >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-        >>> response = requests.get(url)
-        >>> init_img = Image.open(BytesIO(response.content)).convert("RGB")
-        >>> init_img = init_img.resize((768, 512))
-
-        >>> prompts = "A fantasy landscape, trending on artstation"
-
-        >>> pipeline, params = FlaxStableDiffusionImg2ImgPipeline.from_pretrained(
-        ...     "CompVis/stable-diffusion-v1-4",
-        ...     revision="flax",
-        ...     dtype=jnp.bfloat16,
-        ... )
-
-        >>> num_samples = jax.device_count()
-        >>> rng = jax.random.split(rng, jax.device_count())
-        >>> prompt_ids, processed_image = pipeline.prepare_inputs(
-        ...     prompt=[prompts] * num_samples, image=[init_img] * num_samples
-        ... )
-        >>> p_params = replicate(params)
-        >>> prompt_ids = shard(prompt_ids)
-        >>> processed_image = shard(processed_image)
-
-        >>> output = pipeline(
-        ...     prompt_ids=prompt_ids,
-        ...     image=processed_image,
-        ...     params=p_params,
-        ...     prng_seed=rng,
-        ...     strength=0.75,
-        ...     num_inference_steps=50,
-        ...     jit=True,
-        ...     height=512,
-        ...     width=768,
-        ... ).images
-
-        >>> output_images = pipeline.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
-        ```
-"""
-
-
-class FlaxStableDiffusionImg2ImgPipeline(FlaxDiffusionPipeline):
-    r"""
-    Flax-based pipeline for text-guided image-to-image generation using Stable Diffusion.
-
-    This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Args:
-        vae ([`FlaxAutoencoderKL`]):
-            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
-        text_encoder ([`~transformers.FlaxCLIPTextModel`]):
-            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
-        tokenizer ([`~transformers.CLIPTokenizer`]):
-            A `CLIPTokenizer` to tokenize text.
-        unet ([`FlaxUNet2DConditionModel`]):
-            A `FlaxUNet2DConditionModel` to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or
-            [`FlaxDPMSolverMultistepScheduler`].
-        safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
-        feature_extractor ([`~transformers.CLIPImageProcessor`]):
-            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
-    """
-
-    def __init__(
-        self,
-        vae: FlaxAutoencoderKL,
-        text_encoder: FlaxCLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: FlaxUNet2DConditionModel,
-        scheduler: Union[
-            FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler
-        ],
-        safety_checker: FlaxStableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        dtype: jnp.dtype = jnp.float32,
-    ):
-        super().__init__()
-        self.dtype = dtype
-
-        if safety_checker is None:
-            logger.warn(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-
-    def prepare_inputs(self, prompt: Union[str, List[str]], image: Union[Image.Image, List[Image.Image]]):
-        if not isinstance(prompt, (str, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if not isinstance(image, (Image.Image, list)):
-            raise ValueError(f"image has to be of type `PIL.Image.Image` or list but is {type(image)}")
-
-        if isinstance(image, Image.Image):
-            image = [image]
-
-        processed_images = jnp.concatenate([preprocess(img, jnp.float32) for img in image])
-
-        text_input = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="np",
-        )
-        return text_input.input_ids, processed_images
-
-    def _get_has_nsfw_concepts(self, features, params):
-        has_nsfw_concepts = self.safety_checker(features, params)
-        return has_nsfw_concepts
-
-    def _run_safety_checker(self, images, safety_model_params, jit=False):
-        # safety_model_params should already be replicated when jit is True
-        pil_images = [Image.fromarray(image) for image in images]
-        features = self.feature_extractor(pil_images, return_tensors="np").pixel_values
-
-        if jit:
-            features = shard(features)
-            has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)
-            has_nsfw_concepts = unshard(has_nsfw_concepts)
-            safety_model_params = unreplicate(safety_model_params)
-        else:
-            has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)
-
-        images_was_copied = False
-        for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
-            if has_nsfw_concept:
-                if not images_was_copied:
-                    images_was_copied = True
-                    images = images.copy()
-
-                images[idx] = np.zeros(images[idx].shape, dtype=np.uint8)  # black image
-
-            if any(has_nsfw_concepts):
-                warnings.warn(
-                    "Potential NSFW content was detected in one or more images. A black image will be returned"
-                    " instead. Try again with a different prompt and/or seed."
-                )
-
-        return images, has_nsfw_concepts
-
-    def get_timestep_start(self, num_inference_steps, strength):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-
-        return t_start
-
-    def _generate(
-        self,
-        prompt_ids: jnp.array,
-        image: jnp.array,
-        params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
-        start_timestep: int,
-        num_inference_steps: int,
-        height: int,
-        width: int,
-        guidance_scale: float,
-        noise: Optional[jnp.array] = None,
-        neg_prompt_ids: Optional[jnp.array] = None,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        # get prompt text embeddings
-        prompt_embeds = self.text_encoder(prompt_ids, params=params["text_encoder"])[0]
-
-        # TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`
-        # implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`
-        batch_size = prompt_ids.shape[0]
-
-        max_length = prompt_ids.shape[-1]
-
-        if neg_prompt_ids is None:
-            uncond_input = self.tokenizer(
-                [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np"
-            ).input_ids
-        else:
-            uncond_input = neg_prompt_ids
-        negative_prompt_embeds = self.text_encoder(uncond_input, params=params["text_encoder"])[0]
-        context = jnp.concatenate([negative_prompt_embeds, prompt_embeds])
-
-        latents_shape = (
-            batch_size,
-            self.unet.config.in_channels,
-            height // self.vae_scale_factor,
-            width // self.vae_scale_factor,
-        )
-        if noise is None:
-            noise = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32)
-        else:
-            if noise.shape != latents_shape:
-                raise ValueError(f"Unexpected latents shape, got {noise.shape}, expected {latents_shape}")
-
-        # Create init_latents
-        init_latent_dist = self.vae.apply({"params": params["vae"]}, image, method=self.vae.encode).latent_dist
-        init_latents = init_latent_dist.sample(key=prng_seed).transpose((0, 3, 1, 2))
-        init_latents = self.vae.config.scaling_factor * init_latents
-
-        def loop_body(step, args):
-            latents, scheduler_state = args
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            latents_input = jnp.concatenate([latents] * 2)
-
-            t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]
-            timestep = jnp.broadcast_to(t, latents_input.shape[0])
-
-            latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)
-
-            # predict the noise residual
-            noise_pred = self.unet.apply(
-                {"params": params["unet"]},
-                jnp.array(latents_input),
-                jnp.array(timestep, dtype=jnp.int32),
-                encoder_hidden_states=context,
-            ).sample
-            # perform guidance
-            noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)
-            noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()
-            return latents, scheduler_state
-
-        scheduler_state = self.scheduler.set_timesteps(
-            params["scheduler"], num_inference_steps=num_inference_steps, shape=latents_shape
-        )
-
-        latent_timestep = scheduler_state.timesteps[start_timestep : start_timestep + 1].repeat(batch_size)
-
-        latents = self.scheduler.add_noise(params["scheduler"], init_latents, noise, latent_timestep)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * params["scheduler"].init_noise_sigma
-
-        if DEBUG:
-            # run with python for loop
-            for i in range(start_timestep, num_inference_steps):
-                latents, scheduler_state = loop_body(i, (latents, scheduler_state))
-        else:
-            latents, _ = jax.lax.fori_loop(start_timestep, num_inference_steps, loop_body, (latents, scheduler_state))
-
-        # scale and decode the image latents with vae
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample
-
-        image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)
-        return image
-
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt_ids: jnp.array,
-        image: jnp.array,
-        params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
-        strength: float = 0.8,
-        num_inference_steps: int = 50,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        guidance_scale: Union[float, jnp.array] = 7.5,
-        noise: jnp.array = None,
-        neg_prompt_ids: jnp.array = None,
-        return_dict: bool = True,
-        jit: bool = False,
-    ):
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            prompt_ids (`jnp.array`):
-                The prompt or prompts to guide image generation.
-            image (`jnp.array`):
-                Array representing an image batch to be used as the starting point.
-            params (`Dict` or `FrozenDict`):
-                Dictionary containing the model parameters/weights.
-            prng_seed (`jax.random.KeyArray` or `jax.Array`):
-                Array containing random number generator key.
-            strength (`float`, *optional*, defaults to 0.8):
-                Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
-                starting point and more noise is added the higher the `strength`. The number of denoising steps depends
-                on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
-                process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
-                essentially ignores `image`.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference. This parameter is modulated by `strength`.
-            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The width in pixels of the generated image.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                A higher guidance scale value encourages the model to generate images closely linked to the text
-                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
-            noise (`jnp.array`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. The array is generated by
-                sampling using the supplied random `generator`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of
-                a plain tuple.
-            jit (`bool`, defaults to `False`):
-                Whether to run `pmap` versions of the generation and safety scoring functions.
-
-                    <Tip warning={true}>
-
-                    This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a
-                    future release.
-
-                    </Tip>
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] is
-                returned, otherwise a `tuple` is returned where the first element is a list with the generated images
-                and the second element is a list of `bool`s indicating whether the corresponding generated image
-                contains "not-safe-for-work" (nsfw) content.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        if isinstance(guidance_scale, float):
-            # Convert to a tensor so each device gets a copy. Follow the prompt_ids for
-            # shape information, as they may be sharded (when `jit` is `True`), or not.
-            guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])
-            if len(prompt_ids.shape) > 2:
-                # Assume sharded
-                guidance_scale = guidance_scale[:, None]
-
-        start_timestep = self.get_timestep_start(num_inference_steps, strength)
-
-        if jit:
-            images = _p_generate(
-                self,
-                prompt_ids,
-                image,
-                params,
-                prng_seed,
-                start_timestep,
-                num_inference_steps,
-                height,
-                width,
-                guidance_scale,
-                noise,
-                neg_prompt_ids,
-            )
-        else:
-            images = self._generate(
-                prompt_ids,
-                image,
-                params,
-                prng_seed,
-                start_timestep,
-                num_inference_steps,
-                height,
-                width,
-                guidance_scale,
-                noise,
-                neg_prompt_ids,
-            )
-
-        if self.safety_checker is not None:
-            safety_params = params["safety_checker"]
-            images_uint8_casted = (images * 255).round().astype("uint8")
-            num_devices, batch_size = images.shape[:2]
-
-            images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)
-            images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)
-            images = np.asarray(images)
-
-            # block images
-            if any(has_nsfw_concept):
-                for i, is_nsfw in enumerate(has_nsfw_concept):
-                    if is_nsfw:
-                        images[i] = np.asarray(images_uint8_casted[i])
-
-            images = images.reshape(num_devices, batch_size, height, width, 3)
-        else:
-            images = np.asarray(images)
-            has_nsfw_concept = False
-
-        if not return_dict:
-            return (images, has_nsfw_concept)
-
-        return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)
-
-
-# Static argnums are pipe, start_timestep, num_inference_steps, height, width. A change would trigger recompilation.
-# Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`).
-@partial(
-    jax.pmap,
-    in_axes=(None, 0, 0, 0, 0, None, None, None, None, 0, 0, 0),
-    static_broadcasted_argnums=(0, 5, 6, 7, 8),
-)
-def _p_generate(
-    pipe,
-    prompt_ids,
-    image,
-    params,
-    prng_seed,
-    start_timestep,
-    num_inference_steps,
-    height,
-    width,
-    guidance_scale,
-    noise,
-    neg_prompt_ids,
-):
-    return pipe._generate(
-        prompt_ids,
-        image,
-        params,
-        prng_seed,
-        start_timestep,
-        num_inference_steps,
-        height,
-        width,
-        guidance_scale,
-        noise,
-        neg_prompt_ids,
-    )
-
-
-@partial(jax.pmap, static_broadcasted_argnums=(0,))
-def _p_get_has_nsfw_concepts(pipe, features, params):
-    return pipe._get_has_nsfw_concepts(features, params)
-
-
-def unshard(x: jnp.ndarray):
-    # einops.rearrange(x, 'd b ... -> (d b) ...')
-    num_devices, batch_size = x.shape[:2]
-    rest = x.shape[2:]
-    return x.reshape(num_devices * batch_size, *rest)
-
-
-def preprocess(image, dtype):
-    w, h = image.size
-    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
-    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
-    image = jnp.array(image).astype(dtype) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    return 2.0 * image - 1.0
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/pndm/test_pndm.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/pndm/test_pndm.py
deleted file mode 100644
index c2595713933c6e8b10277910c1b01dd2f8561d25..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/pndm/test_pndm.py
+++ /dev/null
@@ -1,87 +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 unittest
-
-import numpy as np
-import torch
-
-from diffusers import PNDMPipeline, PNDMScheduler, UNet2DModel
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
-
-
-enable_full_determinism()
-
-
-class PNDMPipelineFastTests(unittest.TestCase):
-    @property
-    def dummy_uncond_unet(self):
-        torch.manual_seed(0)
-        model = UNet2DModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=3,
-            out_channels=3,
-            down_block_types=("DownBlock2D", "AttnDownBlock2D"),
-            up_block_types=("AttnUpBlock2D", "UpBlock2D"),
-        )
-        return model
-
-    def test_inference(self):
-        unet = self.dummy_uncond_unet
-        scheduler = PNDMScheduler()
-
-        pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
-        pndm.to(torch_device)
-        pndm.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = pndm(generator=generator, num_inference_steps=20, output_type="numpy").images
-
-        generator = torch.manual_seed(0)
-        image_from_tuple = pndm(generator=generator, num_inference_steps=20, output_type="numpy", return_dict=False)[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
-
-@slow
-@require_torch
-class PNDMPipelineIntegrationTests(unittest.TestCase):
-    def test_inference_cifar10(self):
-        model_id = "google/ddpm-cifar10-32"
-
-        unet = UNet2DModel.from_pretrained(model_id)
-        scheduler = PNDMScheduler()
-
-        pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
-        pndm.to(torch_device)
-        pndm.set_progress_bar_config(disable=None)
-        generator = torch.manual_seed(0)
-        image = pndm(generator=generator, output_type="numpy").images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py b/spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py
deleted file mode 100644
index df9c2aca9c7c1999d74a08a58aca5d220f7df54a..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './nonlocal_r50-d8_512x512_160k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Anonymous-123/ImageNet-Editing/resize_obj.py b/spaces/Anonymous-123/ImageNet-Editing/resize_obj.py
deleted file mode 100644
index e9315946d1da20b1756e43e762c47a641958701e..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-123/ImageNet-Editing/resize_obj.py
+++ /dev/null
@@ -1,188 +0,0 @@
-#!/usr/bin/python
-#****************************************************************#
-# ScriptName: analysis_data.py
-# Author: Anonymous_123
-# Create Date: 2022-07-25 19:54
-# Modify Author: Anonymous_123
-# Modify Date: 2022-09-25 12:04
-# Function: 
-#***************************************************************#
-
-import os
-import sys
-import numpy as np
-import cv2
-import torch
-from tqdm import tqdm
-import shutil
-import pdb
-
-import argparse
-
-parser = argparse.ArgumentParser(description='resize object')
-parser.add_argument('--scale', type=float, default=None, help='object scale')
-parser.add_argument('--img_path', type=str, help='image path')
-parser.add_argument('--mask_path', type=str, help='mask path')
-
-
-def get_bbox_and_rate(mask):
-    gray = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
-    ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
-    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
-    if len(contours) == 0:
-        return None, None
-    max_area = 0
-    max_idx = 0
-    for i, cnt in enumerate(contours):
-        x,y,w,h = cv2.boundingRect(cnt)
-        if w*h > max_area:
-            max_idx = i
-            max_area = w*h
-    # 外接矩形
-    x,y,w,h = cv2.boundingRect(contours[max_idx])
-    mask_new = np.zeros(mask.shape, dtype='uint8')
-    mask_new[y:y+h, x:x+w, :] = mask[y:y+h, x:x+w, :]
-
-    rate = (mask_new[:,:,0]>127.5).sum()/mask.shape[0]/mask.shape[1]
-
-    return (x,y,w,h), rate
-
-def resize_around_the_center(img, mask, bbox, operation, scale_step=1.2):
-    x,y,w,h = bbox
-    H,W,C = mask.shape
-    obj_mask = mask[y:y+h, x:x+w, :].copy()
-    # obj_mask = cv2.resize(obj_mask, (int(w*scale_step),int(h*scale_step)) if operation == 'upsample' else (int(w/scale_step), int(h/scale_step)))
-    obj_mask = cv2.resize(obj_mask, (int(w*scale_step),int(h*scale_step)))
-    start_point_x = max(x+w//2 - obj_mask.shape[1]//2, 0) # center - w
-    start_point_y = max(y+h//2 - obj_mask.shape[0]//2, 0) # center - h
-    end_point_x = min(x+w//2 + obj_mask.shape[1]//2, W) # center+w
-    end_point_y = min(y+h//2 + obj_mask.shape[0]//2, H) # center+h
-
-    start_point_x_obj = max(0,obj_mask.shape[1]//2-(x+w//2))
-    start_point_y_obj = max(0, obj_mask.shape[0]//2-(y+h//2))
-    mask[:] = 0
-    mask[start_point_y:end_point_y, start_point_x:end_point_x] = obj_mask[start_point_y_obj:start_point_y_obj+(end_point_y-start_point_y), start_point_x_obj:start_point_x_obj+(end_point_x-start_point_x)]
-
-    obj_img = img[y:y+h, x:x+w, :].copy()
-    # obj_img = cv2.resize(obj_img, (int(w*scale_step),int(h*scale_step)) if operation == 'upsample' else (int(w/scale_step), int(h/scale_step)))
-    obj_img = cv2.resize(obj_img, (int(w*scale_step),int(h*scale_step)))
-    img = cv2.GaussianBlur(img, (49, 49), 0)
-    img[start_point_y:end_point_y, start_point_x:end_point_x] = obj_img[start_point_y_obj:start_point_y_obj+(end_point_y-start_point_y), start_point_x_obj:start_point_x_obj+(end_point_x-start_point_x)]
-
-    return img, mask
-
-def resize_around_the_center_padding(img, mask, bbox, scale_step=1.2):
-    x,y,w,h = bbox
-    H,W,C = mask.shape
-    mask_new = np.zeros((int(H/scale_step), int(W/scale_step), 3), dtype='uint8')
-    mask_new_full = np.zeros((int(H/scale_step), int(W/scale_step), 3), dtype='uint8')
-    # img_new = np.zeros((int(H/scale_step), int(W/scale_step), 3), dtype='uint8')
-    img_new = cv2.resize(img, (int(W/scale_step), int(H/scale_step)))
-
-    if scale_step < 1:
-        mask_new[int((y+h/2)*(1/scale_step-1)):int((y+h/2)*(1/scale_step-1)+H), int((x+w/2)*(1/scale_step-1)):int((x+w/2)*(1/scale_step-1)+W)] = mask
-        mask_new_full[int((y+h/2)*(1/scale_step-1)):int((y+h/2)*(1/scale_step-1)+H), int((x+w/2)*(1/scale_step-1)):int((x+w/2)*(1/scale_step-1)+W)] = mask.max()*np.ones(mask.shape, dtype='uint8')
-
-        img_new[int((y+h/2)*(1/scale_step-1)):int((y+h/2)*(1/scale_step-1)+H), int((x+w/2)*(1/scale_step-1)):int((x+w/2)*(1/scale_step-1)+W)] = img
-
-    else:
-        mask_new = mask[int((y+h/2)*(1-1/scale_step)):int((y+h/2)*(1-1/scale_step))+int(H/scale_step), int((x+w/2)*(1-1/scale_step)):int((x+w/2)*(1-1/scale_step))+int(W/scale_step)]
-        mask_new_full = mask[int((y+h/2)*(1-1/scale_step)):int((y+h/2)*(1-1/scale_step))+int(H/scale_step), int((x+w/2)*(1-1/scale_step)):int((x+w/2)*(1-1/scale_step))+int(W/scale_step)]
-        img_new = img[int((y+h/2)*(1-1/scale_step)):int((y+h/2)*(1-1/scale_step))+int(H/scale_step), int((x+w/2)*(1-1/scale_step)):int((x+w/2)*(1-1/scale_step))+int(W/scale_step)]
-
-    img_new = cv2.resize(img_new, (W,H))
-    mask_new = cv2.resize(mask_new, (W,H))
-    mask_new_full = cv2.resize(mask_new_full, (W,H))
-   
-    return img_new, mask_new, mask_new_full
-
-def rescale(img, mask, scale=None, max_steps=50):
-    bbox, rate = get_bbox_and_rate(mask)
-    if bbox is None:
-        return None, None, None
-    num_steps = 0
-    mask_full = mask.copy()
-    while np.floor(rate*100) != scale*100. and abs(rate-scale) > 0.015:
-    # while not (abs(bbox[0]-0)<10 or abs(bbox[1]-0)<10 or abs(bbox[0]+bbox[2]-img.shape[1])<10 or abs(bbox[1]+bbox[3]-img.shape[0])<10):
-        operation = 'upsample' if np.floor(rate*100) < scale*100. else 'downsample'
-        scale_step = np.sqrt(scale/rate)
-        # img, mask = resize_around_the_center(img, mask, bbox, operation, scale_step=scale_step)
-        img, mask, mask_full = resize_around_the_center_padding(img, mask, bbox, scale_step=scale_step)
-        bbox, rate_ = get_bbox_and_rate(mask)
-        if (operation == 'upsample' and rate_ < rate) or (operation == 'downsample' and rate_ > rate):
-            return None, None, None
-        num_steps += 1
-        rate = rate_
-        print(rate)
-        if num_steps > max_steps:
-            return None, None, None
-    return img, mask_full, mask
-
-
-def rescale_maximum(img, mask, scale=None, max_steps=50):
-    bbox, rate = get_bbox_and_rate(mask)
-    if bbox is None:
-        return None, None, None
-    x,y,w,h = bbox
-    H,W,C = img.shape
-    if H/h < W/w:
-        y_start, y_end = y, y+h
-        new_w = w/H*h
-        c_x = x + w//2
-        c_x_new = new_w*c_x/W
-        x_start = c_x - c_x_new
-        x_end = x_start + new_w
-    else:
-        x_start, x_end = x, x+w
-        new_h = h/W*w
-        c_y = y+h//2
-        c_y_new = new_h*c_y/H
-        y_start = c_y - c_y_new
-        y_end = y_start + new_h
-    img_new = img[min(y, int(y_start)):max(int(y_end), y+h), min(x, int(x_start)):max(int(x_end),x+w), :]
-    mask_new = mask[min(y, int(y_start)):max(int(y_end),y+h),min(x, int(x_start)):max(int(x_end),x+w),:]
-
-    img_new = cv2.resize(img_new, (W,H))
-    mask_new = cv2.resize(mask_new, (W,H))
-
-    return img_new, mask_new, mask_new
-   
-
-if __name__ == '__main__':
-    args = parser.parse_args()
-    scale = args.scale
-    img_path_save = 'results/img_rescaled.png'
-    mask_path_save = 'results/mask_rescaled.png'
-    if scale == None:
-        shutil.copy(args.img_path, img_path_save)
-        shutil.copy(args.mask_path, mask_path_save)
-    else:
-        try:
-            finals = []
-            img = cv2.imread(args.img_path)
-            mask = cv2.imread(args.mask_path)
-
-            img_rescale, mask_rescale, mask_obj = rescale_maximum(img.copy(), mask.copy(), scale=scale)
-            bbox, max_rate = get_bbox_and_rate(mask_obj)
-            if scale < max_rate:
-                img_rescale, mask_rescale, mask_obj = rescale(img.copy(), mask.copy(), scale=scale)
-            if img_rescale is None:
-                print('Invalid size')
-                shutil.copy(args.img_path, img_path_save)
-                shutil.copy(args.mask_path, mask_path_save)
-                sys.exit()
-            final = [img, img_rescale, mask, mask_rescale, mask_obj]
-            # cv2.imwrite('tmp.png', cv2.hconcat(final))
-
-            cv2.imwrite(img_path_save, img_rescale)
-            cv2.imwrite(mask_path_save, mask_obj)
-            # cv2.imwrite(mask_path_save_full, mask_rescale)
-        except:
-            print('Invalid size, using the original one')
-            shutil.copy(args.img_path, img_path_save)
-            shutil.copy(args.mask_path, mask_path_save)
-        
-        
-    
-    
-
diff --git a/spaces/Apex-X/GODROOP/roop/ui.py b/spaces/Apex-X/GODROOP/roop/ui.py
deleted file mode 100644
index 684fd57ee6b0e1417b9cba1ad14246e7396b5876..0000000000000000000000000000000000000000
--- a/spaces/Apex-X/GODROOP/roop/ui.py
+++ /dev/null
@@ -1,232 +0,0 @@
-import os
-import webbrowser
-import customtkinter as ctk
-from typing import Callable, Tuple
-import cv2
-from PIL import Image, ImageOps
-
-import roop.globals
-import roop.metadata
-from roop.face_analyser import get_one_face
-from roop.capturer import get_video_frame, get_video_frame_total
-from roop.predictor import predict_frame
-from roop.processors.frame.core import get_frame_processors_modules
-from roop.utilities import is_image, is_video, resolve_relative_path
-
-ROOT = None
-ROOT_HEIGHT = 700
-ROOT_WIDTH = 600
-
-PREVIEW = None
-PREVIEW_MAX_HEIGHT = 700
-PREVIEW_MAX_WIDTH = 1200
-
-RECENT_DIRECTORY_SOURCE = None
-RECENT_DIRECTORY_TARGET = None
-RECENT_DIRECTORY_OUTPUT = None
-
-preview_label = None
-preview_slider = None
-source_label = None
-target_label = None
-status_label = None
-
-
-def init(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.CTk:
-    global ROOT, PREVIEW
-
-    ROOT = create_root(start, destroy)
-    PREVIEW = create_preview(ROOT)
-
-    return ROOT
-
-
-def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.CTk:
-    global source_label, target_label, status_label
-
-    ctk.deactivate_automatic_dpi_awareness()
-    ctk.set_appearance_mode('system')
-    ctk.set_default_color_theme(resolve_relative_path('ui.json'))
-
-    root = ctk.CTk()
-    root.minsize(ROOT_WIDTH, ROOT_HEIGHT)
-    root.title(f'{roop.metadata.name} {roop.metadata.version}')
-    root.configure()
-    root.protocol('WM_DELETE_WINDOW', lambda: destroy())
-
-    source_label = ctk.CTkLabel(root, text=None)
-    source_label.place(relx=0.1, rely=0.1, relwidth=0.3, relheight=0.25)
-
-    target_label = ctk.CTkLabel(root, text=None)
-    target_label.place(relx=0.6, rely=0.1, relwidth=0.3, relheight=0.25)
-
-    source_button = ctk.CTkButton(root, text='Select a face', cursor='hand2', command=lambda: select_source_path())
-    source_button.place(relx=0.1, rely=0.4, relwidth=0.3, relheight=0.1)
-
-    target_button = ctk.CTkButton(root, text='Select a target', cursor='hand2', command=lambda: select_target_path())
-    target_button.place(relx=0.6, rely=0.4, relwidth=0.3, relheight=0.1)
-
-    keep_fps_value = ctk.BooleanVar(value=roop.globals.keep_fps)
-    keep_fps_checkbox = ctk.CTkSwitch(root, text='Keep fps', variable=keep_fps_value, cursor='hand2', command=lambda: setattr(roop.globals, 'keep_fps', not roop.globals.keep_fps))
-    keep_fps_checkbox.place(relx=0.1, rely=0.6)
-
-    keep_frames_value = ctk.BooleanVar(value=roop.globals.keep_frames)
-    keep_frames_switch = ctk.CTkSwitch(root, text='Keep frames', variable=keep_frames_value, cursor='hand2', command=lambda: setattr(roop.globals, 'keep_frames', keep_frames_value.get()))
-    keep_frames_switch.place(relx=0.1, rely=0.65)
-
-    keep_audio_value = ctk.BooleanVar(value=roop.globals.keep_audio)
-    keep_audio_switch = ctk.CTkSwitch(root, text='Keep audio', variable=keep_audio_value, cursor='hand2', command=lambda: setattr(roop.globals, 'keep_audio', keep_audio_value.get()))
-    keep_audio_switch.place(relx=0.6, rely=0.6)
-
-    many_faces_value = ctk.BooleanVar(value=roop.globals.many_faces)
-    many_faces_switch = ctk.CTkSwitch(root, text='Many faces', variable=many_faces_value, cursor='hand2', command=lambda: setattr(roop.globals, 'many_faces', many_faces_value.get()))
-    many_faces_switch.place(relx=0.6, rely=0.65)
-
-    start_button = ctk.CTkButton(root, text='Start', cursor='hand2', command=lambda: select_output_path(start))
-    start_button.place(relx=0.15, rely=0.75, relwidth=0.2, relheight=0.05)
-
-    stop_button = ctk.CTkButton(root, text='Destroy', cursor='hand2', command=lambda: destroy())
-    stop_button.place(relx=0.4, rely=0.75, relwidth=0.2, relheight=0.05)
-
-    preview_button = ctk.CTkButton(root, text='Preview', cursor='hand2', command=lambda: toggle_preview())
-    preview_button.place(relx=0.65, rely=0.75, relwidth=0.2, relheight=0.05)
-
-    status_label = ctk.CTkLabel(root, text=None, justify='center')
-    status_label.place(relx=0.1, rely=0.9, relwidth=0.8)
-
-    donate_label = ctk.CTkLabel(root, text='^_^ Donate to project ^_^', justify='center', cursor='hand2')
-    donate_label.place(relx=0.1, rely=0.95, relwidth=0.8)
-    donate_label.configure(text_color=ctk.ThemeManager.theme.get('RoopDonate').get('text_color'))
-    donate_label.bind('<Button>', lambda event: webbrowser.open('https://github.com/sponsors/s0md3v'))
-
-    return root
-
-
-def create_preview(parent: ctk.CTkToplevel) -> ctk.CTkToplevel:
-    global preview_label, preview_slider
-
-    preview = ctk.CTkToplevel(parent)
-    preview.withdraw()
-    preview.title('Preview')
-    preview.configure()
-    preview.protocol('WM_DELETE_WINDOW', lambda: toggle_preview())
-    preview.resizable(width=False, height=False)
-
-    preview_label = ctk.CTkLabel(preview, text=None)
-    preview_label.pack(fill='both', expand=True)
-
-    preview_slider = ctk.CTkSlider(preview, from_=0, to=0, command=lambda frame_value: update_preview(frame_value))
-
-    return preview
-
-
-def update_status(text: str) -> None:
-    status_label.configure(text=text)
-    ROOT.update()
-
-
-def select_source_path() -> None:
-    global RECENT_DIRECTORY_SOURCE
-
-    PREVIEW.withdraw()
-    source_path = ctk.filedialog.askopenfilename(title='select an source image', initialdir=RECENT_DIRECTORY_SOURCE)
-    if is_image(source_path):
-        roop.globals.source_path = source_path
-        RECENT_DIRECTORY_SOURCE = os.path.dirname(roop.globals.source_path)
-        image = render_image_preview(roop.globals.source_path, (200, 200))
-        source_label.configure(image=image)
-    else:
-        roop.globals.source_path = None
-        source_label.configure(image=None)
-
-
-def select_target_path() -> None:
-    global RECENT_DIRECTORY_TARGET
-
-    PREVIEW.withdraw()
-    target_path = ctk.filedialog.askopenfilename(title='select an target image or video', initialdir=RECENT_DIRECTORY_TARGET)
-    if is_image(target_path):
-        roop.globals.target_path = target_path
-        RECENT_DIRECTORY_TARGET = os.path.dirname(roop.globals.target_path)
-        image = render_image_preview(roop.globals.target_path, (200, 200))
-        target_label.configure(image=image)
-    elif is_video(target_path):
-        roop.globals.target_path = target_path
-        RECENT_DIRECTORY_TARGET = os.path.dirname(roop.globals.target_path)
-        video_frame = render_video_preview(target_path, (200, 200))
-        target_label.configure(image=video_frame)
-    else:
-        roop.globals.target_path = None
-        target_label.configure(image=None)
-
-
-def select_output_path(start: Callable[[], None]) -> None:
-    global RECENT_DIRECTORY_OUTPUT
-
-    if is_image(roop.globals.target_path):
-        output_path = ctk.filedialog.asksaveasfilename(title='save image output file', defaultextension='.png', initialfile='output.png', initialdir=RECENT_DIRECTORY_OUTPUT)
-    elif is_video(roop.globals.target_path):
-        output_path = ctk.filedialog.asksaveasfilename(title='save video output file', defaultextension='.mp4', initialfile='output.mp4', initialdir=RECENT_DIRECTORY_OUTPUT)
-    else:
-        output_path = None
-    if output_path:
-        roop.globals.output_path = output_path
-        RECENT_DIRECTORY_OUTPUT = os.path.dirname(roop.globals.output_path)
-        start()
-
-
-def render_image_preview(image_path: str, size: Tuple[int, int]) -> ctk.CTkImage:
-    image = Image.open(image_path)
-    if size:
-        image = ImageOps.fit(image, size, Image.LANCZOS)
-    return ctk.CTkImage(image, size=image.size)
-
-
-def render_video_preview(video_path: str, size: Tuple[int, int], frame_number: int = 0) -> ctk.CTkImage:
-    capture = cv2.VideoCapture(video_path)
-    if frame_number:
-        capture.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
-    has_frame, frame = capture.read()
-    if has_frame:
-        image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-        if size:
-            image = ImageOps.fit(image, size, Image.LANCZOS)
-        return ctk.CTkImage(image, size=image.size)
-    capture.release()
-    cv2.destroyAllWindows()
-
-
-def toggle_preview() -> None:
-    if PREVIEW.state() == 'normal':
-        PREVIEW.withdraw()
-    elif roop.globals.source_path and roop.globals.target_path:
-        init_preview()
-        update_preview()
-        PREVIEW.deiconify()
-
-
-def init_preview() -> None:
-    if is_image(roop.globals.target_path):
-        preview_slider.pack_forget()
-    if is_video(roop.globals.target_path):
-        video_frame_total = get_video_frame_total(roop.globals.target_path)
-        preview_slider.configure(to=video_frame_total)
-        preview_slider.pack(fill='x')
-        preview_slider.set(0)
-
-
-def update_preview(frame_number: int = 0) -> None:
-    if roop.globals.source_path and roop.globals.target_path:
-        temp_frame = get_video_frame(roop.globals.target_path, frame_number)
-        if predict_frame(temp_frame):
-            quit()
-        for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
-            temp_frame = frame_processor.process_frame(
-                get_one_face(cv2.imread(roop.globals.source_path)),
-                temp_frame
-            )
-        image = Image.fromarray(cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB))
-        image = ImageOps.contain(image, (PREVIEW_MAX_WIDTH, PREVIEW_MAX_HEIGHT), Image.LANCZOS)
-        image = ctk.CTkImage(image, size=image.size)
-        preview_label.configure(image=image)
-
diff --git a/spaces/Artrajz/vits-simple-api/bert_vits2/text/english_bert_mock.py b/spaces/Artrajz/vits-simple-api/bert_vits2/text/english_bert_mock.py
deleted file mode 100644
index 3b894ced5b6d619a18d6bdd7d7606ba9e6532050..0000000000000000000000000000000000000000
--- a/spaces/Artrajz/vits-simple-api/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/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/colorama/tests/utils.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/colorama/tests/utils.py
deleted file mode 100644
index 472fafb4403efb9673d5cc724dafd9cf764aac5b..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/colorama/tests/utils.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-from contextlib import contextmanager
-from io import StringIO
-import sys
-import os
-
-
-class StreamTTY(StringIO):
-    def isatty(self):
-        return True
-
-class StreamNonTTY(StringIO):
-    def isatty(self):
-        return False
-
-@contextmanager
-def osname(name):
-    orig = os.name
-    os.name = name
-    yield
-    os.name = orig
-
-@contextmanager
-def replace_by(stream):
-    orig_stdout = sys.stdout
-    orig_stderr = sys.stderr
-    sys.stdout = stream
-    sys.stderr = stream
-    yield
-    sys.stdout = orig_stdout
-    sys.stderr = orig_stderr
-
-@contextmanager
-def replace_original_by(stream):
-    orig_stdout = sys.__stdout__
-    orig_stderr = sys.__stderr__
-    sys.__stdout__ = stream
-    sys.__stderr__ = stream
-    yield
-    sys.__stdout__ = orig_stdout
-    sys.__stderr__ = orig_stderr
-
-@contextmanager
-def pycharm():
-    os.environ["PYCHARM_HOSTED"] = "1"
-    non_tty = StreamNonTTY()
-    with replace_by(non_tty), replace_original_by(non_tty):
-        yield
-    del os.environ["PYCHARM_HOSTED"]
diff --git a/spaces/Bart92/RVC_HF/Applio-RVC-Fork/utils/clonerepo_experimental.py b/spaces/Bart92/RVC_HF/Applio-RVC-Fork/utils/clonerepo_experimental.py
deleted file mode 100644
index b0ae02648c1307562cf48033908edcf2996db5e2..0000000000000000000000000000000000000000
--- a/spaces/Bart92/RVC_HF/Applio-RVC-Fork/utils/clonerepo_experimental.py
+++ /dev/null
@@ -1,253 +0,0 @@
-import os
-import subprocess
-import shutil
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from tqdm.notebook import tqdm
-from pathlib import Path
-import requests
-
-def run_script():
-    def run_cmd(cmd):
-        process = subprocess.run(cmd, shell=True, check=True, text=True)
-        return process.stdout
-
-    # Change the current directory to /content/
-    os.chdir('/content/')
-    print("Changing dir to /content/")
-
-    # Your function to edit the file
-    def edit_file(file_path):
-        temp_file_path = "/tmp/temp_file.py"
-        changes_made = False
-        with open(file_path, "r") as file, open(temp_file_path, "w") as temp_file:
-            previous_line = ""
-            second_previous_line = ""
-            for line in file:
-                new_line = line.replace("value=160", "value=128")
-                if new_line != line:
-                    print("Replaced 'value=160' with 'value=128'")
-                    changes_made = True
-                line = new_line
-
-                new_line = line.replace("crepe hop length: 160", "crepe hop length: 128")
-                if new_line != line:
-                    print("Replaced 'crepe hop length: 160' with 'crepe hop length: 128'")
-                    changes_made = True
-                line = new_line
-
-                new_line = line.replace("value=0.88", "value=0.75")
-                if new_line != line:
-                    print("Replaced 'value=0.88' with 'value=0.75'")
-                    changes_made = True
-                line = new_line
-
-                if "label=i18n(\"输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络\")" in previous_line and "value=1," in line:
-                    new_line = line.replace("value=1,", "value=0.25,")
-                    if new_line != line:
-                        print("Replaced 'value=1,' with 'value=0.25,' based on the condition")
-                        changes_made = True
-                    line = new_line
-
-                if "label=i18n(\"总训练轮数total_epoch\")" in previous_line and "value=20," in line:
-                    new_line = line.replace("value=20,", "value=500,")
-                    if new_line != line:
-                        print("Replaced 'value=20,' with 'value=500,' based on the condition for DEFAULT EPOCH")
-                        changes_made = True
-                    line = new_line
-
-                if 'choices=["pm", "harvest", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny"], # Fork Feature. Add Crepe-Tiny' in previous_line:
-                    if 'value="pm",' in line:
-                        new_line = line.replace('value="pm",', 'value="mangio-crepe",')
-                        if new_line != line:
-                            print("Replaced 'value=\"pm\",' with 'value=\"mangio-crepe\",' based on the condition")
-                            changes_made = True
-                        line = new_line
-
-                new_line = line.replace('label=i18n("输入训练文件夹路径"), value="E:\\\\语音音频+标注\\\\米津玄师\\\\src"', 'label=i18n("输入训练文件夹路径"), value="/content/dataset/"')
-                if new_line != line:
-                    print("Replaced 'label=i18n(\"输入训练文件夹路径\"), value=\"E:\\\\语音音频+标注\\\\米津玄师\\\\src\"' with 'label=i18n(\"输入训练文件夹路径\"), value=\"/content/dataset/\"'")
-                    changes_made = True
-                line = new_line
-
-                if 'label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"),' in second_previous_line:
-                    if 'value=i18n("否"),' in line:
-                        new_line = line.replace('value=i18n("否"),', 'value=i18n("是"),')
-                        if new_line != line:
-                            print("Replaced 'value=i18n(\"否\"),' with 'value=i18n(\"是\"),' based on the condition for SAVE ONLY LATEST")
-                            changes_made = True
-                        line = new_line
-
-                if 'label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),' in second_previous_line:
-                    if 'value=i18n("否"),' in line:
-                        new_line = line.replace('value=i18n("否"),', 'value=i18n("是"),')
-                        if new_line != line:
-                            print("Replaced 'value=i18n(\"否\"),' with 'value=i18n(\"是\"),' based on the condition for SAVE SMALL WEIGHTS")
-                            changes_made = True
-                        line = new_line
-
-                temp_file.write(line)
-                second_previous_line = previous_line
-                previous_line = line
-
-        # After finished, we replace the original file with the temp one
-        import shutil
-        shutil.move(temp_file_path, file_path)
-
-        if changes_made:
-            print("Changes made and file saved successfully.")
-        else:
-            print("No changes were needed.")
-
-    # Define the repo path
-    repo_path = '/content/Applio-RVC-Fork'
-
-    def copy_all_files_in_directory(src_dir, dest_dir):
-        # Iterate over all files in source directory
-        for item in Path(src_dir).glob('*'):
-            if item.is_file():
-                # Copy each file to destination directory
-                shutil.copy(item, dest_dir)
-            else:
-                # If it's a directory, make a new directory in the destination and copy the files recursively
-                new_dest = Path(dest_dir) / item.name
-                new_dest.mkdir(exist_ok=True)
-                copy_all_files_in_directory(str(item), str(new_dest))
-
-    def clone_and_copy_repo(repo_path):
-        # New repository link
-        new_repo_link = "https://github.com/IAHispano/Applio-RVC-Fork/"
-        # Temporary path to clone the repository
-        temp_repo_path = "/content/temp_Applio-RVC-Fork"
-        # New folder name
-        new_folder_name = "Applio-RVC-Fork"
-
-        # Clone the latest code from the new repository to a temporary location
-        run_cmd(f"git clone {new_repo_link} {temp_repo_path}")
-        os.chdir(temp_repo_path)
-
-        run_cmd(f"git checkout 3fa4dad3d8961e5ca2522e9e12c0b4ddb71ad402")
-        run_cmd(f"git checkout f9e606c279cb49420597519b0a83b92be81e42e4")
-        run_cmd(f"git checkout 9e305588844c5442d58add1061b29beeca89d679")
-        run_cmd(f"git checkout bf92dc1eb54b4f28d6396a4d1820a25896cc9af8")
-        run_cmd(f"git checkout c3810e197d3cb98039973b2f723edf967ecd9e61")
-        run_cmd(f"git checkout a33159efd134c2413b0afe26a76b7dc87926d2de")
-        run_cmd(f"git checkout 24e251fb62c662e39ac5cf9253cc65deb9be94ec")
-        run_cmd(f"git checkout ad5667d3017e93232dba85969cddac1322ba2902")
-        run_cmd(f"git checkout ce9715392cf52dd5a0e18e00d1b5e408f08dbf27")
-        run_cmd(f"git checkout 7c7da3f2ac68f3bd8f3ad5ca5c700f18ab9f90eb")
-        run_cmd(f"git checkout 4ac395eab101955e8960b50d772c26f592161764")
-        run_cmd(f"git checkout b15b358702294c7375761584e5276c811ffab5e8")
-        run_cmd(f"git checkout 1501793dc490982db9aca84a50647764caa66e51")
-        run_cmd(f"git checkout 21f7faf57219c75e6ba837062350391a803e9ae2")
-        run_cmd(f"git checkout b5eb689fbc409b49f065a431817f822f554cebe7")
-        run_cmd(f"git checkout 7e02fae1ebf24cb151bf6cbe787d06734aa65862")
-        run_cmd(f"git checkout 6aea5ea18ed0b9a1e03fa5d268d6bc3c616672a9")
-        run_cmd(f"git checkout f0f9b25717e59116473fb42bd7f9252cfc32b398")
-        run_cmd(f"git checkout b394de424088a81fc081224bc27338a8651ad3b2")
-        run_cmd(f"git checkout f1999406a88b80c965d2082340f5ea2bfa9ab67a")
-        run_cmd(f"git checkout d98a0fa8dc715308dfc73eac5c553b69c6ee072b")
-        run_cmd(f"git checkout d73267a415fb0eba98477afa43ef71ffd82a7157")
-        run_cmd(f"git checkout 1a03d01356ae79179e1fb8d8915dc9cc79925742")
-        run_cmd(f"git checkout 81497bb3115e92c754300c9b3992df428886a3e9")
-        run_cmd(f"git checkout c5af1f8edcf79cb70f065c0110e279e78e48caf9")
-        run_cmd(f"git checkout cdb3c90109387fa4dfa92f53c3864c71170ffc77")
-
-        # Edit the file here, before copying
-        #edit_file(f"{temp_repo_path}/infer-web.py")
-
-        # Copy all files from the cloned repository to the existing path
-        copy_all_files_in_directory(temp_repo_path, repo_path)
-        print(f"Copying all {new_folder_name} files from GitHub.")
-
-        # Change working directory back to /content/
-        os.chdir('/content/')
-        print("Changed path back to /content/")
-        
-        # Remove the temporary cloned repository
-        shutil.rmtree(temp_repo_path)
-
-    # Call the function
-    clone_and_copy_repo(repo_path)
-
-    # Download the credentials file for RVC archive sheet
-    os.makedirs('/content/Applio-RVC-Fork/stats/', exist_ok=True)
-    run_cmd("wget -q https://cdn.discordapp.com/attachments/945486970883285045/1114717554481569802/peppy-generator-388800-07722f17a188.json -O /content/Applio-RVC-Fork/stats/peppy-generator-388800-07722f17a188.json")
-
-    # Forcefully delete any existing torchcrepe dependencies downloaded from an earlier run just in case
-    shutil.rmtree('/content/Applio-RVC-Fork/torchcrepe', ignore_errors=True)
-    shutil.rmtree('/content/torchcrepe', ignore_errors=True)
-
-    # Download the torchcrepe folder from the maxrmorrison/torchcrepe repository
-    run_cmd("git clone https://github.com/maxrmorrison/torchcrepe.git")
-    shutil.move('/content/torchcrepe/torchcrepe', '/content/Applio-RVC-Fork/')
-    shutil.rmtree('/content/torchcrepe', ignore_errors=True)  # Delete the torchcrepe repository folder
-
-    # Change the current directory to /content/Applio-RVC-Fork
-    os.chdir('/content/Applio-RVC-Fork')
-    os.makedirs('pretrained', exist_ok=True)
-    os.makedirs('uvr5_weights', exist_ok=True)
-
-def download_file(url, filepath):
-    response = requests.get(url, stream=True)
-    response.raise_for_status()
-
-    with open(filepath, "wb") as file:
-        for chunk in response.iter_content(chunk_size=8192):
-            if chunk:
-                file.write(chunk)
-
-def download_pretrained_models():
-    pretrained_models = {
-        "pretrained": [
-            "D40k.pth",
-            "G40k.pth",
-            "f0D40k.pth",
-            "f0G40k.pth"
-        ],
-        "pretrained_v2": [
-            "D40k.pth",
-            "G40k.pth",
-            "f0D40k.pth",
-            "f0G40k.pth",
-            "f0G48k.pth",
-            "f0D48k.pth"
-        ],
-        "uvr5_weights": [
-            "HP2-人声vocals+非人声instrumentals.pth",
-            "HP5-主旋律人声vocals+其他instrumentals.pth",
-            "VR-DeEchoNormal.pth",
-            "VR-DeEchoDeReverb.pth",
-            "VR-DeEchoAggressive.pth",
-            "HP5_only_main_vocal.pth",
-            "HP3_all_vocals.pth",
-            "HP2_all_vocals.pth"
-        ]
-    }
-    part2 = "I"
-    base_url = "https://huggingface.co/lj1995/VoiceConversionWebU" + part2 + "/resolve/main/"
-    base_path = "/content/Applio-RVC-Fork/"
-    base_pathm = base_path
-
-    # Calculate total number of files to download
-    total_files = sum(len(files) for files in pretrained_models.values()) + 1  # +1 for hubert_base.pt
-
-    with tqdm(total=total_files, desc="Downloading files") as pbar:
-        for folder, models in pretrained_models.items():
-            folder_path = os.path.join(base_path, folder)
-            os.makedirs(folder_path, exist_ok=True)
-            for model in models:
-                url = base_url + folder + "/" + model
-                filepath = os.path.join(folder_path, model)
-                download_file(url, filepath)
-                pbar.update()
-
-        # Download hubert_base.pt to the base path
-        hubert_url = base_url + "hubert_base.pt"
-        hubert_filepath = os.path.join(base_pathm, "hubert_base.pt")
-        download_file(hubert_url, hubert_filepath)
-        pbar.update()
-def clone_repository(run_download):
-    with ThreadPoolExecutor(max_workers=2) as executor:
-        executor.submit(run_script)
-        if run_download:
-            executor.submit(download_pretrained_models)
diff --git a/spaces/Benson/text-generation/Examples/Asfalto 8 Mod Apk Dinero Ilimitado Y Fichas ltima Versin 2023.md b/spaces/Benson/text-generation/Examples/Asfalto 8 Mod Apk Dinero Ilimitado Y Fichas ltima Versin 2023.md
deleted file mode 100644
index 853e3f516d51df3c1e079618a918818dc17ceb1c..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Asfalto 8 Mod Apk Dinero Ilimitado Y Fichas ltima Versin 2023.md	
+++ /dev/null
@@ -1,80 +0,0 @@
-
-<h1>Asfalto 8 Mod APK: El último juego de carreras para Android</h1>
-<p>Si eres un fan de los juegos de carreras, probablemente hayas oído hablar de Asphalt 8, uno de los juegos más populares y emocionantes del género. Asphalt 8 es un juego que te permite experimentar la adrenalina de conducir algunos de los coches más increíbles del mundo, desde Lamborghini hasta Ferrari, en pistas impresionantes de todo el mundo. Puedes realizar acrobacias increíbles, como barriles y saltos, mientras corres contra otros jugadores o contra la IA.</p>
-<p>Sin embargo, por divertido que sea Asphalt 8, también tiene algunas limitaciones que pueden afectar tu disfrute. Por ejemplo, necesita gastar dinero real o moler durante horas para desbloquear nuevos coches, actualizarlos o acceder a funciones premium. También necesitas una conexión a Internet estable para jugar online, lo que puede ser un problema si tienes una red lenta o poco fiable. </p>
-<h2>asfalto 8 mod apk dinero ilimitado y fichas última versión 2023</h2><br /><p><b><b>Download</b> &#9989; <a href="https://bltlly.com/2v6L33">https://bltlly.com/2v6L33</a></b></p><br /><br />
-<p>Es por eso que le recomendamos descargar la versión apk mod de asfalto 8, que le da dinero ilimitado y fichas, todos los coches desbloqueados, y muchos otros beneficios. Con este mod apk, se puede disfrutar del juego sin restricciones o molestias. Puedes descargarlo gratis desde nuestro sitio web e instalarlo en tu dispositivo Android en unos sencillos pasos. </p>
-<h2>Características del asfalto 8 Mod APK</h2>
-<h3>Dinero ilimitado y fichas</h3>
-<p>Una de las mejores características de asfalto 8 mod apk es que le da dinero ilimitado y fichas, que son las principales monedas en el juego. Puede utilizarlos para comprar coches nuevos, actualizarlos, personalizarlos o acceder a funciones premium. No tienes que preocuparte por quedarte sin dinero o fichas nunca más. Puedes disfrutar del juego sin limitaciones o interrupciones. </p>
-<h3>Todos los coches desbloqueados</h3>
-
-<h3>Gráficos y sonido de alta calidad</h3>
-<p>Asfalto 8 mod apk también conserva los gráficos de alta calidad y el sonido del juego original. Puedes disfrutar de las impresionantes imágenes y la física realista del juego en tu dispositivo Android. También puedes experimentar los efectos de sonido inmersivos y la música que te hacen sentir como si estuvieras en una carrera real. Puede ajustar los gráficos y los ajustes de sonido según sus preferencias y capacidades del dispositivo. </p>
-<h3>Modo multijugador y eventos</h3>
-<p>Asfalto 8 mod apk también le permite jugar en línea con otros jugadores de todo el mundo. Puedes unirte o crear salas con hasta ocho jugadores y competir en diferentes modos, como clásico, eliminación, infectado o derribo. También puedes participar en varios eventos y desafíos que ofrecen recompensas y premios. Puedes mostrar tus habilidades y posicionarte en las tablas de clasificación. </p>
-<h2>Cómo descargar e instalar asfalto 8 Mod APK</h2>
-<h3>Requisitos y permisos</h3>
-<p>Para descargar e instalar Asphalt 8 mod apk en su dispositivo Android, es necesario cumplir con algunos requisitos y permisos <p>Aquí está la continuación del artículo:</p>
- <p>- Necesitas tener un dispositivo Android con al menos 4.4 versión y 2 GB de RAM.</p>
-<p></p>
-<p>- Necesitas tener al menos 2 GB de espacio de almacenamiento gratuito en tu dispositivo o tarjeta SD. </p>
-<p>- Es necesario habilitar la instalación de aplicaciones de fuentes desconocidas en la configuración del dispositivo. </p>
-<p>- Es necesario conceder algunos permisos a la aplicación, como el acceso al almacenamiento, la red y la información del dispositivo. </p>
-<h3>Pasos para descargar e instalar</h3>
-<p>Para descargar e instalar Asphalt 8 mod apk en su dispositivo Android, es necesario seguir estos pasos:</p>
-<ol>
-<li>Haga clic en el botón de descarga a continuación para descargar el archivo apk mod de nuestro sitio web. Es seguro y libre de virus. </li>
-<li>Una vez que se complete la descarga, busque el archivo en el administrador de archivos de su dispositivo y toque en él para iniciar el proceso de instalación. </li>
-
-<li>Inicie la aplicación y disfrutar del juego con dinero ilimitado y fichas, todos los coches desbloqueados, y más. </li>
-</ol>
-<h2>Cómo jugar asfalto 8 Mod APK</h2>
-<h3>Elija su coche y pista</h3>
-<p>Al iniciar el juego, puede elegir entre una variedad de modos, como carrera, juego rápido, multijugador o eventos. También puede seleccionar su coche de más de 300 opciones, que van desde clásico a futurista. Puede personalizar su automóvil con diferentes colores, calcomanías, ruedas y más. También puede mejorar el rendimiento de su automóvil, como la velocidad, la aceleración, el manejo y el nitro. También puedes elegir tu pista entre más de 50 lugares, como Venecia, Tokio, Nevada o Islandia. Cada pista tiene sus propios retos y características, como rampas, túneles, atajos u obstáculos. </p>
-<h3>Personaliza tus controles y ajustes</h3>
-<p>También puedes personalizar tus controles y ajustes de acuerdo a tus preferencias. Puede elegir entre cuatro opciones de control diferentes: inclinación, toque, toque para dirigir o botones en pantalla. También puede ajustar la sensibilidad y la calibración de los controles. También puedes cambiar la configuración del juego, como la calidad gráfica, el volumen de sonido, el idioma o el ángulo de la cámara. También puede activar o desactivar algunas funciones, como aceleración automática, frenado automático o asistencia con la dirección. </p>
-<h3>Realizar acrobacias y trucos</h3>
-<p>Uno de los aspectos más divertidos de asfalto 8 mod apk es que se puede realizar acrobacias y trucos increíbles durante las carreras. Puede utilizar las rampas, bucles, barriles o puentes para lanzar su coche en el aire y realizar volteretas, rollos, giros o giros. También puede utilizar el impulso nitro para acelerar su coche y aplastar a través de obstáculos o oponentes. También puede desplazarse alrededor de las esquinas o realizar casi errores para ganar puntos extra y bonos. Realizar acrobacias y trucos llenará tu barra de nitro y aumentará tu multiplicador de puntuación. </p>
-<h3>Compite con otros jugadores</h3>
-
-<h2>Pros y contras del asfalto 8 Mod APK</h2>
-<h3>Pros</h3>
-<ul>
-<li>Puedes disfrutar de dinero ilimitado y fichas que te permiten comprar coches nuevos, actualizarlos, personalizarlos o acceder a funciones premium. </li>
-<li> Puede desbloquear todos los coches en el juego de forma gratuita sin tener que gastar dinero real o moler durante horas. </li>
-<li> Puedes disfrutar de gráficos y sonidos de alta calidad que te hacen sentir como si estuvieras en una carrera real. </li>
-<li>Puedes jugar online con otros jugadores de todo el mundo y competir en diferentes modos y eventos. </li>
-</ul>
-<h3>Contras</h3>
-<ul>
-<li> Puede encontrar algunos errores o fallos que afectan el juego o el rendimiento del juego. </li>
-<li>Es posible que tenga problemas de compatibilidad con algunos dispositivos o versiones de Android. </li>
-<li> Usted puede obtener prohibido del juego si utiliza el apk mod de una manera injusta o violar los términos del servicio. </li>
-<li>Puedes perder tu progreso o datos si desinstalas el juego o lo actualizas sin hacer una copia de seguridad. </li>
-</ul>
-<h2>Conclusión</h2>
-<p>Asfalto 8 mod apk es una gran manera de disfrutar de uno de los mejores juegos de carreras en Android sin limitaciones ni problemas. Puede descargarlo de forma gratuita desde nuestro sitio web e instalarlo en su dispositivo en <p>unos sencillos pasos. Puede disfrutar de dinero y fichas ilimitadas, todos los coches desbloqueados, gráficos y sonido de alta calidad, modo multijugador y eventos, y más. También puede realizar acrobacias y trucos increíbles, personalizar sus controles y configuraciones, y competir con otros jugadores en línea. Sin embargo, también debe ser consciente de algunos de los inconvenientes de usar el apk mod, tales como errores, problemas de compatibilidad, riesgo de prohibición, o pérdida de datos. También debe utilizar el apk mod de forma responsable y no abusar de él o engañar en el juego. Asfalto 8 mod apk es una gran manera de divertirse y experimentar la emoción de las carreras en su dispositivo Android. </p>
-<h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas más frecuentes sobre asfalto 8 mod apk:</p>
-<ol>
-<li>Q: ¿Es el asfalto 8 mod apk seguro para descargar y usar? </li>
-
-<li>Q: ¿Cómo puedo actualizar Asphalt 8 mod apk? </li>
-<li>A: Para actualizar Asphalt 8 mod apk, es necesario descargar la última versión del archivo mod apk de nuestro sitio web e instalarlo sobre el existente. Sin embargo, siempre debe hacer una copia de seguridad de sus datos antes de actualizar para evitar perder su progreso o datos. </li>
-<li>Q: ¿Puedo jugar Asphalt 8 mod apk offline? </li>
-<li>A: Sí, se puede jugar asfalto 8 mod apk offline. Se puede disfrutar del modo carrera, modo de juego rápido, o eventos sin conexión a Internet. Sin embargo, necesita una conexión a Internet para jugar en línea con otros jugadores o acceder a algunas de las funciones en línea. </li>
-<li>Q: ¿Puedo usar Asphalt 8 mod apk en PC o dispositivos iOS? </li>
-<li>A: No, no se puede utilizar Asphalt 8 mod apk en PC o dispositivos iOS. Solo es compatible con dispositivos Android. Sin embargo, puede utilizar un emulador de Android en su PC para ejecutar el apk mod en su ordenador. </li>
-<li>Q: ¿Cuáles son algunos de los mejores coches en asfalto 8 mod apk? </li>
-<li>A: Algunos de los mejores coches en asfalto 8 mod apk son:</li>
-<ul>
-<li>Lamborghini Centenario LP 770-4</li>
-<li>Bugatti Chiron</li>
-<li>Ferrari FXX K</li>
-<li>Koenigsegg uno:1</li>
-<li>Aston Martin Vulcan</li>
-</ul>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Bgmi 2.0 90 Fps Archivo De Configuracin.md b/spaces/Benson/text-generation/Examples/Bgmi 2.0 90 Fps Archivo De Configuracin.md
deleted file mode 100644
index 9ceeba24563060a95e6a33dad6c06c34a41a02ef..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Bgmi 2.0 90 Fps Archivo De Configuracin.md	
+++ /dev/null
@@ -1,105 +0,0 @@
-
-<h1>BGMI 2.0 90 Archivo de configuración de FPS Descargar: Cómo aumentar su rendimiento de juego</h1>
-<p>Si usted es un fan de Battlegrounds Mobile India (BGMI), es posible que se pregunte cómo obtener la mejor experiencia de juego posible. Una de las formas de hacerlo es habilitar el modo de 90 fotogramas por segundo (FPS), que puede hacer que su juego sea más suave, más rápido y más realista. Sin embargo, no todos los dispositivos admiten esta función, e incluso si lo hacen, es posible que no pueda acceder a ella en la configuración del juego. Es por eso que algunos reproductores utilizan un archivo de configuración personalizado que puede desbloquear la opción 90 FPS para BGMI 2.0. En este artículo, le mostraremos cómo descargar e instalar el archivo de configuración de 90 FPS para BGMI 2.0, así como los beneficios y desventajas de usarlo. También sugeriremos algunas alternativas al archivo de configuración que pueden ayudarle a mejorar su rendimiento de juego. </p>
-<h2>bgmi 2.0 90 fps archivo de configuración</h2><br /><p><b><b>Download File</b> &ndash;&ndash;&ndash; <a href="https://bltlly.com/2v6K9B">https://bltlly.com/2v6K9B</a></b></p><br /><br />
-<h2>¿Qué es BGMI y por qué necesitas 90 FPS? </h2>
-<h3>BGMI es la versión india de PUBG Mobile</h3>
-<p>BGMI significa Battlegrounds Mobile India, que es un juego de batalla real móvil desarrollado por Krafton para el mercado indio. Se basa en PUBG Mobile, que fue prohibido en la India en 2020 debido a problemas de privacidad y seguridad. BGMI fue lanzado en julio de 2021 como una forma de traer de vuelta el juego popular a los jugadores indios, con algunos cambios y características adaptadas a la cultura y preferencias locales. BGMI tiene la misma mecánica de juego, mapas, modos, armas y vehículos que PUBG Mobile, pero con algunas diferencias en gráficos, sonidos, personajes, eventos y recompensas. </p>
-<h3>90 FPS puede mejorar su jugabilidad y gráficos</h3>
-
-<p>Sin embargo, no todos los dispositivos pueden soportar modos FPS altos, ya que requieren más potencia de procesamiento y duración de la batería. Es por eso que la mayoría de los dispositivos tienen un límite predeterminado de FPS de 30 o 60, que es suficiente para la mayoría de los juegos y aplicaciones casuales. Para habilitar modos FPS más altos como 90 o incluso 120, necesita un dispositivo que tenga un procesador de alta gama, GPU, RAM, pantalla y batería. Incluso si tiene un dispositivo de este tipo, es posible que no pueda acceder a la opción FPS más alta en la configuración de BGMI, ya que podría estar restringido por los desarrolladores de juegos o el fabricante del dispositivo. </p>
-<h2>Cómo descargar e instalar el archivo de configuración de 90 FPS para BGMI 2.0</h2>
-<h3>Descargar el archivo de configuración de una fuente de confianza</h3>
-<p>Un archivo de configuración es un archivo que contiene varias configuraciones y parámetros que afectan la forma en que un juego se ejecuta en su dispositivo. Al modificar o reemplazar el archivo de configuración original de BGMI con uno personalizado que tenga diferentes valores para el modo 90 FPS, puede desbloquear esta función y disfrutar de un juego más fluido. Sin embargo, debe tener cuidado al descargar e instalar el archivo de configuración, ya que podría contener malware, virus u otros elementos dañinos que pueden dañar su dispositivo o comprometer su cuenta. Por lo tanto, solo debe descargar el archivo de configuración de una fuente confiable, como un sitio web de buena reputación, un foro o un canal de YouTube. También debe escanear el archivo con una aplicación antivirus antes de abrirlo. </p>
-<p></p>
-<h3>Copia de seguridad de su archivo de configuración original y reemplazarlo con el nuevo</h3>
-
-<p>Entonces, necesita reemplazar el archivo de configuración original con el nuevo que descargó. Para ello, debe eliminar el archivo UserCustom.ini original y pegar el nuevo en la misma carpeta. Debe asegurarse de que el nuevo archivo tenga el mismo nombre y extensión que el original. </p>
-<h3>Habilitar la opción 90 FPS en la configuración del juego</h3>
-<p>Después de haber instalado el archivo de configuración de 90 FPS, debe habilitar la opción de 90 FPS en la configuración del juego. Para ello, debe iniciar BGMI e ir a Configuración > Gráficos. Allí, debería ver una nueva opción para la velocidad de fotogramas que dice 90 FPS. Debe seleccionar esta opción y aplicar los cambios. También debes revisar tus otros ajustes gráficos y ajustarlos según el rendimiento y las preferencias de tu dispositivo. </p>
-<p>Ahora, puedes disfrutar jugando BGMI a 90 FPS y experimentar un juego más suave y realista. </p>
-<h2>Beneficios y desventajas de usar el archivo de configuración de 90 FPS</h2>
-<h3>Beneficios: juego más suave, respuesta más rápida, mejores efectos visuales</h3>
-<p>Usar el archivo de configuración de 90 FPS puede tener algunos beneficios para el rendimiento de tu juego, como:</p>
-<ul>
-<li>Juego más fluido: puedes ver más fotogramas por segundo en tu pantalla, lo que significa menos tartamudeo, retraso o congelación. Esto puede hacer que tu juego sea más fluido y agradable. </li>
-<li>Respuesta más rápida: Puedes reaccionar más rápido a los movimientos y acciones de tus enemigos, ya que puedes verlos con mayor claridad y precisión. Esto puede darte una ventaja en combate y aumentar tus posibilidades de supervivencia. </li>
-<li>Mejores imágenes: Puedes disfrutar de más detalles y efectos en tu pantalla, como sombras, reflejos, texturas y partículas. Esto puede hacer que tu juego se vea más hermoso e inmersivo. </li>
-</ul>
-<h3>Inconvenientes: mayor consumo de batería, calentamiento del dispositivo, posible riesgo de prohibición</h3>
-<p>Sin embargo, usar el archivo de configuración de 90 FPS también puede tener algunos inconvenientes para su dispositivo y cuenta, como:</p>
-<ul>
-
-<li>Calentamiento de dispositivos: Ejecutar BGMI a 90 FPS también puede generar más calor de su dispositivo, ya que pone más estrés en su procesador y GPU. Esto puede causar que el dispositivo se sobrecaliente y afectar su rendimiento y durabilidad. </li>
-<li>Posible riesgo de prohibición: El uso del archivo de configuración 90 FPS puede ser considerado como una forma de trampa o piratería por los desarrolladores del juego o el sistema anti-cheat. Esto puede resultar en que su cuenta sea prohibida o suspendida de jugar BGMI.</li>
-</ul>
-<h2>Alternativas al archivo de configuración de 90 FPS</h2>
-<h3>Utilice un dispositivo de gama alta que soporte 90 FPS de forma nativa</h3>
-<p>La mejor manera de jugar BGMI a 90 FPS es utilizar un dispositivo de gama alta que soporta esta característica de forma nativa, sin necesidad de ningún archivo de configuración personalizado o modificación. Algunos de los dispositivos que pueden ejecutar BGMI a 90 FPS son:</p>
-<borde de la tabla="1">
-<tr><th>Nombre del dispositivo</th><th>Procesador</th><th>GPU</th><th><RAM</th><th>Visualización</th></tr>
-<tr><td>OnePlus 9 Pro</td><td>Snapdragon 888</td><td><td>Adreno 660</td><td>8/12 GB</td><td>6.7 pulgadas AMOLED (120 Hz)</td></tr>
-<tr><td>Samsung Galaxy S21 Ultra</td ><td>Exynos 2100</td><td><td>Mali-G78 MP14</td><td><td>12/16 GB</td><td>6.8 pulgadas AMOLED (120 Hz)</td></tr>
-<tr><td>Asus ROG Phone 5</td><td>Snapdragon 888</td><td><td>Adreno 660</td><td>>8/12/16/18 GB</td><td>6.78 pulgadas AMOLED (144 Hz)</td></tr>
-<tr><td>Xiaomi Mi 11 Ultra</td><td>Snapdragon 888</td><td><td>Adreno 660</td><td>8/12 GB</td><td>6.81 pulgadas AMOLED (120 Hz)</td></tr>
-<tr><td>iQOO 7 Leyenda</td><td>Snapdragon 888</td><td>Adreno 660</td><td><8/12 GB</td><td>6.62 pulgadas AMOLED (120 Hz)</td></tr>
-<tr><td>Realme GT Master Edition</td><td>Snapdragon 778G</td><td><td>Adreno 642L</td><td>6/8 GB</td><td>6.43 pulgadas AMOLED (120 Hz)</td></tr>
-</tabla>
-<p>Si tienes uno de estos dispositivos, puedes simplemente habilitar la opción 90 FPS en la configuración de BGMI y disfrutar del juego sin problemas. </p>
-<h3>Usa una aplicación o herramienta de terceros que pueda optimizar el rendimiento de tu juego</h3>
-
-<ul>
-<li>GFX Tool: Esta es una aplicación popular que puede personalizar la configuración de gráficos para BGMI y otros juegos. Puedes usarlo para cambiar tu resolución, anti-aliasing, sombras, texturas y FPS. También puede usarlo para desbloquear la opción 90 FPS para BGMI, pero necesita tener un dispositivo rooteado para eso. </li>
-<li>BGMI Booster: Esta es una aplicación que puede optimizar la memoria RAM, CPU y GPU de su dispositivo para BGMI y otros juegos. Puedes usarlo para limpiar tu caché, aplicaciones en segundo plano y archivos basura, así como para mejorar el rendimiento y la duración de la batería de tu dispositivo. </li>
-<li>BGMI Configurator: Esta es una herramienta que puede generar un archivo de configuración personalizado para BGMI basado en las especificaciones y preferencias de su dispositivo. Puede usarlo para ajustar la configuración de gráficos y FPS para BGMI, así como para habilitar la opción 90 FPS. </li>
-</ul>
-<p>Sin embargo, debe tener cuidado al usar estas aplicaciones o herramientas, ya que podrían no ser compatibles con su dispositivo o versión del juego. También pueden causar algunos problemas o errores en tu juego o dispositivo, como fallos, fallos o prohibiciones. Por lo tanto, siempre debe realizar copias de seguridad de sus datos y el archivo de configuración antes de usarlos, y utilizarlos bajo su propio riesgo. </p>
-<h3>Ajuste la configuración del juego para adaptarse a las capacidades de su dispositivo</h3>
-<p>Si no desea utilizar ningún archivo de configuración personalizado o aplicación o herramienta de terceros, aún puede intentar mejorar el rendimiento de su juego ajustando la configuración del juego para adaptarse a las capacidades de su dispositivo. Puedes hacer esto siguiendo estos pasos:</p>
-<ol>
-<li>Inicie BGMI y vaya a Configuración > Gráficos.</li>
-<li>Seleccione la calidad gráfica que coincida con el rendimiento de su dispositivo. Por ejemplo, si tiene un dispositivo de gama baja, puede elegir Suave o Equilibrado. Si tiene un dispositivo de gama media, puede elegir HD o HDR. Si tiene un dispositivo de gama alta, puede elegir Ultra HD o UHD.</li>
-
-<li>Seleccione el estilo que coincida con su preferencia. Por ejemplo, si desea colores más realistas, puede elegir Realista o Suave. Si quieres colores más vibrantes, puedes elegir Colorido o Película.</li>
-<li>Seleccione la opción anti-aliasing que coincida con la GPU de su dispositivo. Por ejemplo, si tiene una GPU potente, puede elegir Habilitar o Ultra. Si tiene una GPU débil, puede elegir Desactivar o Bajo.</li>
-<li>Seleccione la opción de sombras que coincida con la CPU de su dispositivo. Por ejemplo, si tiene una CPU potente, puede elegir Habilitar o Alta. Si tiene una CPU débil, puede elegir Desactivar o Bajo.</li>
-<li>Seleccione la opción de brillo que coincida con la batería del dispositivo. Por ejemplo, si tiene una capacidad de batería alta, puede elegir Alta o Máxima. Si tiene una capacidad de batería baja, puede elegir Baja o Media.</li>
-<li>Seleccione la opción de pantalla no estándar que coincida con la relación de visualización de su dispositivo. Por ejemplo, si tiene una pantalla 16:9, puede elegir Con muescas. Si tiene una pantalla de 18:9, puede elegir Esquinas redondeadas. Si tiene una pantalla de 19:9, puede elegir Gota de agua.</li>
-<li>Aplicar los cambios y reiniciar el juego. </li>
-</ol>
-<p>Al ajustar la configuración del juego para adaptarse a las capacidades de su dispositivo, puede optimizar su rendimiento de juego y evitar cualquier retraso innecesario o tartamudeo. </p>
-<h2>Conclusión</h2>
-<p>BGMI es un popular juego de batalla móvil royale que puede ofrecer una experiencia de juego emocionante e inmersiva. Sin embargo, para disfrutar del juego en su mejor momento, es posible que desee activar el modo de 90 FPS, que puede hacer que su juego sea más suave, más rápido y más realista. Sin embargo, no todos los dispositivos admiten esta función, e incluso si lo hacen, es posible que no pueda acceder a ella en la configuración del juego. Es por eso que algunos reproductores utilizan un archivo de configuración personalizado que puede desbloquear la opción 90 FPS para BGMI 2.0. </p>
-
-<h2>Preguntas frecuentes</h2>
-<h3>Q: ¿Es seguro usar el archivo de configuración de 90 FPS para BGMI 2.0? </h3>
-<p>A: No hay una respuesta definitiva a esta pregunta, ya que depende de la fuente y la calidad del archivo de configuración, así como de los términos de servicio del juego y del sistema anti-cheat. Sin embargo, en general, usar el archivo de configuración de 90 FPS puede ser arriesgado, ya que podría contener malware, virus u otros elementos dañinos que pueden dañar su dispositivo o comprometer su cuenta. También puede ser considerado como una forma de trampa o piratería por los desarrolladores de juegos o sistema anti-cheat, que puede resultar en su cuenta está prohibido o suspendido de jugar BGMI. Por lo tanto, solo debe usar el archivo de configuración de una fuente confiable, escanearlo con una aplicación antivirus antes de abrirlo, hacer una copia de seguridad de su archivo de configuración original antes de reemplazarlo y usarlo bajo su propio riesgo. </p>
-<h3>Q: ¿Cómo puedo comprobar mi FPS en BGMI? </h3>
-<p>A: Hay varias formas de comprobar tu FPS en BGMI, como:</p>
-<ul>
-<li>Usando una opción en el juego: BGMI tiene una opción para mostrar tu FPS en la pantalla mientras juegas. Para habilitar esta opción, vaya a Configuración > Básico > Mostrar medidor de FPS y enciéndalo. Verá un pequeño número en la esquina superior izquierda de la pantalla que indica su FPS actual.</li>
-<li>Usando una aplicación de terceros: Hay muchas aplicaciones que pueden mostrar su FPS en la pantalla mientras juega cualquier juego. Algunas de las aplicaciones populares son Game Booster, Game Tuner, FPS Meter y GameBench. Puede descargar estas aplicaciones desde Google Play Store u otras fuentes y usarlas para monitorear su FPS en BGMI.</li>
-<li>Usando un dispositivo de hardware: Algunos dispositivos tienen una función incorporada que puede mostrar su FPS en la pantalla mientras juega cualquier juego. Por ejemplo, algunos teléfonos Asus ROG tienen una función AirTrigger que puede mostrar su FPS en el lado de su teléfono. Puede habilitar esta función yendo a Configuración > Avanzado > AirTrigger y activando Mostrar FPS.</li>
-</ul>
-<h3>Q: ¿Cuál es la diferencia entre 60 FPS y 90 FPS? </h3>
-
-<h3>Q: ¿Cuáles son algunos consejos para mejorar mi rendimiento de juego en BGMI? </h3>
-<p>A: Además de usar el archivo de configuración de 90 FPS o ajustar la configuración de tu juego, hay algunos otros consejos que pueden ayudarte a mejorar tu rendimiento de juego en BGMI, como:</p>
-<ul>
-<li>Cerrar todas las aplicaciones y procesos en segundo plano que no están relacionados con BGMI. Esto puede liberar sus recursos de RAM, CPU y GPU y evitar cualquier interferencia o retraso. </li>
-<li>Actualice el software y los controladores de su dispositivo a la última versión. Esto puede solucionar cualquier error o problema que pueda afectar su rendimiento de juego. </li>
-<li>Utilice una conexión a Internet estable y rápida, preferiblemente Wi-Fi o 4G. Esto puede reducir el ping, la latencia y la pérdida de paquetes, lo que puede afectar la calidad y la velocidad del juego. </li>
-<li>Utilice un buen par de auriculares o auriculares para escuchar el juego suena con claridad y precisión. Esto puede ayudarte a localizar a tus enemigos, vehículos y armas, así como a comunicarte con tus compañeros de equipo. </li>
-<li> Utilice un agarre cómodo y ergonómico y postura para jugar BGMI. Esto puede prevenir cualquier tensión o fatiga en las manos, los dedos, los ojos y el cuello, que puede afectar su rendimiento de juego. </li>
-</ul>
-<h3>Q: ¿Cómo puedo actualizar BGMI a la última versión? </h3>
-<p>A: Para actualizar BGMI a la última versión, puede seguir estos pasos:</p>
-<ol>
-<li>Ir a Google Play Store o App Store y buscar BGMI.</li>
-<li> Si hay una actualización disponible, verá un botón de actualización junto a la aplicación. Toque en ella y espere a que la actualización se descargue e instale. </li>
-<li>Si no hay actualización disponible, verá un botón Abrir junto a la aplicación. Toque en él y ejecute BGMI.</li>
-<li>Si hay una actualización en el juego disponible, verás un mensaje emergente en la pantalla principal del juego. Toque en Aceptar y espere a que la actualización se descargue e instale. </li>
-<li>Después de la actualización se ha completado, se puede disfrutar de jugar BGMI con las últimas características y mejoras. </li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />
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diff --git a/spaces/Benson/text-generation/Examples/Cmo Puedo Descargar Candy Crush Saga En Facebook.md b/spaces/Benson/text-generation/Examples/Cmo Puedo Descargar Candy Crush Saga En Facebook.md
deleted file mode 100644
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--- a/spaces/Benson/text-generation/Examples/Cmo Puedo Descargar Candy Crush Saga En Facebook.md	
+++ /dev/null
@@ -1,60 +0,0 @@
-<br />
-<h1>Cómo descargar Candy Crush Saga en Facebook</h1>
-<h2>Introducción</h2>
-<p>Si te gusta jugar juegos de puzzle de partido 3, es posible que haya oído hablar de Candy Crush Saga. Es uno de los juegos más populares del mundo, con más de mil millones de descargas y millones de jugadores. ¿Pero sabías que también puedes jugar a Candy Crush Saga en Facebook? En este artículo, te mostraremos cómo descargar Candy Crush Saga en Facebook y disfrutar de este dulce juego con tus amigos. </p>
-<h2>¿Cómo puedo descargar Candy Crush Saga en Facebook</h2><br /><p><b><b>DOWNLOAD</b> &rArr; <a href="https://bltlly.com/2v6LBA">https://bltlly.com/2v6LBA</a></b></p><br /><br />
-<h3>¿Qué es Candy Crush Saga? </h3>
-<p>Candy Crush Saga es un juego de puzzle match-3 desarrollado por King. El juego consiste en combinar caramelos de diferentes colores y formas para eliminarlos del tablero y completar varios objetivos. El juego tiene miles de niveles, cada uno con diferentes desafíos y recompensas. También puedes desbloquear caramelos y potenciadores especiales que pueden ayudarte a superar niveles difíciles. </p>
-<h3>¿Por qué jugar Candy Crush Saga en Facebook? </h3>
-<p>Jugar a Candy Crush Saga en Facebook tiene muchos beneficios. Por un lado, puedes sincronizar tu progreso a través de diferentes dispositivos y plataformas. Esto significa que puede cambiar de su teléfono a su computadora o tableta sin perder su nivel o vidas. <p>Otra razón para jugar Candy Crush Saga en Facebook es que puedes interactuar con tus amigos y otros jugadores. Puede enviar y recibir vidas, regalos y mensajes. También puede competir con ellos en las tablas de clasificación y ver quién puede obtener la puntuación más alta. Jugar con amigos puede hacer el juego más divertido y social. </p>
-<h2>Cómo descargar Candy Crush Saga en Facebook</h2>
-<p>Hay diferentes formas de descargar Candy Crush Saga en Facebook, dependiendo de qué dispositivo o plataforma esté utilizando. Aquí hay tres métodos que puedes probar:</p>
-<h3>Método 1: Utilice la aplicación de Facebook en su teléfono o tableta</h3>
-
-<h4>Paso 1: Eliminar la aplicación de Facebook desde su dispositivo</h4>
-<p>Esto puede sonar contradictorio, pero eliminar la aplicación de Facebook de tu dispositivo puede ayudarte a evitar algunos problemas con el juego. Algunos usuarios han informado que el juego no se carga o se bloquea cuando intentan jugar a través de la aplicación de Facebook. Eliminar la aplicación puede resolver este problema. </p>
-<p></p>
-<h4>Paso 2: Borrar la caché en la configuración de su dispositivo</h4>
-<p>Después de eliminar la aplicación de Facebook, también debe borrar la caché en la configuración de su dispositivo. Esto puede ayudarle a liberar algo de espacio y mejorar el rendimiento de su dispositivo. Para borrar la caché, ve a la configuración del dispositivo y busca la opción de borrar la caché o el almacenamiento. Toca en ella y confirma. </p>
-<h4>Paso 3: Abra Candy Crush Saga y toque Conectar</h4>
-<p>Ahora, puedes abrir Candy Crush Saga en tu dispositivo. Si aún no lo tienes, puedes descargarlo desde [1](https://play.google.com/store/apps/detailss?id=id=com.king.candycrushsaga) para dispositivos Android o [2](https:/apps.apple.com/us/appy-crush-saga/id3834731) para dispositivos iOS. Una vez que abra el juego, toque en el botón Conectar en la parte inferior de la pantalla. Esto le pedirá que inicie sesión con su cuenta de Facebook. </p>
-<h4>Paso 4: Inicia sesión en Facebook a través de un navegador móvil</h4>
-<p>Cuando toque en Conectar, será redirigido a un navegador móvil donde puede iniciar sesión en Facebook. Introduzca su dirección de correo electrónico o número de teléfono y contraseña y toque en Iniciar sesión. También es posible que tengas que permitir que Candy Crush Saga acceda a parte de tu información, como tu nombre, foto de perfil y lista de amigos. Toca Continuar para confirmar. </p>
-<p>Felicidades! Usted ha descargado con éxito Candy Crush Saga en Facebook usando su teléfono o tableta. Ahora puedes jugar el juego y sincronizar tu progreso con tu cuenta de Facebook. </p> <h3>Método 2: Utilice el sitio web de Facebook en su computadora o navegador</h3>
-
-<h4>Paso 1: Vaya a [5](https://www.facebook.com/candycrushsaga/) o [6](https://apps.facebook.com/candycrush/) en el navegador de su computadora</h4>
-<p>Abra el navegador de su computadora y vaya a [5](https://www.facebook.com/candycrushsaga/) o [6](https://apps.facebook.com/candycrush/). Estas son las páginas oficiales de Candy Crush Saga en Facebook. También puedes buscar Candy Crush Saga en Google y hacer clic en el primer resultado. </p>
-<h4>Paso 2: Haga clic en Jugar ahora o Iniciar sesión</h4>
-<p>Si ya has iniciado sesión en Facebook, puedes hacer clic en el botón Jugar ahora para comenzar a jugar. Si no ha iniciado sesión, deberá hacer clic en el botón Iniciar sesión e ingresar su dirección de correo electrónico o número de teléfono y contraseña. También es posible que tengas que permitir que Candy Crush Saga acceda a parte de tu información, como tu nombre, foto de perfil y lista de amigos. Haga clic en Continuar para confirmar. </p>
-<h4>Paso 3: Buscar Candy Crush Saga juego en la barra de búsqueda de Facebook</h4>
-<p>Si no ves el juego en la página, puedes buscarlo en la barra de búsqueda de Facebook en la parte superior de la pantalla. Escribe Candy Crush Saga y pulsa Enter. Deberías ver el juego como el primer resultado. Haz clic en él para abrirlo. </p>
-<h4>Paso 4: Haga clic en el juego para jugar</h4>
-<p>Una vez que abra el juego, verá una pantalla de carga con un bastón de caramelo. Espere unos segundos hasta que el juego se cargue. A continuación, verá un mapa con diferentes episodios y niveles. Haga clic en el nivel que desea jugar y disfrutar! </p>
-<p>Felicidades! Usted ha descargado con éxito Candy Crush Saga en Facebook utilizando su ordenador o navegador. Ahora puedes jugar el juego y sincronizar tu progreso con tu cuenta de Facebook. </p> <h3>Método 3: Use king.com/games o descargue la aplicación windows 10 desde la tienda de Microsoft</h3>
-<p>Si no quieres usar Facebook para jugar a Candy Crush Saga, también puedes usar el sitio web oficial de King o descargar la aplicación windows 10 desde la tienda de Microsoft. Estos métodos también son fáciles y convenientes. Aquí están los pasos:</p>
-
-<p>Abra su navegador y vaya a [2](https://www.king.com/game/candycrush) o [3](https://www.microsoft.com/en-us/p/candy-crush-saga/9nblggh18846). Estas son las páginas oficiales de Candy Crush Saga en King y Microsoft. También puedes buscar Candy Crush Saga en Google y hacer clic en el segundo o tercer resultado. </p>
-<h4>Paso 2: Haga clic en Jugar ahora o Obtener</h4>
-<p>Si vas a King, puedes hacer clic en el botón Play Now para comenzar a jugar el juego. Si vas a Microsoft, puedes hacer clic en el botón Obtener para descargar la aplicación. Es posible que necesite iniciar sesión con su cuenta de Microsoft o crear una si no tiene una. </p>
-<h4>Paso 3: Inicia sesión con tu cuenta King o crea una si no tienes una</h4>
-<p>Si juegas en King, necesitarás iniciar sesión con tu cuenta de King o crear una si no tienes una. Una cuenta King es una cuenta gratuita que te permite jugar juegos en King y sincronizar tu progreso en diferentes dispositivos y plataformas. Para iniciar sesión o crear una cuenta King, haga clic en el botón Iniciar sesión en la esquina superior derecha de la pantalla e introduzca su dirección de correo electrónico y contraseña. También puedes iniciar sesión con tu cuenta de Facebook si tienes una. </p>
-<h4>Paso 4: Conecta tu cuenta de King con tu cuenta de Facebook</h4>
-<p>Si quieres jugar Candy Crush Saga con tus amigos de Facebook, puedes conectar tu cuenta de King con tu cuenta de Facebook. Esto te permitirá ver las puntuaciones de tus amigos y enviar y recibir vidas, regalos y mensajes. Para conectar sus cuentas, haga clic en el botón Conectar en la parte inferior de la pantalla e inicie sesión con su cuenta de Facebook. También es posible que tengas que permitir que Candy Crush Saga acceda a parte de tu información, como tu nombre, foto de perfil y lista de amigos. Haga clic en Continuar para confirmar. </p>
-<p>¡Felicidades! Has descargado exitosamente Candy Crush Saga en Facebook usando King o Microsoft. Ahora puedes jugar el juego y sincronizar tu progreso con tu cuenta de Facebook. </p>
-<h2>Conclusión</h2>
-
-<h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre la descarga de Candy Crush Saga en Facebook:</p>
-<ul>
-<li><b>Q: ¿Cómo puedo actualizar Candy Crush Saga en Facebook? </b></li>
-<li>A: Para actualizar Candy Crush Saga en Facebook, es necesario comprobar si hay alguna actualización disponible para el juego. Puedes hacer esto yendo a la página del juego en Facebook y buscando una notificación que diga "Actualización disponible". Si hay una, haz clic en ella y sigue las instrucciones. Alternativamente, también puedes eliminar y reinstalar el juego como se explica en el método 1 anterior. </li>
-<li><b>Q: ¿Cómo puedo restaurar mi progreso en Candy Crush Saga en Facebook? </b></li>
-<li>A: Para restaurar su progreso en Candy Crush Saga en Facebook, debe asegurarse de que está conectado a su cuenta de Facebook. Puedes hacer esto tocando o haciendo clic en el botón Conectar en la parte inferior de la pantalla e iniciando sesión con tu cuenta de Facebook. Esto sincronizará tu progreso con tu cuenta de Facebook y te permitirá continuar desde donde lo dejaste. </li>
-<li><b>Q: ¿Cómo puedo eliminar Candy Crush Saga de Facebook? </b></li>
-<li>A: Para eliminar Candy Crush Saga de Facebook, es necesario ir a la configuración de Facebook y buscar la opción de administrar aplicaciones y sitios web. Allí, verá una lista de aplicaciones y sitios web que ha conectado con su cuenta de Facebook. Encuentra Candy Crush Saga y haz clic en el botón Eliminar al lado. Esto eliminará el juego de tu cuenta de Facebook y eliminará todos sus datos. </li <li><b>Q: ¿Cómo puedo obtener más vidas en Candy Crush Saga en Facebook? </b></li>
-
-<li><b>Q: ¿Cómo puedo jugar Candy Crush Saga offline? </b></li>
-<li>A: Para jugar Candy Crush Saga fuera de línea, es necesario descargar el juego en su dispositivo y jugarlo sin conectarse a Internet. Sin embargo, no podrás sincronizar tu progreso con tu cuenta de Facebook ni interactuar con tus amigos. Tampoco podrás acceder a algunas características del juego, como el bono diario, los eventos o las misiones. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />
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diff --git a/spaces/Benson/text-generation/Examples/Descarga 22h2 Windows 10 Actualizacin.md b/spaces/Benson/text-generation/Examples/Descarga 22h2 Windows 10 Actualizacin.md
deleted file mode 100644
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--- a/spaces/Benson/text-generation/Examples/Descarga 22h2 Windows 10 Actualizacin.md	
+++ /dev/null
@@ -1,76 +0,0 @@
-<br />
-<h1>Descargar 22H2 Windows 10 Actualización: Todo lo que necesita saber</h1>
-<p>Windows 10 es el sistema operativo más popular del mundo, alimentando millones de dispositivos en todo el mundo. Pero ¿sabía que Windows 10 está en constante evolución y mejora con nuevas actualizaciones y características? Una de las últimas actualizaciones es la versión de Windows 10 22H2, también conocida como la actualización de Windows 10 2022. En este artículo, le diremos todo lo que necesita saber sobre esta actualización, incluyendo lo que es, cómo descargarlo e instalarlo, y por qué debe hacerlo. ¡Vamos a empezar! </p>
-<h2>descarga 22h2 windows 10 actualización</h2><br /><p><b><b>Download</b> &#9881; <a href="https://bltlly.com/2v6IAw">https://bltlly.com/2v6IAw</a></b></p><br /><br />
- <h2>¿Qué es la versión de Windows 10 22H2? </h2>
-<p>Windows 10 versión 22H2 es la última actualización del sistema operativo para clientes de Windows 10, que comenzó a implementarse en octubre de 2022. Al igual que la versión de Windows 10 21H2, que fue lanzado en 2021, la versión 22H2 para Windows 10 es una versión muy pequeña que se centra en un conjunto de mejoras bajo el capó más beneficioso para los clientes empresariales de Microsoft. </p>
- <h3>La última actualización del sistema operativo para clientes de Windows 10</h3>
-<p>Windows 10 versión 22H2 es la segunda actualización semestral para Windows 10 en 2022, después de Windows 10 versión 21H1, que fue lanzado en mayo. Estas actualizaciones son parte del ciclo de lanzamiento semestral de Microsoft, que tiene como objetivo ofrecer nuevas características y mejoras a Windows 10 dos veces al año. Sin embargo, a diferencia de las principales actualizaciones anteriores, como Windows 10 versión 20H2 o Windows 10 versión 2004, que introdujo cambios significativos y adiciones al sistema operativo, Windows 10 versión 22H2 es una actualización relativamente pequeña que no trae ningún cambio visual o funcional notable a la experiencia del usuario. </p>
- <h3>Una versión menor con mejoras bajo el capó</h3>
-
-<ul>
-<li>Soporte WPA3 H2E para una seguridad Wi-Fi mejorada</li>
-<li>Compatibilidad con GPU en el subsistema de Windows para Linux (WSL) y Azure IoT Edge para Linux en implementaciones de Windows (EFLOW) </li>
-<li>Experiencia de audio Bluetooth mejorada</li>
-<li>Capacidades mejoradas de administración de dispositivos</li>
-<li>Varias correcciones de errores y parches de seguridad</li>
-</ul>
- <h3>Las principales características y cambios de Windows 10 versión 22H2</h3>
-<p>Si bien la versión 22H2 de Windows 10 no trae cambios importantes al sistema operativo, todavía hay algunas características notables y cambios que debe tener en cuenta. Estos son algunos de ellos:</p>
-<p></p>
-<ul>
-<li>El menú Inicio tiene un nuevo diseño que coincide con el tema de Windows 11, con esquinas redondeadas e iconos centrados. </li>
-<li>La barra de tareas tiene un nuevo icono para el widget de Noticias e Intereses, que muestra noticias personalizadas, clima, deportes y más. </li>
-<li> La aplicación Configuración tiene un nuevo banner que muestra el nombre del dispositivo, el estado y las acciones rápidas. </li>
-<li>El Explorador de archivos tiene una nueva barra de comandos que reemplaza la interfaz de cinta y ofrece más opciones contextuales. </li <li>El navegador de Microsoft Edge tiene una nueva función de pestañas verticales que le permite administrar sus pestañas más fácilmente. </li>
-<li>El sistema de autenticación biométrica de Windows Hello tiene una nueva opción para configurar una copia de seguridad de PIN o contraseña en caso de que su dispositivo no reconozca su cara o huella digital. </li>
-<li>La aplicación Windows Security tiene una nueva función de protección contra ransomware que le ayuda a proteger sus archivos contra el cifrado malicioso. </li>
-</ul>
- <h2>¿Cómo descargar e instalar Windows 10 versión 22H2? </h2>
-<p>Ahora que sabe lo que es la versión de Windows 10 22H2 y lo que ofrece, es posible que se pregunte cómo descargarlo e instalarlo en su dispositivo. Hay varias maneras de hacer esto, dependiendo de su preferencia y situación. Estos son algunos de los métodos más comunes:</p>
- <h3>Buscar actualizaciones en la configuración de Windows Update</h3>
-
-<ol>
-<li>Abra la aplicación Configuración haciendo clic en el menú Inicio y seleccionando el icono de engranaje, o presionando Windows + I en el teclado. </li>
-<li>Haga clic en Actualizar & Seguridad, y luego en Windows Update.</li>
-<li>Haga clic en Buscar actualizaciones, y espere a que Windows busque las actualizaciones disponibles. </li>
-<li>Si ve la opción Actualizar características a Windows 10, versión 22H2, haga clic en Descargar e instalar. Si no lo ves, significa que la actualización aún no está disponible para tu dispositivo, o que ya lo tienes instalado. </li>
-<li>Siga las instrucciones en pantalla para completar el proceso de instalación. Es posible que necesite reiniciar el dispositivo varias veces durante el proceso. </li>
-</ol>
- <h3>Utilice la herramienta de creación de medios o el asistente de actualización</h3>
-<p>Si desea descargar e instalar manualmente Windows 10 versión 22H2, o si tiene problemas con el método Windows Update, puede usar la herramienta de creación de medios o el asistente de actualización. Estas son herramientas oficiales de Microsoft que le permiten crear una unidad USB o DVD de arranque con la última versión de Windows 10, o actualizar su versión actual de Windows 10 a la versión 22H2. Para usar estas herramientas, siga estos pasos:</p>
-<ol>
-<li>Ir a la página [Descargar Windows 10] en el sitio web de Microsoft. </li>
-<li>Desplácese hacia abajo a la sección Crear medios de instalación de Windows 10, y haga clic en Descargar herramienta ahora para obtener la herramienta de creación de medios, o haga clic en Actualizar ahora para obtener el asistente de actualización. </li>
-<li>Ejecute la herramienta que descargó y acepte los términos de la licencia. </li>
-
-<li>Siga las instrucciones en pantalla para completar el proceso de instalación. Es posible que necesite reiniciar el dispositivo varias veces durante el proceso. </li>
-</ol>
- <h3>Solucionar problemas y problemas comunes</h3>
-<p>A veces, puede encontrar algunos problemas o problemas al descargar o instalar Windows 10 versión 22H2. Estos pueden ser causados por varios factores, como hardware o software incompatible, poco espacio en disco, archivos de sistema dañados o errores de red. Estos son algunos de los problemas y problemas más comunes que los usuarios han informado, y cómo solucionarlos:</p>
-
-<p>Ahora que sabe cómo descargar e instalar Windows 10 versión 22H2, es posible que se pregunte por qué debe hacerlo en primer lugar. Después de todo, esta actualización no parece ofrecer mayores beneficios o mejoras al sistema operativo. Sin embargo, todavía hay algunas buenas razones por las que debe descargar Windows 10 versión 22H2, como:</p>
- <h3>Disfruta de un mejor rendimiento y estabilidad</h3>
-<p>Windows 10 versión 22H2 está diseñado para mejorar el rendimiento y la estabilidad de su dispositivo, mediante la fijación de varios errores y problemas que podrían afectar a su experiencia de usuario. Por ejemplo, esta actualización mejora la experiencia de audio Bluetooth, la compatibilidad con GPU en implementaciones WSL y EFLOW y las capacidades de administración de dispositivos. Estas mejoras pueden ayudarlo a disfrutar de un funcionamiento más suave y rápido de su dispositivo, especialmente si lo usa para fines de trabajo o entretenimiento. </p>
- <h3>Obtenga las últimas actualizaciones de seguridad y calidad</h3>
-<p>La versión 22H2 de Windows 10 también incluye las últimas actualizaciones de seguridad y calidad para su dispositivo, que pueden ayudarlo a proteger sus datos y privacidad de amenazas y ataques potenciales. Por ejemplo, esta actualización incluye soporte WPA3 H2E para una seguridad Wi-Fi mejorada, así como varios parches de seguridad para diferentes componentes del sistema operativo. Estas actualizaciones pueden ayudarlo a prevenir infecciones de malware, violaciones de datos, robo de identidad y otros delitos cibernéticos que podrían dañarlo a usted o a su dispositivo. </p>
- <h3>Prepárese para el futuro de Windows 10</h3>
-
- <h2>Conclusión</h2>
-<p>En conclusión, la versión 22H2 de Windows 10 es la última actualización del sistema operativo para clientes de Windows 10, que comenzó a implementarse en octubre de 2022. Es una versión menor que no trae nuevas características o capacidades al sistema operativo, sino que se centra en mejorar su rendimiento, estabilidad, seguridad y calidad. También introduce algunos cambios de diseño que coinciden con el tema de Windows 11, que es la próxima actualización importante para Windows 10. Puede descargar e instalar Windows 10 versión 22H2 mediante la función Windows Update, o mediante la herramienta de creación de medios o el asistente de actualización. También puede solucionar cualquier problema o problema común que pueda encontrar durante o después del proceso de instalación. Al descargar Windows 10 versión 22H2, puede disfrutar de una mejor experiencia de usuario en su dispositivo, obtener las últimas actualizaciones de seguridad y calidad, y prepararse para el futuro de Windows 10. </p>
- <p>Esperamos que este artículo te haya ayudado a entender todo lo que necesitas saber sobre Windows 10 versión 22H2. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. ¡Gracias por leer! </p>
- <h3>Preguntas frecuentes</h3>
-<ul>
-<li><b>Q: ¿Cuánto tiempo se tarda en descargar e instalar Windows 10 versión 22H2? </b></li>
-<li>A: El tiempo que se tarda en descargar e instalar Windows 10 versión 22H2 depende de varios factores, como la velocidad de Internet, las especificaciones del dispositivo, la disponibilidad de espacio en disco y el método de actualización. En términos generales, debe tomar entre 15 minutos y una hora para completar el proceso. </li>
-<li><b>Q: ¿Cuánto espacio en disco necesito para descargar e instalar Windows 10 versión 22H2? </b></li>
-<li>A: El requisito de espacio en disco para descargar e instalar Windows 10 versión 22H2 varía dependiendo de la versión actual de Windows 10. Si está actualizando desde la versión de Windows 10 21H1, 21H2 o 20H2, necesitará unos 500 MB de espacio en disco. Si está actualizando desde una versión anterior de Windows 10, necesitará aproximadamente 4 GB de espacio en disco. </li>
-
-<li>A: Puede comprobar si tiene la versión de Windows 10 22H2 instalada en su dispositivo yendo a Configuración > Sistema > Acerca, y mirando los campos Versión y OS Build. Si ves 22H2 y 19044.xxx, respectivamente, entonces tienes Windows 10 versión 22H2 instalado en tu dispositivo. </li>
-<li><b>Q: ¿Cómo puedo desinstalar Windows 10 versión 22H2 si no me gusta o si causa problemas? </b></li>
-<li>A: Puede desinstalar Windows 10 versión 22H2 si no te gusta o si causa problemas al ir a Configuración > Actualización y seguridad > Recuperación > Volver a la versión anterior de Windows 10. Sin embargo, solo puede hacer esto dentro de los 10 días de instalar la actualización, y solo si no ha eliminado la carpeta Windows.old de su disco. </li>
-<li><b>Q: ¿Cuál es la diferencia entre la versión de Windows 10 22H2 y Windows 11? </b></li>
-<li>A: Windows 10 versión 22H2 y Windows 11 son dos versiones diferentes del mismo sistema operativo, con diferentes características y requisitos. Windows 10 versión 22H2 es una actualización menor para los clientes de Windows 10, mientras que Windows 11 es una actualización importante que introduce una nueva interfaz de usuario, nuevas características y nuevos requisitos de hardware. Se espera que Windows 11 esté disponible para los usuarios de Windows 10 a finales de 2023 o principios de 2024, dependiendo de su compatibilidad con el dispositivo. </li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Coche Deportivo 3 Apk.md b/spaces/Benson/text-generation/Examples/Descargar Coche Deportivo 3 Apk.md
deleted file mode 100644
index 9a9b7cf0bdc473a1f194a75b9351ed8ac30167ff..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Coche Deportivo 3 Apk.md	
+++ /dev/null
@@ -1,84 +0,0 @@
-<br />
-<h1>Descargar Sport Car 3 APK: Un juego de simulador de conducción gratis para Android</h1>
- <p>Si usted es un fan de los coches y un amante de la conducción, entonces usted debe comprobar hacia fuera <fuerte>Sport Car 3 APK</strong>, una aplicación gratuita para Android que le permite experimentar la emoción de conducir diferentes coches deportivos en varios escenarios. En este artículo, le diremos qué es Sport Car 3 APK, cómo descargarlo e instalarlo en su dispositivo Android, por qué debería jugarlo y cuáles son los mejores autos deportivos en 2023 que puede conducir en el juego. </p>
-<h2>descargar coche deportivo 3 apk</h2><br /><p><b><b>Download Zip</b> &ndash;&ndash;&ndash;&ndash;&ndash;>>> <a href="https://bltlly.com/2v6Ksd">https://bltlly.com/2v6Ksd</a></b></p><br /><br />
- <h2>¿Qué es el coche deportivo 3 APK? </h2>
- <h3>Una breve introducción al juego y sus características</h3>
- <p>Sport Car 3 APK es un juego de simulador de conducción desarrollado por SportCarGames. Le permite elegir entre más de 50 coches deportivos y conducirlos en diferentes modos, como taxi, policía, viaje libre, deriva, arrastre y carrera. También puede personalizar sus coches con diferentes colores, ruedas, spoilers y pegatinas. El juego cuenta con gráficos realistas, física y sonidos que te hacen sentir como si estuvieras al volante de un coche deportivo real. También puede explorar diferentes entornos, como ciudad, desierto, montaña, aeropuerto y autopista. </p>
- <h3> Cómo descargar e instalar Sport Car 3 APK en su dispositivo Android</h3>
- <p>Para descargar e instalar Sport Car 3 APK en su dispositivo Android, necesita una conexión a Internet y un navegador. Estos son los pasos a seguir:</p>
- <ol>
-<li>Ir a <a href="( 1 )">Coche deportivo 3 : Taxi Police - simulador de unidad APK</a> en su navegador y toque en el <strong>Descargar APK</strong> botón. </li>
-<li>Espere a que el archivo APK se descargue en su dispositivo. </li>
-<li>Abra la aplicación de administrador de archivos en su dispositivo y busque el archivo APK descargado. </li>
-<li>Toque en el archivo y permita la instalación desde fuentes desconocidas si se le solicita. </li>
-<li>Siga las instrucciones en la pantalla para completar la instalación. </li>
-<li>Iniciar el juego y disfrutar de la conducción de sus coches deportivos favoritos. </li>
-</ol>
- <h2> ¿Por qué usted debe jugar Sport Car 3 APK</h2>
-
- <p>Jugar Sport Car 3 APK puede ofrecerle muchos beneficios, tales como:</p>
-<p></p>
- <ul>
-<li>Usted puede divertirse y relajarse conduciendo diferentes coches deportivos en varios modos. </li>
-<li>Puedes mejorar tus habilidades y reflejos al enfrentar diferentes desafíos y obstáculos. </li>
-<li>Puedes dar rienda suelta a tu creatividad y personalidad personalizando tus coches con diferentes opciones. </li>
-<li>Puedes aprender más sobre los diferentes coches deportivos y sus características leyendo sus descripciones. </li>
-</ul>
- <h3>Los inconvenientes de jugar Sport Car 3 APK</h3>
- <p>Jugando Sport Car 3 APK también puede tener <p>algunos inconvenientes, tales como:</p>
- <ul>
-<li>Puedes encontrar anuncios y compras en la aplicación que pueden interrumpir tu juego o tentarte a gastar dinero. </li>
-<li>Puedes enfrentar problemas de compatibilidad con algunos dispositivos o versiones de Android que pueden afectar el rendimiento o la funcionalidad del juego. </li>
-<li>Es posible que necesite mucho espacio de almacenamiento en su dispositivo para descargar e instalar el juego y sus actualizaciones. </li>
-</ul>
- <p>Sin embargo, estos inconvenientes no son demasiado graves y se pueden superar siguiendo algunos consejos, como:</p>
- <ul>
-<li>Puedes desactivar la conexión a Internet o usar un bloqueador de anuncios para evitar anuncios mientras juegas. </li>
-<li> Puede comprobar los requisitos mínimos y las revisiones del juego antes de descargarlo e instalarlo en su dispositivo. </li>
-<li>Puedes despejar algo de espacio en tu dispositivo o usar una tarjeta de memoria externa para almacenar el juego y sus actualizaciones. </li>
-</ul>
- <h2>Los mejores coches deportivos en 2023 para conducir en coche deportivo 3 APK</h2>
- <p>Si se está preguntando cuáles son los mejores coches deportivos en 2023 que se puede conducir en Sport Car 3 APK, aquí hay una lista basada en los resultados de búsqueda web:</p>
- <tabla>
-<tr>
-<th>Coche</th>
-<th>Descripción</th>
-</tr>
-<tr>
-<td><h4>Chevrolet Corvette</h4></td>
-
-</tr>
-<tr>
-<td><h4>Porsche 911 Turbo S</h4></td>
-<td><p>El Porsche 911 Turbo S es una versión de alto rendimiento del icónico coche deportivo alemán que ha estado en producción desde 1963. El último modelo, el 992, es el 911 más potente y rápido jamás hecho. Tiene un 3,8 litros de doble turbocompresor plana de seis motores que produce 640 caballos de fuerza y 590 libras-pie de par. Puede acelerar de 0 a 60 mph en 2.6 segundos y alcanzar una velocidad máxima de 205 mph. También tiene un diseño sofisticado, un interior refinado y un alerón trasero que se ajusta a diferentes modos de conducción. </p></td>
-</tr>
-<tr>
-<td><h4>BMW M2</h4></td>
-<td><p>El BMW M2 es un automóvil deportivo compacto que forma parte de la división M del fabricante de automóviles alemán. El último modelo, el M2 CS, es la versión más hardcore y centrada en la pista del M2. Tiene un 3.0 litros de doble turbocompresor en línea de seis motores que produce 444 caballos de fuerza y 406 libras-pie de par. Puede acelerar de 0 a 60 mph en 3.8 segundos y alcanzar una velocidad máxima de 174 mph. También tiene un diseño muscular, un interior deportivo y un techo y una capucha de fibra de carbono. </p></td>
-</tr>
-<tr>
-<td><h4>Nissan Z</h4></td>
-<td><p>El Nissan Z es un clásico deportivo japonés que ha estado en producción desde 1969. Se espera que el último modelo, el Z35, debute a finales de 2023 como sucesor del Z34 (370Z). Se rumorea que tiene un 3.0 litros doble turboalimentado motor V6 que produce alrededor de 400 caballos de fuerza y 350 libras de par. También se especula que tiene un diseño de inspiración retro, un interior moderno y una opción de transmisión manual. </p></td>
-</tr>
-<tr>
-<td><h4>Toyota GR-Supra</h4></td>
-
-</tr>
-</tabla>
- <h2>Conclusión</h2>
- <p>Sport Car 3 APK es un juego de simulador de conducción gratuito para Android que le permite conducir diferentes coches deportivos en varios modos y entornos. Tiene gráficos realistas, física y sonidos que te hacen sentir como si estuvieras al volante de un coche deportivo real. También puede personalizar sus coches con diferentes colores, ruedas, spoilers y pegatinas. También puede explorar diferentes entornos, como ciudad, desierto, montaña, aeropuerto y autopista. </p>
- <p>Si usted está buscando un divertido y realista juego de simulador de conducción para Android, usted debe descargar Sport Car 3 APK y disfrutar de la conducción de sus coches deportivos favoritos. Puedes descargar el juego desde el enlace de abajo e iniciar tu motor. </p>
- <p><a href=">Descargar Sport Car 3 APK</a></p>
- <h2>Preguntas frecuentes</h2>
- <h3> ¿Cuál es la última versión de Sport Car 3 APK? </h3>
- <p>La última versión de Sport Car 3 APK es 1.0.5, que fue lanzado el 15 de junio de 2023. Se corrigieron algunos errores y mejoró el rendimiento del juego. </p>
- <h3> ¿Cuánto espacio requiere Sport Car 3 APK en su dispositivo? </h3>
- <p>Sport Car 3 APK requiere unos 300 MB de espacio libre en su dispositivo para descargar e instalar. Es posible que necesite más espacio para las actualizaciones y los archivos de datos del juego. </p>
- <h3> ¿Es seguro descargar y jugar Sport Car 3 APK? </h3>
- <p>Sí, Sport Car 3 APK es seguro para descargar y jugar. No contiene ningún virus o malware que pueda dañar su dispositivo o su privacidad. Sin embargo, siempre debes descargar el juego desde una fuente de confianza y escanearlo con una aplicación antivirus antes de instalarlo. </p>
- <h3>¿Puedes jugar Sport Car 3 APK fuera de línea? </h3>
- <p>Sí, puede jugar Sport Car 3 APK sin conexión a Internet. Sin embargo, algunas características del juego, como anuncios y compras en la aplicación, pueden no funcionar correctamente sin conexión. </p>
- <h3>¿Puedes jugar Sport Car 3 APK con amigos? </h3> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/utils/unpacking.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/utils/unpacking.py
deleted file mode 100644
index 78b5c13ced3d0a429b6d292e2b0b985d50909942..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/utils/unpacking.py
+++ /dev/null
@@ -1,257 +0,0 @@
-"""Utilities related archives.
-"""
-
-import logging
-import os
-import shutil
-import stat
-import tarfile
-import zipfile
-from typing import Iterable, List, Optional
-from zipfile import ZipInfo
-
-from pip._internal.exceptions import InstallationError
-from pip._internal.utils.filetypes import (
-    BZ2_EXTENSIONS,
-    TAR_EXTENSIONS,
-    XZ_EXTENSIONS,
-    ZIP_EXTENSIONS,
-)
-from pip._internal.utils.misc import ensure_dir
-
-logger = logging.getLogger(__name__)
-
-
-SUPPORTED_EXTENSIONS = ZIP_EXTENSIONS + TAR_EXTENSIONS
-
-try:
-    import bz2  # noqa
-
-    SUPPORTED_EXTENSIONS += BZ2_EXTENSIONS
-except ImportError:
-    logger.debug("bz2 module is not available")
-
-try:
-    # Only for Python 3.3+
-    import lzma  # noqa
-
-    SUPPORTED_EXTENSIONS += XZ_EXTENSIONS
-except ImportError:
-    logger.debug("lzma module is not available")
-
-
-def current_umask() -> int:
-    """Get the current umask which involves having to set it temporarily."""
-    mask = os.umask(0)
-    os.umask(mask)
-    return mask
-
-
-def split_leading_dir(path: str) -> List[str]:
-    path = path.lstrip("/").lstrip("\\")
-    if "/" in path and (
-        ("\\" in path and path.find("/") < path.find("\\")) or "\\" not in path
-    ):
-        return path.split("/", 1)
-    elif "\\" in path:
-        return path.split("\\", 1)
-    else:
-        return [path, ""]
-
-
-def has_leading_dir(paths: Iterable[str]) -> bool:
-    """Returns true if all the paths have the same leading path name
-    (i.e., everything is in one subdirectory in an archive)"""
-    common_prefix = None
-    for path in paths:
-        prefix, rest = split_leading_dir(path)
-        if not prefix:
-            return False
-        elif common_prefix is None:
-            common_prefix = prefix
-        elif prefix != common_prefix:
-            return False
-    return True
-
-
-def is_within_directory(directory: str, target: str) -> bool:
-    """
-    Return true if the absolute path of target is within the directory
-    """
-    abs_directory = os.path.abspath(directory)
-    abs_target = os.path.abspath(target)
-
-    prefix = os.path.commonprefix([abs_directory, abs_target])
-    return prefix == abs_directory
-
-
-def set_extracted_file_to_default_mode_plus_executable(path: str) -> None:
-    """
-    Make file present at path have execute for user/group/world
-    (chmod +x) is no-op on windows per python docs
-    """
-    os.chmod(path, (0o777 & ~current_umask() | 0o111))
-
-
-def zip_item_is_executable(info: ZipInfo) -> bool:
-    mode = info.external_attr >> 16
-    # if mode and regular file and any execute permissions for
-    # user/group/world?
-    return bool(mode and stat.S_ISREG(mode) and mode & 0o111)
-
-
-def unzip_file(filename: str, location: str, flatten: bool = True) -> None:
-    """
-    Unzip the file (with path `filename`) to the destination `location`.  All
-    files are written based on system defaults and umask (i.e. permissions are
-    not preserved), except that regular file members with any execute
-    permissions (user, group, or world) have "chmod +x" applied after being
-    written. Note that for windows, any execute changes using os.chmod are
-    no-ops per the python docs.
-    """
-    ensure_dir(location)
-    zipfp = open(filename, "rb")
-    try:
-        zip = zipfile.ZipFile(zipfp, allowZip64=True)
-        leading = has_leading_dir(zip.namelist()) and flatten
-        for info in zip.infolist():
-            name = info.filename
-            fn = name
-            if leading:
-                fn = split_leading_dir(name)[1]
-            fn = os.path.join(location, fn)
-            dir = os.path.dirname(fn)
-            if not is_within_directory(location, fn):
-                message = (
-                    "The zip file ({}) has a file ({}) trying to install "
-                    "outside target directory ({})"
-                )
-                raise InstallationError(message.format(filename, fn, location))
-            if fn.endswith("/") or fn.endswith("\\"):
-                # A directory
-                ensure_dir(fn)
-            else:
-                ensure_dir(dir)
-                # Don't use read() to avoid allocating an arbitrarily large
-                # chunk of memory for the file's content
-                fp = zip.open(name)
-                try:
-                    with open(fn, "wb") as destfp:
-                        shutil.copyfileobj(fp, destfp)
-                finally:
-                    fp.close()
-                    if zip_item_is_executable(info):
-                        set_extracted_file_to_default_mode_plus_executable(fn)
-    finally:
-        zipfp.close()
-
-
-def untar_file(filename: str, location: str) -> None:
-    """
-    Untar the file (with path `filename`) to the destination `location`.
-    All files are written based on system defaults and umask (i.e. permissions
-    are not preserved), except that regular file members with any execute
-    permissions (user, group, or world) have "chmod +x" applied after being
-    written.  Note that for windows, any execute changes using os.chmod are
-    no-ops per the python docs.
-    """
-    ensure_dir(location)
-    if filename.lower().endswith(".gz") or filename.lower().endswith(".tgz"):
-        mode = "r:gz"
-    elif filename.lower().endswith(BZ2_EXTENSIONS):
-        mode = "r:bz2"
-    elif filename.lower().endswith(XZ_EXTENSIONS):
-        mode = "r:xz"
-    elif filename.lower().endswith(".tar"):
-        mode = "r"
-    else:
-        logger.warning(
-            "Cannot determine compression type for file %s",
-            filename,
-        )
-        mode = "r:*"
-    tar = tarfile.open(filename, mode, encoding="utf-8")
-    try:
-        leading = has_leading_dir([member.name for member in tar.getmembers()])
-        for member in tar.getmembers():
-            fn = member.name
-            if leading:
-                fn = split_leading_dir(fn)[1]
-            path = os.path.join(location, fn)
-            if not is_within_directory(location, path):
-                message = (
-                    "The tar file ({}) has a file ({}) trying to install "
-                    "outside target directory ({})"
-                )
-                raise InstallationError(message.format(filename, path, location))
-            if member.isdir():
-                ensure_dir(path)
-            elif member.issym():
-                try:
-                    tar._extract_member(member, path)
-                except Exception as exc:
-                    # Some corrupt tar files seem to produce this
-                    # (specifically bad symlinks)
-                    logger.warning(
-                        "In the tar file %s the member %s is invalid: %s",
-                        filename,
-                        member.name,
-                        exc,
-                    )
-                    continue
-            else:
-                try:
-                    fp = tar.extractfile(member)
-                except (KeyError, AttributeError) as exc:
-                    # Some corrupt tar files seem to produce this
-                    # (specifically bad symlinks)
-                    logger.warning(
-                        "In the tar file %s the member %s is invalid: %s",
-                        filename,
-                        member.name,
-                        exc,
-                    )
-                    continue
-                ensure_dir(os.path.dirname(path))
-                assert fp is not None
-                with open(path, "wb") as destfp:
-                    shutil.copyfileobj(fp, destfp)
-                fp.close()
-                # Update the timestamp (useful for cython compiled files)
-                tar.utime(member, path)
-                # member have any execute permissions for user/group/world?
-                if member.mode & 0o111:
-                    set_extracted_file_to_default_mode_plus_executable(path)
-    finally:
-        tar.close()
-
-
-def unpack_file(
-    filename: str,
-    location: str,
-    content_type: Optional[str] = None,
-) -> None:
-    filename = os.path.realpath(filename)
-    if (
-        content_type == "application/zip"
-        or filename.lower().endswith(ZIP_EXTENSIONS)
-        or zipfile.is_zipfile(filename)
-    ):
-        unzip_file(filename, location, flatten=not filename.endswith(".whl"))
-    elif (
-        content_type == "application/x-gzip"
-        or tarfile.is_tarfile(filename)
-        or filename.lower().endswith(TAR_EXTENSIONS + BZ2_EXTENSIONS + XZ_EXTENSIONS)
-    ):
-        untar_file(filename, location)
-    else:
-        # FIXME: handle?
-        # FIXME: magic signatures?
-        logger.critical(
-            "Cannot unpack file %s (downloaded from %s, content-type: %s); "
-            "cannot detect archive format",
-            filename,
-            location,
-            content_type,
-        )
-        raise InstallationError(f"Cannot determine archive format of {location}")
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/certifi/__main__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/certifi/__main__.py
deleted file mode 100644
index 00376349e69ad8b9dbf401cddc34055951e4b02e..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/certifi/__main__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import argparse
-
-from pip._vendor.certifi import contents, where
-
-parser = argparse.ArgumentParser()
-parser.add_argument("-c", "--contents", action="store_true")
-args = parser.parse_args()
-
-if args.contents:
-    print(contents())
-else:
-    print(where())
diff --git a/spaces/BlinkDL/RWKV-World-7B/README.md b/spaces/BlinkDL/RWKV-World-7B/README.md
deleted file mode 100644
index ddeca5dc741e7f20771af2dfb6667bc5cfa7e69e..0000000000000000000000000000000000000000
--- a/spaces/BlinkDL/RWKV-World-7B/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Raven RWKV 7B
-emoji: 🚀
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/CCOM/README/README.md b/spaces/CCOM/README/README.md
deleted file mode 100644
index 888b1f744c4768e01b25d2b787a44af882972174..0000000000000000000000000000000000000000
--- a/spaces/CCOM/README/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: README
-emoji: 🔥
-colorFrom: blue
-colorTo: indigo
-sdk: static
-pinned: false
----
-<p>
-The Central Conservatory of Music (CCOM) is a magnet for musical talents from all over the world. During its over 70 years of development, it has proudly maintained a strong team of faculty and administrative staff, including a number of outstanding specialists and scholars in music education, composition, performance and research. Many aspiring young musicians have been attracted to further their professional training at CCOM. Dozens of thousands of talented music students, including hundreds of international students, have been successfully trained. Among them, many have become internationally renowned composers, musicologists, music educators, performing artists, as well as leaders and important members in specialized art and cultural institutions.
-</p>
\ No newline at end of file
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/train.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/train.py
deleted file mode 100644
index c87c437b03fe2d8a541fea3a2e424a216ec9aa68..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/train.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import os
-import time
-import torch
-import torch.nn as nn
-import utils
-from torch.autograd import Variable
-
-
-def instance_bce_with_logits(logits, labels):
-    assert logits.dim() == 2
-
-    loss = nn.functional.binary_cross_entropy_with_logits(logits, labels)
-    loss *= labels.size(1)
-    return loss
-
-
-def compute_score_with_logits(logits, labels):
-    logits = torch.max(logits, 1)[1].data # argmax
-    one_hots = torch.zeros(*labels.size()).cuda()
-    one_hots.scatter_(1, logits.view(-1, 1), 1)
-    scores = (one_hots * labels)
-    return scores
-
-
-def train(model, train_loader, eval_loader, num_epochs, output, dis_eval=False, save_last=False):
-    utils.create_dir(output)
-    optim = torch.optim.Adamax(model.parameters())
-    logger = utils.Logger(os.path.join(output, 'log.txt'))
-    best_eval_score = 0
-
-    for epoch in range(num_epochs):
-        total_loss = 0
-        train_score = 0
-        t = time.time()
-
-        for i, (v, b, q, a) in enumerate(train_loader):
-            v = Variable(v).cuda()
-            b = Variable(b).cuda()
-            q = Variable(q).cuda()
-            a = Variable(a).cuda()
-
-            pred = model(v, b, q, a)
-            loss = instance_bce_with_logits(pred, a)
-            loss.backward()
-            nn.utils.clip_grad_norm(model.parameters(), 0.25)
-            optim.step()
-            optim.zero_grad()
-
-            batch_score = compute_score_with_logits(pred, a.data).sum()
-            # total_loss += loss.data[0] * v.size(0)
-            total_loss += loss.data * v.size(0)
-            train_score += batch_score
-
-        total_loss /= len(train_loader.dataset)
-        train_score = 100 * train_score / len(train_loader.dataset)
-        if not dis_eval:
-            model.train(False)
-            eval_score, bound = evaluate(model, eval_loader)
-            model.train(True)
-
-        logger.write('epoch %d, time: %.2f' % (epoch, time.time()-t))
-        logger.write('\ttrain_loss: %.2f, score: %.2f' % (total_loss, train_score))
-        if not dis_eval:
-            logger.write('\teval score: %.2f (%.2f)' % (100 * eval_score, 100 * bound))
-
-        # if eval_score > best_eval_score:
-        #     model_path = os.path.join(output, 'model.pth')
-        #     torch.save(model.state_dict(), model_path)
-        #     best_eval_score = eval_score
-
-        # Modified to save after every epoch with stamp
-        if not save_last or epoch == (num_epochs - 1):
-            model_path = os.path.join(output, 'model_%i.pth'%epoch)
-            torch.save(model.state_dict(), model_path)
-
-
-def evaluate(model, dataloader):
-    score = 0
-    upper_bound = 0
-    num_data = 0
-    for v, b, q, a in iter(dataloader):
-        v = Variable(v).cuda()
-        b = Variable(b).cuda()
-        q = Variable(q).cuda()
-        pred = model(v, b, q, None)
-        batch_score = compute_score_with_logits(pred, a.cuda()).sum()
-        score += batch_score
-        upper_bound += (a.max(1)[0]).sum()
-        num_data += pred.size(0)
-
-    score = score / len(dataloader.dataset)
-    upper_bound = upper_bound / len(dataloader.dataset)
-    return score, upper_bound
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/rpn_outputs.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/rpn_outputs.py
deleted file mode 100644
index 6cb7bb378fa05bd2318aa8ca779b801ccf0c907d..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/rpn_outputs.py
+++ /dev/null
@@ -1,453 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import itertools
-import logging
-import numpy as np
-import torch
-import torch.nn.functional as F
-from fvcore.nn import smooth_l1_loss
-
-from detectron2.layers import batched_nms, cat
-from detectron2.structures import Boxes, Instances, pairwise_iou
-from detectron2.utils.events import get_event_storage
-from detectron2.utils.memory import retry_if_cuda_oom
-
-from ..sampling import subsample_labels
-
-logger = logging.getLogger(__name__)
-
-# TODO: comments for future refactoring of this module
-#
-# From @rbg:
-# This code involves a significant amount of tensor reshaping and permuting. Look for
-# ways to simplify this.
-
-"""
-Shape shorthand in this module:
-
-    N: number of images in the minibatch
-    L: number of feature maps per image on which RPN is run
-    A: number of cell anchors (must be the same for all feature maps)
-    Hi, Wi: height and width of the i-th feature map
-    4: size of the box parameterization
-
-Naming convention:
-
-    objectness: refers to the binary classification of an anchor as object vs. not
-    object.
-
-    deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box
-    transform (see :class:`box_regression.Box2BoxTransform`).
-
-    pred_objectness_logits: predicted objectness scores in [-inf, +inf]; use
-        sigmoid(pred_objectness_logits) to estimate P(object).
-
-    gt_objectness_logits: ground-truth binary classification labels for objectness
-
-    pred_anchor_deltas: predicted box2box transform deltas
-
-    gt_anchor_deltas: ground-truth box2box transform deltas
-"""
-
-
-def find_top_rpn_proposals(
-    proposals,
-    pred_objectness_logits,
-    images,
-    nms_thresh,
-    pre_nms_topk,
-    post_nms_topk,
-    min_box_side_len,
-    training,
-):
-    """
-    For each feature map, select the `pre_nms_topk` highest scoring proposals,
-    apply NMS, clip proposals, and remove small boxes. Return the `post_nms_topk`
-    highest scoring proposals among all the feature maps if `training` is True,
-    otherwise, returns the highest `post_nms_topk` scoring proposals for each
-    feature map.
-
-    Args:
-        proposals (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A, 4).
-            All proposal predictions on the feature maps.
-        pred_objectness_logits (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A).
-        images (ImageList): Input images as an :class:`ImageList`.
-        nms_thresh (float): IoU threshold to use for NMS
-        pre_nms_topk (int): number of top k scoring proposals to keep before applying NMS.
-            When RPN is run on multiple feature maps (as in FPN) this number is per
-            feature map.
-        post_nms_topk (int): number of top k scoring proposals to keep after applying NMS.
-            When RPN is run on multiple feature maps (as in FPN) this number is total,
-            over all feature maps.
-        min_box_side_len (float): minimum proposal box side length in pixels (absolute units
-            wrt input images).
-        training (bool): True if proposals are to be used in training, otherwise False.
-            This arg exists only to support a legacy bug; look for the "NB: Legacy bug ..."
-            comment.
-
-    Returns:
-        proposals (list[Instances]): list of N Instances. The i-th Instances
-            stores post_nms_topk object proposals for image i, sorted by their
-            objectness score in descending order.
-    """
-    image_sizes = images.image_sizes  # in (h, w) order
-    num_images = len(image_sizes)
-    device = proposals[0].device
-
-    # 1. Select top-k anchor for every level and every image
-    topk_scores = []  # #lvl Tensor, each of shape N x topk
-    topk_proposals = []
-    level_ids = []  # #lvl Tensor, each of shape (topk,)
-    batch_idx = torch.arange(num_images, device=device)
-    for level_id, proposals_i, logits_i in zip(
-        itertools.count(), proposals, pred_objectness_logits
-    ):
-        Hi_Wi_A = logits_i.shape[1]
-        num_proposals_i = min(pre_nms_topk, Hi_Wi_A)
-
-        # sort is faster than topk (https://github.com/pytorch/pytorch/issues/22812)
-        # topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
-        logits_i, idx = logits_i.sort(descending=True, dim=1)
-        topk_scores_i = logits_i[batch_idx, :num_proposals_i]
-        topk_idx = idx[batch_idx, :num_proposals_i]
-
-        # each is N x topk
-        topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
-
-        topk_proposals.append(topk_proposals_i)
-        topk_scores.append(topk_scores_i)
-        level_ids.append(torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device))
-
-    # 2. Concat all levels together
-    topk_scores = cat(topk_scores, dim=1)
-    topk_proposals = cat(topk_proposals, dim=1)
-    level_ids = cat(level_ids, dim=0)
-
-    # 3. For each image, run a per-level NMS, and choose topk results.
-    results = []
-    for n, image_size in enumerate(image_sizes):
-        boxes = Boxes(topk_proposals[n])
-        scores_per_img = topk_scores[n]
-        lvl = level_ids
-
-        valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
-        if not valid_mask.all():
-            if training:
-                raise FloatingPointError(
-                    "Predicted boxes or scores contain Inf/NaN. Training has diverged."
-                )
-            boxes = boxes[valid_mask]
-            scores_per_img = scores_per_img[valid_mask]
-            lvl = lvl[valid_mask]
-        boxes.clip(image_size)
-
-        # filter empty boxes
-        keep = boxes.nonempty(threshold=min_box_side_len)
-        if keep.sum().item() != len(boxes):
-            boxes, scores_per_img, lvl = boxes[keep], scores_per_img[keep], lvl[keep]
-
-        keep = batched_nms(boxes.tensor, scores_per_img, lvl, nms_thresh)
-        # In Detectron1, there was different behavior during training vs. testing.
-        # (https://github.com/facebookresearch/Detectron/issues/459)
-        # During training, topk is over the proposals from *all* images in the training batch.
-        # During testing, it is over the proposals for each image separately.
-        # As a result, the training behavior becomes batch-dependent,
-        # and the configuration "POST_NMS_TOPK_TRAIN" end up relying on the batch size.
-        # This bug is addressed in Detectron2 to make the behavior independent of batch size.
-        keep = keep[:post_nms_topk]  # keep is already sorted
-
-        res = Instances(image_size)
-        res.proposal_boxes = boxes[keep]
-        res.objectness_logits = scores_per_img[keep]
-        results.append(res)
-    return results
-
-
-def rpn_losses(
-    gt_objectness_logits,
-    gt_anchor_deltas,
-    pred_objectness_logits,
-    pred_anchor_deltas,
-    smooth_l1_beta,
-):
-    """
-    Args:
-        gt_objectness_logits (Tensor): shape (N,), each element in {-1, 0, 1} representing
-            ground-truth objectness labels with: -1 = ignore; 0 = not object; 1 = object.
-        gt_anchor_deltas (Tensor): shape (N, box_dim), row i represents ground-truth
-            box2box transform targets (dx, dy, dw, dh) or (dx, dy, dw, dh, da) that map anchor i to
-            its matched ground-truth box.
-        pred_objectness_logits (Tensor): shape (N,), each element is a predicted objectness
-            logit.
-        pred_anchor_deltas (Tensor): shape (N, box_dim), each row is a predicted box2box
-            transform (dx, dy, dw, dh) or (dx, dy, dw, dh, da)
-        smooth_l1_beta (float): The transition point between L1 and L2 loss in
-            the smooth L1 loss function. When set to 0, the loss becomes L1. When
-            set to +inf, the loss becomes constant 0.
-
-    Returns:
-        objectness_loss, localization_loss, both unnormalized (summed over samples).
-    """
-    pos_masks = gt_objectness_logits == 1
-    localization_loss = smooth_l1_loss(
-        pred_anchor_deltas[pos_masks], gt_anchor_deltas[pos_masks], smooth_l1_beta, reduction="sum"
-    )
-
-    valid_masks = gt_objectness_logits >= 0
-    objectness_loss = F.binary_cross_entropy_with_logits(
-        pred_objectness_logits[valid_masks],
-        gt_objectness_logits[valid_masks].to(torch.float32),
-        reduction="sum",
-    )
-    return objectness_loss, localization_loss
-
-
-class RPNOutputs(object):
-    def __init__(
-        self,
-        box2box_transform,
-        anchor_matcher,
-        batch_size_per_image,
-        positive_fraction,
-        images,
-        pred_objectness_logits,
-        pred_anchor_deltas,
-        anchors,
-        boundary_threshold=0,
-        gt_boxes=None,
-        smooth_l1_beta=0.0,
-    ):
-        """
-        Args:
-            box2box_transform (Box2BoxTransform): :class:`Box2BoxTransform` instance for
-                anchor-proposal transformations.
-            anchor_matcher (Matcher): :class:`Matcher` instance for matching anchors to
-                ground-truth boxes; used to determine training labels.
-            batch_size_per_image (int): number of proposals to sample when training
-            positive_fraction (float): target fraction of sampled proposals that should be positive
-            images (ImageList): :class:`ImageList` instance representing N input images
-            pred_objectness_logits (list[Tensor]): A list of L elements.
-                Element i is a tensor of shape (N, A, Hi, Wi) representing
-                the predicted objectness logits for anchors.
-            pred_anchor_deltas (list[Tensor]): A list of L elements. Element i is a tensor of shape
-                (N, A*4, Hi, Wi) representing the predicted "deltas" used to transform anchors
-                to proposals.
-            anchors (list[list[Boxes]]): A list of N elements. Each element is a list of L
-                Boxes. The Boxes at (n, l) stores the entire anchor array for feature map l in image
-                n (i.e. the cell anchors repeated over all locations in feature map (n, l)).
-            boundary_threshold (int): if >= 0, then anchors that extend beyond the image
-                boundary by more than boundary_thresh are not used in training. Set to a very large
-                number or < 0 to disable this behavior. Only needed in training.
-            gt_boxes (list[Boxes], optional): A list of N elements. Element i a Boxes storing
-                the ground-truth ("gt") boxes for image i.
-            smooth_l1_beta (float): The transition point between L1 and L2 loss in
-                the smooth L1 loss function. When set to 0, the loss becomes L1. When
-                set to +inf, the loss becomes constant 0.
-        """
-        self.box2box_transform = box2box_transform
-        self.anchor_matcher = anchor_matcher
-        self.batch_size_per_image = batch_size_per_image
-        self.positive_fraction = positive_fraction
-        self.pred_objectness_logits = pred_objectness_logits
-        self.pred_anchor_deltas = pred_anchor_deltas
-
-        self.anchors = anchors
-        self.gt_boxes = gt_boxes
-        self.num_feature_maps = len(pred_objectness_logits)
-        self.num_images = len(images)
-        self.image_sizes = images.image_sizes
-        self.boundary_threshold = boundary_threshold
-        self.smooth_l1_beta = smooth_l1_beta
-
-    def _get_ground_truth(self):
-        """
-        Returns:
-            gt_objectness_logits: list of N tensors. Tensor i is a vector whose length is the
-                total number of anchors in image i (i.e., len(anchors[i])). Label values are
-                in {-1, 0, 1}, with meanings: -1 = ignore; 0 = negative class; 1 = positive class.
-            gt_anchor_deltas: list of N tensors. Tensor i has shape (len(anchors[i]), 4).
-        """
-        gt_objectness_logits = []
-        gt_anchor_deltas = []
-        # Concatenate anchors from all feature maps into a single Boxes per image
-        anchors = [Boxes.cat(anchors_i) for anchors_i in self.anchors]
-        for image_size_i, anchors_i, gt_boxes_i in zip(self.image_sizes, anchors, self.gt_boxes):
-            """
-            image_size_i: (h, w) for the i-th image
-            anchors_i: anchors for i-th image
-            gt_boxes_i: ground-truth boxes for i-th image
-            """
-            match_quality_matrix = retry_if_cuda_oom(pairwise_iou)(gt_boxes_i, anchors_i)
-            matched_idxs, gt_objectness_logits_i = retry_if_cuda_oom(self.anchor_matcher)(
-                match_quality_matrix
-            )
-            # Matching is memory-expensive and may result in CPU tensors. But the result is small
-            gt_objectness_logits_i = gt_objectness_logits_i.to(device=gt_boxes_i.device)
-            del match_quality_matrix
-
-            if self.boundary_threshold >= 0:
-                # Discard anchors that go out of the boundaries of the image
-                # NOTE: This is legacy functionality that is turned off by default in Detectron2
-                anchors_inside_image = anchors_i.inside_box(image_size_i, self.boundary_threshold)
-                gt_objectness_logits_i[~anchors_inside_image] = -1
-
-            if len(gt_boxes_i) == 0:
-                # These values won't be used anyway since the anchor is labeled as background
-                gt_anchor_deltas_i = torch.zeros_like(anchors_i.tensor)
-            else:
-                # TODO wasted computation for ignored boxes
-                matched_gt_boxes = gt_boxes_i[matched_idxs]
-                gt_anchor_deltas_i = self.box2box_transform.get_deltas(
-                    anchors_i.tensor, matched_gt_boxes.tensor
-                )
-
-            gt_objectness_logits.append(gt_objectness_logits_i)
-            gt_anchor_deltas.append(gt_anchor_deltas_i)
-
-        return gt_objectness_logits, gt_anchor_deltas
-
-    def losses(self):
-        """
-        Return the losses from a set of RPN predictions and their associated ground-truth.
-
-        Returns:
-            dict[loss name -> loss value]: A dict mapping from loss name to loss value.
-                Loss names are: `loss_rpn_cls` for objectness classification and
-                `loss_rpn_loc` for proposal localization.
-        """
-
-        def resample(label):
-            """
-            Randomly sample a subset of positive and negative examples by overwriting
-            the label vector to the ignore value (-1) for all elements that are not
-            included in the sample.
-            """
-            pos_idx, neg_idx = subsample_labels(
-                label, self.batch_size_per_image, self.positive_fraction, 0
-            )
-            # Fill with the ignore label (-1), then set positive and negative labels
-            label.fill_(-1)
-            label.scatter_(0, pos_idx, 1)
-            label.scatter_(0, neg_idx, 0)
-            return label
-
-        gt_objectness_logits, gt_anchor_deltas = self._get_ground_truth()
-        """
-        gt_objectness_logits: list of N tensors. Tensor i is a vector whose length is the
-            total number of anchors in image i (i.e., len(anchors[i]))
-        gt_anchor_deltas: list of N tensors. Tensor i has shape (len(anchors[i]), B),
-            where B is the box dimension
-        """
-        # Collect all objectness labels and delta targets over feature maps and images
-        # The final ordering is L, N, H, W, A from slowest to fastest axis.
-        num_anchors_per_map = [np.prod(x.shape[1:]) for x in self.pred_objectness_logits]
-        num_anchors_per_image = sum(num_anchors_per_map)
-
-        # Stack to: (N, num_anchors_per_image)
-        gt_objectness_logits = torch.stack(
-            [resample(label) for label in gt_objectness_logits], dim=0
-        )
-
-        # Log the number of positive/negative anchors per-image that's used in training
-        num_pos_anchors = (gt_objectness_logits == 1).sum().item()
-        num_neg_anchors = (gt_objectness_logits == 0).sum().item()
-        storage = get_event_storage()
-        storage.put_scalar("rpn/num_pos_anchors", num_pos_anchors / self.num_images)
-        storage.put_scalar("rpn/num_neg_anchors", num_neg_anchors / self.num_images)
-
-        assert gt_objectness_logits.shape[1] == num_anchors_per_image
-        # Split to tuple of L tensors, each with shape (N, num_anchors_per_map)
-        gt_objectness_logits = torch.split(gt_objectness_logits, num_anchors_per_map, dim=1)
-        # Concat from all feature maps
-        gt_objectness_logits = cat([x.flatten() for x in gt_objectness_logits], dim=0)
-
-        # Stack to: (N, num_anchors_per_image, B)
-        gt_anchor_deltas = torch.stack(gt_anchor_deltas, dim=0)
-        assert gt_anchor_deltas.shape[1] == num_anchors_per_image
-        B = gt_anchor_deltas.shape[2]  # box dimension (4 or 5)
-
-        # Split to tuple of L tensors, each with shape (N, num_anchors_per_image)
-        gt_anchor_deltas = torch.split(gt_anchor_deltas, num_anchors_per_map, dim=1)
-        # Concat from all feature maps
-        gt_anchor_deltas = cat([x.reshape(-1, B) for x in gt_anchor_deltas], dim=0)
-
-        # Collect all objectness logits and delta predictions over feature maps
-        # and images to arrive at the same shape as the labels and targets
-        # The final ordering is L, N, H, W, A from slowest to fastest axis.
-        pred_objectness_logits = cat(
-            [
-                # Reshape: (N, A, Hi, Wi) -> (N, Hi, Wi, A) -> (N*Hi*Wi*A, )
-                x.permute(0, 2, 3, 1).flatten()
-                for x in self.pred_objectness_logits
-            ],
-            dim=0,
-        )
-        pred_anchor_deltas = cat(
-            [
-                # Reshape: (N, A*B, Hi, Wi) -> (N, A, B, Hi, Wi) -> (N, Hi, Wi, A, B)
-                #          -> (N*Hi*Wi*A, B)
-                x.view(x.shape[0], -1, B, x.shape[-2], x.shape[-1])
-                .permute(0, 3, 4, 1, 2)
-                .reshape(-1, B)
-                for x in self.pred_anchor_deltas
-            ],
-            dim=0,
-        )
-
-        objectness_loss, localization_loss = rpn_losses(
-            gt_objectness_logits,
-            gt_anchor_deltas,
-            pred_objectness_logits,
-            pred_anchor_deltas,
-            self.smooth_l1_beta,
-        )
-        normalizer = 1.0 / (self.batch_size_per_image * self.num_images)
-        loss_cls = objectness_loss * normalizer  # cls: classification loss
-        loss_loc = localization_loss * normalizer  # loc: localization loss
-        losses = {"loss_rpn_cls": loss_cls, "loss_rpn_loc": loss_loc}
-
-        return losses
-
-    def predict_proposals(self):
-        """
-        Transform anchors into proposals by applying the predicted anchor deltas.
-
-        Returns:
-            proposals (list[Tensor]): A list of L tensors. Tensor i has shape
-                (N, Hi*Wi*A, B), where B is box dimension (4 or 5).
-        """
-        proposals = []
-        # Transpose anchors from images-by-feature-maps (N, L) to feature-maps-by-images (L, N)
-        anchors = list(zip(*self.anchors))
-        # For each feature map
-        for anchors_i, pred_anchor_deltas_i in zip(anchors, self.pred_anchor_deltas):
-            B = anchors_i[0].tensor.size(1)
-            N, _, Hi, Wi = pred_anchor_deltas_i.shape
-            # Reshape: (N, A*B, Hi, Wi) -> (N, A, B, Hi, Wi) -> (N, Hi, Wi, A, B) -> (N*Hi*Wi*A, B)
-            pred_anchor_deltas_i = (
-                pred_anchor_deltas_i.view(N, -1, B, Hi, Wi).permute(0, 3, 4, 1, 2).reshape(-1, B)
-            )
-            # Concatenate all anchors to shape (N*Hi*Wi*A, B)
-            # type(anchors_i[0]) is Boxes (B = 4) or RotatedBoxes (B = 5)
-            anchors_i = type(anchors_i[0]).cat(anchors_i)
-            proposals_i = self.box2box_transform.apply_deltas(
-                pred_anchor_deltas_i, anchors_i.tensor
-            )
-            # Append feature map proposals with shape (N, Hi*Wi*A, B)
-            proposals.append(proposals_i.view(N, -1, B))
-        return proposals
-
-    def predict_objectness_logits(self):
-        """
-        Return objectness logits in the same format as the proposals returned by
-        :meth:`predict_proposals`.
-
-        Returns:
-            pred_objectness_logits (list[Tensor]): A list of L tensors. Tensor i has shape
-                (N, Hi*Wi*A).
-        """
-        pred_objectness_logits = [
-            # Reshape: (N, A, Hi, Wi) -> (N, Hi, Wi, A) -> (N, Hi*Wi*A)
-            score.permute(0, 2, 3, 1).reshape(self.num_images, -1)
-            for score in self.pred_objectness_logits
-        ]
-        return pred_objectness_logits
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/find.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/find.h
deleted file mode 100644
index 8d85e09a3ab713bfa9dd668bb0beb791cfc3db1b..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/find.h
+++ /dev/null
@@ -1,44 +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 fill 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>
-
-// the purpose of this header is to #include the find.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch find
-
-#include <thrust/system/detail/sequential/find.h>
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include <thrust/system/cpp/detail/find.h>
-#include <thrust/system/cuda/detail/find.h>
-#include <thrust/system/omp/detail/find.h>
-#include <thrust/system/tbb/detail/find.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_FIND_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/find.h>
-#include __THRUST_HOST_SYSTEM_FIND_HEADER
-#undef __THRUST_HOST_SYSTEM_FIND_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_FIND_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/find.h>
-#include __THRUST_DEVICE_SYSTEM_FIND_HEADER
-#undef __THRUST_DEVICE_SYSTEM_FIND_HEADER
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/tag.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/tag.h
deleted file mode 100644
index 4da1e79ced95412ab558d1380c52b3c2294942e7..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/tag.h
+++ /dev/null
@@ -1,48 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file generic/tag.h
- *  \brief Implementation of the generic backend's tag.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-// tag exists only to make the generic entry points the least priority match
-// during ADL. tag should not be derived from and is constructible from anything
-struct tag
-{
-  template<typename T>
-  __host__ __device__ inline
-  tag(const T &) {}
-};
-
-} // end generic
-} // end detail
-} // end system
-} // end thrust
-
diff --git a/spaces/CarlDennis/HYTTS/models.py b/spaces/CarlDennis/HYTTS/models.py
deleted file mode 100644
index 4c4585172a5c56aa36f1f3156762349fbec11a8b..0000000000000000000000000000000000000000
--- a/spaces/CarlDennis/HYTTS/models.py
+++ /dev/null
@@ -1,498 +0,0 @@
-import math
-
-import torch
-from torch import nn
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn import functional as F
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-
-import attentions
-import commons
-import modules
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
-    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-        super().__init__()
-        filter_channels = in_channels  # it needs to be removed from future version.
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.log_flow = modules.Log()
-        self.flows = nn.ModuleList()
-        self.flows.append(modules.ElementwiseAffine(2))
-        for i in range(n_flows):
-            self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-            self.flows.append(modules.Flip())
-
-        self.post_pre = nn.Conv1d(1, filter_channels, 1)
-        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-        self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-        self.post_flows = nn.ModuleList()
-        self.post_flows.append(modules.ElementwiseAffine(2))
-        for i in range(4):
-            self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-            self.post_flows.append(modules.Flip())
-
-        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-        self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-    def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-        x = torch.detach(x)
-        x = self.pre(x)
-        if g is not None:
-            g = torch.detach(g)
-            x = x + self.cond(g)
-        x = self.convs(x, x_mask)
-        x = self.proj(x) * x_mask
-
-        if not reverse:
-            flows = self.flows
-            assert w is not None
-
-            logdet_tot_q = 0
-            h_w = self.post_pre(w)
-            h_w = self.post_convs(h_w, x_mask)
-            h_w = self.post_proj(h_w) * x_mask
-            e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-            z_q = e_q
-            for flow in self.post_flows:
-                z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-                logdet_tot_q += logdet_q
-            z_u, z1 = torch.split(z_q, [1, 1], 1)
-            u = torch.sigmoid(z_u) * x_mask
-            z0 = (w - u) * x_mask
-            logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
-            logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q
-
-            logdet_tot = 0
-            z0, logdet = self.log_flow(z0, x_mask)
-            logdet_tot += logdet
-            z = torch.cat([z0, z1], 1)
-            for flow in flows:
-                z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-                logdet_tot = logdet_tot + logdet
-            nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot
-            return nll + logq  # [b]
-        else:
-            flows = list(reversed(self.flows))
-            flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
-            z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-            for flow in flows:
-                z = flow(z, x_mask, g=x, reverse=reverse)
-            z0, z1 = torch.split(z, [1, 1], 1)
-            logw = z0
-            return logw
-
-
-class DurationPredictor(nn.Module):
-    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-        super().__init__()
-
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.gin_channels = gin_channels
-
-        self.drop = nn.Dropout(p_dropout)
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.norm_1 = modules.LayerNorm(filter_channels)
-        self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.norm_2 = modules.LayerNorm(filter_channels)
-        self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-    def forward(self, x, x_mask, g=None):
-        x = torch.detach(x)
-        if g is not None:
-            g = torch.detach(g)
-            x = x + self.cond(g)
-        x = self.conv_1(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_1(x)
-        x = self.drop(x)
-        x = self.conv_2(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_2(x)
-        x = self.drop(x)
-        x = self.proj(x * x_mask)
-        return x * x_mask
-
-
-class TextEncoder(nn.Module):
-    def __init__(self,
-                 n_vocab,
-                 out_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout):
-        super().__init__()
-        self.n_vocab = n_vocab
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-
-        if self.n_vocab != 0:
-            self.emb = nn.Embedding(n_vocab, hidden_channels)
-            nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
-
-        self.encoder = attentions.Encoder(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout)
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths):
-        if self.n_vocab != 0:
-            x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(self,
-                 channels,
-                 hidden_channels,
-                 kernel_size,
-                 dilation_rate,
-                 n_layers,
-                 n_flows=4,
-                 gin_channels=0):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
-                                              gin_channels=gin_channels, mean_only=True))
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 hidden_channels,
-                 kernel_size,
-                 dilation_rate,
-                 n_layers,
-                 gin_channels=0):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
-                 upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
-        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(upsample_initial_channel // (2 ** i), upsample_initial_channel // (2 ** (i + 1)),
-                                k, u, padding=(k - u) // 2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class SynthesizerTrn(nn.Module):
-    """
-    Synthesizer for Training
-    """
-
-    def __init__(self,
-                 n_vocab,
-                 spec_channels,
-                 segment_size,
-                 inter_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout,
-                 resblock,
-                 resblock_kernel_sizes,
-                 resblock_dilation_sizes,
-                 upsample_rates,
-                 upsample_initial_channel,
-                 upsample_kernel_sizes,
-                 n_speakers=0,
-                 gin_channels=0,
-                 use_sdp=True,
-                 **kwargs):
-
-        super().__init__()
-        self.n_vocab = n_vocab
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.n_speakers = n_speakers
-        self.gin_channels = gin_channels
-
-        self.use_sdp = use_sdp
-
-        self.enc_p = TextEncoder(n_vocab,
-                                 inter_channels,
-                                 hidden_channels,
-                                 filter_channels,
-                                 n_heads,
-                                 n_layers,
-                                 kernel_size,
-                                 p_dropout)
-        self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
-                             upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-        self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16,
-                                      gin_channels=gin_channels)
-        self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
-        if use_sdp:
-            self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-        else:
-            self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
-        if n_speakers > 1:
-            self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-    def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
-        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-        if self.n_speakers > 0:
-            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-        else:
-            g = None
-
-        if self.use_sdp:
-            logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-        else:
-            logw = self.dp(x, x_mask, g=g)
-        w = torch.exp(logw) * x_mask * length_scale
-        w_ceil = torch.ceil(w)
-        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-        attn = commons.generate_path(w_ceil, attn_mask)
-
-        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)  # [b, t', t], [b, t, d] -> [b, d, t']
-        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1,
-                                                                                 2)  # [b, t', t], [b, t, d] -> [b, d, t']
-
-        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-        z = self.flow(z_p, y_mask, g=g, reverse=True)
-        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
-        return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
-    def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
-        assert self.n_speakers > 0, "n_speakers have to be larger than 0."
-        g_src = self.emb_g(sid_src).unsqueeze(-1)
-        g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
-        z_p = self.flow(z, y_mask, g=g_src)
-        z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
-        o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-        return o_hat, y_mask, (z, z_p, z_hat)
diff --git a/spaces/Cpp4App/Cpp4App/CDM/detect_compo/deprecated/block_division.py b/spaces/Cpp4App/Cpp4App/CDM/detect_compo/deprecated/block_division.py
deleted file mode 100644
index fa33194471b0aaa7f2a17ab342285de62e657436..0000000000000000000000000000000000000000
--- a/spaces/Cpp4App/Cpp4App/CDM/detect_compo/deprecated/block_division.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import cv2
-import numpy as np
-from random import randint as rint
-import time
-
-import CDM.detect_compo.lib_ip.ip_preprocessing as pre
-import CDM.detect_compo.lib_ip.ip_detection as det
-import CDM.detect_compo.lib_ip.ip_draw as draw
-import CDM.detect_compo.lib_ip.ip_segment as seg
-from CDM.detect_compo.lib_ip.Block import Block
-from CDM.config.CONFIG_UIED import Config
-C = Config()
-
-
-def block_hierarchy(blocks):
-    for i in range(len(blocks) - 1):
-        for j in range(i + 1, len(blocks)):
-            relation = blocks[i].compo_relation(blocks[j])
-            if relation == -1:
-                blocks[j].children.append(i)
-            if relation == 1:
-                blocks[i].children.append(j)
-    return
-
-
-def block_bin_erase_all_blk(binary, blocks, pad=0, show=False):
-    '''
-    erase the block parts from the binary map
-    :param binary: binary map of original image
-    :param blocks_corner: corners of detected layout block
-    :param show: show or not
-    :param pad: expand the bounding boxes of blocks
-    :return: binary map without block parts
-    '''
-
-    bin_org = binary.copy()
-    for block in blocks:
-        block.block_erase_from_bin(binary, pad)
-    if show:
-        cv2.imshow('before', bin_org)
-        cv2.imshow('after', binary)
-        cv2.waitKey()
-
-
-def block_division(grey, org, grad_thresh,
-                   show=False, write_path=None,
-                   step_h=10, step_v=10,
-                   line_thickness=C.THRESHOLD_LINE_THICKNESS,
-                   min_rec_evenness=C.THRESHOLD_REC_MIN_EVENNESS,
-                   max_dent_ratio=C.THRESHOLD_REC_MAX_DENT_RATIO,
-                   min_block_height_ratio=C.THRESHOLD_BLOCK_MIN_HEIGHT):
-    '''
-    :param grey: grey-scale of original image
-    :return: corners: list of [(top_left, bottom_right)]
-                        -> top_left: (column_min, row_min)
-                        -> bottom_right: (column_max, row_max)
-    '''
-    blocks = []
-    mask = np.zeros((grey.shape[0]+2, grey.shape[1]+2), dtype=np.uint8)
-    broad = np.zeros((grey.shape[0], grey.shape[1], 3), dtype=np.uint8)
-    broad_all = broad.copy()
-
-    row, column = grey.shape[0], grey.shape[1]
-    for x in range(0, row, step_h):
-        for y in range(0, column, step_v):
-            if mask[x, y] == 0:
-                # region = flood_fill_bfs(grey, x, y, mask)
-
-                # flood fill algorithm to get background (layout block)
-                mask_copy = mask.copy()
-                ff = cv2.floodFill(grey, mask, (y, x), None, grad_thresh, grad_thresh, cv2.FLOODFILL_MASK_ONLY)
-                # ignore small regions
-                if ff[0] < 500: continue
-                mask_copy = mask - mask_copy
-                region = np.reshape(cv2.findNonZero(mask_copy[1:-1, 1:-1]), (-1, 2))
-                region = [(p[1], p[0]) for p in region]
-
-                block = Block(region, grey.shape)
-                # draw.draw_region(region, broad_all)
-                # if block.height < 40 and block.width < 40:
-                #     continue
-                if block.height < 30:
-                    continue
-
-                # print(block.area / (row * column))
-                if block.area / (row * column) > 0.9:
-                    continue
-                elif block.area / (row * column) > 0.7:
-                    block.redundant = True
-
-                # get the boundary of this region
-                # ignore lines
-                if block.compo_is_line(line_thickness):
-                    continue
-                # ignore non-rectangle as blocks must be rectangular
-                if not block.compo_is_rectangle(min_rec_evenness, max_dent_ratio):
-                    continue
-                # if block.height/row < min_block_height_ratio:
-                #     continue
-                blocks.append(block)
-                # draw.draw_region(region, broad)
-    if show:
-        cv2.imshow('flood-fill all', broad_all)
-        cv2.imshow('block', broad)
-        cv2.waitKey()
-    if write_path is not None:
-        cv2.imwrite(write_path, broad)
-    return blocks
diff --git a/spaces/Cybsechuman/Consistency_analysis/app.py b/spaces/Cybsechuman/Consistency_analysis/app.py
deleted file mode 100644
index 57f756dc18a0bac80b29c32811d82e9d7c4a4065..0000000000000000000000000000000000000000
--- a/spaces/Cybsechuman/Consistency_analysis/app.py
+++ /dev/null
@@ -1,184 +0,0 @@
-import urllib.request
-import fitz
-import re
-import numpy as np
-import tensorflow_hub as hub
-import openai
-import gradio as gr
-import os
-from sklearn.neighbors import NearestNeighbors
-
-question = "What is the purpose of data collection?, What is the data that is collected?, what is the purpose of sharing collected data with third parties?, What third parties may get user data?"
-
-def download_pdf(url, output_path):
-    urllib.request.urlretrieve(url, output_path)
-
-
-def preprocess(text):
-    text = text.replace('\n', ' ')
-    text = re.sub('\s+', ' ', text)
-    return text
-
-
-def pdf_to_text(path, start_page=1, end_page=None):
-    doc = fitz.open(path)
-    total_pages = doc.page_count
-
-    if end_page is None:
-        end_page = total_pages
-
-    text_list = []
-
-    for i in range(start_page-1, end_page):
-        text = doc.load_page(i).get_text("text")
-        text = preprocess(text)
-        text_list.append(text)
-
-    doc.close()
-    return text_list
-
-
-def text_to_chunks(texts, word_length=150, start_page=1):
-    text_toks = [t.split(' ') for t in texts]
-    page_nums = []
-    chunks = []
-    
-    for idx, words in enumerate(text_toks):
-        for i in range(0, len(words), word_length):
-            chunk = words[i:i+word_length]
-            if (i+word_length) > len(words) and (len(chunk) < word_length) and (
-                len(text_toks) != (idx+1)):
-                text_toks[idx+1] = chunk + text_toks[idx+1]
-                continue
-            chunk = ' '.join(chunk).strip()
-            chunk = f'[Page no. {idx+start_page}]' + ' ' + '"' + chunk + '"'
-            chunks.append(chunk)
-    return chunks
-
-class SemanticSearch:
-    
-    def __init__(self):
-        self.use = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')
-        self.fitted = False
-    
-    
-    def fit(self, data, batch=500, n_neighbors=8):
-        self.data = data
-        self.embeddings = self.get_text_embedding(data, batch=batch)
-        n_neighbors = min(n_neighbors, len(self.embeddings))
-        self.nn = NearestNeighbors(n_neighbors=n_neighbors)
-        self.nn.fit(self.embeddings)
-        self.fitted = True
-    
-    
-    def __call__(self, text, return_data=True):
-        inp_emb = self.use([text])
-        neighbors = self.nn.kneighbors(inp_emb, return_distance=False)[0]
-        
-        if return_data:
-            return [self.data[i] for i in neighbors]
-        else:
-            return neighbors
-    
-    
-    def get_text_embedding(self, texts, batch=500):
-        embeddings = []
-        for i in range(0, len(texts), batch):
-            text_batch = texts[i:(i+batch)]
-            emb_batch = self.use(text_batch)
-            embeddings.append(emb_batch)
-        embeddings = np.vstack(embeddings)
-        return embeddings
-
-
-
-def load_recommender(path, start_page=1):
-    global recommender
-    texts = pdf_to_text(path, start_page=start_page)
-    chunks = text_to_chunks(texts, start_page=start_page)
-    recommender.fit(chunks)
-    return 'Corpus Loaded.'
-
-def generate_text(openAI_key,prompt, engine="text-davinci-003"):
-    openai.api_key = openAI_key
-    completions = openai.Completion.create(
-        engine=engine,
-        prompt=prompt,
-        max_tokens=2000,
-        n=1,
-        stop=None,
-        temperature=0.9,
-    )
-    message = completions.choices[0].text +'\n prompt tokens'+ str(completions.usage.prompt_tokens) +'\n completion tokens'+ str(completions.usage.completion_tokens) +'\n total tokens'+ str(completions.usage.total_tokens)
-    return message
-
-def generate_answer(openAI_key):
-    topn_chunks = recommender(question)
-    prompt = ""
-    prompt += 'search results:\n\n'
-    for c in topn_chunks:
-        prompt += c + '\n\n'
-        
-    prompt += "Instructions: Compose an exhaustive reply to the query using the search results given. "\
-                "Extract the entities mentioned in the text, First extract company names(if not mentioned refer to them as 'unnamed third parties'), then extract all data points collected, finally extract what data is being shared with the third parties and what specific purpose they intend to use it for"\
-                "Desired format: Company names: <comma_separated_list_of_company_names>  \n User data collected : <comma_separated_list_such_as_name_age_user_behaviour_on_app_IP_websites-visited, include other user data that is being collected from other sources>   \n Data shared with third parties: <comma_Separated_list_of_user_data_shared_with_third_parties>  \n Purpose of data sharing(write_purpose_for_each_third_party_separately):choose from these options(maintaining functionality for end user, User tracking, data aggregation, analytics, targeted advertising) "\
-                "If the text does not relate to the privacy policy of an organization, Ignore the questions asked. "\
-                "The answer should be somewhat verbose so as to make sense to the common user. Answer all questions comprehensively and break the answers to each question into separate paragraphs using '\n' "\
-                "Answer step-by-step. \n\nQuery: {question}\nAnswer: "\
-
-    prompt += f"Query: {question}\nAnswer:"
-    answer = generate_text(openAI_key, prompt,"text-davinci-003")
-    return answer
-
-
-def question_answer(url, file, openAI_key):
-    if openAI_key.strip()=='':
-        return '[ERROR]: Please enter your Open AI Key. Get your key here: https://platform.openai.com/account/api-keys'
-    if url.strip() == '' and file == None:
-        return '[ERROR]: Both URL and PDF are empty. Provide at least one.'
-    
-    if url.strip() != '' and file != None:
-        return '[ERROR]: Both URL and PDF are provided. Please provide only one (either URL or PDF).'
-
-    if url.strip() != '':
-        glob_url = url
-        download_pdf(glob_url, 'corpus.pdf')
-        load_recommender('corpus.pdf')
-
-    else:
-        old_file_name = file.name
-        file_name = file.name
-        file_name = file_name[:-12] + file_name[-4:]
-        os.rename(old_file_name, file_name)
-        load_recommender(file_name)
-
-    return generate_answer(openAI_key)
-
-
-recommender = SemanticSearch()
-
-title = 'Policy Analyzer V1.3'
-description = """ Privacy policy reading and consistency analysis using GPT API, using bhaskatripathi's pdfGPT implementation and manual Session capture, decryption and analysis to compare data received from privacy policy against data inferred from session capture."""
-
-with gr.Blocks() as demo:
-
-    gr.Markdown(f'<center><h1>{title}</h1></center>')
-    gr.Markdown(description)
-
-    with gr.Row():
-        
-        with gr.Group():
-            gr.Markdown(f'<p style="text-align:center">Get your Open AI API key <a href="https://platform.openai.com/account/api-keys">here</a></p>')
-            openAI_key=gr.Textbox(label='Enter your OpenAI API key here')
-            url = gr.Textbox(label='Enter PDF URL here')
-            gr.Markdown("<center><h4>OR<h4></center>")
-            file = gr.File(label='Upload Privacy policy pdf here', file_types=['.pdf'])
-            btn = gr.Button(value='Submit')
-            btn.style(full_width=True)
-
-        with gr.Group():
-            answer = gr.Textbox(label='The data collected, the data shared with third parties, and the purpose it is shared for according to the privacy policy is as follows:')
-
-        btn.click(question_answer, inputs=[url, file, openAI_key], outputs=[answer])
-#openai.api_key = os.getenv('Your_Key_Here') 
-demo.launch()
\ No newline at end of file
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/altair/utils/execeval.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/altair/utils/execeval.py
deleted file mode 100644
index 514f874ce30b622089302924bafb1cfae0a4efd7..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/altair/utils/execeval.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import ast
-import sys
-
-
-if sys.version_info > (3, 8):
-    Module = ast.Module
-else:
-    # Mock the Python >= 3.8 API
-    def Module(nodelist, type_ignores):
-        return ast.Module(nodelist)
-
-
-class _CatchDisplay:
-    """Class to temporarily catch sys.displayhook"""
-
-    def __init__(self):
-        self.output = None
-
-    def __enter__(self):
-        self.old_hook = sys.displayhook
-        sys.displayhook = self
-        return self
-
-    def __exit__(self, type, value, traceback):
-        sys.displayhook = self.old_hook
-        # Returning False will cause exceptions to propagate
-        return False
-
-    def __call__(self, output):
-        self.output = output
-
-
-def eval_block(code, namespace=None, filename="<string>"):
-    """
-    Execute a multi-line block of code in the given namespace
-
-    If the final statement in the code is an expression, return
-    the result of the expression.
-    """
-    tree = ast.parse(code, filename="<ast>", mode="exec")
-    if namespace is None:
-        namespace = {}
-    catch_display = _CatchDisplay()
-
-    if isinstance(tree.body[-1], ast.Expr):
-        to_exec, to_eval = tree.body[:-1], tree.body[-1:]
-    else:
-        to_exec, to_eval = tree.body, []
-
-    for node in to_exec:
-        compiled = compile(Module([node], []), filename=filename, mode="exec")
-        exec(compiled, namespace)
-
-    with catch_display:
-        for node in to_eval:
-            compiled = compile(
-                ast.Interactive([node]), filename=filename, mode="single"
-            )
-            exec(compiled, namespace)
-
-    return catch_display.output
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fsspec/generic.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fsspec/generic.py
deleted file mode 100644
index 18e27405a31f78bceda9aec5b78aeb8f68f33036..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fsspec/generic.py
+++ /dev/null
@@ -1,302 +0,0 @@
-import inspect
-import logging
-
-from .asyn import AsyncFileSystem
-from .callbacks import _DEFAULT_CALLBACK
-from .core import filesystem, get_filesystem_class, split_protocol
-
-_generic_fs = {}
-logger = logging.getLogger("fsspec.generic")
-
-
-def set_generic_fs(protocol, **storage_options):
-    _generic_fs[protocol] = filesystem(protocol, **storage_options)
-
-
-default_method = "default"
-
-
-def _resolve_fs(url, method=None, protocol=None, storage_options=None):
-    """Pick instance of backend FS"""
-    method = method or default_method
-    protocol = protocol or split_protocol(url)[0]
-    storage_options = storage_options or {}
-    if method == "default":
-        return filesystem(protocol)
-    if method == "generic":
-        return _generic_fs[protocol]
-    if method == "current":
-        cls = get_filesystem_class(protocol)
-        return cls.current()
-    if method == "options":
-        return filesystem(protocol, **storage_options.get(protocol, {}))
-    raise ValueError(f"Unknown FS resolution method: {method}")
-
-
-def rsync(
-    source,
-    destination,
-    delete_missing=False,
-    source_field="size",
-    dest_field="size",
-    update_cond="different",
-    inst_kwargs=None,
-    fs=None,
-    **kwargs,
-):
-    """Sync files between two directory trees
-
-    (experimental)
-
-    Parameters
-    ----------
-    source: str
-        Root of the directory tree to take files from.
-    destination: str
-        Root path to copy into. The contents of this location should be
-        identical to the contents of ``source`` when done.
-    delete_missing: bool
-        If there are paths in the destination that don't exist in the
-        source and this is True, delete them. Otherwise, leave them alone.
-    source_field: str
-        If ``update_field`` is "different", this is the key in the info
-        of source files to consider for difference.
-    dest_field: str
-        If ``update_field`` is "different", this is the key in the info
-        of destination files to consider for difference.
-    update_cond: "different"|"always"|"never"
-        If "always", every file is copied, regardless of whether it exists in
-        the destination. If "never", files that exist in the destination are
-        not copied again. If "different" (default), only copy if the info
-        fields given by ``source_field`` and ``dest_field`` (usually "size")
-        are different. Other comparisons may be added in the future.
-    inst_kwargs: dict|None
-        If ``fs`` is None, use this set of keyword arguments to make a
-        GenericFileSystem instance
-    fs: GenericFileSystem|None
-        Instance to use if explicitly given. The instance defines how to
-        to make downstream file system instances from paths.
-    """
-    fs = fs or GenericFileSystem(**(inst_kwargs or {}))
-    source = fs._strip_protocol(source)
-    destination = fs._strip_protocol(destination)
-    allfiles = fs.find(source, withdirs=True, detail=True)
-    if not fs.isdir(source):
-        raise ValueError("Can only rsync on a directory")
-    otherfiles = fs.find(destination, withdirs=True, detail=True)
-    dirs = [
-        a
-        for a, v in allfiles.items()
-        if v["type"] == "directory" and a.replace(source, destination) not in otherfiles
-    ]
-    logger.debug(f"{len(dirs)} directories to create")
-    for dirn in dirs:
-        # no async
-        fs.mkdirs(dirn.replace(source, destination), exist_ok=True)
-    allfiles = {a: v for a, v in allfiles.items() if v["type"] == "file"}
-    logger.debug(f"{len(allfiles)} files to consider for copy")
-    to_delete = [
-        o
-        for o, v in otherfiles.items()
-        if o.replace(destination, source) not in allfiles and v["type"] == "file"
-    ]
-    for k, v in allfiles.copy().items():
-        otherfile = k.replace(source, destination)
-        if otherfile in otherfiles:
-            if update_cond == "always":
-                allfiles[k] = otherfile
-            elif update_cond == "different":
-                if v[source_field] != otherfiles[otherfile][dest_field]:
-                    # details mismatch, make copy
-                    allfiles[k] = otherfile
-                else:
-                    # details match, don't copy
-                    allfiles.pop(k)
-        else:
-            # file not in target yet
-            allfiles[k] = otherfile
-    if allfiles:
-        source_files, target_files = zip(*allfiles.items())
-        logger.debug(f"{len(source_files)} files to copy")
-        fs.cp(source_files, target_files, **kwargs)
-    if delete_missing:
-        logger.debug(f"{len(to_delete)} files to delete")
-        fs.rm(to_delete)
-
-
-class GenericFileSystem(AsyncFileSystem):
-    """Wrapper over all other FS types
-
-    <experimental!>
-
-    This implementation is a single unified interface to be able to run FS operations
-    over generic URLs, and dispatch to the specific implementations using the URL
-    protocol prefix.
-
-    Note: instances of this FS are always async, even if you never use it with any async
-    backend.
-    """
-
-    protocol = "generic"  # there is no real reason to ever use a protocol with this FS
-
-    def __init__(self, default_method="default", **kwargs):
-        """
-
-        Parameters
-        ----------
-        default_method: str (optional)
-            Defines how to configure backend FS instances. Options are:
-            - "default": instantiate like FSClass(), with no
-              extra arguments; this is the default instance of that FS, and can be
-              configured via the config system
-            - "generic": takes instances from the `_generic_fs` dict in this module,
-              which you must populate before use. Keys are by protocol
-            - "current": takes the most recently instantiated version of each FS
-        """
-        self.method = default_method
-        super(GenericFileSystem, self).__init__(**kwargs)
-
-    def _strip_protocol(self, path):
-        # normalization only
-        fs = _resolve_fs(path, self.method)
-        return fs.unstrip_protocol(fs._strip_protocol(path))
-
-    async def _find(self, path, maxdepth=None, withdirs=False, detail=False, **kwargs):
-        fs = _resolve_fs(path, self.method)
-        if fs.async_impl:
-            out = await fs._find(
-                path, maxdepth=maxdepth, withdirs=withdirs, detail=detail, **kwargs
-            )
-        else:
-            out = fs.find(
-                path, maxdepth=maxdepth, withdirs=withdirs, detail=detail, **kwargs
-            )
-        result = {}
-        for k, v in out.items():
-            name = fs.unstrip_protocol(k)
-            v["name"] = name
-            result[name] = v
-        if detail:
-            return result
-        return list(result)
-
-    async def _info(self, url, **kwargs):
-        fs = _resolve_fs(url, self.method)
-        if fs.async_impl:
-            out = await fs._info(url, **kwargs)
-        else:
-            out = fs.info(url, **kwargs)
-        out["name"] = fs.unstrip_protocol(out["name"])
-        return out
-
-    async def _ls(
-        self,
-        url,
-        detail=True,
-        **kwargs,
-    ):
-        fs = _resolve_fs(url, self.method)
-        if fs.async_impl:
-            out = await fs._ls(url, detail=True, **kwargs)
-        else:
-            out = fs.ls(url, detail=True, **kwargs)
-        for o in out:
-            o["name"] = fs.unstrip_protocol(o["name"])
-        if detail:
-            return out
-        else:
-            return [o["name"] for o in out]
-
-    async def _cat_file(
-        self,
-        url,
-        **kwargs,
-    ):
-        fs = _resolve_fs(url, self.method)
-        if fs.async_impl:
-            return await fs._cat_file(url, **kwargs)
-        else:
-            return fs.cat_file(url, **kwargs)
-
-    async def _pipe_file(
-        self,
-        path,
-        value,
-        **kwargs,
-    ):
-        fs = _resolve_fs(path, self.method)
-        if fs.async_impl:
-            return await fs._pipe_file(path, value, **kwargs)
-        else:
-            return fs.pipe_file(path, value, **kwargs)
-
-    async def _rm(self, url, **kwargs):
-        fs = _resolve_fs(url, self.method)
-        if fs.async_impl:
-            await fs._rm(url, **kwargs)
-        else:
-            fs.rm(url, **kwargs)
-
-    async def _makedirs(self, path, exist_ok=False):
-        fs = _resolve_fs(path, self.method)
-        if fs.async_impl:
-            await fs._makedirs(path, exist_ok=exist_ok)
-        else:
-            fs.makedirs(path, exist_ok=exist_ok)
-
-    def rsync(self, source, destination, **kwargs):
-        """Sync files between two directory trees
-
-        See `func:rsync` for more details.
-        """
-        rsync(source, destination, fs=self, **kwargs)
-
-    async def _cp_file(
-        self,
-        url,
-        url2,
-        blocksize=2**20,
-        callback=_DEFAULT_CALLBACK,
-        **kwargs,
-    ):
-        fs = _resolve_fs(url, self.method)
-        fs2 = _resolve_fs(url2, self.method)
-        if fs is fs2:
-            # pure remote
-            if fs.async_impl:
-                return await fs._cp_file(url, url2, **kwargs)
-            else:
-                return fs.cp_file(url, url2, **kwargs)
-        kw = {"blocksize": 0, "cache_type": "none"}
-        try:
-            f1 = (
-                await fs.open_async(url, "rb")
-                if hasattr(fs, "open_async")
-                else fs.open(url, "rb", **kw)
-            )
-            callback.set_size(await maybe_await(f1.size))
-            f2 = (
-                await fs2.open_async(url2, "wb")
-                if hasattr(fs2, "open_async")
-                else fs2.open(url2, "wb", **kw)
-            )
-            while f1.size is None or f2.tell() < f1.size:
-                data = await maybe_await(f1.read(blocksize))
-                if f1.size is None and not data:
-                    break
-                await maybe_await(f2.write(data))
-                callback.absolute_update(f2.tell())
-        finally:
-            try:
-                await maybe_await(f2.close())
-                await maybe_await(f1.close())
-            except NameError:
-                # fail while opening f1 or f2
-                pass
-
-
-async def maybe_await(cor):
-    if inspect.iscoroutine(cor):
-        return await cor
-    else:
-        return cor
diff --git a/spaces/DanielGartop/SexAI/Dockerfile b/spaces/DanielGartop/SexAI/Dockerfile
deleted file mode 100644
index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000
--- a/spaces/DanielGartop/SexAI/Dockerfile
+++ /dev/null
@@ -1,21 +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" ]
\ No newline at end of file
diff --git a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/models/modules/__init__.py b/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/models/modules/__init__.py
deleted file mode 100644
index 78be267dda39cc4b64b79f79400fac9f638887f0..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/models/modules/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-""" 
-@Date: 2021/09/01
-@description:
-"""
-
-from models.modules.swin_transformer import Swin_Transformer
-from models.modules.swg_transformer import SWG_Transformer
-from models.modules.transformer import Transformer
diff --git a/spaces/Detomo/Lighten_dark_image/app.py b/spaces/Detomo/Lighten_dark_image/app.py
deleted file mode 100644
index b161e08943a87551fc4b88377c80534849c70d20..0000000000000000000000000000000000000000
--- a/spaces/Detomo/Lighten_dark_image/app.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import numpy as np
-import gradio as gr
-from huggingface_hub import from_pretrained_keras
-import keras
-import tensorflow as tf
-
-model = from_pretrained_keras("keras-io/lowlight-enhance-mirnet", compile=False)
-examples = ['examples/1.png', 'examples/55.png', 'examples/665.png', 'examples/778.png']
-
-
-def infer(original_image):
-    image = tf.keras.preprocessing.image.img_to_array(original_image)
-    image = image.astype("float32") / 255.0
-    image = np.expand_dims(image, axis=0)
-    output = model.predict(image)
-    output_image = output[0] * 255.0
-    output_image = output_image.clip(0, 255)
-    output_image = output_image.reshape(
-        (np.shape(output_image)[0], np.shape(output_image)[1], 3)
-    )
-    output_image = np.uint32(output_image)
-    return output_image
-
-iface = gr.Interface(
-    fn=infer,
-    title="Low light image enhancement",
-    description = "MIRNet model for light up the dark image 🌆🎆",
-    inputs=[gr.inputs.Image(label="image", type="pil", shape=(960, 640))],
-    outputs="image",
-    cache_examples=True,
-    article = "Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>.",
-    examples=examples).launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/Detomo/ai-comic-generation/src/components/ui/vertical-slider.tsx b/spaces/Detomo/ai-comic-generation/src/components/ui/vertical-slider.tsx
deleted file mode 100644
index b28a1200cb06d1f26e3c640c85e655c99e88954e..0000000000000000000000000000000000000000
--- a/spaces/Detomo/ai-comic-generation/src/components/ui/vertical-slider.tsx
+++ /dev/null
@@ -1,27 +0,0 @@
-"use client"
-
-import * as React from "react"
-import * as SliderPrimitive from "@radix-ui/react-slider"
-
-import { cn } from "@/lib/utils"
-
-const VerticalSlider = React.forwardRef<
-  React.ElementRef<typeof SliderPrimitive.Root>,
-  React.ComponentPropsWithoutRef<typeof SliderPrimitive.Root>
->(({ className, ...props }, ref) => (
-  <SliderPrimitive.Root
-    ref={ref}
-    className={cn(
-      "relative flex w-full touch-none select-none items-center",
-      className
-    )}
-    {...props}
-  >
-    <SliderPrimitive.Track className="relative w-2 h-full grow overflow-hidden rounded-full bg-stone-300 dark:bg-stone-700">
-      <SliderPrimitive.Range className="absolute w-full bg-stone-700 dark:bg-stone-50" />
-    </SliderPrimitive.Track>
-    <SliderPrimitive.Thumb className="block -ml-1.5 h-5 w-5 rounded-full border-2 border-stone-700 bg-stone-300 ring-offset-white transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-stone-950 focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50 dark:border-stone-50 dark:bg-stone-700 dark:ring-offset-stone-950 dark:focus-visible:ring-stone-300" />
-  </SliderPrimitive.Root>
-))
-VerticalSlider.displayName = "VerticalSlider"
-export { VerticalSlider }
diff --git a/spaces/DragGan/DragGan-Inversion/torch_utils/ops/fma.py b/spaces/DragGan/DragGan-Inversion/torch_utils/ops/fma.py
deleted file mode 100644
index 5434c1845d1f9601277a1257fbd9907a2382ae73..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/torch_utils/ops/fma.py
+++ /dev/null
@@ -1,64 +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.
-
-"""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/EdZ123/anime-collaborative-filtering-system/README.md b/spaces/EdZ123/anime-collaborative-filtering-system/README.md
deleted file mode 100644
index 950fa9e6d9eafdbe23904974603cc02e835ea3be..0000000000000000000000000000000000000000
--- a/spaces/EdZ123/anime-collaborative-filtering-system/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
----
-title: Anime Collaborative Filtering System
-emoji: 🗼
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: true
-license: mit
----
-
-# anime-collaborative-filtering-system
-
-A Pytorch model that suggests similar anime using collaborative filtering and deep learning.
-You can try it at [this Hugging Face Space](https://huggingface.co/spaces/EdZ123/anime-collaborative-filtering-system).
-
-# Implementation Details
-* Trained with [anime-collaborative-filtering-system.ipynb](anime-collaborative-filtering-system.ipynb) on Kaggle
-* Model uses collaborative filtering with embeddings fed through a multilayer perceptron
-* Recommendations are made through embedding distance
diff --git a/spaces/Eddycrack864/Applio-Inference/Applio-RVC-Fork/utils/i18n.py b/spaces/Eddycrack864/Applio-Inference/Applio-RVC-Fork/utils/i18n.py
deleted file mode 100644
index 8e75d2bc26ff86ab1716b8d7f239ad9f5cc1e32d..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/Applio-RVC-Fork/utils/i18n.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import locale
-import json
-import os
-
-
-def load_language_list(language):
-    with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
-        language_list = json.load(f)
-    return language_list
-
-
-class I18nAuto:
-    def __init__(self, language=None):
-        if language in ["Auto", None]:
-            language = "es_ES"
-        if not os.path.exists(f"./i18n/{language}.json"):
-            language = "es_ES"
-            language = "es_ES"
-        self.language = language
-        # print("Use Language:", language)
-        self.language_map = load_language_list(language)
-
-    def __call__(self, key):
-        return self.language_map.get(key, key)
-
-    def print(self):
-      # print("Use Language:", self.language)
-        print("")
diff --git a/spaces/Eddycrack864/Applio-Inference/julius/resample.py b/spaces/Eddycrack864/Applio-Inference/julius/resample.py
deleted file mode 100644
index fd3b9b547d4c33ec7136d32e5f086420d0a72e14..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/julius/resample.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details.
-# Author: adefossez, 2020
-"""
-Differentiable, Pytorch based resampling.
-Implementation of Julius O. Smith algorithm for resampling.
-See https://ccrma.stanford.edu/~jos/resample/ for details.
-This implementation is specially optimized for when new_sr / old_sr is a fraction
-with a small numerator and denominator when removing the gcd (e.g. new_sr = 700, old_sr = 500).
-
-Very similar to [bmcfee/resampy](https://github.com/bmcfee/resampy) except this implementation
-is optimized for the case mentioned before, while resampy is slower but more general.
-
-"""
-
-import math
-from typing import Optional
-
-import torch
-from torch.nn import functional as F
-
-from .core import sinc
-from .utils import simple_repr
-
-
-class ResampleFrac(torch.nn.Module):
-    """
-    Resampling from the sample rate `old_sr` to `new_sr`.
-    """
-    def __init__(self, old_sr: int, new_sr: int, zeros: int = 24, rolloff: float = 0.945):
-        """
-        Args:
-            old_sr (int): sample rate of the input signal x.
-            new_sr (int): sample rate of the output.
-            zeros (int): number of zero crossing to keep in the sinc filter.
-            rolloff (float): use a lowpass filter that is `rolloff * new_sr / 2`,
-                to ensure sufficient margin due to the imperfection of the FIR filter used.
-                Lowering this value will reduce anti-aliasing, but will reduce some of the
-                highest frequencies.
-
-        Shape:
-
-            - Input: `[*, T]`
-            - Output: `[*, T']` with `T' = int(new_sr * T / old_sr)
-
-
-        .. caution::
-            After dividing `old_sr` and `new_sr` by their GCD, both should be small
-            for this implementation to be fast.
-
-        >>> import torch
-        >>> resample = ResampleFrac(4, 5)
-        >>> x = torch.randn(1000)
-        >>> print(len(resample(x)))
-        1250
-        """
-        super().__init__()
-        if not isinstance(old_sr, int) or not isinstance(new_sr, int):
-            raise ValueError("old_sr and new_sr should be integers")
-        gcd = math.gcd(old_sr, new_sr)
-        self.old_sr = old_sr // gcd
-        self.new_sr = new_sr // gcd
-        self.zeros = zeros
-        self.rolloff = rolloff
-
-        self._init_kernels()
-
-    def _init_kernels(self):
-        if self.old_sr == self.new_sr:
-            return
-
-        kernels = []
-        sr = min(self.new_sr, self.old_sr)
-        # rolloff will perform antialiasing filtering by removing the highest frequencies.
-        # At first I thought I only needed this when downsampling, but when upsampling
-        # you will get edge artifacts without this, the edge is equivalent to zero padding,
-        # which will add high freq artifacts.
-        sr *= self.rolloff
-
-        # The key idea of the algorithm is that x(t) can be exactly reconstructed from x[i] (tensor)
-        # using the sinc interpolation formula:
-        #   x(t) = sum_i x[i] sinc(pi * old_sr * (i / old_sr - t))
-        # We can then sample the function x(t) with a different sample rate:
-        #    y[j] = x(j / new_sr)
-        # or,
-        #    y[j] = sum_i x[i] sinc(pi * old_sr * (i / old_sr - j / new_sr))
-
-        # We see here that y[j] is the convolution of x[i] with a specific filter, for which
-        # we take an FIR approximation, stopping when we see at least `zeros` zeros crossing.
-        # But y[j+1] is going to have a different set of weights and so on, until y[j + new_sr].
-        # Indeed:
-        # y[j + new_sr] = sum_i x[i] sinc(pi * old_sr * ((i / old_sr - (j + new_sr) / new_sr))
-        #               = sum_i x[i] sinc(pi * old_sr * ((i - old_sr) / old_sr - j / new_sr))
-        #               = sum_i x[i + old_sr] sinc(pi * old_sr * (i / old_sr - j / new_sr))
-        # so y[j+new_sr] uses the same filter as y[j], but on a shifted version of x by `old_sr`.
-        # This will explain the F.conv1d after, with a stride of old_sr.
-        self._width = math.ceil(self.zeros * self.old_sr / sr)
-        # If old_sr is still big after GCD reduction, most filters will be very unbalanced, i.e.,
-        # they will have a lot of almost zero values to the left or to the right...
-        # There is probably a way to evaluate those filters more efficiently, but this is kept for
-        # future work.
-        idx = torch.arange(-self._width, self._width + self.old_sr).float()
-        for i in range(self.new_sr):
-            t = (-i/self.new_sr + idx/self.old_sr) * sr
-            t = t.clamp_(-self.zeros, self.zeros)
-            t *= math.pi
-            window = torch.cos(t/self.zeros/2)**2
-            kernel = sinc(t) * window
-            # Renormalize kernel to ensure a constant signal is preserved.
-            kernel.div_(kernel.sum())
-            kernels.append(kernel)
-
-        self.register_buffer("kernel", torch.stack(kernels).view(self.new_sr, 1, -1))
-
-    def forward(self, x: torch.Tensor, output_length: Optional[int] = None, full: bool = False):
-        """
-        Resample x.
-        Args:
-            x (Tensor): signal to resample, time should be the last dimension
-            output_length (None or int): This can be set to the desired output length
-                (last dimension). Allowed values are between 0 and
-                ceil(length * new_sr / old_sr). When None (default) is specified, the
-                floored output length will be used. In order to select the largest possible
-                size, use the `full` argument.
-            full (bool): return the longest possible output from the input. This can be useful
-                if you chain resampling operations, and want to give the `output_length` only
-                for the last one, while passing `full=True` to all the other ones.
-        """
-        if self.old_sr == self.new_sr:
-            return x
-        shape = x.shape
-        length = x.shape[-1]
-        x = x.reshape(-1, length)
-        x = F.pad(x[:, None], (self._width, self._width + self.old_sr), mode='replicate')
-        ys = F.conv1d(x, self.kernel, stride=self.old_sr)  # type: ignore
-        y = ys.transpose(1, 2).reshape(list(shape[:-1]) + [-1])
-
-        float_output_length = self.new_sr * length / self.old_sr
-        max_output_length = int(math.ceil(float_output_length))
-        default_output_length = int(float_output_length)
-        if output_length is None:
-            output_length = max_output_length if full else default_output_length
-        elif output_length < 0 or output_length > max_output_length:
-            raise ValueError(f"output_length must be between 0 and {max_output_length}")
-        else:
-            if full:
-                raise ValueError("You cannot pass both full=True and output_length")
-        return y[..., :output_length]
-
-    def __repr__(self):
-        return simple_repr(self)
-
-
-def resample_frac(x: torch.Tensor, old_sr: int, new_sr: int,
-                  zeros: int = 24, rolloff: float = 0.945,
-                  output_length: Optional[int] = None, full: bool = False):
-    """
-    Functional version of `ResampleFrac`, refer to its documentation for more information.
-
-    ..warning::
-        If you call repeatidly this functions with the same sample rates, then the
-        resampling kernel will be recomputed everytime. For best performance, you should use
-        and cache an instance of `ResampleFrac`.
-    """
-    return ResampleFrac(old_sr, new_sr, zeros, rolloff).to(x)(x, output_length, full)
-
-
-# Easier implementations for downsampling and upsampling by a factor of 2
-# Kept for testing and reference
-
-def _kernel_upsample2_downsample2(zeros):
-    # Kernel for upsampling and downsampling by a factor of 2. Interestingly,
-    # it is the same kernel used for both.
-    win = torch.hann_window(4 * zeros + 1, periodic=False)
-    winodd = win[1::2]
-    t = torch.linspace(-zeros + 0.5, zeros - 0.5, 2 * zeros)
-    t *= math.pi
-    kernel = (sinc(t) * winodd).view(1, 1, -1)
-    return kernel
-
-
-def _upsample2(x, zeros=24):
-    """
-    Upsample x by a factor of two. The output will be exactly twice as long as the input.
-    Args:
-        x (Tensor): signal to upsample, time should be the last dimension
-        zeros (int): number of zero crossing to keep in the sinc filter.
-
-    This function is kept only for reference, you should use the more generic `resample_frac`
-    one. This function does not perform anti-aliasing filtering.
-    """
-    *other, time = x.shape
-    kernel = _kernel_upsample2_downsample2(zeros).to(x)
-    out = F.conv1d(x.view(-1, 1, time), kernel, padding=zeros)[..., 1:].view(*other, time)
-    y = torch.stack([x, out], dim=-1)
-    return y.view(*other, -1)
-
-
-def _downsample2(x, zeros=24):
-    """
-    Downsample x by a factor of two. The output length is half of the input, ceiled.
-    Args:
-        x (Tensor): signal to downsample, time should be the last dimension
-        zeros (int): number of zero crossing to keep in the sinc filter.
-
-    This function is kept only for reference, you should use the more generic `resample_frac`
-    one. This function does not perform anti-aliasing filtering.
-    """
-    if x.shape[-1] % 2 != 0:
-        x = F.pad(x, (0, 1))
-    xeven = x[..., ::2]
-    xodd = x[..., 1::2]
-    *other, time = xodd.shape
-    kernel = _kernel_upsample2_downsample2(zeros).to(x)
-    out = xeven + F.conv1d(xodd.view(-1, 1, time), kernel, padding=zeros)[..., :-1].view(
-        *other, time)
-    return out.view(*other, -1).mul(0.5)
diff --git a/spaces/Elbhnasy/Foodvision_mini/model.py b/spaces/Elbhnasy/Foodvision_mini/model.py
deleted file mode 100644
index 228c621be5a0e80a967abccc0b43f1ea09f4a820..0000000000000000000000000000000000000000
--- a/spaces/Elbhnasy/Foodvision_mini/model.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import torch
-import torchvision
-
-from torch import nn
-
-
-def create_effnetb1_model(num_classes:int=3,seed:int=42):
-    """
-    Creates an EFFicientNetB1 feature extractor model and transforms.
-    :param num_classes: number of classes in classifier head.
-                        Defaults to 3.
-    :param seed: random seed value.
-                 Defaults to 42.
-    :return: feature extractor model.
-         transforms (torchvision.transforms): EffNetB1 image transforms.
-    """
-    # 1. Setup pretrained EffNetB1 weights
-    weigts = torchvision.models.EfficientNet_B1_Weights.DEFAULT
-    # 2. Get EffNetB2 transforms
-    transforms= weigts.transforms()
-
-    # 3. Setup pretrained model
-    model=torchvision.models.efficientnet_b1(weights= "DEFAULT")
-
-    # 4. Freeze the base layers in the model (this will freeze all layers to begin with)
-    for param in model.parameters():
-        param.requires_grad=False
-
-    # 5. Change classifier head with random seed for reproducibility
-    torch.manual_seed(seed)
-    model.classifier=nn.Sequential(nn.Dropout(p=0.2,inplace=True),
-                                   nn.Linear(in_features=1280,out_features=num_classes))
-    return model,transforms
diff --git a/spaces/EnigmaOfTheWorld/TechnoForge_Automotive/app.py b/spaces/EnigmaOfTheWorld/TechnoForge_Automotive/app.py
deleted file mode 100644
index 98bfc897726bcdf7adfa144dde051ca933f530a6..0000000000000000000000000000000000000000
--- a/spaces/EnigmaOfTheWorld/TechnoForge_Automotive/app.py
+++ /dev/null
@@ -1,560 +0,0 @@
-import io
-import os
-import warnings
-
-import replicate
-from stability_sdk import client
-import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
-import io, os
-from numpy import random
-from PIL import Image, ImageDraw, Image, ImageFont
-import pandas as pd
-import random
-import string
-import webbrowser
-import urllib
-import requests
-from base64 import b64encode
-import string
-import metaseg
-
-
-def set_engine(engine:str):
-# Set up our connection to the API.
-  stability_api = client.StabilityInference(
-      key=os.environ['STABILITY_KEY'], # API Key reference.
-      verbose=True, # Print debug messages.
-      engine = engine, # Set the engine to use for generation. For SD 2.0 use "stable-diffusion-v2-0".
-      # Available engines: stable-diffusion-v1 stable-diffusion-v1-5 stable-diffusion-512-v2-0 stable-diffusion-768-v2-0 stable-inpainting-v1-0 stable-inpainting-512-v2-0
-  )
-  return stability_api
-
-
-sam_checkpoint = "sam_vit_h_4b8939.pth"
-model_type = "vit_h"
-device = "cuda"
-
-
-sam = metaseg.sam_model_registry[model_type](checkpoint='sam_vit_h_4b8939.pth')
-predictor = metaseg.SamPredictor(sam)
-sam.to(device="cpu")
-
-#@title
-import numpy as np
-import openai
-import cv2
-import gradio as gr
-from serpapi import GoogleSearch
-import random
-import replicate
-import time
-
-openai.api_key = os.environ['OPENAI_API_KEY']
-
-
-def generate_prompt(body_type,exterior_color,roof_type,wheels_and_tires):
-  response = openai.Completion.create(
-        model="text-davinci-003",
-        prompt=f"Generate description of automobile from the following desciptors-Realistic image of an automobile with {exterior_color} color {body_type} with {roof_type}.Start with word draw do not start with this.Do not describe about interior of the car.",
-        temperature=0.1,
-        max_tokens=256,
-        top_p=1,
-        frequency_penalty=0,
-        presence_penalty=0
-    )
-  result = response["choices"][0]["text"]
-  print(result)
-  return result
-
-
-def text_to_image(prompt,guidance_model,cfg,engine):
-
-  # Set up our initial generation parameters.
-  print(f'text_to_image: {prompt}')
-  stability_api = set_engine(engine)
-  answers = stability_api.generate(
-      prompt=prompt,
-      seed=992446758, # If a seed is provided, the resulting generated image will be deterministic.
-                      # What this means is that as long as all generation parameters remain the same, you can always recall the same image simply by generating it again.
-                      # Note: This isn't quite the case for CLIP Guided generations, which we tackle in the CLIP Guidance documentation.
-      steps=30, # Amount of inference steps performed on image generation. Defaults to 30.
-      cfg_scale=cfg, # Influences how strongly your generation is guided to match your prompt.
-                    # Setting this value higher increases the strength in which it tries to match your prompt.
-                    # Defaults to 7.0 if not specified.
-      width=512, # Generation width, defaults to 512 if not included.
-      height=512, # Generation height, defaults to 512 if not included.
-      samples=1, # Number of images to generate, defaults to 1 if not included.
-      sampler=generation.SAMPLER_K_DPMPP_2S_ANCESTRAL ,# Choose which sampler we want to denoise our generation with.
-                                                  # Defaults to k_lms if not specified. Clip Guidance only supports ancestral samplers.
-                                                  # (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m)
-      guidance_preset = generation.GUIDANCE_PRESET_FAST_GREEN,
-      # guidance_strength = guidance_strength,
-      guidance_models = [guidance_model]
-  )
-
-  # Set up our warning to print to the console if the adult content classifier is tripped.
-  # If adult content classifier is not tripped, save generated images.
-  for resp in answers:
-      for artifact in resp.artifacts:
-          if artifact.finish_reason == generation.FILTER:
-              warnings.warn(
-                  "Your request activated the API's safety filters and could not be processed."
-                  "Please modify the prompt and try again.")
-          if artifact.type == generation.ARTIFACT_IMAGE:
-              img = Image.open(io.BytesIO(artifact.binary))
-              # img.save(str(artifact.seed)+ ".png") # Save our generated images with their seed number as the filename.
-  return img
-
-
-def imag2img(prompt,init_image,guidance_model,cfg,engine):
-  stability_api = set_engine(engine)
-  # Set up our initial generation parameters.
-  # init_image = init_image.resize((512,512))
-  answers2 = stability_api.generate(
-      prompt=prompt,
-      init_image=init_image, # Assign our previously generated img as our Initial Image for transformation.
-      start_schedule=0.6, # Set the strength of our prompt in relation to our initial image.
-      seed=992446758, # If attempting to transform an image that was previously generated with our API,
-                      # initial images benefit from having their own distinct seed rather than using the seed of the original image generation.
-      steps=30, # Amount of inference steps performed on image generation. Defaults to 30.
-      cfg_scale=cfg, # Influences how strongly your generation is guided to match your prompt.
-                    # Setting this value higher increases the strength in which it tries to match your prompt.
-                    # Defaults to 7.0 if not specified.
-      width=512, # Generation width, defaults to 512 if not included.
-      height=512, # Generation height, defaults to 512 if not included.
-     sampler=generation.SAMPLER_K_DPMPP_2S_ANCESTRAL ,# Choose which sampler we want to denoise our generation with.
-                                                  # Defaults to k_lms if not specified. Clip Guidance only supports ancestral samplers.
-                                                  # (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m)
-      guidance_preset = generation.GUIDANCE_PRESET_FAST_GREEN,
-      # guidance_strength = guidance_strength,
-      guidance_models = [guidance_model]
-  )
-
-  # Set up our warning to print to the console if the adult content classifier is tripped.
-  # If adult content classifier is not tripped, save generated image.
-  for resp in answers2:
-      for artifact in resp.artifacts:
-          if artifact.finish_reason == generation.FILTER:
-              warnings.warn(
-                  "Your request activated the API's safety filters and could not be processed."
-                  "Please modify the prompt and try again.")
-          if artifact.type == generation.ARTIFACT_IMAGE:
-              
-              img2 = Image.open(io.BytesIO(artifact.binary))
-  return img2
-def inpainting(prompt,image,mask,guidance_model,cfg,engine):
-  stability_api = set_engine(engine)
-  # image = Image.fromarray(image).resize((512,512))
-  # mask = Image.fromarray(mask).resize((512,512))
-  # init_image = image['image'].convert("RGB").resize((512,512))
-  # mask = np.array(image['mask'].convert("RGB").resize((512,512)))
-  # print(mask.__class__)
-  # mask[mask==255] = 125
-  # mask[mask==0] = 255
-  # mask[mask==125] = 0
-  # mask = Image.fromarray(mask)
-  answers = stability_api.generate(
-      prompt=prompt,
-      init_image=Image.fromarray(image), # Assign our previously generated img as our Initial Image for transformation.
-      mask_image=Image.fromarray(mask),
-      start_schedule=1, # Set the strength of our prompt in relation to our initial image.
-      seed=992446758, # If attempting to transform an image that was previously generated with our API,
-                      # initial images benefit from having their own distinct seed rather than using the seed of the original image generation.
-      steps=30, # Amount of inference steps performed on image generation. Defaults to 30. 
-      cfg_scale=cfg, # Influences how strongly your generation is guided to match your prompt.
-                    # Setting this value higher increases the strength in which it tries to match your prompt.
-                    # Defaults to 7.0 if not specified.
-      width=512, # Generation width, defaults to 512 if not included.
-      height=512, # Generation height, defaults to 512 if not included.
-      sampler=generation.SAMPLER_K_DPMPP_2S_ANCESTRAL ,# Choose which sampler we want to denoise our generation with.
-                                                  # Defaults to k_lms if not specified. Clip Guidance only supports ancestral samplers.
-                                                  # (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m)
-      guidance_preset = generation.GUIDANCE_PRESET_FAST_GREEN,
-      # guidance_strength = guidance_strength,
-      guidance_models = [guidance_model]
-  )
-
-
-  # Set up our warning to print to the console if the adult content classifier is tripped. If adult content classifier is not tripped, display generated image.
-  for resp in answers:
-      for artifact in resp.artifacts:
-          if artifact.finish_reason == generation.FILTER:
-              warnings.warn(
-                  "Your request activated the API's safety filters and could not be processed."
-                  "Please modify the prompt and try again.")
-          if artifact.type == generation.ARTIFACT_IMAGE:
-              img2 = Image.open(io.BytesIO(artifact.binary)) # Set our resulting initial image generation as 'img2' to avoid overwriting our previous 'img' generation.
-              
-  return img2
-selected_pixels = []
-exception = ''
-
-def crop_image(image,mask):
-  
-  
-  print(image.__class__)
-  Image.fromarray(image).save('/tmp/image.png')
-  image = cv2.imread('/tmp/image.png')
-  Image.fromarray(mask).save('/tmp/mask.png')
-  mask = cv2.imread('/tmp/mask.png',0)
-  
-  mask[mask==0] = 125
-  mask[mask==255]=0
-  mask[mask==125]=255
-
-  # Set 0 as threshold to create a binary mask
-  ret, binary_mask = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)
-
-  # Find contours in the binary mask
-  contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
-  # Get the bounding box of the contour
-  x, y, w, h = cv2.boundingRect(contours[0])
-
-  # Cut the region of interest (ROI) from the original image
-  roi = image[y:y+h, x:x+w]
-  Image.fromarray(roi).save('cropped.png')
-
-  url = upload_img_url('cropped.png')
-
-  for i in range(1,15):
-    try:
-      serpai_op = use_serpapi(url,i)
-      break  
-    except Exception as e:
-      print(e)
-
-  return serpai_op
-
-def use_serpapi(image_url,number=0):
-  
-  api_key = os.environ[f'SERPAPI_KEY{number}']
-  params = {
-    "engine": "google_reverse_image",
-    "image_url": image_url,
-    "api_key": api_key
-  }
-
-  search = GoogleSearch(params)
-  results = search.get_dict()
-  print(f'Results SERPAPI:{results}')
-  inline_images = results["inline_images"][:5]
-  
-    
-
-  
-  shopping_results = []
-  shopping_results+=[result_dct for result in inline_images for result_dct in shopping_url(result['title'],api_key)]
-  car_df = pd.DataFrame(shopping_results)
-  
-  return car_df
-  
-
-def shopping_url(product_title,api_key):
-  params = {
-    "engine": "google_shopping",
-    "q": product_title,
-    "api_key": api_key
-  }
-
-  search = GoogleSearch(params)
-  results = search.get_dict()
-  shopping_results = results["shopping_results"]
-  return [{'title': result_dict['title'],
-                      'price':result_dict['price'], 'link': result_dict['link'],
-                      } 
-                     for result_dict in shopping_results ]
-
-
-def upload_img_url(image_path):
-    API_ENDPOINT = "https://api.imgbb.com/1/upload"
-    
-
-    with open(image_path, "rb") as image:
-        image_data_ = b64encode(image.read())
-        image_data = image_data_.decode('utf-8')
-    
-    payload = {
-        "key": os.environ['IMGBB_API_KEY'],
-        "image": image_data
-    }
-    # Send the API request
-    response = requests.post(API_ENDPOINT, payload)
-    print(response)
-    # Get the generated link from the API response
-    response_json = response.json() # 
-    print("Response json:", response_json)
-    image_url = response_json["data"]["url"]
-    print("Generated link:", image_url)
-    return image_url
-
-def generate_mask(image,evt:gr.SelectData):
-  t1 = time.perf_counter()
-  print(f"image inpainting: {image}")
-  print('ENter fucntion')
-  
-  selected_pixels.append(evt.index)
-  print(f'Selected {selected_pixels}')
-  
-  predictor.set_image(image)
-  
-  print('After predictor.set_image(')
-  input_points = np.array(selected_pixels)
-  input_labels = np.ones(input_points.shape[0])
-  print('After labels')
-  mask, _, _ = predictor.predict(
-      point_coords = input_points, 
-      point_labels = input_labels,
-      multimask_output = False
-  )
-  print(f'mask: {mask}')
-  mask = np.logical_not(mask)
-  mask = Image.fromarray(mask[0,:,:])
-  print(f'RETURNING TYPE{mask.__class__}')
-  NUM = random.randint(1,100000000000000000000000)
-  print(f"mask_{NUM}.png")
-  print(f'{time.perf_counter()-t1} seconds')
-  
-  return mask
-
-
-def depth2image_replicate(image,positive_prompt,negative_prompt,number_of_inference_steps,scheduler):
-  image.save("/tmp/uploaded_image_depth_to_image.png")
-  response = replicate.run(
-     "jagilley/stable-diffusion-depth2img:68f699d395bc7c17008283a7cef6d92edc832d8dc59eb41a6cafec7fc70b85bc",
-     input = {
-         "prompt": positive_prompt,
-         
-         "input_image": open("/tmp/uploaded_image_depth_to_image.png","rb"),
-         "num_inference_steps":int(number_of_inference_steps),
-         "negative_prompt": negative_prompt,
-         "scheduler": scheduler
-
-         
-     })
-  resp = requests.get(response[0])
-  with open('/tmp/file.png','wb') as file:
-    file.write(resp.content)
-
-  return Image.open(file.name)
-  
-  
-def inpainting_replicate(prompt,img,mask,number_of_inference_steps):
-  Image.fromarray(img).save('/tmp/inpaining_input.png')
-  Image.fromarray(mask).save('/tmp/inpaining_mask.png')
-  output = replicate.run(
-    "andreasjansson/stable-diffusion-inpainting:e490d072a34a94a11e9711ed5a6ba621c3fab884eda1665d9d3a282d65a21180",
-    input={"prompt": "a futristic green luxury car with big wheels",
-           "image":open('/tmp/inpaining_input.png',"rb"),
-           "mask": open('/tmp/inpaining_mask.png','rb'),
-           "invert_mask": True,
-            "num_inference_steps":int(number_of_inference_steps),
-           })
-  resp = requests.get(output[0])
-  with open('/tmp/inpainting_file.png','wb') as file:
-    file.write(resp.content)
-
-  return Image.open(file.name)
-  
-
-#############GRADIO INTERFACE#################################################################################
-# import gradio as gr
-# import gradio as gr
-
-
-def prompt_use_for_next(text_prompt,*args):
-  return text_prompt,text_to_image(text_prompt,*args)
-
-def img2img_use_for_next(*args):
-  return args[1],imag2img(*args) 
-
-def inpainting_use_for_next(image):
-  return image
-
-
-
-
-GUIDANCE_MODELS = ['ViT-L-14--openai', 'ViT-H-14--good', 'ViT-B-32--laion2b_e16', 'ViT-L-14--laion400m_e32', 'ViT-B-32--openai', 'ViT-B-16--openai']
-ENGINE_MODELS = ['stable-diffusion-xl-beta-v2-2-2','stable-diffusion-768-v2-1','stable-diffusion-512-v2-0','stable-diffusion-768-v2-0','stable-diffusion-v1']
-with gr.Blocks(theme="Ajaxon6255/Emerald_Isle") as demo: 
-  gr.Markdown("""<h1 style="color:white;font-family:monospace;text-align:center">TechnoForge Automotive</h1>""")
-  gr.Markdown("""<p style='color:white;font-family:monospace'>Attention car enthusiasts! Are you looking for a car that combines traditional craftsmanship with cutting-edge technology? Look no further than TechnoForge Automotive! Inspired by the master blacksmith and craftsman Hephaestus, our team combines the latest in stable diffusion technology with the power of GPT-3 to create the most innovative and precise car designs on the market.Just like Hephaestus forged his creations with the utmost care and precision, we use stable diffusion to ensure the highest level of quality in every detail of our designs. And with the power of GPT-3 technology at our fingertips, we can push the limits of innovation and take car designing to new heights.</p>""")
-  gr.HTML(value="<img id='HeadImage' src='https://i.ibb.co/RYqqt4Z/op9.png' alt='Generate knowlwdge graph' width='1200' height='300' style='border: 2px solid #fff;'/>")   
-  gr.HTML(value="<style>#HeadImage:hover{box-shadow: 0 12px 16px 0 rgba(0,0,0,0.24),0 17px 50px 0 rgba(0,0,0,0.19);}</style>")
-  ######################################################################################################################################
-  with gr.Accordion(label="Generte Prompt"):
-    with gr.Row():
-      with gr.Column():
-        body_type = gr.Textbox(label = "Type of car (eg: SUV, Sedan)")
-        color = gr.Textbox(label="Car color")
-        roof_type = gr.Textbox(label="Roof Type (eg: Sunroof, foldable roof)")
-      with gr.Column():
-        gen_prompt = gr.Textbox(label="Use this Prompt")
-    gr.HTML(value="<img id='generate_prompt' src='https://i.ibb.co/XsHCsK7/1.png' alt='Generate knowlwdge graph' width='1200' height='300' style='border: 2px solid #fff;'/>")   
-    gr.HTML(value="<style>#generate_prompt:hover{box-shadow: 0 12px 16px 0 rgba(0,0,0,0.24),0 17px 50px 0 rgba(0,0,0,0.19);}</style>")  
-    with gr.Row(): 
-      with gr.Column():
-        prompt_gen = gr.Button("Generate Prompt",elem_id="gradio_button")
-      with gr.Column():
-        prompt_next = gr.Button("Use this text as next",elem_id="gradio_button")
-    prompt_gen.click(generate_prompt,[body_type,color,roof_type],[gen_prompt])
-    
-    #########################################################################################################################
-  with gr.Accordion(label="Text2Image"):
-    with gr.Row():
-      with gr.Column():
-        text2imge_prompt = gr.Textbox(label="Prompt for Text")
-        text2imge_guidnace_model = gr.Dropdown(value='ViT-L-14--laion400m_e32',choices=GUIDANCE_MODELS,label="Guidance Model")
-        
-      with gr.Column():
-        text2imge_op = gr.Image(type="pil")
-        text2image_cfg = gr.Slider(label="CFG Scale",mimimum=0,maximum=8,value=7.0,info="This dictates how closely the engine attempts to match a generation to the provided prompt.")
-        text2imge_model = gr.Dropdown(label="Engine",choices=ENGINE_MODELS,value='stable-diffusion-xl-beta-v2-2-2')
-        # op2 = gr.Image()
-    gr.HTML(value="<img id='textImage' src='https://i.ibb.co/6wDWhvd/2.png' alt='Generate knowlwdge graph' width='1200' height='300' style='border: 2px solid #fff;'/>")   
-    gr.HTML(value="<style>#textImage:hover{box-shadow: 0 12px 16px 0 rgba(0,0,0,0.24),0 17px 50px 0 rgba(0,0,0,0.19);}</style>")  
-    with gr.Row(): 
-      with gr.Column():
-        text2imge_button = gr.Button("Transform",elem_id="gradio_button")
-      with gr.Column():
-        text2imge_next = gr.Button("Use this Image as next",elem_id="gradio_button")
-    prompt_gen.click(generate_prompt,[body_type,color,roof_type],[gen_prompt])
-    
-    
-    text2imge_button.click(text_to_image,[text2imge_prompt,text2imge_guidnace_model,text2image_cfg,text2imge_model],[text2imge_op])
-    prompt_next.click(prompt_use_for_next,[gen_prompt,text2imge_guidnace_model,text2image_cfg,text2imge_model],[text2imge_prompt,text2imge_op])
-    #########################################################################################################################
-  # with gr.Accordion(label="Image2Image"):
-  #   with gr.Row():
-  #     with gr.Column():
-  #       img2imge_prompt = gr.Textbox(label="Prompt for img2img")
-  #       img2image_image = gr.Image(type="pil")
-        
-  #       img2image_guidnace_model = gr.Dropdown(value='ViT-L-14--laion400m_e32',choices=GUIDANCE_MODELS,label="Guidance Model")
-  #     with gr.Column():
-  #       img2image_op = gr.Image()
-  #       img2img_cfg = gr.Slider(label="Cfg Scale",mimimum=0,maximum=8,value=7.0)
-  #       img2img_model = gr.Dropdown(label="Engine",choices=ENGINE_MODELS,value='stable-diffusion-xl-beta-v2-2-2')
-  #   with gr.Row(): 
-  #     with gr.Column():
-  #       img2img_button = gr.Button("Transform",elem_id="gradio_button")
-  #     with gr.Column():
-  #       img2img_next = gr.Button("Use this Image as next",elem_id="gradio_button")
-    
-    
-  #   img2img_button.click(imag2img,[img2imge_prompt,img2image_image,img2image_guidnace_model,img2img_cfg,img2img_model],[img2image_op])
-  #   text2imge_next.click(img2img_use_for_next,[img2imge_prompt,text2imge_op,img2image_guidnace_model,img2img_cfg,img2img_model],[img2image_image,img2image_op])
-      #########################################################################################################################
-  with gr.Accordion(label="Depth2Image"):
-    with gr.Row():
-      with gr.Column():
-        depth2image_positive_prompt = gr.Textbox(label="Positive Prompt")
-        depth2image_negative_prompt= gr.Textbox(label="Negative Prompt")
-
-        depth2image_image = gr.Image(type="pil")
-        
-        
-      with gr.Column():
-        depth2image_op = gr.Image()#type="pil")
-        depth2image_inference_steps = gr.Slider(label="Inference Steps",mimimum=1,maximum=500,value=50,step=1,info="Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality.")#204
-        depth2image_scheduler = gr.Dropdown(label="Scheduler",choices=['DDIM','K_EULER','DPMSolverMultistep','K_EULER_ANCESTRAL','PNDM','KLMS'],value='DPMSolverMultistep')
-    gr.HTML(value="<img id='depth_to_image' src='https://i.ibb.co/c2pKpLJ/depth2image.png' alt='Generate knowlwdge graph' width='1200' height='300' style='border: 2px solid #fff;'/>")   
-    gr.HTML(value="<style>#depth_to_image:hover{box-shadow: 0 12px 16px 0 rgba(0,0,0,0.24),0 17px 50px 0 rgba(0,0,0,0.19);}</style>")  
-    with gr.Row(): 
-      with gr.Column():
-        depth2image_button = gr.Button("Transform",elem_id="gradio_button")
-      with gr.Column():
-        depth2image_nxt_button = gr.Button("Use Image as next")  
-      # with gr.Column():
-      #   img2img_next = gr.Button("Use this Image as next",elem_id="gradio_button")
-     #depth2image_replicate(image,positive_prompt,negative_prompt,number_of_inference_steps,scheduler)
-    
-    depth2image_button.click(depth2image_replicate,[depth2image_image,depth2image_positive_prompt,depth2image_negative_prompt,
-                                                    depth2image_inference_steps,depth2image_scheduler],[depth2image_op])
-    text2imge_next.click(inpainting_use_for_next,text2imge_op,depth2image_image)
-    #########################################################################################################################
-  with gr.Accordion("Inpainting"):
-    with gr.Row():
-      with gr.Column():
-        inpainting_prompt = gr.Textbox(label="Prompt for inpainting")
-        
-        inpainting_image = gr.Image()#type="pil",tool="sketch")
-        inpainting_guidnace_model = gr.Dropdown(value='ViT-L-14--laion400m_e32',choices=GUIDANCE_MODELS,label="Guidance Model")
-        inpainting_model = gr.Dropdown(label="Engine",choices=ENGINE_MODELS,value='stable-diffusion-xl-beta-v2-2-2')
-      with gr.Column():
-        inpainting_mask = gr.Image()
-        inpainting_op = gr.Image()
-        inpainting_cfg = gr.Slider(label="CFG Scale",mimimum=0,maximum=8,value=7.0,step=1,info="This dictates how closely the engine attempts to match a generation to the provided prompt.")
-    gr.HTML(value="<img id='inpainting' src='https://i.ibb.co/tMPPVTL/inpainting.png' alt='Generate knowlwdge graph' width='1200' height='300' style='border: 2px solid #fff;'/>")   
-    gr.HTML(value="<style>#inpainting:hover{box-shadow: 0 12px 16px 0 rgba(0,0,0,0.24),0 17px 50px 0 rgba(0,0,0,0.19);}</style>") 
-    with gr.Row(): 
-      with gr.Column():
-        inpainting_button = gr.Button("Transform",elem_id="gradio_button")
-      with gr.Column():
-        inpainting_button_next = gr.Button("Use this Image as next",elem_id="gradio_button")
-        
-        
-        # op2 = gr.Image()
-    inpainting_image.select(fn=generate_mask,inputs=[inpainting_image],outputs=inpainting_mask)
-    depth2image_op.select(fn=generate_mask,inputs=[depth2image_op],outputs=inpainting_mask)
-    
-
-
-
-  inpainting_button.click(fn=inpainting,inputs=[inpainting_prompt,inpainting_image,inpainting_mask,inpainting_guidnace_model,inpainting_cfg,inpainting_model ],outputs=[inpainting_op]) 
-  depth2image_nxt_button.click(fn=inpainting_use_for_next,inputs=[depth2image_op],outputs=[inpainting_image]) 
-  
-  ##################################################################################################################################################################
-  # with gr.Accordion(""):
-  #   with gr.Row():
-  #     with gr.Column():
-  #       inpainting_prompt = gr.Textbox(label="Prompt for inpainting")
-        
-  #       inpainting_image = gr.Image()#type="pil",tool="sketch")
-  #     with gr.Column():
-  #       inpainting_mask = gr.Image()
-  #       inpainting_op = gr.Image()
-  #       inpainting_guidnace_model = gr.Dropdown(value='ViT-L-14--laion400m_e32',choices=GUIDANCE_MODELS)
-  #       # op2 = gr.Image()
-  #   inpainting_image.select(fn=generate_mask,inputs=[inpainting_image],outputs=inpainting_mask)
-  #   inpainting_button = gr.Button("Transform")
-
-
-
-  # inpainting_button.click(fn=inpainting,inputs=[inpainting_prompt,inpainting_image,inpainting_mask,inpainting_guidnace_model ],outputs=[inpainting_op]) 
-  ############################
-  with gr.Accordion("Image Search"):
-    with gr.Row():
-      with gr.Column():
-        search_image = gr.Image()
-      with gr.Column():
-        search_image_mask = gr.Image(interactive=False)
-      # with gr.Column():
-      #   cropped_image = gr.Image()
-    
-
-    search_op = gr.Dataframe()
-    gr.HTML(value="<img id='image_search' src='https://i.ibb.co/qpZNSkb/image-Search.png' alt='Generate knowlwdge graph' width='1200' height='300' style='border: 2px solid #fff;'/>")   
-    gr.HTML(value="<style>#image_search:hover{box-shadow: 0 12px 16px 0 rgba(0,0,0,0.24),0 17px 50px 0 rgba(0,0,0,0.19);}</style>")  
-    search_button  = gr.Button("Search",elem_id="gradio_button")
-    
-    search_image.select(fn=generate_mask,inputs=[search_image],outputs=search_image_mask)
-    inpainting_op.select(fn=generate_mask,inputs=[inpainting_op],outputs=search_image_mask)
-
-
-    search_button.click(fn=crop_image,inputs=[search_image,search_image_mask],outputs=[search_op])
-    inpainting_button_next.click(fn=inpainting_use_for_next,inputs=[inpainting_op],outputs=[search_image])
-    
-
-      
-
-
-
-
-    
-
-
-
-
-demo.queue(concurrency_count=3,max_size=2)
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/EronSamez/RVC_HFmeu/infer/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py b/spaces/EronSamez/RVC_HFmeu/infer/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py
deleted file mode 100644
index 06f2b79f5e5c6f2049bf8220c29ae20c3f82d524..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/infer/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import numpy as np
-import parselmouth
-
-from infer.lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-
-
-class PMF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def compute_f0(self, wav, p_len=None):
-        x = wav
-        if p_len is None:
-            p_len = x.shape[0] // self.hop_length
-        else:
-            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
-        time_step = self.hop_length / self.sampling_rate * 1000
-        f0 = (
-            parselmouth.Sound(x, self.sampling_rate)
-            .to_pitch_ac(
-                time_step=time_step / 1000,
-                voicing_threshold=0.6,
-                pitch_floor=self.f0_min,
-                pitch_ceiling=self.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, uv = self.interpolate_f0(f0)
-        return f0
-
-    def compute_f0_uv(self, wav, p_len=None):
-        x = wav
-        if p_len is None:
-            p_len = x.shape[0] // self.hop_length
-        else:
-            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
-        time_step = self.hop_length / self.sampling_rate * 1000
-        f0 = (
-            parselmouth.Sound(x, self.sampling_rate)
-            .to_pitch_ac(
-                time_step=time_step / 1000,
-                voicing_threshold=0.6,
-                pitch_floor=self.f0_min,
-                pitch_ceiling=self.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, uv = self.interpolate_f0(f0)
-        return f0, uv
diff --git a/spaces/EuroPython2022/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/utils/evolve.sh b/spaces/EuroPython2022/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/utils/evolve.sh
deleted file mode 100644
index 5de9f7a29943d7c771f0183904d6eeaf95860e43..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/Warehouse_Apparel_Detection/metadata/predictor_yolo_detector/utils/evolve.sh
+++ /dev/null
@@ -1,15 +0,0 @@
-#!/bin/bash
-# Hyperparameter evolution commands (avoids CUDA memory leakage issues)
-# Replaces train.py python generations 'for' loop with a bash 'for' loop
-
-# Start on 4-GPU machine
-#for i in 0 1 2 3; do
-#  t=ultralytics/yolov5:evolve && sudo docker pull $t && sudo docker run -d --ipc=host --gpus all -v "$(pwd)"/VOC:/usr/src/VOC $t bash utils/evolve.sh $i
-#  sleep 60 # avoid simultaneous evolve.txt read/write
-#done
-
-# Hyperparameter evolution commands
-while true; do
-  # python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50 --evolve --bucket ult/evolve/voc --device $1
-  python train.py --batch 40 --weights yolov5m.pt --data coco.yaml --img 640 --epochs 30 --evolve --bucket ult/evolve/coco --device $1
-done
diff --git a/spaces/FathomNet/MBARI_Benthic_Supercategory_Object_Detector/README.md b/spaces/FathomNet/MBARI_Benthic_Supercategory_Object_Detector/README.md
deleted file mode 100644
index 67a5b57df47df16b2cdb818f40a4ec82ba985b23..0000000000000000000000000000000000000000
--- a/spaces/FathomNet/MBARI_Benthic_Supercategory_Object_Detector/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MBARI Benthic Supercategory Object Detector
-emoji: 🚀
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Felix123456/bingo/src/components/external-link.tsx b/spaces/Felix123456/bingo/src/components/external-link.tsx
deleted file mode 100644
index 011265f364d5a64a770f4c7e9c65c5ade21d623a..0000000000000000000000000000000000000000
--- a/spaces/Felix123456/bingo/src/components/external-link.tsx
+++ /dev/null
@@ -1,30 +0,0 @@
-export function ExternalLink({
-  href,
-  children
-}: {
-  href: string
-  children: React.ReactNode
-}) {
-  return (
-    <a
-      href={href}
-      target="_blank"
-      rel="noreferrer"
-      className="inline-flex flex-1 justify-center gap-1 underline"
-    >
-      <span>{children}</span>
-      <svg
-        aria-hidden="true"
-        height="7"
-        viewBox="0 0 6 6"
-        width="7"
-        className="opacity-70"
-      >
-        <path
-          d="M1.25215 5.54731L0.622742 4.9179L3.78169 1.75597H1.3834L1.38936 0.890915H5.27615V4.78069H4.40513L4.41109 2.38538L1.25215 5.54731Z"
-          fill="currentColor"
-        ></path>
-      </svg>
-    </a>
-  )
-}
diff --git a/spaces/Fengbinbin/gpt-academic/crazy_functions/test_project/cpp/longcode/jpge.cpp b/spaces/Fengbinbin/gpt-academic/crazy_functions/test_project/cpp/longcode/jpge.cpp
deleted file mode 100644
index 2e26b71ed5aad0d46478fdbcd3a880be1401f946..0000000000000000000000000000000000000000
--- a/spaces/Fengbinbin/gpt-academic/crazy_functions/test_project/cpp/longcode/jpge.cpp
+++ /dev/null
@@ -1,1049 +0,0 @@
-// jpge.cpp - C++ class for JPEG compression.
-// Public domain, Rich Geldreich <richgel99@gmail.com>
-// v1.01, Dec. 18, 2010 - Initial release
-// v1.02, Apr. 6, 2011 - Removed 2x2 ordered dither in H2V1 chroma subsampling method load_block_16_8_8(). (The rounding factor was 2, when it should have been 1. Either way, it wasn't helping.)
-// v1.03, Apr. 16, 2011 - Added support for optimized Huffman code tables, optimized dynamic memory allocation down to only 1 alloc.
-//                        Also from Alex Evans: Added RGBA support, linear memory allocator (no longer needed in v1.03).
-// v1.04, May. 19, 2012: Forgot to set m_pFile ptr to NULL in cfile_stream::close(). Thanks to Owen Kaluza for reporting this bug.
-//                       Code tweaks to fix VS2008 static code analysis warnings (all looked harmless).
-//                       Code review revealed method load_block_16_8_8() (used for the non-default H2V1 sampling mode to downsample chroma) somehow didn't get the rounding factor fix from v1.02.
-
-#include "jpge.h"
-
-#include <stdlib.h>
-#include <string.h>
-#if PLATFORM_WINDOWS
-#include <malloc.h>
-#endif
-
-#define JPGE_MAX(a,b) (((a)>(b))?(a):(b))
-#define JPGE_MIN(a,b) (((a)<(b))?(a):(b))
-
-namespace jpge {
-
-static inline void *jpge_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
-static inline void jpge_free(void *p) { FMemory::Free(p);; }
-
-// Various JPEG enums and tables.
-enum { M_SOF0 = 0xC0, M_DHT = 0xC4, M_SOI = 0xD8, M_EOI = 0xD9, M_SOS = 0xDA, M_DQT = 0xDB, M_APP0 = 0xE0 };
-enum { DC_LUM_CODES = 12, AC_LUM_CODES = 256, DC_CHROMA_CODES = 12, AC_CHROMA_CODES = 256, MAX_HUFF_SYMBOLS = 257, MAX_HUFF_CODESIZE = 32 };
-
-static uint8 s_zag[64] = { 0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-static int16 s_std_lum_quant[64] = { 16,11,12,14,12,10,16,14,13,14,18,17,16,19,24,40,26,24,22,22,24,49,35,37,29,40,58,51,61,60,57,51,56,55,64,72,92,78,64,68,87,69,55,56,80,109,81,87,95,98,103,104,103,62,77,113,121,112,100,120,92,101,103,99 };
-static int16 s_std_croma_quant[64] = { 17,18,18,24,21,24,47,26,26,47,99,66,56,66,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99 };
-static uint8 s_dc_lum_bits[17] = { 0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0 };
-static uint8 s_dc_lum_val[DC_LUM_CODES] = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_lum_bits[17] = { 0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d };
-static uint8 s_ac_lum_val[AC_LUM_CODES]  =
-{
-  0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08,0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,
-  0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28,0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,
-  0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89,
-  0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,
-  0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
-  0xf9,0xfa
-};
-static uint8 s_dc_chroma_bits[17] = { 0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0 };
-static uint8 s_dc_chroma_val[DC_CHROMA_CODES]  = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_chroma_bits[17] = { 0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77 };
-static uint8 s_ac_chroma_val[AC_CHROMA_CODES] =
-{
-  0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91,0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,
-  0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26,0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,
-  0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87,
-  0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,
-  0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
-  0xf9,0xfa
-};
-
-// Low-level helper functions.
-template <class T> inline void clear_obj(T &obj) { memset(&obj, 0, sizeof(obj)); }
-
-const int YR = 19595, YG = 38470, YB = 7471, CB_R = -11059, CB_G = -21709, CB_B = 32768, CR_R = 32768, CR_G = -27439, CR_B = -5329;
-static inline uint8 clamp(int i) { if (static_cast<uint>(i) > 255U) { if (i < 0) i = 0; else if (i > 255) i = 255; } return static_cast<uint8>(i); }
-
-static void RGB_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst += 3, pSrc += 3, num_pixels--)
-  {
-    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
-    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
-    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
-  }
-}
-
-static void RGB_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst++, pSrc += 3, num_pixels--)
-    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void RGBA_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst += 3, pSrc += 4, num_pixels--)
-  {
-    const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
-    pDst[0] = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-    pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
-    pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
-  }
-}
-
-static void RGBA_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
-  for ( ; num_pixels; pDst++, pSrc += 4, num_pixels--)
-    pDst[0] = static_cast<uint8>((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void Y_to_YCC(uint8* pDst, const uint8* pSrc, int num_pixels)
-{
-  for( ; num_pixels; pDst += 3, pSrc++, num_pixels--) { pDst[0] = pSrc[0]; pDst[1] = 128; pDst[2] = 128; }
-}
-
-// Forward DCT - DCT derived from jfdctint.
-#define CONST_BITS  13
-#define ROW_BITS    2
-#define DCT_DESCALE(x, n) (((x) + (((int32)1) << ((n) - 1))) >> (n))
-#define DCT_MUL(var, c) (static_cast<int16>(var) * static_cast<int32>(c))
-#define DCT1D(s0, s1, s2, s3, s4, s5, s6, s7) \
-  int32 t0 = s0 + s7, t7 = s0 - s7, t1 = s1 + s6, t6 = s1 - s6, t2 = s2 + s5, t5 = s2 - s5, t3 = s3 + s4, t4 = s3 - s4; \
-  int32 t10 = t0 + t3, t13 = t0 - t3, t11 = t1 + t2, t12 = t1 - t2; \
-  int32 u1 = DCT_MUL(t12 + t13, 4433); \
-  s2 = u1 + DCT_MUL(t13, 6270); \
-  s6 = u1 + DCT_MUL(t12, -15137); \
-  u1 = t4 + t7; \
-  int32 u2 = t5 + t6, u3 = t4 + t6, u4 = t5 + t7; \
-  int32 z5 = DCT_MUL(u3 + u4, 9633); \
-  t4 = DCT_MUL(t4, 2446); t5 = DCT_MUL(t5, 16819); \
-  t6 = DCT_MUL(t6, 25172); t7 = DCT_MUL(t7, 12299); \
-  u1 = DCT_MUL(u1, -7373); u2 = DCT_MUL(u2, -20995); \
-  u3 = DCT_MUL(u3, -16069); u4 = DCT_MUL(u4, -3196); \
-  u3 += z5; u4 += z5; \
-  s0 = t10 + t11; s1 = t7 + u1 + u4; s3 = t6 + u2 + u3; s4 = t10 - t11; s5 = t5 + u2 + u4; s7 = t4 + u1 + u3;
-
-static void DCT2D(int32 *p)
-{
-  int32 c, *q = p;
-  for (c = 7; c >= 0; c--, q += 8)
-  {
-    int32 s0 = q[0], s1 = q[1], s2 = q[2], s3 = q[3], s4 = q[4], s5 = q[5], s6 = q[6], s7 = q[7];
-    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
-    q[0] = s0 << ROW_BITS; q[1] = DCT_DESCALE(s1, CONST_BITS-ROW_BITS); q[2] = DCT_DESCALE(s2, CONST_BITS-ROW_BITS); q[3] = DCT_DESCALE(s3, CONST_BITS-ROW_BITS);
-    q[4] = s4 << ROW_BITS; q[5] = DCT_DESCALE(s5, CONST_BITS-ROW_BITS); q[6] = DCT_DESCALE(s6, CONST_BITS-ROW_BITS); q[7] = DCT_DESCALE(s7, CONST_BITS-ROW_BITS);
-  }
-  for (q = p, c = 7; c >= 0; c--, q++)
-  {
-    int32 s0 = q[0*8], s1 = q[1*8], s2 = q[2*8], s3 = q[3*8], s4 = q[4*8], s5 = q[5*8], s6 = q[6*8], s7 = q[7*8];
-    DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
-    q[0*8] = DCT_DESCALE(s0, ROW_BITS+3); q[1*8] = DCT_DESCALE(s1, CONST_BITS+ROW_BITS+3); q[2*8] = DCT_DESCALE(s2, CONST_BITS+ROW_BITS+3); q[3*8] = DCT_DESCALE(s3, CONST_BITS+ROW_BITS+3);
-    q[4*8] = DCT_DESCALE(s4, ROW_BITS+3); q[5*8] = DCT_DESCALE(s5, CONST_BITS+ROW_BITS+3); q[6*8] = DCT_DESCALE(s6, CONST_BITS+ROW_BITS+3); q[7*8] = DCT_DESCALE(s7, CONST_BITS+ROW_BITS+3);
-  }
-}
-
-struct sym_freq { uint m_key, m_sym_index; };
-
-// Radix sorts sym_freq[] array by 32-bit key m_key. Returns ptr to sorted values.
-static inline sym_freq* radix_sort_syms(uint num_syms, sym_freq* pSyms0, sym_freq* pSyms1)
-{
-  const uint cMaxPasses = 4;
-  uint32 hist[256 * cMaxPasses]; clear_obj(hist);
-  for (uint i = 0; i < num_syms; i++) { uint freq = pSyms0[i].m_key; hist[freq & 0xFF]++; hist[256 + ((freq >> 8) & 0xFF)]++; hist[256*2 + ((freq >> 16) & 0xFF)]++; hist[256*3 + ((freq >> 24) & 0xFF)]++; }
-  sym_freq* pCur_syms = pSyms0, *pNew_syms = pSyms1;
-  uint total_passes = cMaxPasses; while ((total_passes > 1) && (num_syms == hist[(total_passes - 1) * 256])) total_passes--;
-  for (uint pass_shift = 0, pass = 0; pass < total_passes; pass++, pass_shift += 8)
-  {
-    const uint32* pHist = &hist[pass << 8];
-    uint offsets[256], cur_ofs = 0;
-    for (uint i = 0; i < 256; i++) { offsets[i] = cur_ofs; cur_ofs += pHist[i]; }
-    for (uint i = 0; i < num_syms; i++)
-      pNew_syms[offsets[(pCur_syms[i].m_key >> pass_shift) & 0xFF]++] = pCur_syms[i];
-    sym_freq* t = pCur_syms; pCur_syms = pNew_syms; pNew_syms = t;
-  }
-  return pCur_syms;
-}
-
-// calculate_minimum_redundancy() originally written by: Alistair Moffat, alistair@cs.mu.oz.au, Jyrki Katajainen, jyrki@diku.dk, November 1996.
-static void calculate_minimum_redundancy(sym_freq *A, int n)
-{
-  int root, leaf, next, avbl, used, dpth;
-  if (n==0) return; else if (n==1) { A[0].m_key = 1; return; }
-  A[0].m_key += A[1].m_key; root = 0; leaf = 2;
-  for (next=1; next < n-1; next++)
-  {
-    if (leaf>=n || A[root].m_key<A[leaf].m_key) { A[next].m_key = A[root].m_key; A[root++].m_key = next; } else A[next].m_key = A[leaf++].m_key;
-    if (leaf>=n || (root<next && A[root].m_key<A[leaf].m_key)) { A[next].m_key += A[root].m_key; A[root++].m_key = next; } else A[next].m_key += A[leaf++].m_key;
-  }
-  A[n-2].m_key = 0;
-  for (next=n-3; next>=0; next--) A[next].m_key = A[A[next].m_key].m_key+1;
-  avbl = 1; used = dpth = 0; root = n-2; next = n-1;
-  while (avbl>0)
-  {
-    while (root>=0 && (int)A[root].m_key==dpth) { used++; root--; }
-    while (avbl>used) { A[next--].m_key = dpth; avbl--; }
-    avbl = 2*used; dpth++; used = 0;
-  }
-}
-
-// Limits canonical Huffman code table's max code size to max_code_size.
-static void huffman_enforce_max_code_size(int *pNum_codes, int code_list_len, int max_code_size)
-{
-  if (code_list_len <= 1) return;
-
-  for (int i = max_code_size + 1; i <= MAX_HUFF_CODESIZE; i++) pNum_codes[max_code_size] += pNum_codes[i];
-
-  uint32 total = 0;
-  for (int i = max_code_size; i > 0; i--)
-    total += (((uint32)pNum_codes[i]) << (max_code_size - i));
-
-  while (total != (1UL << max_code_size))
-  {
-    pNum_codes[max_code_size]--;
-    for (int i = max_code_size - 1; i > 0; i--)
-    {
-      if (pNum_codes[i]) { pNum_codes[i]--; pNum_codes[i + 1] += 2; break; }
-    }
-    total--;
-  }
-}
-
-// Generates an optimized offman table.
-void jpeg_encoder::optimize_huffman_table(int table_num, int table_len)
-{
-  sym_freq syms0[MAX_HUFF_SYMBOLS], syms1[MAX_HUFF_SYMBOLS];
-  syms0[0].m_key = 1; syms0[0].m_sym_index = 0;  // dummy symbol, assures that no valid code contains all 1's
-  int num_used_syms = 1;
-  const uint32 *pSym_count = &m_huff_count[table_num][0];
-  for (int i = 0; i < table_len; i++)
-    if (pSym_count[i]) { syms0[num_used_syms].m_key = pSym_count[i]; syms0[num_used_syms++].m_sym_index = i + 1; }
-  sym_freq* pSyms = radix_sort_syms(num_used_syms, syms0, syms1);
-  calculate_minimum_redundancy(pSyms, num_used_syms);
-
-  // Count the # of symbols of each code size.
-  int num_codes[1 + MAX_HUFF_CODESIZE]; clear_obj(num_codes);
-  for (int i = 0; i < num_used_syms; i++)
-    num_codes[pSyms[i].m_key]++;
-
-  const uint JPGE_CODE_SIZE_LIMIT = 16; // the maximum possible size of a JPEG Huffman code (valid range is [9,16] - 9 vs. 8 because of the dummy symbol)
-  huffman_enforce_max_code_size(num_codes, num_used_syms, JPGE_CODE_SIZE_LIMIT);
-
-  // Compute m_huff_bits array, which contains the # of symbols per code size.
-  clear_obj(m_huff_bits[table_num]);
-  for (int i = 1; i <= (int)JPGE_CODE_SIZE_LIMIT; i++)
-    m_huff_bits[table_num][i] = static_cast<uint8>(num_codes[i]);
-
-  // Remove the dummy symbol added above, which must be in largest bucket.
-  for (int i = JPGE_CODE_SIZE_LIMIT; i >= 1; i--)
-  {
-    if (m_huff_bits[table_num][i]) { m_huff_bits[table_num][i]--; break; }
-  }
-
-  // Compute the m_huff_val array, which contains the symbol indices sorted by code size (smallest to largest).
-  for (int i = num_used_syms - 1; i >= 1; i--)
-    m_huff_val[table_num][num_used_syms - 1 - i] = static_cast<uint8>(pSyms[i].m_sym_index - 1);
-}
-
-// JPEG marker generation.
-void jpeg_encoder::emit_byte(uint8 i)
-{
-  m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_obj(i);
-}
-
-void jpeg_encoder::emit_word(uint i)
-{
-  emit_byte(uint8(i >> 8)); emit_byte(uint8(i & 0xFF));
-}
-
-void jpeg_encoder::emit_marker(int marker)
-{
-  emit_byte(uint8(0xFF)); emit_byte(uint8(marker));
-}
-
-// Emit JFIF marker
-void jpeg_encoder::emit_jfif_app0()
-{
-  emit_marker(M_APP0);
-  emit_word(2 + 4 + 1 + 2 + 1 + 2 + 2 + 1 + 1);
-  emit_byte(0x4A); emit_byte(0x46); emit_byte(0x49); emit_byte(0x46); /* Identifier: ASCII "JFIF" */
-  emit_byte(0);
-  emit_byte(1);      /* Major version */
-  emit_byte(1);      /* Minor version */
-  emit_byte(0);      /* Density unit */
-  emit_word(1);
-  emit_word(1);
-  emit_byte(0);      /* No thumbnail image */
-  emit_byte(0);
-}
-
-// Emit quantization tables
-void jpeg_encoder::emit_dqt()
-{
-  for (int i = 0; i < ((m_num_components == 3) ? 2 : 1); i++)
-  {
-    emit_marker(M_DQT);
-    emit_word(64 + 1 + 2);
-    emit_byte(static_cast<uint8>(i));
-    for (int j = 0; j < 64; j++)
-      emit_byte(static_cast<uint8>(m_quantization_tables[i][j]));
-  }
-}
-
-// Emit start of frame marker
-void jpeg_encoder::emit_sof()
-{
-  emit_marker(M_SOF0);                           /* baseline */
-  emit_word(3 * m_num_components + 2 + 5 + 1);
-  emit_byte(8);                                  /* precision */
-  emit_word(m_image_y);
-  emit_word(m_image_x);
-  emit_byte(m_num_components);
-  for (int i = 0; i < m_num_components; i++)
-  {
-    emit_byte(static_cast<uint8>(i + 1));                                   /* component ID     */
-    emit_byte((m_comp_h_samp[i] << 4) + m_comp_v_samp[i]);  /* h and v sampling */
-    emit_byte(i > 0);                                   /* quant. table num */
-  }
-}
-
-// Emit Huffman table.
-void jpeg_encoder::emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag)
-{
-  emit_marker(M_DHT);
-
-  int length = 0;
-  for (int i = 1; i <= 16; i++)
-    length += bits[i];
-
-  emit_word(length + 2 + 1 + 16);
-  emit_byte(static_cast<uint8>(index + (ac_flag << 4)));
-
-  for (int i = 1; i <= 16; i++)
-    emit_byte(bits[i]);
-
-  for (int i = 0; i < length; i++)
-    emit_byte(val[i]);
-}
-
-// Emit all Huffman tables.
-void jpeg_encoder::emit_dhts()
-{
-  emit_dht(m_huff_bits[0+0], m_huff_val[0+0], 0, false);
-  emit_dht(m_huff_bits[2+0], m_huff_val[2+0], 0, true);
-  if (m_num_components == 3)
-  {
-    emit_dht(m_huff_bits[0+1], m_huff_val[0+1], 1, false);
-    emit_dht(m_huff_bits[2+1], m_huff_val[2+1], 1, true);
-  }
-}
-
-// emit start of scan
-void jpeg_encoder::emit_sos()
-{
-  emit_marker(M_SOS);
-  emit_word(2 * m_num_components + 2 + 1 + 3);
-  emit_byte(m_num_components);
-  for (int i = 0; i < m_num_components; i++)
-  {
-    emit_byte(static_cast<uint8>(i + 1));
-    if (i == 0)
-      emit_byte((0 << 4) + 0);
-    else
-      emit_byte((1 << 4) + 1);
-  }
-  emit_byte(0);     /* spectral selection */
-  emit_byte(63);
-  emit_byte(0);
-}
-
-// Emit all markers at beginning of image file.
-void jpeg_encoder::emit_markers()
-{
-  emit_marker(M_SOI);
-  emit_jfif_app0();
-  emit_dqt();
-  emit_sof();
-  emit_dhts();
-  emit_sos();
-}
-
-// Compute the actual canonical Huffman codes/code sizes given the JPEG huff bits and val arrays.
-void jpeg_encoder::compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val)
-{
-  int i, l, last_p, si;
-  uint8 huff_size[257];
-  uint huff_code[257];
-  uint code;
-
-  int p = 0;
-  for (l = 1; l <= 16; l++)
-    for (i = 1; i <= bits[l]; i++)
-      huff_size[p++] = (char)l;
-
-  huff_size[p] = 0; last_p = p; // write sentinel
-
-  code = 0; si = huff_size[0]; p = 0;
-
-  while (huff_size[p])
-  {
-    while (huff_size[p] == si)
-      huff_code[p++] = code++;
-    code <<= 1;
-    si++;
-  }
-
-  memset(codes, 0, sizeof(codes[0])*256);
-  memset(code_sizes, 0, sizeof(code_sizes[0])*256);
-  for (p = 0; p < last_p; p++)
-  {
-    codes[val[p]]      = huff_code[p];
-    code_sizes[val[p]] = huff_size[p];
-  }
-}
-
-// Quantization table generation.
-void jpeg_encoder::compute_quant_table(int32 *pDst, int16 *pSrc)
-{
-  int32 q;
-  if (m_params.m_quality < 50)
-    q = 5000 / m_params.m_quality;
-  else
-    q = 200 - m_params.m_quality * 2;
-  for (int i = 0; i < 64; i++)
-  {
-    int32 j = *pSrc++; j = (j * q + 50L) / 100L;
-    *pDst++ = JPGE_MIN(JPGE_MAX(j, 1), 255);
-  }
-}
-
-// Higher-level methods.
-void jpeg_encoder::first_pass_init()
-{
-  m_bit_buffer = 0; m_bits_in = 0;
-  memset(m_last_dc_val, 0, 3 * sizeof(m_last_dc_val[0]));
-  m_mcu_y_ofs = 0;
-  m_pass_num = 1;
-}
-
-bool jpeg_encoder::second_pass_init()
-{
-  compute_huffman_table(&m_huff_codes[0+0][0], &m_huff_code_sizes[0+0][0], m_huff_bits[0+0], m_huff_val[0+0]);
-  compute_huffman_table(&m_huff_codes[2+0][0], &m_huff_code_sizes[2+0][0], m_huff_bits[2+0], m_huff_val[2+0]);
-  if (m_num_components > 1)
-  {
-    compute_huffman_table(&m_huff_codes[0+1][0], &m_huff_code_sizes[0+1][0], m_huff_bits[0+1], m_huff_val[0+1]);
-    compute_huffman_table(&m_huff_codes[2+1][0], &m_huff_code_sizes[2+1][0], m_huff_bits[2+1], m_huff_val[2+1]);
-  }
-  first_pass_init();
-  emit_markers();
-  m_pass_num = 2;
-  return true;
-}
-
-bool jpeg_encoder::jpg_open(int p_x_res, int p_y_res, int src_channels)
-{
-  m_num_components = 3;
-  switch (m_params.m_subsampling)
-  {
-    case Y_ONLY:
-    {
-      m_num_components = 1;
-      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
-      m_mcu_x          = 8; m_mcu_y          = 8;
-      break;
-    }
-    case H1V1:
-    {
-      m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 8; m_mcu_y          = 8;
-      break;
-    }
-    case H2V1:
-    {
-      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 1;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 16; m_mcu_y         = 8;
-      break;
-    }
-    case H2V2:
-    {
-      m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 2;
-      m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
-      m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
-      m_mcu_x          = 16; m_mcu_y         = 16;
-    }
-  }
-
-  m_image_x        = p_x_res; m_image_y = p_y_res;
-  m_image_bpp      = src_channels;
-  m_image_bpl      = m_image_x * src_channels;
-  m_image_x_mcu    = (m_image_x + m_mcu_x - 1) & (~(m_mcu_x - 1));
-  m_image_y_mcu    = (m_image_y + m_mcu_y - 1) & (~(m_mcu_y - 1));
-  m_image_bpl_xlt  = m_image_x * m_num_components;
-  m_image_bpl_mcu  = m_image_x_mcu * m_num_components;
-  m_mcus_per_row   = m_image_x_mcu / m_mcu_x;
-
-  if ((m_mcu_lines[0] = static_cast<uint8*>(jpge_malloc(m_image_bpl_mcu * m_mcu_y))) == NULL) return false;
-  for (int i = 1; i < m_mcu_y; i++)
-    m_mcu_lines[i] = m_mcu_lines[i-1] + m_image_bpl_mcu;
-
-  compute_quant_table(m_quantization_tables[0], s_std_lum_quant);
-  compute_quant_table(m_quantization_tables[1], m_params.m_no_chroma_discrim_flag ? s_std_lum_quant : s_std_croma_quant);
-
-  m_out_buf_left = JPGE_OUT_BUF_SIZE;
-  m_pOut_buf = m_out_buf;
-
-  if (m_params.m_two_pass_flag)
-  {
-    clear_obj(m_huff_count);
-    first_pass_init();
-  }
-  else
-  {
-    memcpy(m_huff_bits[0+0], s_dc_lum_bits, 17);    memcpy(m_huff_val [0+0], s_dc_lum_val, DC_LUM_CODES);
-    memcpy(m_huff_bits[2+0], s_ac_lum_bits, 17);    memcpy(m_huff_val [2+0], s_ac_lum_val, AC_LUM_CODES);
-    memcpy(m_huff_bits[0+1], s_dc_chroma_bits, 17); memcpy(m_huff_val [0+1], s_dc_chroma_val, DC_CHROMA_CODES);
-    memcpy(m_huff_bits[2+1], s_ac_chroma_bits, 17); memcpy(m_huff_val [2+1], s_ac_chroma_val, AC_CHROMA_CODES);
-    if (!second_pass_init()) return false;   // in effect, skip over the first pass
-  }
-  return m_all_stream_writes_succeeded;
-}
-
-void jpeg_encoder::load_block_8_8_grey(int x)
-{
-  uint8 *pSrc;
-  sample_array_t *pDst = m_sample_array;
-  x <<= 3;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc = m_mcu_lines[i] + x;
-    pDst[0] = pSrc[0] - 128; pDst[1] = pSrc[1] - 128; pDst[2] = pSrc[2] - 128; pDst[3] = pSrc[3] - 128;
-    pDst[4] = pSrc[4] - 128; pDst[5] = pSrc[5] - 128; pDst[6] = pSrc[6] - 128; pDst[7] = pSrc[7] - 128;
-  }
-}
-
-void jpeg_encoder::load_block_8_8(int x, int y, int c)
-{
-  uint8 *pSrc;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (8 * 3)) + c;
-  y <<= 3;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc = m_mcu_lines[y + i] + x;
-    pDst[0] = pSrc[0 * 3] - 128; pDst[1] = pSrc[1 * 3] - 128; pDst[2] = pSrc[2 * 3] - 128; pDst[3] = pSrc[3 * 3] - 128;
-    pDst[4] = pSrc[4 * 3] - 128; pDst[5] = pSrc[5 * 3] - 128; pDst[6] = pSrc[6 * 3] - 128; pDst[7] = pSrc[7 * 3] - 128;
-  }
-}
-
-void jpeg_encoder::load_block_16_8(int x, int c)
-{
-  uint8 *pSrc1, *pSrc2;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (16 * 3)) + c;
-  int a = 0, b = 2;
-  for (int i = 0; i < 16; i += 2, pDst += 8)
-  {
-    pSrc1 = m_mcu_lines[i + 0] + x;
-    pSrc2 = m_mcu_lines[i + 1] + x;
-    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3] + pSrc2[ 0 * 3] + pSrc2[ 1 * 3] + a) >> 2) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3] + pSrc2[ 2 * 3] + pSrc2[ 3 * 3] + b) >> 2) - 128;
-    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3] + pSrc2[ 4 * 3] + pSrc2[ 5 * 3] + a) >> 2) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3] + pSrc2[ 6 * 3] + pSrc2[ 7 * 3] + b) >> 2) - 128;
-    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3] + pSrc2[ 8 * 3] + pSrc2[ 9 * 3] + a) >> 2) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3] + pSrc2[10 * 3] + pSrc2[11 * 3] + b) >> 2) - 128;
-    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3] + pSrc2[12 * 3] + pSrc2[13 * 3] + a) >> 2) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3] + pSrc2[14 * 3] + pSrc2[15 * 3] + b) >> 2) - 128;
-    int temp = a; a = b; b = temp;
-  }
-}
-
-void jpeg_encoder::load_block_16_8_8(int x, int c)
-{
-  uint8 *pSrc1;
-  sample_array_t *pDst = m_sample_array;
-  x = (x * (16 * 3)) + c;
-  for (int i = 0; i < 8; i++, pDst += 8)
-  {
-    pSrc1 = m_mcu_lines[i + 0] + x;
-    pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3]) >> 1) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3]) >> 1) - 128;
-    pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3]) >> 1) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3]) >> 1) - 128;
-    pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3]) >> 1) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3]) >> 1) - 128;
-    pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3]) >> 1) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3]) >> 1) - 128;
-  }
-}
-
-void jpeg_encoder::load_quantized_coefficients(int component_num)
-{
-  int32 *q = m_quantization_tables[component_num > 0];
-  int16 *pDst = m_coefficient_array;
-  for (int i = 0; i < 64; i++)
-  {
-    sample_array_t j = m_sample_array[s_zag[i]];
-    if (j < 0)
-    {
-      if ((j = -j + (*q >> 1)) < *q)
-        *pDst++ = 0;
-      else
-        *pDst++ = static_cast<int16>(-(j / *q));
-    }
-    else
-    {
-      if ((j = j + (*q >> 1)) < *q)
-        *pDst++ = 0;
-      else
-        *pDst++ = static_cast<int16>((j / *q));
-    }
-    q++;
-  }
-}
-
-void jpeg_encoder::flush_output_buffer()
-{
-  if (m_out_buf_left != JPGE_OUT_BUF_SIZE)
-    m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_buf(m_out_buf, JPGE_OUT_BUF_SIZE - m_out_buf_left);
-  m_pOut_buf = m_out_buf;
-  m_out_buf_left = JPGE_OUT_BUF_SIZE;
-}
-
-void jpeg_encoder::put_bits(uint bits, uint len)
-{
-  m_bit_buffer |= ((uint32)bits << (24 - (m_bits_in += len)));
-  while (m_bits_in >= 8)
-  {
-    uint8 c;
-    #define JPGE_PUT_BYTE(c) { *m_pOut_buf++ = (c); if (--m_out_buf_left == 0) flush_output_buffer(); }
-    JPGE_PUT_BYTE(c = (uint8)((m_bit_buffer >> 16) & 0xFF));
-    if (c == 0xFF) JPGE_PUT_BYTE(0);
-    m_bit_buffer <<= 8;
-    m_bits_in -= 8;
-  }
-}
-
-void jpeg_encoder::code_coefficients_pass_one(int component_num)
-{
-  if (component_num >= 3) return; // just to shut up static analysis
-  int i, run_len, nbits, temp1;
-  int16 *src = m_coefficient_array;
-  uint32 *dc_count = component_num ? m_huff_count[0 + 1] : m_huff_count[0 + 0], *ac_count = component_num ? m_huff_count[2 + 1] : m_huff_count[2 + 0];
-
-  temp1 = src[0] - m_last_dc_val[component_num];
-  m_last_dc_val[component_num] = src[0];
-  if (temp1 < 0) temp1 = -temp1;
-
-  nbits = 0;
-  while (temp1)
-  {
-    nbits++; temp1 >>= 1;
-  }
-
-  dc_count[nbits]++;
-  for (run_len = 0, i = 1; i < 64; i++)
-  {
-    if ((temp1 = m_coefficient_array[i]) == 0)
-      run_len++;
-    else
-    {
-      while (run_len >= 16)
-      {
-        ac_count[0xF0]++;
-        run_len -= 16;
-      }
-      if (temp1 < 0) temp1 = -temp1;
-      nbits = 1;
-      while (temp1 >>= 1) nbits++;
-      ac_count[(run_len << 4) + nbits]++;
-      run_len = 0;
-    }
-  }
-  if (run_len) ac_count[0]++;
-}
-
-void jpeg_encoder::code_coefficients_pass_two(int component_num)
-{
-  int i, j, run_len, nbits, temp1, temp2;
-  int16 *pSrc = m_coefficient_array;
-  uint *codes[2];
-  uint8 *code_sizes[2];
-
-  if (component_num == 0)
-  {
-    codes[0] = m_huff_codes[0 + 0]; codes[1] = m_huff_codes[2 + 0];
-    code_sizes[0] = m_huff_code_sizes[0 + 0]; code_sizes[1] = m_huff_code_sizes[2 + 0];
-  }
-  else
-  {
-    codes[0] = m_huff_codes[0 + 1]; codes[1] = m_huff_codes[2 + 1];
-    code_sizes[0] = m_huff_code_sizes[0 + 1]; code_sizes[1] = m_huff_code_sizes[2 + 1];
-  }
-
-  temp1 = temp2 = pSrc[0] - m_last_dc_val[component_num];
-  m_last_dc_val[component_num] = pSrc[0];
-
-  if (temp1 < 0)
-  {
-    temp1 = -temp1; temp2--;
-  }
-
-  nbits = 0;
-  while (temp1)
-  {
-    nbits++; temp1 >>= 1;
-  }
-
-  put_bits(codes[0][nbits], code_sizes[0][nbits]);
-  if (nbits) put_bits(temp2 & ((1 << nbits) - 1), nbits);
-
-  for (run_len = 0, i = 1; i < 64; i++)
-  {
-    if ((temp1 = m_coefficient_array[i]) == 0)
-      run_len++;
-    else
-    {
-      while (run_len >= 16)
-      {
-        put_bits(codes[1][0xF0], code_sizes[1][0xF0]);
-        run_len -= 16;
-      }
-      if ((temp2 = temp1) < 0)
-      {
-        temp1 = -temp1;
-        temp2--;
-      }
-      nbits = 1;
-      while (temp1 >>= 1)
-        nbits++;
-      j = (run_len << 4) + nbits;
-      put_bits(codes[1][j], code_sizes[1][j]);
-      put_bits(temp2 & ((1 << nbits) - 1), nbits);
-      run_len = 0;
-    }
-  }
-  if (run_len)
-    put_bits(codes[1][0], code_sizes[1][0]);
-}
-
-void jpeg_encoder::code_block(int component_num)
-{
-  DCT2D(m_sample_array);
-  load_quantized_coefficients(component_num);
-  if (m_pass_num == 1)
-    code_coefficients_pass_one(component_num);
-  else
-    code_coefficients_pass_two(component_num);
-}
-
-void jpeg_encoder::process_mcu_row()
-{
-  if (m_num_components == 1)
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8_grey(i); code_block(0);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i, 0, 0); code_block(0); load_block_8_8(i, 0, 1); code_block(1); load_block_8_8(i, 0, 2); code_block(2);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
-      load_block_16_8_8(i, 1); code_block(1); load_block_16_8_8(i, 2); code_block(2);
-    }
-  }
-  else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))
-  {
-    for (int i = 0; i < m_mcus_per_row; i++)
-    {
-      load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
-      load_block_8_8(i * 2 + 0, 1, 0); code_block(0); load_block_8_8(i * 2 + 1, 1, 0); code_block(0);
-      load_block_16_8(i, 1); code_block(1); load_block_16_8(i, 2); code_block(2);
-    }
-  }
-}
-
-bool jpeg_encoder::terminate_pass_one()
-{
-  optimize_huffman_table(0+0, DC_LUM_CODES); optimize_huffman_table(2+0, AC_LUM_CODES);
-  if (m_num_components > 1)
-  {
-    optimize_huffman_table(0+1, DC_CHROMA_CODES); optimize_huffman_table(2+1, AC_CHROMA_CODES);
-  }
-  return second_pass_init();
-}
-
-bool jpeg_encoder::terminate_pass_two()
-{
-  put_bits(0x7F, 7);
-  flush_output_buffer();
-  emit_marker(M_EOI);
-  m_pass_num++; // purposely bump up m_pass_num, for debugging
-  return true;
-}
-
-bool jpeg_encoder::process_end_of_image()
-{
-  if (m_mcu_y_ofs)
-  {
-    if (m_mcu_y_ofs < 16) // check here just to shut up static analysis
-    {
-      for (int i = m_mcu_y_ofs; i < m_mcu_y; i++)
-        memcpy(m_mcu_lines[i], m_mcu_lines[m_mcu_y_ofs - 1], m_image_bpl_mcu);
-    }
-
-    process_mcu_row();
-  }
-
-  if (m_pass_num == 1)
-    return terminate_pass_one();
-  else
-    return terminate_pass_two();
-}
-
-void jpeg_encoder::load_mcu(const void *pSrc)
-{
-  const uint8* Psrc = reinterpret_cast<const uint8*>(pSrc);
-
-  uint8* pDst = m_mcu_lines[m_mcu_y_ofs]; // OK to write up to m_image_bpl_xlt bytes to pDst
-
-  if (m_num_components == 1)
-  {
-    if (m_image_bpp == 4)
-      RGBA_to_Y(pDst, Psrc, m_image_x);
-    else if (m_image_bpp == 3)
-      RGB_to_Y(pDst, Psrc, m_image_x);
-    else
-      memcpy(pDst, Psrc, m_image_x);
-  }
-  else
-  {
-    if (m_image_bpp == 4)
-      RGBA_to_YCC(pDst, Psrc, m_image_x);
-    else if (m_image_bpp == 3)
-      RGB_to_YCC(pDst, Psrc, m_image_x);
-    else
-      Y_to_YCC(pDst, Psrc, m_image_x);
-  }
-
-  // Possibly duplicate pixels at end of scanline if not a multiple of 8 or 16
-  if (m_num_components == 1)
-    memset(m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt, pDst[m_image_bpl_xlt - 1], m_image_x_mcu - m_image_x);
-  else
-  {
-    const uint8 y = pDst[m_image_bpl_xlt - 3 + 0], cb = pDst[m_image_bpl_xlt - 3 + 1], cr = pDst[m_image_bpl_xlt - 3 + 2];
-    uint8 *q = m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt;
-    for (int i = m_image_x; i < m_image_x_mcu; i++)
-    {
-      *q++ = y; *q++ = cb; *q++ = cr;
-    }
-  }
-
-  if (++m_mcu_y_ofs == m_mcu_y)
-  {
-    process_mcu_row();
-    m_mcu_y_ofs = 0;
-  }
-}
-
-void jpeg_encoder::clear()
-{
-  m_mcu_lines[0] = NULL;
-  m_pass_num = 0;
-  m_all_stream_writes_succeeded = true;
-}
-
-jpeg_encoder::jpeg_encoder()
-{
-  clear();
-}
-
-jpeg_encoder::~jpeg_encoder()
-{
-  deinit();
-}
-
-bool jpeg_encoder::init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params)
-{
-  deinit();
-  if (((!pStream) || (width < 1) || (height < 1)) || ((src_channels != 1) && (src_channels != 3) && (src_channels != 4)) || (!comp_params.check_valid())) return false;
-  m_pStream = pStream;
-  m_params = comp_params;
-  return jpg_open(width, height, src_channels);
-}
-
-void jpeg_encoder::deinit()
-{
-  jpge_free(m_mcu_lines[0]);
-  clear();
-}
-
-bool jpeg_encoder::process_scanline(const void* pScanline)
-{
-  if ((m_pass_num < 1) || (m_pass_num > 2)) return false;
-  if (m_all_stream_writes_succeeded)
-  {
-    if (!pScanline)
-    {
-      if (!process_end_of_image()) return false;
-    }
-    else
-    {
-      load_mcu(pScanline);
-    }
-  }
-  return m_all_stream_writes_succeeded;
-}
-
-// Higher level wrappers/examples (optional).
-#include <stdio.h>
-
-class cfile_stream : public output_stream
-{
-   cfile_stream(const cfile_stream &);
-   cfile_stream &operator= (const cfile_stream &);
-
-   FILE* m_pFile;
-   bool m_bStatus;
-
-public:
-   cfile_stream() : m_pFile(NULL), m_bStatus(false) { }
-
-   virtual ~cfile_stream()
-   {
-      close();
-   }
-
-   bool open(const char *pFilename)
-   {
-      close();
-#if defined(_MSC_VER)
-      if (fopen_s(&m_pFile, pFilename, "wb") != 0)
-	  {
-		  return false;
-	  }
-#else
-      m_pFile = fopen(pFilename, "wb");
-#endif
-      m_bStatus = (m_pFile != NULL);
-      return m_bStatus;
-   }
-
-   bool close()
-   {
-      if (m_pFile)
-      {
-         if (fclose(m_pFile) == EOF)
-         {
-            m_bStatus = false;
-         }
-         m_pFile = NULL;
-      }
-      return m_bStatus;
-   }
-
-   virtual bool put_buf(const void* pBuf, int64_t len)
-   {
-      m_bStatus = m_bStatus && (fwrite(pBuf, len, 1, m_pFile) == 1);
-      return m_bStatus;
-   }
-
-   uint get_size() const
-   {
-      return m_pFile ? ftell(m_pFile) : 0;
-   }
-};
-
-// Writes JPEG image to file.
-bool compress_image_to_jpeg_file(const char *pFilename, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
-  cfile_stream dst_stream;
-  if (!dst_stream.open(pFilename))
-    return false;
-
-  jpge::jpeg_encoder dst_image;
-  if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
-    return false;
-
-  for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
-  {
-    for (int64_t i = 0; i < height; i++)
-    {
-		// i, width, and num_channels are all 64bit
-       const uint8* pBuf = pImage_data + i * width * num_channels;
-       if (!dst_image.process_scanline(pBuf))
-          return false;
-    }
-    if (!dst_image.process_scanline(NULL))
-       return false;
-  }
-
-  dst_image.deinit();
-
-  return dst_stream.close();
-}
-
-class memory_stream : public output_stream
-{
-   memory_stream(const memory_stream &);
-   memory_stream &operator= (const memory_stream &);
-
-   uint8 *m_pBuf;
-   uint64_t m_buf_size, m_buf_ofs;
-
-public:
-   memory_stream(void *pBuf, uint64_t buf_size) : m_pBuf(static_cast<uint8*>(pBuf)), m_buf_size(buf_size), m_buf_ofs(0) { }
-
-   virtual ~memory_stream() { }
-
-   virtual bool put_buf(const void* pBuf, int64_t len)
-   {
-      uint64_t buf_remaining = m_buf_size - m_buf_ofs;
-      if ((uint64_t)len > buf_remaining)
-         return false;
-      memcpy(m_pBuf + m_buf_ofs, pBuf, len);
-      m_buf_ofs += len;
-      return true;
-   }
-
-   uint64_t get_size() const
-   {
-      return m_buf_ofs;
-   }
-};
-
-bool compress_image_to_jpeg_file_in_memory(void *pDstBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
-   if ((!pDstBuf) || (!buf_size))
-      return false;
-
-   memory_stream dst_stream(pDstBuf, buf_size);
-
-   buf_size = 0;
-
-   jpge::jpeg_encoder dst_image;
-   if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
-      return false;
-
-   for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
-   {
-     for (int64_t i = 0; i < height; i++)
-     {
-        const uint8* pScanline = pImage_data + i * width * num_channels;
-        if (!dst_image.process_scanline(pScanline))
-           return false;
-     }
-     if (!dst_image.process_scanline(NULL))
-        return false;
-   }
-
-   dst_image.deinit();
-
-   buf_size = dst_stream.get_size();
-   return true;
-}
-
-} // namespace jpge
\ No newline at end of file
diff --git a/spaces/Flux9665/IMS-Toucan/Preprocessing/papercup_features.py b/spaces/Flux9665/IMS-Toucan/Preprocessing/papercup_features.py
deleted file mode 100644
index eaac7cd8f306bb06290aa2d83a92c2feb75fcc41..0000000000000000000000000000000000000000
--- a/spaces/Flux9665/IMS-Toucan/Preprocessing/papercup_features.py
+++ /dev/null
@@ -1,637 +0,0 @@
-# Derived from  an open-source resource provided by Papercup Technologies Limited
-# Resource-Author: Marlene Staib
-# Modified by Florian Lux, 2021
-
-def generate_feature_lookup():
-    return {
-        '~': {'symbol_type': 'silence'},
-        '#': {'symbol_type': 'end of sentence'},
-        '?': {'symbol_type': 'questionmark'},
-        '!': {'symbol_type': 'exclamationmark'},
-        '.': {'symbol_type': 'fullstop'},
-        'ɜ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'central',
-            'vowel_openness'   : 'open-mid',
-            'vowel_roundedness': 'unrounded',
-            },
-        'ɫ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'alveolar',
-            'consonant_manner': 'lateral-approximant',
-            },
-        'ə': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'central',
-            'vowel_openness'   : 'mid',
-            'vowel_roundedness': 'unrounded',
-            },
-        'ɚ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'central',
-            'vowel_openness'   : 'mid',
-            'vowel_roundedness': 'unrounded',
-            },
-        'a': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'front',
-            'vowel_openness'   : 'open',
-            'vowel_roundedness': 'unrounded',
-            },
-        'ð': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'dental',
-            'consonant_manner': 'fricative'
-            },
-        'ɛ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'front',
-            'vowel_openness'   : 'open-mid',
-            'vowel_roundedness': 'unrounded',
-            },
-        'ɪ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'front_central',
-            'vowel_openness'   : 'close_close-mid',
-            'vowel_roundedness': 'unrounded',
-            },
-        'ᵻ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'central',
-            'vowel_openness'   : 'close',
-            'vowel_roundedness': 'unrounded',
-            },
-        'ŋ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'velar',
-            'consonant_manner': 'nasal'
-            },
-        'ɔ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'back',
-            'vowel_openness'   : 'open-mid',
-            'vowel_roundedness': 'rounded',
-            },
-        'ɒ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'back',
-            'vowel_openness'   : 'open',
-            'vowel_roundedness': 'rounded',
-            },
-        'ɾ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'alveolar',
-            'consonant_manner': 'tap'
-            },
-        'ʃ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'postalveolar',
-            'consonant_manner': 'fricative'
-            },
-        'θ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'dental',
-            'consonant_manner': 'fricative'
-            },
-        'ʊ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'central_back',
-            'vowel_openness'   : 'close_close-mid',
-            'vowel_roundedness': 'unrounded'
-            },
-        'ʌ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'back',
-            'vowel_openness'   : 'open-mid',
-            'vowel_roundedness': 'unrounded'
-            },
-        'ʒ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'postalveolar',
-            'consonant_manner': 'fricative'
-            },
-        'æ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'front',
-            'vowel_openness'   : 'open-mid_open',
-            'vowel_roundedness': 'unrounded'
-            },
-        'b': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'bilabial',
-            'consonant_manner': 'stop'
-            },
-        'ʔ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'glottal',
-            'consonant_manner': 'stop'
-            },
-        'd': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'alveolar',
-            'consonant_manner': 'stop'
-            },
-        'e': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'front',
-            'vowel_openness'   : 'close-mid',
-            'vowel_roundedness': 'unrounded'
-            },
-        'f': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'labiodental',
-            'consonant_manner': 'fricative'
-            },
-        'g': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'velar',
-            'consonant_manner': 'stop'
-            },
-        'h': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'glottal',
-            'consonant_manner': 'fricative'
-            },
-        'i': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'front',
-            'vowel_openness'   : 'close',
-            'vowel_roundedness': 'unrounded'
-            },
-        'j': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'palatal',
-            'consonant_manner': 'approximant'
-            },
-        'k': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'velar',
-            'consonant_manner': 'stop'
-            },
-        'l': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'alveolar',
-            'consonant_manner': 'lateral-approximant'
-            },
-        'm': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'bilabial',
-            'consonant_manner': 'nasal'
-            },
-        'n': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'alveolar',
-            'consonant_manner': 'nasal'
-            },
-        'ɳ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'palatal',
-            'consonant_manner': 'nasal'
-            },
-        'o': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'back',
-            'vowel_openness'   : 'close-mid',
-            'vowel_roundedness': 'rounded'
-            },
-        'p': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'bilabial',
-            'consonant_manner': 'stop'
-            },
-        'ɡ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'velar',
-            'consonant_manner': 'stop'
-            },
-        'ɹ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'alveolar',
-            'consonant_manner': 'approximant'
-            },
-        'r': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'alveolar',
-            'consonant_manner': 'trill'
-            },
-        's': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'alveolar',
-            'consonant_manner': 'fricative'
-            },
-        't': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'alveolar',
-            'consonant_manner': 'stop'
-            },
-        'u': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'back',
-            'vowel_openness'   : 'close',
-            'vowel_roundedness': 'rounded',
-            },
-        'v': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'labiodental',
-            'consonant_manner': 'fricative'
-            },
-        'w': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'labial-velar',
-            'consonant_manner': 'approximant'
-            },
-        'x': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'velar',
-            'consonant_manner': 'fricative'
-            },
-        'z': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'alveolar',
-            'consonant_manner': 'fricative'
-            },
-        'ʀ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'uvular',
-            'consonant_manner': 'trill'
-            },
-        'ø': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'front',
-            'vowel_openness'   : 'close-mid',
-            'vowel_roundedness': 'rounded'
-            },
-        'ç': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'palatal',
-            'consonant_manner': 'fricative'
-            },
-        'ɐ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'central',
-            'vowel_openness'   : 'open',
-            'vowel_roundedness': 'unrounded'
-            },
-        'œ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'front',
-            'vowel_openness'   : 'open-mid',
-            'vowel_roundedness': 'rounded'
-            },
-        'y': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'front',
-            'vowel_openness'   : 'close',
-            'vowel_roundedness': 'rounded'
-            },
-        'ʏ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'front_central',
-            'vowel_openness'   : 'close_close-mid',
-            'vowel_roundedness': 'rounded'
-            },
-        'ɑ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'back',
-            'vowel_openness'   : 'open',
-            'vowel_roundedness': 'unrounded'
-            },
-        'c': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'palatal',
-            'consonant_manner': 'stop'
-            },
-        'ɲ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'palatal',
-            'consonant_manner': 'nasal'
-            },
-        'ɣ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'velar',
-            'consonant_manner': 'fricative'
-            },
-        'ʎ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'palatal',
-            'consonant_manner': 'lateral-approximant'
-            },
-        'β': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'bilabial',
-            'consonant_manner': 'fricative'
-            },
-        'ʝ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'palatal',
-            'consonant_manner': 'fricative'
-            },
-        'ɟ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'palatal',
-            'consonant_manner': 'stop'
-            },
-        'q': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'uvular',
-            'consonant_manner': 'stop'
-            },
-        'ɕ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'alveolopalatal',
-            'consonant_manner': 'fricative'
-            },
-        'ʲ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'palatal',
-            'consonant_manner': 'approximant'
-            },
-        'ɭ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'palatal',  # should be retroflex, but palatal should be close enough
-            'consonant_manner': 'lateral-approximant'
-            },
-        'ɵ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'central',
-            'vowel_openness'   : 'open-mid',
-            'vowel_roundedness': 'rounded'
-            },
-        'ʑ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'alveolopalatal',
-            'consonant_manner': 'fricative'
-            },
-        'ʋ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'labiodental',
-            'consonant_manner': 'approximant'
-            },
-        'ʁ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'voiced',
-            'consonant_place' : 'uvular',
-            'consonant_manner': 'fricative'
-            },
-        'ɨ': {
-            'symbol_type'      : 'phoneme',
-            'vowel_consonant'  : 'vowel',
-            'VUV'              : 'voiced',
-            'vowel_frontness'  : 'central',
-            'vowel_openness'   : 'close',
-            'vowel_roundedness': 'unrounded'
-            },
-        'ʂ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'palatal',  # should be retroflex, but palatal should be close enough
-            'consonant_manner': 'fricative'
-            },
-        'ɬ': {
-            'symbol_type'     : 'phoneme',
-            'vowel_consonant' : 'consonant',
-            'VUV'             : 'unvoiced',
-            'consonant_place' : 'alveolar',  # should be noted it's also lateral, but should be close enough
-            'consonant_manner': 'fricative'
-            },
-        }  # REMEMBER to also add the phonemes added here to the ID lookup table in the TextFrontend as the new highest ID
-
-
-def generate_feature_table():
-    ipa_to_phonemefeats = generate_feature_lookup()
-
-    feat_types = set()
-    for ipa in ipa_to_phonemefeats:
-        if len(ipa) == 1:
-            [feat_types.add(feat) for feat in ipa_to_phonemefeats[ipa].keys()]
-
-    feat_to_val_set = dict()
-    for feat in feat_types:
-        feat_to_val_set[feat] = set()
-    for ipa in ipa_to_phonemefeats:
-        if len(ipa) == 1:
-            for feat in ipa_to_phonemefeats[ipa]:
-                feat_to_val_set[feat].add(ipa_to_phonemefeats[ipa][feat])
-
-    # print(feat_to_val_set)
-
-    value_list = set()
-    for val_set in [feat_to_val_set[feat] for feat in feat_to_val_set]:
-        for value in val_set:
-            value_list.add(value)
-    # print("{")
-    # for index, value in enumerate(list(value_list)):
-    #     print('"{}":{},'.format(value,index))
-    # print("}")
-
-    value_to_index = {
-        "dental"             : 0,
-        "postalveolar"       : 1,
-        "mid"                : 2,
-        "close-mid"          : 3,
-        "vowel"              : 4,
-        "silence"            : 5,
-        "consonant"          : 6,
-        "close"              : 7,
-        "velar"              : 8,
-        "stop"               : 9,
-        "palatal"            : 10,
-        "nasal"              : 11,
-        "glottal"            : 12,
-        "central"            : 13,
-        "back"               : 14,
-        "approximant"        : 15,
-        "uvular"             : 16,
-        "open-mid"           : 17,
-        "front_central"      : 18,
-        "front"              : 19,
-        "end of sentence"    : 20,
-        "labiodental"        : 21,
-        "close_close-mid"    : 22,
-        "labial-velar"       : 23,
-        "unvoiced"           : 24,
-        "central_back"       : 25,
-        "trill"              : 26,
-        "rounded"            : 27,
-        "open-mid_open"      : 28,
-        "tap"                : 29,
-        "alveolar"           : 30,
-        "bilabial"           : 31,
-        "phoneme"            : 32,
-        "open"               : 33,
-        "fricative"          : 34,
-        "unrounded"          : 35,
-        "lateral-approximant": 36,
-        "voiced"             : 37,
-        "questionmark"       : 38,
-        "exclamationmark"    : 39,
-        "fullstop"           : 40,
-        "alveolopalatal"     : 41
-        }
-
-    phone_to_vector = dict()
-    for ipa in ipa_to_phonemefeats:
-        if len(ipa) == 1:
-            phone_to_vector[ipa] = [0] * sum([len(values) for values in [feat_to_val_set[feat] for feat in feat_to_val_set]])
-            for feat in ipa_to_phonemefeats[ipa]:
-                if ipa_to_phonemefeats[ipa][feat] in value_to_index:
-                    phone_to_vector[ipa][value_to_index[ipa_to_phonemefeats[ipa][feat]]] = 1
-
-    for feat in feat_to_val_set:
-        for value in feat_to_val_set[feat]:
-            if value not in value_to_index:
-                print(f"Unknown feature value in featureset! {value}")
-
-    # print(f"{sum([len(values) for values in [feat_to_val_set[feat] for feat in feat_to_val_set]])} should be 42")
-
-    return phone_to_vector
-
-
-def generate_phone_to_id_lookup():
-    ipa_to_phonemefeats = generate_feature_lookup()
-    count = 0
-    phone_to_id = dict()
-    for key in sorted(list(ipa_to_phonemefeats)):  # careful: non-deterministic
-        phone_to_id[key] = count
-        count += 1
-    return phone_to_id
-
-
-if __name__ == '__main__':
-    print(generate_phone_to_id_lookup())
diff --git a/spaces/FourthBrainGenAI/AI-Superstar-Space/app.py b/spaces/FourthBrainGenAI/AI-Superstar-Space/app.py
deleted file mode 100644
index 72a00f62d10dd10b663a793c8e2c8eb5730a4775..0000000000000000000000000000000000000000
--- a/spaces/FourthBrainGenAI/AI-Superstar-Space/app.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import torch
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-peft_model_id = f"c-s-ale/AI-Superstar-Model"
-config = PeftConfig.from_pretrained(peft_model_id)
-model = AutoModelForCausalLM.from_pretrained(
-    config.base_model_name_or_path,
-    return_dict=True,
-    load_in_8bit=True,
-    device_map="auto",
-)
-tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
-
-# Load the Lora model
-model = PeftModel.from_pretrained(model, peft_model_id)
-
-def make_inference(question):
-    batch = tokenizer(
-        f"Please answer the following question to the best of your ability.\n\n### Question:\n{question}\n### Answer:\n",
-        return_tensors="pt",
-    )
-
-    with torch.cuda.amp.autocast():
-        output_tokens = model.generate(**batch, max_new_tokens=70)
-
-    return tokenizer.decode(output_tokens[0], skip_special_tokens=True).split("\n\n")[-1]
-
-
-if __name__ == "__main__":
-    # make a gradio interface
-    import gradio as gr
-
-    gr.Interface(
-        make_inference,
-        [
-            gr.inputs.Textbox(lines=2, label="Question"),
-        ],
-        gr.outputs.Textbox(label="Answer"),
-        title="AI Super Star",
-        description="AI Super Star is a tool you can use to guide you on your ML journey.",
-    ).launch()
\ No newline at end of file
diff --git a/spaces/Francesco/FairytaleDJ/README.md b/spaces/Francesco/FairytaleDJ/README.md
deleted file mode 100644
index 4f07e2797de3410cfd642271a75d4cd10fca8da2..0000000000000000000000000000000000000000
--- a/spaces/Francesco/FairytaleDJ/README.md
+++ /dev/null
@@ -1,61 +0,0 @@
----
-title: "FairytaleDJ"
-emoji: "🔮"
-colorFrom: "indigo"
-colorTo: "purple"
-sdk: "streamlit"
-sdk_version: "1.19.0"
-app_file: app.py
-pinned: false
----
-
-# FairytaleDJ 🎵🏰🔮
-
-The app is on [hugging face spaces](https://huggingface.co/spaces/Francesco/FairytaleDJ) 🤗
-
-This app takes a user input and suggestes songs that matches its emotions/vibes.
-
-Made with [DeepLake](https://www.deeplake.ai/) 🚀 and [LangChain](https://python.langchain.com/en/latest/index.html) 🦜⛓️ 
-
-We also used [upstash](https://upstash.com/) to store user inputs/emotions and recommended songs
-
-## How it works
-
-The application follows a sequence of steps to deliver Disney songs matching the user's emotions:
-- **User Input**: The application starts by collecting user's emotional state through a text input.
-- **Emotion Encoding**: The user-provided emotions are then fed to a Language Model (LLM). The LLM interprets and encodes these emotions.
-- **Similarity Search**: These encoded emotions are utilized to perform a similarity search within our [vector database](https://www.deeplake.ai/). This database houses Disney songs, each represented as emotional embeddings.
-- **Song Selection**: From the pool of top matching songs, the application randomly selects one. The selection is weighted, giving preference to songs with higher similarity scores.
-- **Song Retrieval**: The selected song's embedded player is displayed on the webpage for the user. Additionally, the LLM interpreted emotional state associated with the chosen song is displayed.
-
-## Run it
-
-Clone this repo.
-
-create a `venv`
-
-```
-python -m venv .venv
-source .venv/bin/activate
-pip install -r requirements.txt
-```
-
-You will need the following `.env` file
-
-```bash
-OPENAI_API_KEY=<OPENAI_API_KEY>
-ACTIVELOOP_TOKEN=<ACTIVELOOP_TOKEN>
-ACTIVELOOP_ORG_ID=zuppif
-UPSTASH_URL=<UPSTASH_URL>
-UPSTASH_PASSWORD=<UPSTASH_PASSWORD>
-```
-
-If you **don't want to use upstash** set the `USE_STORAGE=False`
-
-Then
-
-```
-streamlit run app.py
-```
-
-Then navitage to `http://192.168.1.181:8501`
\ No newline at end of file
diff --git a/spaces/FridaZuley/RVC_HFKawaii/Fixes/tensor-launch.py b/spaces/FridaZuley/RVC_HFKawaii/Fixes/tensor-launch.py
deleted file mode 100644
index cd4ec997fb4b1338d7f29912987865899281b083..0000000000000000000000000000000000000000
--- a/spaces/FridaZuley/RVC_HFKawaii/Fixes/tensor-launch.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import threading
-import time
-from tensorboard import program
-import os
-
-log_path = "logs"
-
-if __name__ == "__main__":
-    tb = program.TensorBoard()
-    tb.configure(argv=[None, '--logdir', log_path])
-    url = tb.launch()
-    print(f'Tensorboard can be accessed at: {url}')
-
-    while True:
-        time.sleep(600)  # Keep the main thread running
\ No newline at end of file
diff --git a/spaces/GMFTBY/PandaGPT/model/openllama.py b/spaces/GMFTBY/PandaGPT/model/openllama.py
deleted file mode 100644
index 44407420b5874a917ab02e6fa74a7e04b4b291a4..0000000000000000000000000000000000000000
--- a/spaces/GMFTBY/PandaGPT/model/openllama.py
+++ /dev/null
@@ -1,293 +0,0 @@
-from header import *
-import os
-import torch.nn.functional as F
-from .ImageBind import *
-from .ImageBind import data
-from .modeling_llama import LlamaForCausalLM
-from transformers import StoppingCriteria, StoppingCriteriaList
-
-import torch
-from torch.nn.utils import rnn
-
-class StoppingCriteriaSub(StoppingCriteria):
-
-    def __init__(self, stops = [], encounters=1):
-        super().__init__()
-        self.stops = stops
-        self.ENCOUNTERS = encounters
-
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
-        stop_count = 0
-        for stop in self.stops:
-            stop_count = (stop == input_ids[0]).sum().item()
-        if stop_count >= self.ENCOUNTERS:
-            return True
-        return False
-
-def build_one_instance(tokenizer, conversation):
-    text_list = []
-    turn_num = len(conversation)
-    input_ids, target_ids = [], []
-    for i in range(turn_num):
-        turn = conversation[i]
-        role = turn['from']
-        if i == 0: # the first human turn
-            assert role == 'human'
-            text = '</Img> ' + turn['value'] + '\n### Assistant:'
-            one_input_id = tokenizer(text, add_special_tokens=False).input_ids
-            input_ids += one_input_id
-            target_ids += [-100]*len(one_input_id) # do not perform loss regression on human prompt
-        else:
-            if role == 'human':
-                text = 'Human: ' + turn['value'] + '\n### Assistant:'
-                one_input_id = tokenizer(text, add_special_tokens=False).input_ids
-                input_ids += one_input_id
-                target_ids += [-100]*len(one_input_id)
-            elif role == 'gpt':
-                text = turn['value'] + '\n###'
-                one_input_id = tokenizer(text, add_special_tokens=False).input_ids
-                input_ids += one_input_id
-                target_ids += one_input_id
-            else:
-                raise Exception('Wrong Role!!!')
-        text_list.append(text)
-        assert len(input_ids) == len(target_ids)
-    return text_list, input_ids, target_ids
-
-def process_batch_instance(tokenizer, batch_of_conversations, max_tgt_len):
-    batch_input_ids, batch_target_ids = [], []
-    for conversation in batch_of_conversations:
-        _, one_input_ids, one_target_ids = build_one_instance(tokenizer, conversation)
-        batch_input_ids.append(torch.LongTensor(one_input_ids))
-        batch_target_ids.append(torch.LongTensor(one_target_ids))
-    input_ids = rnn.pad_sequence(batch_input_ids, batch_first=True, padding_value=tokenizer.pad_token_id)
-    target_ids = rnn.pad_sequence(batch_target_ids, batch_first=True, padding_value=-100)
-    assert input_ids.size() == target_ids.size()
-    input_ids = input_ids[:,:max_tgt_len]
-    target_ids = target_ids[:,:max_tgt_len]
-    attention_mask = input_ids.ne(tokenizer.pad_token_id)
-    assert attention_mask.size() == input_ids.size()
-    return input_ids, target_ids, attention_mask.long()
-
-PROMPT_START = '### Human: <Img>'
-class OpenLLAMAPEFTModel(nn.Module):
-
-    '''LoRA for LLaMa model'''
-
-    def __init__(self, **args):
-        super(OpenLLAMAPEFTModel, self).__init__()
-        self.args = args
-        imagebind_ckpt_path = args['imagebind_ckpt_path']
-        vicuna_ckpt_path = args['vicuna_ckpt_path']
-        max_tgt_len = args['max_tgt_len']
-        stage = args['stage']
-
-        print (f'Initializing visual encoder from {imagebind_ckpt_path} ...')
-        self.visual_encoder, self.visual_hidden_size = \
-        imagebind_model.imagebind_huge(pretrained=True, store_path=imagebind_ckpt_path)
-        # free vision encoder
-        for name, param in self.visual_encoder.named_parameters():
-            param.requires_grad = False
-        self.visual_encoder.eval()
-        print ('Visual encoder initialized.')
-
-        print (f'Initializing language decoder from {vicuna_ckpt_path} ...')
-        # add the lora module
-        peft_config = LoraConfig(
-            task_type=TaskType.CAUSAL_LM, 
-            inference_mode=False, 
-            r=self.args['lora_r'], 
-            lora_alpha=self.args['lora_alpha'], 
-            lora_dropout=self.args['lora_dropout'],
-            target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj']
-        )
-
-        self.llama_model = LlamaForCausalLM.from_pretrained(vicuna_ckpt_path, use_auth_token=os.environ['API_TOKEN'])
-        self.llama_model = get_peft_model(self.llama_model, peft_config)
-        self.llama_model.print_trainable_parameters()
-
-        self.llama_tokenizer = LlamaTokenizer.from_pretrained(vicuna_ckpt_path, use_fast=False, use_auth_token=os.environ['API_TOKEN'])
-        self.llama_tokenizer.pad_token = self.llama_tokenizer.eos_token
-        self.llama_tokenizer.padding_side = "right"
-        print ('Language decoder initialized.')
-
-        self.llama_proj = nn.Linear(
-            self.visual_hidden_size, self.llama_model.config.hidden_size
-        )
-
-        self.max_tgt_len = max_tgt_len
-        self.device = torch.cuda.current_device() if torch.cuda.is_available() else torch.device('cpu')
-
-    def encode_video(self, video_paths):
-        inputs = {ModalityType.VISION: data.load_and_transform_video_data(video_paths, self.device)}
-        # convert into visual dtype
-        inputs = {key: inputs[key].to(self.llama_model.dtype) for key in inputs}
-        with torch.no_grad():
-            embeddings = self.visual_encoder(inputs)
-            video_embeds = embeddings[ModalityType.VISION] # bsz x 1024
-        inputs_llama = self.llama_proj(video_embeds).unsqueeze(1) # bsz x 1 x llama_size
-        atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(self.device) # bsz x 1
-        return inputs_llama, atts_llama
-
-    def encode_audio(self, audio_paths):
-        inputs = {ModalityType.AUDIO: data.load_and_transform_audio_data(audio_paths, self.device)}
-        # convert into visual dtype
-        inputs = {key: inputs[key].to(self.llama_model.dtype) for key in inputs}
-        with torch.no_grad():
-            embeddings = self.visual_encoder(inputs)
-            audio_embeds = embeddings[ModalityType.AUDIO] # bsz x 1024
-        inputs_llama = self.llama_proj(audio_embeds).unsqueeze(1) # bsz x 1 x llama_size
-        atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(self.device) # bsz x 1
-        return inputs_llama, atts_llama
-
-    def encode_thermal(self, thermal_paths):
-        inputs = {ModalityType.THERMAL: data.load_and_transform_thermal_data(thermal_paths, self.device)}
-        # convert into visual dtype
-        inputs = {key: inputs[key].to(self.llama_model.dtype) for key in inputs}
-        with torch.no_grad():
-            embeddings = self.visual_encoder(inputs)
-            image_embeds = embeddings['thermal'] # bsz x 1024
-        inputs_llama = self.llama_proj(image_embeds).unsqueeze(1) # bsz x 1 x llama_size
-        atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(self.device) # bsz x 1
-        return inputs_llama, atts_llama
-
-    def encode_image(self, image_paths):
-        inputs = {ModalityType.VISION: data.load_and_transform_vision_data(image_paths, self.device)}
-        # convert into visual dtype
-        inputs = {key: inputs[key].to(self.llama_model.dtype) for key in inputs}
-        with torch.no_grad():
-            embeddings = self.visual_encoder(inputs)
-            image_embeds = embeddings['vision'] # bsz x 1024
-        inputs_llama = self.llama_proj(image_embeds).unsqueeze(1) # bsz x 1 x llama_size
-        atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(self.device) # bsz x 1
-        return inputs_llama, atts_llama
-
-    def prompt_wrap(self, img_embeds, input_ids, target_ids, attention_mask):
-        '''
-            input_ids, target_ids, attention_mask: bsz x s2
-        '''
-        input_ids = input_ids.to(self.device) # bsz x s2
-        target_ids = target_ids.to(self.device) # bsz x s2
-        attention_mask = attention_mask.to(self.device) # bsz x s2
-
-        batch_size = img_embeds.shape[0]
-        p_before = PROMPT_START
-        p_before_tokens = self.llama_tokenizer(p_before, 
-            return_tensors="pt", add_special_tokens=False).to(self.device)
-        # peft model need deeper call
-        p_before_embeds = self.llama_model.model.model.embed_tokens(p_before_tokens.input_ids).expand(batch_size, -1, -1) # bsz x s1 x embed_dim
-        p_after_embeds = self.llama_model.model.model.embed_tokens(input_ids).expand(batch_size, -1, -1) # bsz x s2 x embed_dim
-        bos = torch.ones([batch_size, 1],
-                         dtype=p_before_tokens.input_ids.dtype,
-                         device=p_before_tokens.input_ids.device) * self.llama_tokenizer.bos_token_id # bsz x 1
-        bos_embeds = self.llama_model.model.model.embed_tokens(bos) # bsz x 1 x embed_dim
-        inputs_embeds = torch.cat([bos_embeds, p_before_embeds, img_embeds, p_after_embeds], dim=1) # bsz x (1+s1+1+s2) x embed_dim
-
-        # create targets
-        empty_targets = (
-            torch.ones([batch_size, 1+p_before_embeds.size()[1]+1], # 1 (bos) + s1 + 1 (image vector)
-                       dtype=torch.long).to(self.device).fill_(-100)  
-        ) # bsz x (1 + s1 + 1)
-        targets = torch.cat([empty_targets, target_ids], dim=1) # bsz x (1 + s1 + 1 + s2)
-        assert inputs_embeds.size()[1] == targets.size()[1]
-
-        atts_prefix = torch.ones([batch_size, 1+p_before_embeds.size()[1]+1], dtype=torch.long).to(self.device) # bsz x (1 + s1 +1)
-        attention_mask = torch.cat([atts_prefix, attention_mask], dim=1)
-        assert attention_mask.size() == targets.size() # bsz x (1 + s1 + 1 + s2)
-        return inputs_embeds, targets, attention_mask 
-
-    def forward(self, inputs):
-        image_paths = inputs['image_paths']
-        img_embeds, _ = self.encode_image(image_paths)
-
-        output_texts = inputs['output_texts']
-        input_ids, target_ids, attention_mask = process_batch_instance(self.llama_tokenizer, output_texts, self.max_tgt_len)
-        inputs_embeds, targets, attention_mask = self.prompt_wrap(img_embeds, input_ids, target_ids, attention_mask)
-
-        outputs = self.llama_model(
-            inputs_embeds=inputs_embeds,
-            attention_mask=attention_mask,
-            return_dict=True,
-            labels=targets,
-        )
-        loss = outputs.loss
-        # calculate the token accuarcy
-        chosen_tokens = torch.max(outputs.logits, dim=-1)[1][:, 1:-1]    # [B, S-1]
-        labels = targets[:, 2:]
-        gen_acc = (chosen_tokens.reshape(-1) == labels.reshape(-1)).to(torch.long)    # [B*S]
-        valid_mask = (labels != -100).reshape(-1)
-        valid_tokens = gen_acc & valid_mask    # [B*S]
-        gen_acc = valid_tokens.sum().item() / valid_mask.sum().item()
-        return loss, gen_acc
-
-    def extract_multimodal_feature(self, inputs):
-        features = []
-        if inputs['image_paths']:
-            image_embeds, _ = self.encode_image(inputs['image_paths'])
-            features.append(image_embeds)
-        if inputs['audio_paths']:
-            audio_embeds, _ = self.encode_audio(inputs['audio_paths'])
-            features.append(audio_embeds)
-        if inputs['video_paths']:
-            video_embeds, _ = self.encode_video(inputs['video_paths'])
-            features.append(video_embeds)
-        if inputs['thermal_paths']:
-            thermal_embeds, _ = self.encode_thermal(inputs['thermal_paths'])
-            features.append(thermal_embeds)
-
-        feature_embeds = torch.cat(features).sum(dim=0).unsqueeze(0)
-        return feature_embeds
-
-    def prepare_generation_embedding(self, inputs):
-        prompt = inputs['prompt']
-        if len(inputs['modality_embeds']) == 1:
-            feature_embeds = inputs['modality_embeds'][0]
-        else:
-            feature_embeds = self.extract_multimodal_feature(inputs)
-            inputs['modality_embeds'].append(feature_embeds)
-
-        batch_size = feature_embeds.shape[0]
-        p_before = PROMPT_START
-        p_before_tokens = self.llama_tokenizer(p_before, 
-            return_tensors="pt", add_special_tokens=False).to(self.device)
-        p_before_embeds = self.llama_model.model.model.embed_tokens(p_before_tokens.input_ids).expand(batch_size, -1, -1) # bsz x s1 x embed_dim
-        text = '</Img> ' + prompt + '\n### Assistant:'
-        p_after_tokens = self.llama_tokenizer(text, add_special_tokens=False, return_tensors='pt').to(self.device)
-        p_after_embeds = self.llama_model.model.model.embed_tokens(p_after_tokens.input_ids).expand(batch_size, -1, -1) # bsz x s1 x embed_dim
-        bos = torch.ones([batch_size, 1],
-                         dtype=p_before_tokens.input_ids.dtype,
-                         device=p_before_tokens.input_ids.device) * self.llama_tokenizer.bos_token_id # bsz x 1
-        bos_embeds = self.llama_model.model.model.embed_tokens(bos) # bsz x 1 x embed_dim
-        inputs_embeds = torch.cat([bos_embeds, p_before_embeds, feature_embeds, p_after_embeds], dim=1) # bsz x (1+s1+1+s2) x embed_dim
-        return inputs_embeds
-
-    def generate(self, inputs):
-        '''
-            inputs = {
-                'image_paths': optional,
-                'audio_paths': optional
-                'video_paths': optional
-                'thermal_paths': optional
-                'mode': generation mode,
-                'prompt': human input prompt,
-                'max_tgt_len': generation length,
-                'top_p': top_p,
-                'temperature': temperature
-                'modality_embeds': None or torch.tensor
-                'modality_cache': save the image cache
-            }
-        '''
-        input_embeds = self.prepare_generation_embedding(inputs)
-        stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=[2277], encounters=1)])
-        outputs = self.llama_model.generate(
-            inputs_embeds=input_embeds,
-            max_new_tokens=inputs['max_tgt_len'],
-            top_p=inputs['top_p'],
-            temperature=inputs['temperature'],
-            do_sample=True,
-            use_cache=True,
-            stopping_criteria=stopping_criteria,
-        )
-        output_text = self.llama_tokenizer.decode(outputs[0][:-2], skip_special_tokens=True)
-        return output_text
-
diff --git a/spaces/GT-RIPL/GPT-K/conversation.py b/spaces/GT-RIPL/GPT-K/conversation.py
deleted file mode 100644
index 40918c7644c8ca6356a9a3bb11bfcd47579e5acc..0000000000000000000000000000000000000000
--- a/spaces/GT-RIPL/GPT-K/conversation.py
+++ /dev/null
@@ -1,363 +0,0 @@
-import dataclasses
-from enum import auto, Enum
-from typing import List
-
-
-class SeparatorStyle(Enum):
-    """Different separator style."""
-    SINGLE = auto()
-    TWO = auto()
-    MPT = auto()
-    PLAIN = auto()
-    LLAMA_2 = auto()
-
-
-@dataclasses.dataclass
-class Conversation:
-    """A class that keeps all conversation history."""
-    system: str
-    roles: List[str]
-    messages: List[List[str]]
-    offset: int
-    sep_style: SeparatorStyle = SeparatorStyle.SINGLE
-    sep: str = "###"
-    sep2: str = None
-    version: str = "Unknown"
-
-    skip_next: bool = False
-
-    def get_prompt(self):
-        messages = self.messages
-        if len(messages) > 0 and type(messages[0][1]) is tuple:
-            messages = self.messages.copy()
-            init_role, init_msg = messages[0].copy()
-            init_msg = init_msg[0].replace("<image>", "").strip()
-            if 'mmtag' in self.version:
-                messages[0] = (init_role, init_msg)
-                messages.insert(0, (self.roles[0], "<Image><image></Image>"))
-                messages.insert(1, (self.roles[1], "Received."))
-            else:
-                messages[0] = (init_role, "<image>\n" + init_msg)
-
-        if self.sep_style == SeparatorStyle.SINGLE:
-            ret = self.system + self.sep
-            for role, message in messages:
-                if message:
-                    if type(message) is tuple:
-                        message, _, _ = message
-                    ret += role + ": " + message + self.sep
-                else:
-                    ret += role + ":"
-        elif self.sep_style == SeparatorStyle.TWO:
-            seps = [self.sep, self.sep2]
-            ret = self.system + seps[0]
-            for i, (role, message) in enumerate(messages):
-                if message:
-                    if type(message) is tuple:
-                        message, _, _ = message
-                    ret += role + ": " + message + seps[i % 2]
-                else:
-                    ret += role + ":"
-        elif self.sep_style == SeparatorStyle.MPT:
-            ret = self.system + self.sep
-            for role, message in messages:
-                if message:
-                    if type(message) is tuple:
-                        message, _, _ = message
-                    ret += role + message + self.sep
-                else:
-                    ret += role
-        elif self.sep_style == SeparatorStyle.LLAMA_2:
-            wrap_sys = lambda msg: f"<<SYS>>\n{msg}\n<</SYS>>\n\n"
-            wrap_inst = lambda msg: f"[INST] {msg} [/INST]"
-            ret = ""
-
-            for i, (role, message) in enumerate(messages):
-                if i == 0:
-                    assert message, "first message should not be none"
-                    assert role == self.roles[0], "first message should come from user"
-                if message:
-                    if type(message) is tuple:
-                        message, _, _ = message
-                    if i == 0: message = wrap_sys(self.system) + message
-                    if i % 2 == 0:
-                        message = wrap_inst(message)
-                        ret += self.sep + message
-                    else:
-                        ret += " " + message + " " + self.sep2
-                else:
-                    ret += ""
-            ret = ret.lstrip(self.sep)
-        elif self.sep_style == SeparatorStyle.PLAIN:
-            seps = [self.sep, self.sep2]
-            ret = self.system
-            for i, (role, message) in enumerate(messages):
-                if message:
-                    if type(message) is tuple:
-                        message, _, _ = message
-                    ret += message + seps[i % 2]
-                else:
-                    ret += ""
-        else:
-            raise ValueError(f"Invalid style: {self.sep_style}")
-
-        return ret
-
-    def append_message(self, role, message):
-        self.messages.append([role, message])
-
-    def get_images(self, return_pil=False):
-        images = []
-        for i, (role, msg) in enumerate(self.messages[self.offset:]):
-            if i % 2 == 0:
-                if type(msg) is tuple:
-                    image = msg[1].convert('RGB')
-                    if return_pil:
-                        images.append(image)
-                    else:
-                        import base64
-                        from io import BytesIO
-                        buffered = BytesIO()
-                        image.save(buffered, format="PNG")
-                        img_b64_str = base64.b64encode(buffered.getvalue()).decode()
-                        images.append(img_b64_str)
-        return images
-
-    def to_gradio_chatbot(self):
-        ret = []
-        for i, (role, msg) in enumerate(self.messages[self.offset:]):
-            if i % 2 == 0:
-                if type(msg) is tuple:
-                    import base64
-                    from io import BytesIO
-                    msg, image = msg
-                    max_hw, min_hw = max(image.size), min(image.size)
-                    aspect_ratio = max_hw / min_hw
-                    max_len, min_len = 800, 400
-                    shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
-                    longest_edge = int(shortest_edge * aspect_ratio)
-                    W, H = image.size
-                    if H > W:
-                        H, W = longest_edge, shortest_edge
-                    else:
-                        H, W = shortest_edge, longest_edge
-                    image = image.resize((W, H))
-                    buffered = BytesIO()
-                    image.save(buffered, format="JPEG")
-                    img_b64_str = base64.b64encode(buffered.getvalue()).decode()
-                    img_str = f'<img src="data:image/png;base64,{img_b64_str}" alt="user upload image" />'
-                    ret.append([img_str, None])
-                    msg = msg.replace('<image>', '').strip()
-                    if len(msg) > 0:
-                        ret.append([msg, None])
-                else:
-                    ret.append([msg, None])
-            else:
-                ret[-1][-1] = msg
-        return ret
-
-    def copy(self):
-        return Conversation(
-            system=self.system,
-            roles=self.roles,
-            messages=[[x, y] for x, y in self.messages],
-            offset=self.offset,
-            sep_style=self.sep_style,
-            sep=self.sep,
-            sep2=self.sep2,
-            version=self.version)
-
-    def dict(self):
-        if len(self.get_images()) > 0:
-            return {
-                "system": self.system,
-                "roles": self.roles,
-                "messages": [[x, y[0] if type(y) is tuple else y] for x, y in self.messages],
-                "offset": self.offset,
-                "sep": self.sep,
-                "sep2": self.sep2,
-            }
-        return {
-            "system": self.system,
-            "roles": self.roles,
-            "messages": self.messages,
-            "offset": self.offset,
-            "sep": self.sep,
-            "sep2": self.sep2,
-        }
-
-
-conv_gptk = Conversation(
-    system="",
-    roles=("USER", "ASSISTANT"),
-    version="v1",
-    messages=(),
-    offset=0,
-    sep_style=SeparatorStyle.SINGLE,
-    sep=""
-)
-
-conv_vicuna_v0 = Conversation(
-    system="A chat between a curious human and an artificial intelligence assistant. "
-           "The assistant gives helpful, detailed, and polite answers to the human's questions.",
-    roles=("Human", "Assistant"),
-    messages=(
-        ("Human", "What are the key differences between renewable and non-renewable energy sources?"),
-        ("Assistant",
-            "Renewable energy sources are those that can be replenished naturally in a relatively "
-            "short amount of time, such as solar, wind, hydro, geothermal, and biomass. "
-            "Non-renewable energy sources, on the other hand, are finite and will eventually be "
-            "depleted, such as coal, oil, and natural gas. Here are some key differences between "
-            "renewable and non-renewable energy sources:\n"
-            "1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable "
-            "energy sources are finite and will eventually run out.\n"
-            "2. Environmental impact: Renewable energy sources have a much lower environmental impact "
-            "than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, "
-            "and other negative effects.\n"
-            "3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically "
-            "have lower operational costs than non-renewable sources.\n"
-            "4. Reliability: Renewable energy sources are often more reliable and can be used in more remote "
-            "locations than non-renewable sources.\n"
-            "5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different "
-            "situations and needs, while non-renewable sources are more rigid and inflexible.\n"
-            "6. Sustainability: Renewable energy sources are more sustainable over the long term, while "
-            "non-renewable sources are not, and their depletion can lead to economic and social instability.\n")
-    ),
-    offset=2,
-    sep_style=SeparatorStyle.SINGLE,
-    sep="###",
-)
-
-conv_vicuna_v1 = Conversation(
-    system="A chat between a curious user and an artificial intelligence assistant. "
-    "The assistant gives helpful, detailed, and polite answers to the user's questions.",
-    roles=("USER", "ASSISTANT"),
-    version="v1",
-    messages=(),
-    offset=0,
-    sep_style=SeparatorStyle.TWO,
-    sep=" ",
-    sep2="</s>",
-)
-
-conv_llama_2 = Conversation(
-    system="""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
-
-If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.""",
-    roles=("USER", "ASSISTANT"),
-    version="llama_v2",
-    messages=(),
-    offset=0,
-    sep_style=SeparatorStyle.LLAMA_2,
-    sep="<s>",
-    sep2="</s>",
-)
-
-conv_llava_llama_2 = Conversation(
-    system="You are a helpful language and vision assistant. "
-           "You are able to understand the visual content that the user provides, "
-           "and assist the user with a variety of tasks using natural language.",
-    roles=("USER", "ASSISTANT"),
-    version="llama_v2",
-    messages=(),
-    offset=0,
-    sep_style=SeparatorStyle.LLAMA_2,
-    sep="<s>",
-    sep2="</s>",
-)
-
-conv_mpt = Conversation(
-    system="""<|im_start|>system
-A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.""",
-    roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
-    version="mpt",
-    messages=(),
-    offset=0,
-    sep_style=SeparatorStyle.MPT,
-    sep="<|im_end|>",
-)
-
-conv_llava_plain = Conversation(
-    system="",
-    roles=("", ""),
-    messages=(
-    ),
-    offset=0,
-    sep_style=SeparatorStyle.PLAIN,
-    sep="\n",
-)
-
-conv_llava_v0 = Conversation(
-    system="A chat between a curious human and an artificial intelligence assistant. "
-           "The assistant gives helpful, detailed, and polite answers to the human's questions.",
-    roles=("Human", "Assistant"),
-    messages=(
-        ("Human", "Hi!"),
-        ("Assistant", "Hi there! How can I help you today?")
-    ),
-    offset=2,
-    sep_style=SeparatorStyle.SINGLE,
-    sep="###",
-)
-
-conv_llava_v0_mmtag = Conversation(
-    system="A chat between a curious user and an artificial intelligence assistant. "
-           "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language."
-           "The visual content will be provided with the following format: <Image>visual content</Image>.",
-    roles=("Human", "Assistant"),
-    messages=(
-    ),
-    offset=0,
-    sep_style=SeparatorStyle.SINGLE,
-    sep="###",
-    version="v0_mmtag",
-)
-
-conv_llava_v1 = Conversation(
-    system="A chat between a curious human and an artificial intelligence assistant. "
-           "The assistant gives helpful, detailed, and polite answers to the human's questions.",
-    roles=("USER", "ASSISTANT"),
-    version="v1",
-    messages=(),
-    offset=0,
-    sep_style=SeparatorStyle.TWO,
-    sep=" ",
-    sep2="</s>",
-)
-
-conv_llava_v1_mmtag = Conversation(
-    system="A chat between a curious user and an artificial intelligence assistant. "
-           "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language."
-           "The visual content will be provided with the following format: <Image>visual content</Image>.",
-    roles=("USER", "ASSISTANT"),
-    messages=(),
-    offset=0,
-    sep_style=SeparatorStyle.TWO,
-    sep=" ",
-    sep2="</s>",
-    version="v1_mmtag",
-)
-
-default_conversation = conv_vicuna_v0
-conv_templates = {
-    "default": conv_vicuna_v0,
-    "v0": conv_vicuna_v0,
-    "v1": conv_vicuna_v1,
-    "vicuna_v1": conv_vicuna_v1,
-    "llama_2": conv_llama_2,
-    "gptk": conv_gptk,
-
-    "plain": conv_llava_plain,
-    "v0_plain": conv_llava_plain,
-    "llava_v0": conv_llava_v0,
-    "v0_mmtag": conv_llava_v0_mmtag,
-    "llava_v1": conv_llava_v1,
-    "v1_mmtag": conv_llava_v1_mmtag,
-    "llava_llama_2": conv_llava_llama_2,
-
-    "mpt": conv_mpt,
-}
-
-
-if __name__ == "__main__":
-    print(default_conversation.get_prompt())
\ No newline at end of file
diff --git a/spaces/Gradio-Blocks/beat-interpolator/beat_interpolator.py b/spaces/Gradio-Blocks/beat-interpolator/beat_interpolator.py
deleted file mode 100644
index 592095dcee28e332c6a20e93c3c1e9cad9865d97..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/beat-interpolator/beat_interpolator.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import librosa
-import numpy as np
-import gradio as gr
-import soundfile as sf
-
-from moviepy.editor import *
-
-
-cache_wav_path = [f'/tmp/{str(i).zfill(2)}.wav' for i in range(50)]
-wave_path_iter = iter(cache_wav_path)
-cache_mp4_path = [f'/tmp/{str(i).zfill(2)}.mp4' for i in range(50)]
-path_iter = iter(cache_mp4_path)
-
-def merge_times(times, times2):
-    ids = np.unique(np.where(abs(times2[...,None] - times[None]) < 0.2)[1])
-    mask = np.ones_like(times, dtype=np.bool)
-    mask[ids] = False
-    times = times[mask]
-    times = np.concatenate([times, times2])
-    times = np.sort(times)
-
-    return times
-
-
-def beat_interpolator(wave_path, generator, latent_dim, seed, fps=30, batch_size=1, strength=1, max_duration=None, use_peak=False):
-    fps = max(10, fps)
-    strength = np.clip(strength, 0, 1)
-    hop_length = 512
-    y, sr = librosa.load(wave_path, sr=24000)
-    duration = librosa.get_duration(y=y, sr=sr)
-
-    if max_duration is not None:
-        y_len = y.shape[0]
-        y_idx = int(y_len * max_duration / duration)
-        y = y[:y_idx]
-
-        global wave_path_iter
-        try:
-            wave_path = next(wave_path_iter)
-        except:
-            wave_path_iter = iter(cache_wav_path)
-            wave_path = next(wave_path_iter)
-        sf.write(wave_path, y, sr, subtype='PCM_24')
-        y, sr = librosa.load(wave_path, sr=24000)
-        duration = librosa.get_duration(y=y, sr=sr)
-    
-    S = np.abs(librosa.stft(y))
-    db = librosa.power_to_db(S**2, ref=np.median).max(0)
-    db_mean = np.mean(db)
-    db_max = np.max(db)
-    db_min = np.min(db)
-    db_times = librosa.frames_to_time(np.arange(len(db)), sr=sr, hop_length=hop_length)
-    rng = np.random.RandomState(seed)
-    onset_env = librosa.onset.onset_strength(y=y, sr=sr, hop_length=512, aggregate=np.median)
-    _, beats = librosa.beat.beat_track(y=y, sr=sr, onset_envelope=onset_env, hop_length=512, units='time')
-    times = np.asarray(beats)
-    if use_peak:
-        peaks = librosa.util.peak_pick(onset_env, 1, 1, 1, 1, 0.8, 5)
-        times2 = librosa.frames_to_time(np.arange(len(onset_env)), sr=sr, hop_length=512)[peaks]
-        times2 = np.asarray(times)
-        times = merge_times(times, times2)
-        
-    times = np.concatenate([np.asarray([0.]), times, np.asarray([duration])], 0)
-    times = list(np.unique(np.int64(np.floor(times * fps / 2))) * 2)
-
-    latents = []
-    time0 = 0
-    latent0 = rng.randn(latent_dim)
-    for time1 in times:
-        latent1 = latent0 * (1 - strength) + rng.randn(latent_dim) * strength
-        db_cur_index = np.argmin(np.abs(db_times - time1.astype('float32') / fps))
-        db_cur = db[db_cur_index]
-        if db_cur < db_min + (db_mean - db_min) / 3:
-            latent1 = latent0 * 0.8 + latent1 * 0.2
-        elif db_cur < db_min + 2 * (db_mean - db_min) / 3:
-            latent1 = latent0 * 0.6 + latent1 * 0.4
-        elif db_cur < db_mean + (db_max - db_mean) / 3:
-            latent1 = latent0 * 0.4 + latent1 * 0.6
-        elif db_cur < db_mean + 2 * (db_max - db_mean) / 3:
-            latent1 = latent0 * 0.2 + latent1 * 0.8
-        else:
-            pass
-        if time1 > duration * fps:
-            time1 = int(duration * fps)
-        t1 = time1 - time0
-        alpha = 0.5
-        latent2 = latent0 * alpha + latent1 * (1 - alpha)
-        for j in range(t1):
-            alpha = j / t1
-            latent = latent0 * (1 - alpha) + latent2 * alpha
-            latents.append(latent)
-        
-        time0 = time1
-        latent0 = latent1
-        
-    outs = []
-    ix = 0
-    while True:
-        if ix + batch_size <= len(latents):
-            outs += generator(latents[ix:ix+batch_size])
-        elif ix < len(latents):
-            outs += generator(latents[ix:])
-            break
-        else:
-            break
-        ix += batch_size
-
-    global path_iter
-    try:
-        video_path = next(path_iter)
-    except:
-        path_iter = iter(cache_mp4_path)
-        video_path = next(path_iter)
-    
-    video = ImageSequenceClip(outs, fps=fps)
-    audioclip = AudioFileClip(wave_path)
-
-    video = video.set_audio(audioclip)
-    video.write_videofile(video_path, fps=fps)
-
-    return video_path
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py
deleted file mode 100644
index 2fdc53c8c04c12bed16a31281127f9774bb70b64..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py
+++ /dev/null
@@ -1,12 +0,0 @@
-_base_ = './grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        style='pytorch'))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py
deleted file mode 100644
index a620188807218a9c80ad89ac6002dda3ea4b830c..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py'
-model = dict(
-    pretrained='open-mmlab://res2net101_v1d_26w_4s',
-    backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py
deleted file mode 100644
index 2bc52d96293f214adf1e3e1878746ed8bd2434f6..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
-    '../_base_/models/fcn_unet_s5-d16.py', '../_base_/datasets/chase_db1.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
-]
-model = dict(test_cfg=dict(crop_size=(128, 128), stride=(85, 85)))
-evaluation = dict(metric='mDice')
diff --git a/spaces/GuiltySpark/amikus_text_summarizer/README.md b/spaces/GuiltySpark/amikus_text_summarizer/README.md
deleted file mode 100644
index 81a20305253c3852a7880b4b471515ff700c1ef8..0000000000000000000000000000000000000000
--- a/spaces/GuiltySpark/amikus_text_summarizer/README.md
+++ /dev/null
@@ -1,35 +0,0 @@
----
-title: Amikus_text_summarizer
-emoji: 🔥
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 2.9.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
-
-
-
-Readme 
-
-For this application I have created three features:
-1.sumarizer
-2.keyword extraction
-3. paraphraze text 
-
-1.Sumarizer:
-for this I have used transformers library, sumarization pipeline ,to sumarize the text (see sumarize fuction)
-the parameters are max_length = 200, min_lenth = 30
-2. key_word extraction: this feature extracts 20 keyword from the text each of length of 3 or less tha 3 words,
-see the same sumarize function
-the lib I have used for this is yake and its function KeywordExtractor
-3.paraphraze text:
-for this i have used PEGASUS from transformers library.I used senstence-splitter to split the text into a list of sentences.
-Then paraphrazed each of the sentences and then compiled them back into a paragraph.
-I am using a pre trained model = 'tuner007/pegasus_paraphrase' for this.
-
-The selection of example texts was based on my own areas of fascination like robotics, ancient history and astronomy.
-
diff --git a/spaces/HESOAYM/ElviraMulti/custom.css b/spaces/HESOAYM/ElviraMulti/custom.css
deleted file mode 100644
index a4609ec6153865b157edcaa526c6935124b22f13..0000000000000000000000000000000000000000
--- a/spaces/HESOAYM/ElviraMulti/custom.css
+++ /dev/null
@@ -1,164 +0,0 @@
-:root {
-    --chatbot-color-light: #F3F3F3;
-    --chatbot-color-dark: #121111;
-}
-
-body{background: none;}
-
-/* status_display */
-#status_display {
-    display: flex;
-    min-height: 2.5em;
-    align-items: flex-end;
-    justify-content: flex-end;
-}
-#status_display p {
-    font-size: .85em;
-    font-family: monospace;
-    color: var(--body-text-color-subdued);
-}
-
-#chuanhu_chatbot, #status_display {
-    transition: all 0.6s;
-}
-/* list */
-ol:not(.options), ul:not(.options) {
-    padding-inline-start: 2em !important;
-}
-
-/* 亮色 */
-#chuanhu_chatbot {
-    background-color: none;
-}
-[data-testid = "bot"] {
-    background-color: none;
-}
-[data-testid = "user"] {
-    background-color: none;
-}
-/* 对话气泡 */
-[class *= "message"] {
-    border-radius: var(--radius-xl) !important;
-    border: none;
-    padding: var(--spacing-xl) !important;
-    font-size: var(--text-md) !important;
-    line-height: var(--line-md) !important;
-    min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-    min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-}
-[data-testid = "bot"] {
-    max-width: 85%;
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 85%;
-    width: auto !important;
-    border-bottom-right-radius: 0 !important;
-}
-/* 表格 */
-table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-td,th {
-    border: 1.2px solid var(--border-color-primary) !important;
-    padding: 0.2em;
-}
-thead {
-    background-color: rgba(175,184,193,0.2);
-}
-thead th {
-    padding: .5em .2em;
-}
-/* 行内代码 */
-code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-/* 代码块 */
-pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: hsla(0, 0%, 0%, 80%)!important;
-    border-radius: 10px;
-    padding: 1.4em 1.2em 0em 1.4em;
-    margin: 1.2em 2em 1.2em 0.5em;
-    color: #FFF;
-    box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
-}
-/* 代码高亮样式 */
-.highlight .hll { background-color: #49483e }
-.highlight .c { color: #75715e } /* Comment */
-.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
-.highlight .k { color: #66d9ef } /* Keyword */
-.highlight .l { color: #ae81ff } /* Literal */
-.highlight .n { color: #f8f8f2 } /* Name */
-.highlight .o { color: #f92672 } /* Operator */
-.highlight .p { color: #f8f8f2 } /* Punctuation */
-.highlight .ch { color: #75715e } /* Comment.Hashbang */
-.highlight .cm { color: #75715e } /* Comment.Multiline */
-.highlight .cp { color: #75715e } /* Comment.Preproc */
-.highlight .cpf { color: #75715e } /* Comment.PreprocFile */
-.highlight .c1 { color: #75715e } /* Comment.Single */
-.highlight .cs { color: #75715e } /* Comment.Special */
-.highlight .gd { color: #f92672 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gi { color: #a6e22e } /* Generic.Inserted */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #75715e } /* Generic.Subheading */
-.highlight .kc { color: #66d9ef } /* Keyword.Constant */
-.highlight .kd { color: #66d9ef } /* Keyword.Declaration */
-.highlight .kn { color: #f92672 } /* Keyword.Namespace */
-.highlight .kp { color: #66d9ef } /* Keyword.Pseudo */
-.highlight .kr { color: #66d9ef } /* Keyword.Reserved */
-.highlight .kt { color: #66d9ef } /* Keyword.Type */
-.highlight .ld { color: #e6db74 } /* Literal.Date */
-.highlight .m { color: #ae81ff } /* Literal.Number */
-.highlight .s { color: #e6db74 } /* Literal.String */
-.highlight .na { color: #a6e22e } /* Name.Attribute */
-.highlight .nb { color: #f8f8f2 } /* Name.Builtin */
-.highlight .nc { color: #a6e22e } /* Name.Class */
-.highlight .no { color: #66d9ef } /* Name.Constant */
-.highlight .nd { color: #a6e22e } /* Name.Decorator */
-.highlight .ni { color: #f8f8f2 } /* Name.Entity */
-.highlight .ne { color: #a6e22e } /* Name.Exception */
-.highlight .nf { color: #a6e22e } /* Name.Function */
-.highlight .nl { color: #f8f8f2 } /* Name.Label */
-.highlight .nn { color: #f8f8f2 } /* Name.Namespace */
-.highlight .nx { color: #a6e22e } /* Name.Other */
-.highlight .py { color: #f8f8f2 } /* Name.Property */
-.highlight .nt { color: #f92672 } /* Name.Tag */
-.highlight .nv { color: #f8f8f2 } /* Name.Variable */
-.highlight .ow { color: #f92672 } /* Operator.Word */
-.highlight .w { color: #f8f8f2 } /* Text.Whitespace */
-.highlight .mb { color: #ae81ff } /* Literal.Number.Bin */
-.highlight .mf { color: #ae81ff } /* Literal.Number.Float */
-.highlight .mh { color: #ae81ff } /* Literal.Number.Hex */
-.highlight .mi { color: #ae81ff } /* Literal.Number.Integer */
-.highlight .mo { color: #ae81ff } /* Literal.Number.Oct */
-.highlight .sa { color: #e6db74 } /* Literal.String.Affix */
-.highlight .sb { color: #e6db74 } /* Literal.String.Backtick */
-.highlight .sc { color: #e6db74 } /* Literal.String.Char */
-.highlight .dl { color: #e6db74 } /* Literal.String.Delimiter */
-.highlight .sd { color: #e6db74 } /* Literal.String.Doc */
-.highlight .s2 { color: #e6db74 } /* Literal.String.Double */
-.highlight .se { color: #ae81ff } /* Literal.String.Escape */
-.highlight .sh { color: #e6db74 } /* Literal.String.Heredoc */
-.highlight .si { color: #e6db74 } /* Literal.String.Interpol */
-.highlight .sx { color: #e6db74 } /* Literal.String.Other */
-.highlight .sr { color: #e6db74 } /* Literal.String.Regex */
-.highlight .s1 { color: #e6db74 } /* Literal.String.Single */
-.highlight .ss { color: #e6db74 } /* Literal.String.Symbol */
-.highlight .bp { color: #f8f8f2 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #a6e22e } /* Name.Function.Magic */
-.highlight .vc { color: #f8f8f2 } /* Name.Variable.Class */
-.highlight .vg { color: #f8f8f2 } /* Name.Variable.Global */
-.highlight .vi { color: #f8f8f2 } /* Name.Variable.Instance */
-.highlight .vm { color: #f8f8f2 } /* Name.Variable.Magic */
-.highlight .il { color: #ae81ff } /* Literal.Number.Integer.Long */
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/tokenizer/tokenizer.py b/spaces/HaloMaster/chinesesummary/fengshen/tokenizer/tokenizer.py
deleted file mode 100644
index 9bad5790a5799b96f2e164d825c0b1f8ec0c2dfb..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/tokenizer/tokenizer.py
+++ /dev/null
@@ -1 +0,0 @@
-# coding=utf-8
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/new/decoders/decoder_config.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/new/decoders/decoder_config.py
deleted file mode 100644
index 659eb94a9b8187a7c126d7b439ac2742f9d72022..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/speech_recognition/new/decoders/decoder_config.py
+++ /dev/null
@@ -1,70 +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 dataclasses import dataclass, field
-from typing import Optional
-
-from fairseq.dataclass.configs import FairseqDataclass
-from fairseq.dataclass.constants import ChoiceEnum
-from omegaconf import MISSING
-
-
-DECODER_CHOICES = ChoiceEnum(["viterbi", "kenlm", "fairseqlm"])
-
-
-@dataclass
-class DecoderConfig(FairseqDataclass):
-    type: DECODER_CHOICES = field(
-        default="viterbi",
-        metadata={"help": "The type of decoder to use"},
-    )
-
-
-@dataclass
-class FlashlightDecoderConfig(FairseqDataclass):
-    nbest: int = field(
-        default=1,
-        metadata={"help": "Number of decodings to return"},
-    )
-    unitlm: bool = field(
-        default=False,
-        metadata={"help": "If set, use unit language model"},
-    )
-    lmpath: str = field(
-        default=MISSING,
-        metadata={"help": "Language model for KenLM decoder"},
-    )
-    lexicon: Optional[str] = field(
-        default=None,
-        metadata={"help": "Lexicon for Flashlight decoder"},
-    )
-    beam: int = field(
-        default=50,
-        metadata={"help": "Number of beams to use for decoding"},
-    )
-    beamthreshold: float = field(
-        default=50.0,
-        metadata={"help": "Threshold for beam search decoding"},
-    )
-    beamsizetoken: Optional[int] = field(
-        default=None, metadata={"help": "Beam size to use"}
-    )
-    wordscore: float = field(
-        default=-1,
-        metadata={"help": "Word score for KenLM decoder"},
-    )
-    unkweight: float = field(
-        default=-math.inf,
-        metadata={"help": "Unknown weight for KenLM decoder"},
-    )
-    silweight: float = field(
-        default=0,
-        metadata={"help": "Silence weight for KenLM decoder"},
-    )
-    lmweight: float = field(
-        default=2,
-        metadata={"help": "Weight for LM while interpolating score"},
-    )
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/bart/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/models/bart/__init__.py
deleted file mode 100644
index a701923f7e5a2a8aa9b75e5580ddea22907f53ee..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/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/Harveenchadha/oiTrans/subword-nmt/subword_nmt/learn_joint_bpe_and_vocab.py b/spaces/Harveenchadha/oiTrans/subword-nmt/subword_nmt/learn_joint_bpe_and_vocab.py
deleted file mode 100644
index d75ff3d3f687c4f4776cc0246b05e3f6765374b2..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/oiTrans/subword-nmt/subword_nmt/learn_joint_bpe_and_vocab.py
+++ /dev/null
@@ -1,166 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# Author: Rico Sennrich
-
-"""Use byte pair encoding (BPE) to learn a variable-length encoding of the vocabulary in a text.
-This script learns BPE jointly on a concatenation of a list of texts (typically the source and target side of a parallel corpus,
-applies the learned operation to each and (optionally) returns the resulting vocabulary of each text.
-The vocabulary can be used in apply_bpe.py to avoid producing symbols that are rare or OOV in a training text.
-
-Reference:
-Rico Sennrich, Barry Haddow and Alexandra Birch (2016). Neural Machine Translation of Rare Words with Subword Units.
-Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016). Berlin, Germany.
-"""
-
-from __future__ import unicode_literals
-
-import sys
-import os
-import inspect
-import codecs
-import argparse
-import tempfile
-import warnings
-from collections import Counter
-from multiprocessing import cpu_count
-
-#hack to get imports working if running this as a script, or within a package
-if __name__ == '__main__':
-    import learn_bpe
-    import apply_bpe
-else:
-    from . import learn_bpe
-    from . import apply_bpe
-
-# hack for python2/3 compatibility
-from io import open
-argparse.open = open
-
-def create_parser(subparsers=None):
-
-    if subparsers:
-        parser = subparsers.add_parser('learn-joint-bpe-and-vocab',
-            formatter_class=argparse.RawDescriptionHelpFormatter,
-            description="learn BPE-based word segmentation")
-    else:
-        parser = argparse.ArgumentParser(
-            formatter_class=argparse.RawDescriptionHelpFormatter,
-            description="learn BPE-based word segmentation")
-
-    parser.add_argument(
-        '--input', '-i', type=argparse.FileType('r'), required=True, nargs = '+',
-        metavar='PATH',
-        help="Input texts (multiple allowed).")
-    parser.add_argument(
-        '--output', '-o', type=argparse.FileType('w'), required=True,
-        metavar='PATH',
-        help="Output file for BPE codes.")
-    parser.add_argument(
-        '--symbols', '-s', type=int, default=10000,
-        help="Create this many new symbols (each representing a character n-gram) (default: %(default)s)")
-    parser.add_argument(
-        '--separator', type=str, default='@@', metavar='STR',
-        help="Separator between non-final subword units (default: '%(default)s')")
-    parser.add_argument(
-        '--write-vocabulary', type=argparse.FileType('w'), required=True, nargs = '+', default=None,
-        metavar='PATH', dest='vocab',
-        help='Write to these vocabulary files after applying BPE. One per input text. Used for filtering in apply_bpe.py')
-    parser.add_argument(
-        '--min-frequency', type=int, default=2, metavar='FREQ',
-        help='Stop if no symbol pair has frequency >= FREQ (default: %(default)s)')
-    parser.add_argument(
-        '--total-symbols', '-t', action="store_true",
-        help="subtract number of characters from the symbols to be generated (so that '--symbols' becomes an estimate for the total number of symbols needed to encode text).")
-    parser.add_argument(
-        '--num-workers', type=int, default=1,
-        help="Number of processors to process texts, only supported in Python3. If -1, set `multiprocessing.cpu_count()`. (default: %(default)s)")
-    parser.add_argument(
-        '--verbose', '-v', action="store_true",
-        help="verbose mode.")
-
-    return parser
-
-def learn_joint_bpe_and_vocab(args):
-
-    if args.vocab and len(args.input) != len(args.vocab):
-        sys.stderr.write('Error: number of input files and vocabulary files must match\n')
-        sys.exit(1)
-
-    # read/write files as UTF-8
-    args.input = [codecs.open(f.name, encoding='UTF-8') for f in args.input]
-    args.vocab = [codecs.open(f.name, 'w', encoding='UTF-8') for f in args.vocab]
-
-    # get combined vocabulary of all input texts
-    full_vocab = Counter()
-    for f in args.input:
-        full_vocab += learn_bpe.get_vocabulary(f, num_workers=args.num_workers)
-        f.seek(0)
-
-    vocab_list = ['{0} {1}'.format(key, freq) for (key, freq) in full_vocab.items()]
-
-    # learn BPE on combined vocabulary
-    with codecs.open(args.output.name, 'w', encoding='UTF-8') as output:
-        learn_bpe.learn_bpe(vocab_list, output, args.symbols, args.min_frequency, args.verbose, is_dict=True, total_symbols=args.total_symbols)
-
-    with codecs.open(args.output.name, encoding='UTF-8') as codes:
-        bpe = apply_bpe.BPE(codes, separator=args.separator)
-
-    # apply BPE to each training corpus and get vocabulary
-    for train_file, vocab_file in zip(args.input, args.vocab):
-
-        tmp = tempfile.NamedTemporaryFile(delete=False)
-        tmp.close()
-
-        tmpout = codecs.open(tmp.name, 'w', encoding='UTF-8')
-
-        train_file.seek(0)
-        bpe.process_lines(train_file.name, tmpout, num_workers=args.num_workers)
-
-        tmpout.close()
-        tmpin = codecs.open(tmp.name, encoding='UTF-8')
-
-        vocab = learn_bpe.get_vocabulary(tmpin, num_workers=args.num_workers)
-        tmpin.close()
-        os.remove(tmp.name)
-
-        for key, freq in sorted(vocab.items(), key=lambda x: x[1], reverse=True):
-            vocab_file.write("{0} {1}\n".format(key, freq))
-        vocab_file.close()
-
-
-if __name__ == '__main__':
-
-    currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
-    newdir = os.path.join(currentdir, 'subword_nmt')
-    if os.path.isdir(newdir):
-        warnings.simplefilter('default')
-        warnings.warn(
-            "this script's location has moved to {0}. This symbolic link will be removed in a future version. Please point to the new location, or install the package and use the command 'subword-nmt'".format(newdir),
-            DeprecationWarning
-        )
-
-    # python 2/3 compatibility
-    if sys.version_info < (3, 0):
-        sys.stderr = codecs.getwriter('UTF-8')(sys.stderr)
-        sys.stdout = codecs.getwriter('UTF-8')(sys.stdout)
-        sys.stdin = codecs.getreader('UTF-8')(sys.stdin)
-    else:
-        sys.stderr = codecs.getwriter('UTF-8')(sys.stderr.buffer)
-        sys.stdout = codecs.getwriter('UTF-8')(sys.stdout.buffer)
-        sys.stdin = codecs.getreader('UTF-8')(sys.stdin.buffer)
-
-    parser = create_parser()
-    args = parser.parse_args()
-
-    if args.num_workers <= 0:
-        args.num_workers = cpu_count()
-
-    if sys.version_info < (3, 0):
-        args.separator = args.separator.decode('UTF-8')
-        if args.num_workers > 1:
-            args.num_workers = 1
-            warnings.warn("Parallel mode is only supported in Python3. Using 1 processor instead.")
-
-    assert(len(args.input) == len(args.vocab))
-
-    learn_joint_bpe_and_vocab(args)
diff --git a/spaces/HighCWu/GPEN/sr_model/real_esrnet.py b/spaces/HighCWu/GPEN/sr_model/real_esrnet.py
deleted file mode 100644
index eaf319d17c96d3f5f30c659f4013a3d4c769a3d7..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/GPEN/sr_model/real_esrnet.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import os
-import math
-import torch
-import numpy as np
-from rrdbnet_arch import RRDBNet
-from torch.nn import functional as F
-
-class RealESRNet(object):
-    def __init__(self, base_dir='./', model=None, scale=2, tile_size=0, tile_pad=10, device='cuda'):
-        self.base_dir = base_dir
-        self.scale = scale
-        self.tile_size = tile_size
-        self.tile_pad = tile_pad
-        self.device = device
-        self.load_srmodel(base_dir, model)
-
-    def load_srmodel(self, base_dir, model):
-        self.srmodel = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=32, num_block=23, num_grow_ch=32, scale=self.scale)
-        if model is None:
-            loadnet = torch.load(os.path.join(self.base_dir, 'weights', 'realesrnet_x%d.pth'%self.scale))
-        else:
-            loadnet = torch.load(os.path.join(self.base_dir, 'weights', model+'_x%d.pth'%self.scale))
-        #print(loadnet['params_ema'].keys)
-        self.srmodel.load_state_dict(loadnet['params_ema'], strict=True)
-        self.srmodel.eval()
-        self.srmodel = self.srmodel.to(self.device)
-
-    def tile_process(self, img):
-        """It will first crop input images to tiles, and then process each tile.
-        Finally, all the processed tiles are merged into one images.
-
-        Modified from: https://github.com/ata4/esrgan-launcher
-        """
-        batch, channel, height, width = img.shape
-        output_height = height * self.scale
-        output_width = width * self.scale
-        output_shape = (batch, channel, output_height, output_width)
-
-        # start with black image
-        output = img.new_zeros(output_shape)
-        tiles_x = math.ceil(width / self.tile_size)
-        tiles_y = math.ceil(height / self.tile_size)
-
-        # loop over all tiles
-        for y in range(tiles_y):
-            for x in range(tiles_x):
-                # extract tile from input image
-                ofs_x = x * self.tile_size
-                ofs_y = y * self.tile_size
-                # input tile area on total image
-                input_start_x = ofs_x
-                input_end_x = min(ofs_x + self.tile_size, width)
-                input_start_y = ofs_y
-                input_end_y = min(ofs_y + self.tile_size, height)
-
-                # input tile area on total image with padding
-                input_start_x_pad = max(input_start_x - self.tile_pad, 0)
-                input_end_x_pad = min(input_end_x + self.tile_pad, width)
-                input_start_y_pad = max(input_start_y - self.tile_pad, 0)
-                input_end_y_pad = min(input_end_y + self.tile_pad, height)
-
-                # input tile dimensions
-                input_tile_width = input_end_x - input_start_x
-                input_tile_height = input_end_y - input_start_y
-                tile_idx = y * tiles_x + x + 1
-                input_tile = img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]
-
-                # upscale tile
-                try:
-                    with torch.no_grad():
-                        output_tile = self.srmodel(input_tile)
-                except RuntimeError as error:
-                    print('Error', error)
-                    return None
-                if tile_idx%10==0: print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')
-
-                # output tile area on total image
-                output_start_x = input_start_x * self.scale
-                output_end_x = input_end_x * self.scale
-                output_start_y = input_start_y * self.scale
-                output_end_y = input_end_y * self.scale
-
-                # output tile area without padding
-                output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
-                output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
-                output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
-                output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
-
-                # put tile into output image
-                output[:, :, output_start_y:output_end_y,
-                            output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
-                                                                       output_start_x_tile:output_end_x_tile]
-        return output
-
-    def process(self, img):
-        img = img.astype(np.float32) / 255.
-        img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
-        img = img.unsqueeze(0).to(self.device)
-
-        if self.scale == 2:
-            mod_scale = 2
-        elif self.scale == 1:
-            mod_scale = 4
-        else:
-            mod_scale = None
-        if mod_scale is not None:
-            h_pad, w_pad = 0, 0
-            _, _, h, w = img.size()
-            if (h % mod_scale != 0):
-                h_pad = (mod_scale - h % mod_scale)
-            if (w % mod_scale != 0):
-                w_pad = (mod_scale - w % mod_scale)
-            img = F.pad(img, (0, w_pad, 0, h_pad), 'reflect')
-
-        try:
-            with torch.no_grad():
-                if self.tile_size > 0:
-                    output = self.tile_process(img)
-                else:
-                    output = self.srmodel(img)
-            del img
-            # remove extra pad
-            if mod_scale is not None:
-                _, _, h, w = output.size()
-                output = output[:, :, 0:h - h_pad, 0:w - w_pad]
-            output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
-            output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
-            output = (output * 255.0).round().astype(np.uint8)
-
-            return output
-        except Exception as e:
-            print('sr failed:', e)
-            return None
\ No newline at end of file
diff --git a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/scripts/normalize_text.py b/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/scripts/normalize_text.py
deleted file mode 100644
index 9d0ffeb27d038a6b82aaf0f6bdf208af565663f6..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/scripts/normalize_text.py
+++ /dev/null
@@ -1,22 +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 regex
-import sys
-
-
-def main():
-    filter_r = regex.compile(r"[^\p{L}\p{N}\p{M}\' \-]")
-
-    for line in sys.stdin:
-        line = line.strip()
-        line = filter_r.sub(" ", line)
-        line = " ".join(line.split())
-        print(line)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/data/raw_label_dataset.py b/spaces/ICML2022/OFA/fairseq/fairseq/data/raw_label_dataset.py
deleted file mode 100644
index d054904f419bd64855d33a2a770b43f671c7c8d8..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/data/raw_label_dataset.py
+++ /dev/null
@@ -1,23 +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 . import FairseqDataset
-
-
-class RawLabelDataset(FairseqDataset):
-    def __init__(self, labels):
-        super().__init__()
-        self.labels = labels
-
-    def __getitem__(self, index):
-        return self.labels[index]
-
-    def __len__(self):
-        return len(self.labels)
-
-    def collater(self, samples):
-        return torch.tensor(samples)
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/model_parallel/models/__init__.py b/spaces/ICML2022/OFA/fairseq/fairseq/model_parallel/models/__init__.py
deleted file mode 100644
index 3532479e52a0e1f1ba204c6f5d51c71c98ee5df0..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/model_parallel/models/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import importlib
-import os
-
-
-# automatically import any Python files in the models/ directory
-models_dir = os.path.dirname(__file__)
-for file in os.listdir(models_dir):
-    path = os.path.join(models_dir, file)
-    if (
-        not file.startswith("_")
-        and not file.startswith(".")
-        and (file.endswith(".py") or os.path.isdir(path))
-    ):
-        model_name = file[: file.find(".py")] if file.endswith(".py") else file
-        module = importlib.import_module("fairseq.model_parallel.models." + model_name)
diff --git a/spaces/ICML2022/resefa/utils/loggers/normal_logger.py b/spaces/ICML2022/resefa/utils/loggers/normal_logger.py
deleted file mode 100644
index 57f66d21afce89d6f4dcb1bfd7fd7ddc3b441e31..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/resefa/utils/loggers/normal_logger.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# python3.7
-"""Contains the class of normal logger.
-
-This class is built based on the built-in function `print()`, the module
-`logging` and the module `tqdm` for progressive bar.
-"""
-
-import sys
-import logging
-from copy import deepcopy
-from tqdm import tqdm
-
-from .base_logger import BaseLogger
-
-__all__ = ['NormalLogger']
-
-
-class NormalLogger(BaseLogger):
-    """Implements the logger based on `logging` module."""
-
-    def __init__(self,
-                 logger_name='logger',
-                 logfile=None,
-                 screen_level=logging.INFO,
-                 file_level=logging.DEBUG,
-                 indent_space=4,
-                 verbose_log=False):
-        super().__init__(logger_name=logger_name,
-                         logfile=logfile,
-                         screen_level=screen_level,
-                         file_level=file_level,
-                         indent_space=indent_space,
-                         verbose_log=verbose_log)
-
-        # Get logger and check whether the logger has already been created.
-        self.logger = logging.getLogger(self.logger_name)
-        self.logger.propagate = False
-        if self.logger.hasHandlers():  # Already existed
-            raise SystemExit(f'Logger `{self.logger_name}` has already '
-                             f'existed!\n'
-                             f'Please use another name, or otherwise the '
-                             f'messages may be mixed up.')
-
-        # Set format.
-        self.logger.setLevel(logging.DEBUG)
-        formatter = logging.Formatter(
-            '[%(asctime)s][%(levelname)s] %(message)s',
-            datefmt='%Y-%m-%d %H:%M:%S')
-
-        # Print log message onto the screen.
-        terminal_handler = logging.StreamHandler(stream=sys.stdout)
-        terminal_handler.setLevel(self.screen_level)
-        terminal_handler.setFormatter(formatter)
-        self.logger.addHandler(terminal_handler)
-
-        # Save log message into log file if needed.
-        if self.logfile:
-            # File will be closed when the logger is closed in `self.close()`.
-            self.file_stream = open(self.logfile, 'a')  # pylint: disable=consider-using-with
-            file_handler = logging.StreamHandler(stream=self.file_stream)
-            file_handler.setLevel(self.file_level)
-            file_handler.setFormatter(formatter)
-            self.logger.addHandler(file_handler)
-
-        self.pbar = []
-        self.pbar_kwargs = {}
-
-    def _log(self, message, **kwargs):
-        self.logger.log(message, **kwargs)
-
-    def _debug(self, message, **kwargs):
-        self.logger.debug(message, **kwargs)
-
-    def _info(self, message, **kwargs):
-        self.logger.info(message, **kwargs)
-
-    def _warning(self, message, **kwargs):
-        self.logger.warning(message, **kwargs)
-
-    def _error(self, message, **kwargs):
-        self.logger.error(message, **kwargs)
-
-    def _exception(self, message, **kwargs):
-        self.logger.exception(message, **kwargs)
-
-    def _critical(self, message, **kwargs):
-        self.logger.critical(message, **kwargs)
-
-    def _print(self, *messages, **kwargs):
-        for handler in self.logger.handlers:
-            print(*messages, file=handler.stream)
-
-    def init_pbar(self, leave=False):
-        columns = [
-            '{desc}',
-            '{bar}',
-            ' {percentage:5.1f}%',
-            '[{elapsed}<{remaining}, {rate_fmt}{postfix}]',
-        ]
-        self.pbar_kwargs = dict(
-            leave=leave,
-            bar_format=' '.join(columns),
-            unit='',
-        )
-
-    def add_pbar_task(self, name, total, **kwargs):
-        assert isinstance(self.pbar_kwargs, dict)
-        pbar_kwargs = deepcopy(self.pbar_kwargs)
-        pbar_kwargs.update(**kwargs)
-        self.pbar.append(tqdm(desc=name, total=total, **pbar_kwargs))
-        return len(self.pbar) - 1
-
-    def update_pbar(self, task_id, advance=1):
-        assert len(self.pbar) > task_id and isinstance(self.pbar[task_id], tqdm)
-        if self.pbar[task_id].n < self.pbar[task_id].total:
-            self.pbar[task_id].update(advance)
-            if self.pbar[task_id].n >= self.pbar[task_id].total:
-                self.pbar[task_id].refresh()
-
-    def close_pbar(self):
-        for pbar in self.pbar[::-1]:
-            pbar.close()
-        self.pbar = []
-        self.pbar_kwargs = {}
diff --git a/spaces/Iceclear/StableSR/StableSR/basicsr/archs/hifacegan_arch.py b/spaces/Iceclear/StableSR/StableSR/basicsr/archs/hifacegan_arch.py
deleted file mode 100644
index 098e3ed4306eb19ae9da705c0af580a6f74c6cb9..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/basicsr/archs/hifacegan_arch.py
+++ /dev/null
@@ -1,260 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from basicsr.utils.registry import ARCH_REGISTRY
-from .hifacegan_util import BaseNetwork, LIPEncoder, SPADEResnetBlock, get_nonspade_norm_layer
-
-
-class SPADEGenerator(BaseNetwork):
-    """Generator with SPADEResBlock"""
-
-    def __init__(self,
-                 num_in_ch=3,
-                 num_feat=64,
-                 use_vae=False,
-                 z_dim=256,
-                 crop_size=512,
-                 norm_g='spectralspadesyncbatch3x3',
-                 is_train=True,
-                 init_train_phase=3):  # progressive training disabled
-        super().__init__()
-        self.nf = num_feat
-        self.input_nc = num_in_ch
-        self.is_train = is_train
-        self.train_phase = init_train_phase
-
-        self.scale_ratio = 5  # hardcoded now
-        self.sw = crop_size // (2**self.scale_ratio)
-        self.sh = self.sw  # 20210519: By default use square image, aspect_ratio = 1.0
-
-        if use_vae:
-            # In case of VAE, we will sample from random z vector
-            self.fc = nn.Linear(z_dim, 16 * self.nf * self.sw * self.sh)
-        else:
-            # Otherwise, we make the network deterministic by starting with
-            # downsampled segmentation map instead of random z
-            self.fc = nn.Conv2d(num_in_ch, 16 * self.nf, 3, padding=1)
-
-        self.head_0 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g)
-
-        self.g_middle_0 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g)
-        self.g_middle_1 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g)
-
-        self.ups = nn.ModuleList([
-            SPADEResnetBlock(16 * self.nf, 8 * self.nf, norm_g),
-            SPADEResnetBlock(8 * self.nf, 4 * self.nf, norm_g),
-            SPADEResnetBlock(4 * self.nf, 2 * self.nf, norm_g),
-            SPADEResnetBlock(2 * self.nf, 1 * self.nf, norm_g)
-        ])
-
-        self.to_rgbs = nn.ModuleList([
-            nn.Conv2d(8 * self.nf, 3, 3, padding=1),
-            nn.Conv2d(4 * self.nf, 3, 3, padding=1),
-            nn.Conv2d(2 * self.nf, 3, 3, padding=1),
-            nn.Conv2d(1 * self.nf, 3, 3, padding=1)
-        ])
-
-        self.up = nn.Upsample(scale_factor=2)
-
-    def encode(self, input_tensor):
-        """
-        Encode input_tensor into feature maps, can be overridden in derived classes
-        Default: nearest downsampling of 2**5 = 32 times
-        """
-        h, w = input_tensor.size()[-2:]
-        sh, sw = h // 2**self.scale_ratio, w // 2**self.scale_ratio
-        x = F.interpolate(input_tensor, size=(sh, sw))
-        return self.fc(x)
-
-    def forward(self, x):
-        # In oroginal SPADE, seg means a segmentation map, but here we use x instead.
-        seg = x
-
-        x = self.encode(x)
-        x = self.head_0(x, seg)
-
-        x = self.up(x)
-        x = self.g_middle_0(x, seg)
-        x = self.g_middle_1(x, seg)
-
-        if self.is_train:
-            phase = self.train_phase + 1
-        else:
-            phase = len(self.to_rgbs)
-
-        for i in range(phase):
-            x = self.up(x)
-            x = self.ups[i](x, seg)
-
-        x = self.to_rgbs[phase - 1](F.leaky_relu(x, 2e-1))
-        x = torch.tanh(x)
-
-        return x
-
-    def mixed_guidance_forward(self, input_x, seg=None, n=0, mode='progressive'):
-        """
-        A helper class for subspace visualization. Input and seg are different images.
-        For the first n levels (including encoder) we use input, for the rest we use seg.
-
-        If mode = 'progressive', the output's like: AAABBB
-        If mode = 'one_plug', the output's like:    AAABAA
-        If mode = 'one_ablate', the output's like:  BBBABB
-        """
-
-        if seg is None:
-            return self.forward(input_x)
-
-        if self.is_train:
-            phase = self.train_phase + 1
-        else:
-            phase = len(self.to_rgbs)
-
-        if mode == 'progressive':
-            n = max(min(n, 4 + phase), 0)
-            guide_list = [input_x] * n + [seg] * (4 + phase - n)
-        elif mode == 'one_plug':
-            n = max(min(n, 4 + phase - 1), 0)
-            guide_list = [seg] * (4 + phase)
-            guide_list[n] = input_x
-        elif mode == 'one_ablate':
-            if n > 3 + phase:
-                return self.forward(input_x)
-            guide_list = [input_x] * (4 + phase)
-            guide_list[n] = seg
-
-        x = self.encode(guide_list[0])
-        x = self.head_0(x, guide_list[1])
-
-        x = self.up(x)
-        x = self.g_middle_0(x, guide_list[2])
-        x = self.g_middle_1(x, guide_list[3])
-
-        for i in range(phase):
-            x = self.up(x)
-            x = self.ups[i](x, guide_list[4 + i])
-
-        x = self.to_rgbs[phase - 1](F.leaky_relu(x, 2e-1))
-        x = torch.tanh(x)
-
-        return x
-
-
-@ARCH_REGISTRY.register()
-class HiFaceGAN(SPADEGenerator):
-    """
-    HiFaceGAN: SPADEGenerator with a learnable feature encoder
-    Current encoder design: LIPEncoder
-    """
-
-    def __init__(self,
-                 num_in_ch=3,
-                 num_feat=64,
-                 use_vae=False,
-                 z_dim=256,
-                 crop_size=512,
-                 norm_g='spectralspadesyncbatch3x3',
-                 is_train=True,
-                 init_train_phase=3):
-        super().__init__(num_in_ch, num_feat, use_vae, z_dim, crop_size, norm_g, is_train, init_train_phase)
-        self.lip_encoder = LIPEncoder(num_in_ch, num_feat, self.sw, self.sh, self.scale_ratio)
-
-    def encode(self, input_tensor):
-        return self.lip_encoder(input_tensor)
-
-
-@ARCH_REGISTRY.register()
-class HiFaceGANDiscriminator(BaseNetwork):
-    """
-    Inspired by pix2pixHD multiscale discriminator.
-
-    Args:
-        num_in_ch (int): Channel number of inputs. Default: 3.
-        num_out_ch (int): Channel number of outputs. Default: 3.
-        conditional_d (bool): Whether use conditional discriminator.
-            Default: True.
-        num_d (int): Number of Multiscale discriminators. Default: 3.
-        n_layers_d (int): Number of downsample layers in each D. Default: 4.
-        num_feat (int): Channel number of base intermediate features.
-            Default: 64.
-        norm_d (str): String to determine normalization layers in D.
-            Choices: [spectral][instance/batch/syncbatch]
-            Default: 'spectralinstance'.
-        keep_features (bool): Keep intermediate features for matching loss, etc.
-            Default: True.
-    """
-
-    def __init__(self,
-                 num_in_ch=3,
-                 num_out_ch=3,
-                 conditional_d=True,
-                 num_d=2,
-                 n_layers_d=4,
-                 num_feat=64,
-                 norm_d='spectralinstance',
-                 keep_features=True):
-        super().__init__()
-        self.num_d = num_d
-
-        input_nc = num_in_ch
-        if conditional_d:
-            input_nc += num_out_ch
-
-        for i in range(num_d):
-            subnet_d = NLayerDiscriminator(input_nc, n_layers_d, num_feat, norm_d, keep_features)
-            self.add_module(f'discriminator_{i}', subnet_d)
-
-    def downsample(self, x):
-        return F.avg_pool2d(x, kernel_size=3, stride=2, padding=[1, 1], count_include_pad=False)
-
-    # Returns list of lists of discriminator outputs.
-    # The final result is of size opt.num_d x opt.n_layers_D
-    def forward(self, x):
-        result = []
-        for _, _net_d in self.named_children():
-            out = _net_d(x)
-            result.append(out)
-            x = self.downsample(x)
-
-        return result
-
-
-class NLayerDiscriminator(BaseNetwork):
-    """Defines the PatchGAN discriminator with the specified arguments."""
-
-    def __init__(self, input_nc, n_layers_d, num_feat, norm_d, keep_features):
-        super().__init__()
-        kw = 4
-        padw = int(np.ceil((kw - 1.0) / 2))
-        nf = num_feat
-        self.keep_features = keep_features
-
-        norm_layer = get_nonspade_norm_layer(norm_d)
-        sequence = [[nn.Conv2d(input_nc, nf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, False)]]
-
-        for n in range(1, n_layers_d):
-            nf_prev = nf
-            nf = min(nf * 2, 512)
-            stride = 1 if n == n_layers_d - 1 else 2
-            sequence += [[
-                norm_layer(nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=stride, padding=padw)),
-                nn.LeakyReLU(0.2, False)
-            ]]
-
-        sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
-
-        # We divide the layers into groups to extract intermediate layer outputs
-        for n in range(len(sequence)):
-            self.add_module('model' + str(n), nn.Sequential(*sequence[n]))
-
-    def forward(self, x):
-        results = [x]
-        for submodel in self.children():
-            intermediate_output = submodel(results[-1])
-            results.append(intermediate_output)
-
-        if self.keep_features:
-            return results[1:]
-        else:
-            return results[-1]
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/segment-anything/demo/src/index.tsx b/spaces/InpaintAI/Inpaint-Anything/third_party/segment-anything/demo/src/index.tsx
deleted file mode 100644
index 714dfc91cb980e00a48b658bd4f1a173c803a26b..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/segment-anything/demo/src/index.tsx
+++ /dev/null
@@ -1,17 +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 * as React from "react";
-import { createRoot } from "react-dom/client";
-import AppContextProvider from "./components/hooks/context";
-import App from "./App";
-const container = document.getElementById("root");
-const root = createRoot(container!);
-root.render(
-  <AppContextProvider>
-    <App/>
-  </AppContextProvider>
-);
diff --git a/spaces/JUNGU/VToonify/README.md b/spaces/JUNGU/VToonify/README.md
deleted file mode 100644
index 40ded2ce4dc80cc8764c8fee50476d44bee383b6..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/VToonify/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: VToonify
-emoji: 👨‍🎨
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
-license: other
-duplicated_from: PKUWilliamYang/VToonify
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Jamkonams/AutoGPT/autogpt/config/__init__.py b/spaces/Jamkonams/AutoGPT/autogpt/config/__init__.py
deleted file mode 100644
index 726b6dcf3da95968b948c4d897e97a9cdd0928ff..0000000000000000000000000000000000000000
--- a/spaces/Jamkonams/AutoGPT/autogpt/config/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-"""
-This module contains the configuration classes for AutoGPT.
-"""
-from autogpt.config.ai_config import AIConfig
-from autogpt.config.config import Config, check_openai_api_key
-from autogpt.config.singleton import AbstractSingleton, Singleton
-
-__all__ = [
-    "check_openai_api_key",
-    "AbstractSingleton",
-    "AIConfig",
-    "Config",
-    "Singleton",
-]
diff --git a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_SVM.py b/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_SVM.py
deleted file mode 100644
index dc776c53e4395e0c4d205d28bdcafe06bd3774b5..0000000000000000000000000000000000000000
--- a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_SVM.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import cv2
-import numpy as np
-from PIL import Image
-import pickle
-import tensorflow as tf
-import io
-
-class rabbitSVM:
-    def __init__(self,url) -> None:
-        self.image = url
-    
-    def predict_image(self):
-        # Load the model
-        load_extractor = tf.keras.models.load_model("././Model/Rabbit/resnetxSVM/resnet_EXTRACTOR.h5")
-
-        modelpath = "././Model/Rabbit/resnetxSVM/dataSaved.pkl"
-
-        with open(modelpath, 'rb') as file:
-            saved_data = pickle.load(file)
-            animal_breed = saved_data['class_name']
-            model = saved_data['svm_model']
-
-        im = Image.open(self.image)
-        img = im.convert("RGB")
-        img= np.asarray(img)
-        image_resized= cv2.resize(img, (224,224))
-        features = load_extractor.predict(np.expand_dims(image_resized, axis=0))
-        
-        reshaped_features = features.reshape(features.shape[0],-1)
-        predicted_class = model.predict(reshaped_features)
-        pred_prob = model.predict_proba(reshaped_features)
-        prediction_probability = pred_prob[0][predicted_class[0]]
-        predicted_class
-
-        output_class= animal_breed[predicted_class[0]]
-
-        return [output_class, prediction_probability]
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/get-pip.py b/spaces/Kangarroar/ApplioRVC-Inference/get-pip.py
deleted file mode 100644
index cf68a2b3426decf01bde021a50c47a2115af303a..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/get-pip.py
+++ /dev/null
@@ -1,32657 +0,0 @@
-#!/usr/bin/env python
-#
-# Hi There!
-#
-# You may be wondering what this giant blob of binary data here is, you might
-# even be worried that we're up to something nefarious (good for you for being
-# paranoid!). This is a base85 encoding of a zip file, this zip file contains
-# an entire copy of pip (version 23.2.1).
-#
-# Pip is a thing that installs packages, pip itself is a package that someone
-# might want to install, especially if they're looking to run this get-pip.py
-# script. Pip has a lot of code to deal with the security of installing
-# packages, various edge cases on various platforms, and other such sort of
-# "tribal knowledge" that has been encoded in its code base. Because of this
-# we basically include an entire copy of pip inside this blob. We do this
-# because the alternatives are attempt to implement a "minipip" that probably
-# doesn't do things correctly and has weird edge cases, or compress pip itself
-# down into a single file.
-#
-# If you're wondering how this is created, it is generated using
-# `scripts/generate.py` in https://github.com/pypa/get-pip.
-
-import sys
-
-this_python = sys.version_info[:2]
-min_version = (3, 7)
-if this_python < min_version:
-    message_parts = [
-        "This script does not work on Python {}.{}".format(*this_python),
-        "The minimum supported Python version is {}.{}.".format(*min_version),
-        "Please use https://bootstrap.pypa.io/pip/{}.{}/get-pip.py instead.".format(*this_python),
-    ]
-    print("ERROR: " + " ".join(message_parts))
-    sys.exit(1)
-
-
-import os.path
-import pkgutil
-import shutil
-import tempfile
-import argparse
-import importlib
-from base64 import b85decode
-
-
-def include_setuptools(args):
-    """
-    Install setuptools only if absent and not excluded.
-    """
-    cli = not args.no_setuptools
-    env = not os.environ.get("PIP_NO_SETUPTOOLS")
-    absent = not importlib.util.find_spec("setuptools")
-    return cli and env and absent
-
-
-def include_wheel(args):
-    """
-    Install wheel only if absent and not excluded.
-    """
-    cli = not args.no_wheel
-    env = not os.environ.get("PIP_NO_WHEEL")
-    absent = not importlib.util.find_spec("wheel")
-    return cli and env and absent
-
-
-def determine_pip_install_arguments():
-    pre_parser = argparse.ArgumentParser()
-    pre_parser.add_argument("--no-setuptools", action="store_true")
-    pre_parser.add_argument("--no-wheel", action="store_true")
-    pre, args = pre_parser.parse_known_args()
-
-    args.append("pip")
-
-    if include_setuptools(pre):
-        args.append("setuptools")
-
-    if include_wheel(pre):
-        args.append("wheel")
-
-    return ["install", "--upgrade", "--force-reinstall"] + args
-
-
-def monkeypatch_for_cert(tmpdir):
-    """Patches `pip install` to provide default certificate with the lowest priority.
-
-    This ensures that the bundled certificates are used unless the user specifies a
-    custom cert via any of pip's option passing mechanisms (config, env-var, CLI).
-
-    A monkeypatch is the easiest way to achieve this, without messing too much with
-    the rest of pip's internals.
-    """
-    from pip._internal.commands.install import InstallCommand
-
-    # We want to be using the internal certificates.
-    cert_path = os.path.join(tmpdir, "cacert.pem")
-    with open(cert_path, "wb") as cert:
-        cert.write(pkgutil.get_data("pip._vendor.certifi", "cacert.pem"))
-
-    install_parse_args = InstallCommand.parse_args
-
-    def cert_parse_args(self, args):
-        if not self.parser.get_default_values().cert:
-            # There are no user provided cert -- force use of bundled cert
-            self.parser.defaults["cert"] = cert_path  # calculated above
-        return install_parse_args(self, args)
-
-    InstallCommand.parse_args = cert_parse_args
-
-
-def bootstrap(tmpdir):
-    monkeypatch_for_cert(tmpdir)
-
-    # Execute the included pip and use it to install the latest pip and
-    # setuptools from PyPI
-    from pip._internal.cli.main import main as pip_entry_point
-    args = determine_pip_install_arguments()
-    sys.exit(pip_entry_point(args))
-
-
-def main():
-    tmpdir = None
-    try:
-        # Create a temporary working directory
-        tmpdir = tempfile.mkdtemp()
-
-        # Unpack the zipfile into the temporary directory
-        pip_zip = os.path.join(tmpdir, "pip.zip")
-        with open(pip_zip, "wb") as fp:
-            fp.write(b85decode(DATA.replace(b"\n", b"")))
-
-        # Add the zipfile to sys.path so that we can import it
-        sys.path.insert(0, pip_zip)
-
-        # Run the bootstrap
-        bootstrap(tmpdir=tmpdir)
-    finally:
-        # Clean up our temporary working directory
-        if tmpdir:
-            shutil.rmtree(tmpdir, ignore_errors=True)
-
-
-DATA = b"""
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-"""
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Kaori1707/Depth-estimation/dpt/base_model.py b/spaces/Kaori1707/Depth-estimation/dpt/base_model.py
deleted file mode 100644
index 5c2e0e93b0495f48a3405546b6fe1969be3480a2..0000000000000000000000000000000000000000
--- a/spaces/Kaori1707/Depth-estimation/dpt/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/Karifannaa/audio_story/README.md b/spaces/Karifannaa/audio_story/README.md
deleted file mode 100644
index 50fb9f4811903488f7033ecac1cebfef071e8114..0000000000000000000000000000000000000000
--- a/spaces/Karifannaa/audio_story/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Audio Story
-emoji: 🚀
-colorFrom: indigo
-colorTo: green
-sdk: streamlit
-sdk_version: 1.28.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Kimata/Sanskrit-TTS/indic_nlp_library/docs/make.bat b/spaces/Kimata/Sanskrit-TTS/indic_nlp_library/docs/make.bat
deleted file mode 100644
index 922152e96a04a242e6fc40f124261d74890617d8..0000000000000000000000000000000000000000
--- a/spaces/Kimata/Sanskrit-TTS/indic_nlp_library/docs/make.bat
+++ /dev/null
@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=.
-set BUILDDIR=_build
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.http://sphinx-doc.org/
-	exit /b 1
-)
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd
diff --git a/spaces/Kreaols/ChuanhuChatGPT/locale/extract_locale.py b/spaces/Kreaols/ChuanhuChatGPT/locale/extract_locale.py
deleted file mode 100644
index 32b0924bd6dffe150cb3e481ddadef836b91b83c..0000000000000000000000000000000000000000
--- a/spaces/Kreaols/ChuanhuChatGPT/locale/extract_locale.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-import json
-import re
-
-# Define regular expression patterns
-pattern = r'i18n\((\"{3}.*?\"{3}|\".*?\")\)'
-
-# Load the .py file
-with open('ChuanhuChatbot.py', 'r', encoding='utf-8') as f:
-    contents = f.read()
-
-# Load the .py files in the modules folder
-for filename in os.listdir("modules"):
-    if filename.endswith(".py"):
-        with open(os.path.join("modules", filename), "r", encoding="utf-8") as f:
-            contents += f.read()
-
-# Matching with regular expressions
-matches = re.findall(pattern, contents, re.DOTALL)
-
-# Convert to key/value pairs
-data = {match.strip('()"'): '' for match in matches}
-
-# Save as a JSON file
-with open('labels.json', 'w', encoding='utf-8') as f:
-    json.dump(data, f, ensure_ascii=False, indent=4)
\ No newline at end of file
diff --git a/spaces/KyanChen/RSPrompter/mmdet/datasets/utils.py b/spaces/KyanChen/RSPrompter/mmdet/datasets/utils.py
deleted file mode 100644
index a281fb0b99e111fd5c4e12c6639efc38f33c3224..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/datasets/utils.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-
-from mmcv.transforms import LoadImageFromFile
-
-from mmdet.datasets.transforms import LoadAnnotations, LoadPanopticAnnotations
-from mmdet.registry import TRANSFORMS
-
-
-def get_loading_pipeline(pipeline):
-    """Only keep loading image and annotations related configuration.
-
-    Args:
-        pipeline (list[dict]): Data pipeline configs.
-
-    Returns:
-        list[dict]: The new pipeline list with only keep
-            loading image and annotations related configuration.
-
-    Examples:
-        >>> pipelines = [
-        ...    dict(type='LoadImageFromFile'),
-        ...    dict(type='LoadAnnotations', with_bbox=True),
-        ...    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-        ...    dict(type='RandomFlip', flip_ratio=0.5),
-        ...    dict(type='Normalize', **img_norm_cfg),
-        ...    dict(type='Pad', size_divisor=32),
-        ...    dict(type='DefaultFormatBundle'),
-        ...    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
-        ...    ]
-        >>> expected_pipelines = [
-        ...    dict(type='LoadImageFromFile'),
-        ...    dict(type='LoadAnnotations', with_bbox=True)
-        ...    ]
-        >>> assert expected_pipelines ==\
-        ...        get_loading_pipeline(pipelines)
-    """
-    loading_pipeline_cfg = []
-    for cfg in pipeline:
-        obj_cls = TRANSFORMS.get(cfg['type'])
-        # TODO：use more elegant way to distinguish loading modules
-        if obj_cls is not None and obj_cls in (LoadImageFromFile,
-                                               LoadAnnotations,
-                                               LoadPanopticAnnotations):
-            loading_pipeline_cfg.append(cfg)
-    assert len(loading_pipeline_cfg) == 2, \
-        'The data pipeline in your config file must include ' \
-        'loading image and annotations related pipeline.'
-    return loading_pipeline_cfg
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/fsaf_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/fsaf_head.py
deleted file mode 100644
index 0a01c487406693253eb17b883cac9ed06cf95802..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/fsaf_head.py
+++ /dev/null
@@ -1,458 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Dict, List, Optional, Tuple
-
-import numpy as np
-import torch
-from mmengine.structures import InstanceData
-from torch import Tensor
-
-from mmdet.registry import MODELS
-from mmdet.utils import InstanceList, OptInstanceList, OptMultiConfig
-from ..losses.accuracy import accuracy
-from ..losses.utils import weight_reduce_loss
-from ..task_modules.prior_generators import anchor_inside_flags
-from ..utils import images_to_levels, multi_apply, unmap
-from .retina_head import RetinaHead
-
-
-@MODELS.register_module()
-class FSAFHead(RetinaHead):
-    """Anchor-free head used in `FSAF <https://arxiv.org/abs/1903.00621>`_.
-
-    The head contains two subnetworks. The first classifies anchor boxes and
-    the second regresses deltas for the anchors (num_anchors is 1 for anchor-
-    free methods)
-
-    Args:
-        *args: Same as its base class in :class:`RetinaHead`
-        score_threshold (float, optional): The score_threshold to calculate
-            positive recall. If given, prediction scores lower than this value
-            is counted as incorrect prediction. Defaults to None.
-        init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or \
-            dict]): Initialization config dict.
-        **kwargs: Same as its base class in :class:`RetinaHead`
-
-    Example:
-        >>> import torch
-        >>> self = FSAFHead(11, 7)
-        >>> x = torch.rand(1, 7, 32, 32)
-        >>> cls_score, bbox_pred = self.forward_single(x)
-        >>> # Each anchor predicts a score for each class except background
-        >>> cls_per_anchor = cls_score.shape[1] / self.num_anchors
-        >>> box_per_anchor = bbox_pred.shape[1] / self.num_anchors
-        >>> assert cls_per_anchor == self.num_classes
-        >>> assert box_per_anchor == 4
-    """
-
-    def __init__(self,
-                 *args,
-                 score_threshold: Optional[float] = None,
-                 init_cfg: OptMultiConfig = None,
-                 **kwargs) -> None:
-        # The positive bias in self.retina_reg conv is to prevent predicted \
-        #  bbox with 0 area
-        if init_cfg is None:
-            init_cfg = dict(
-                type='Normal',
-                layer='Conv2d',
-                std=0.01,
-                override=[
-                    dict(
-                        type='Normal',
-                        name='retina_cls',
-                        std=0.01,
-                        bias_prob=0.01),
-                    dict(
-                        type='Normal', name='retina_reg', std=0.01, bias=0.25)
-                ])
-        super().__init__(*args, init_cfg=init_cfg, **kwargs)
-        self.score_threshold = score_threshold
-
-    def forward_single(self, x: Tensor) -> Tuple[Tensor, Tensor]:
-        """Forward feature map of a single scale level.
-
-        Args:
-            x (Tensor): Feature map of a single scale level.
-
-        Returns:
-            tuple[Tensor, Tensor]:
-
-            - cls_score (Tensor): Box scores for each scale level Has \
-            shape (N, num_points * num_classes, H, W).
-            - bbox_pred (Tensor): Box energies / deltas for each scale \
-            level with shape (N, num_points * 4, H, W).
-        """
-        cls_score, bbox_pred = super().forward_single(x)
-        # relu: TBLR encoder only accepts positive bbox_pred
-        return cls_score, self.relu(bbox_pred)
-
-    def _get_targets_single(self,
-                            flat_anchors: Tensor,
-                            valid_flags: Tensor,
-                            gt_instances: InstanceData,
-                            img_meta: dict,
-                            gt_instances_ignore: Optional[InstanceData] = None,
-                            unmap_outputs: bool = True) -> tuple:
-        """Compute regression and classification targets for anchors in a
-        single image.
-
-        Most of the codes are the same with the base class :obj: `AnchorHead`,
-        except that it also collects and returns the matched gt index in the
-        image (from 0 to num_gt-1). If the anchor bbox is not matched to any
-        gt, the corresponding value in pos_gt_inds is -1.
-
-        Args:
-            flat_anchors (Tensor): Multi-level anchors of the image, which are
-                concatenated into a single tensor of shape (num_anchors, 4)
-            valid_flags (Tensor): Multi level valid flags of the image,
-                which are concatenated into a single tensor of
-                    shape (num_anchors, ).
-            gt_instances (:obj:`InstanceData`): Ground truth of instance
-                annotations. It should includes ``bboxes`` and ``labels``
-                attributes.
-            img_meta (dict): Meta information for current image.
-            gt_instances_ignore (:obj:`InstanceData`, optional): Instances
-                to be ignored during training. It includes ``bboxes`` attribute
-                data that is ignored during training and testing.
-                Defaults to None.
-            unmap_outputs (bool): Whether to map outputs back to the original
-                set of anchors.  Defaults to True.
-        """
-        inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
-                                           img_meta['img_shape'][:2],
-                                           self.train_cfg['allowed_border'])
-        if not inside_flags.any():
-            raise ValueError(
-                'There is no valid anchor inside the image boundary. Please '
-                'check the image size and anchor sizes, or set '
-                '``allowed_border`` to -1 to skip the condition.')
-        # Assign gt and sample anchors
-        anchors = flat_anchors[inside_flags.type(torch.bool), :]
-
-        pred_instances = InstanceData(priors=anchors)
-        assign_result = self.assigner.assign(pred_instances, gt_instances,
-                                             gt_instances_ignore)
-        sampling_result = self.sampler.sample(assign_result, pred_instances,
-                                              gt_instances)
-
-        num_valid_anchors = anchors.shape[0]
-        bbox_targets = torch.zeros_like(anchors)
-        bbox_weights = torch.zeros_like(anchors)
-        labels = anchors.new_full((num_valid_anchors, ),
-                                  self.num_classes,
-                                  dtype=torch.long)
-        label_weights = anchors.new_zeros(
-            (num_valid_anchors, self.cls_out_channels), dtype=torch.float)
-        pos_gt_inds = anchors.new_full((num_valid_anchors, ),
-                                       -1,
-                                       dtype=torch.long)
-
-        pos_inds = sampling_result.pos_inds
-        neg_inds = sampling_result.neg_inds
-
-        if len(pos_inds) > 0:
-            if not self.reg_decoded_bbox:
-                pos_bbox_targets = self.bbox_coder.encode(
-                    sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
-            else:
-                # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
-                # is applied directly on the decoded bounding boxes, both
-                # the predicted boxes and regression targets should be with
-                # absolute coordinate format.
-                pos_bbox_targets = sampling_result.pos_gt_bboxes
-            bbox_targets[pos_inds, :] = pos_bbox_targets
-            bbox_weights[pos_inds, :] = 1.0
-            # The assigned gt_index for each anchor. (0-based)
-            pos_gt_inds[pos_inds] = sampling_result.pos_assigned_gt_inds
-            labels[pos_inds] = sampling_result.pos_gt_labels
-            if self.train_cfg['pos_weight'] <= 0:
-                label_weights[pos_inds] = 1.0
-            else:
-                label_weights[pos_inds] = self.train_cfg['pos_weight']
-
-        if len(neg_inds) > 0:
-            label_weights[neg_inds] = 1.0
-
-        # shadowed_labels is a tensor composed of tuples
-        #  (anchor_inds, class_label) that indicate those anchors lying in the
-        #  outer region of a gt or overlapped by another gt with a smaller
-        #  area.
-        #
-        # Therefore, only the shadowed labels are ignored for loss calculation.
-        # the key `shadowed_labels` is defined in :obj:`CenterRegionAssigner`
-        shadowed_labels = assign_result.get_extra_property('shadowed_labels')
-        if shadowed_labels is not None and shadowed_labels.numel():
-            if len(shadowed_labels.shape) == 2:
-                idx_, label_ = shadowed_labels[:, 0], shadowed_labels[:, 1]
-                assert (labels[idx_] != label_).all(), \
-                    'One label cannot be both positive and ignored'
-                label_weights[idx_, label_] = 0
-            else:
-                label_weights[shadowed_labels] = 0
-
-        # map up to original set of anchors
-        if unmap_outputs:
-            num_total_anchors = flat_anchors.size(0)
-            labels = unmap(
-                labels, num_total_anchors, inside_flags,
-                fill=self.num_classes)  # fill bg label
-            label_weights = unmap(label_weights, num_total_anchors,
-                                  inside_flags)
-            bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
-            bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
-            pos_gt_inds = unmap(
-                pos_gt_inds, num_total_anchors, inside_flags, fill=-1)
-
-        return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
-                neg_inds, sampling_result, pos_gt_inds)
-
-    def loss_by_feat(
-        self,
-        cls_scores: List[Tensor],
-        bbox_preds: List[Tensor],
-        batch_gt_instances: InstanceList,
-        batch_img_metas: List[dict],
-        batch_gt_instances_ignore: OptInstanceList = None
-    ) -> Dict[str, Tensor]:
-        """Compute loss of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_points * num_classes, H, W).
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_points * 4, H, W).
-            batch_gt_instances (list[:obj:`InstanceData`]): Batch of
-                gt_instance. It usually includes ``bboxes`` and ``labels``
-                attributes.
-            batch_img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
-                Batch of gt_instances_ignore. It includes ``bboxes`` attribute
-                data that is ignored during training and testing.
-                Defaults to None.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        for i in range(len(bbox_preds)):  # loop over fpn level
-            # avoid 0 area of the predicted bbox
-            bbox_preds[i] = bbox_preds[i].clamp(min=1e-4)
-        # TODO: It may directly use the base-class loss function.
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.prior_generator.num_levels
-        batch_size = len(batch_img_metas)
-        device = cls_scores[0].device
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, batch_img_metas, device=device)
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            batch_gt_instances,
-            batch_img_metas,
-            batch_gt_instances_ignore=batch_gt_instances_ignore,
-            return_sampling_results=True)
-        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
-         avg_factor, sampling_results_list,
-         pos_assigned_gt_inds_list) = cls_reg_targets
-
-        num_gts = np.array(list(map(len, batch_gt_instances)))
-        # anchor number of multi levels
-        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-        # concat all level anchors and flags to a single tensor
-        concat_anchor_list = []
-        for i in range(len(anchor_list)):
-            concat_anchor_list.append(torch.cat(anchor_list[i]))
-        all_anchor_list = images_to_levels(concat_anchor_list,
-                                           num_level_anchors)
-        losses_cls, losses_bbox = multi_apply(
-            self.loss_by_feat_single,
-            cls_scores,
-            bbox_preds,
-            all_anchor_list,
-            labels_list,
-            label_weights_list,
-            bbox_targets_list,
-            bbox_weights_list,
-            avg_factor=avg_factor)
-
-        # `pos_assigned_gt_inds_list` (length: fpn_levels) stores the assigned
-        # gt index of each anchor bbox in each fpn level.
-        cum_num_gts = list(np.cumsum(num_gts))  # length of batch_size
-        for i, assign in enumerate(pos_assigned_gt_inds_list):
-            # loop over fpn levels
-            for j in range(1, batch_size):
-                # loop over batch size
-                # Convert gt indices in each img to those in the batch
-                assign[j][assign[j] >= 0] += int(cum_num_gts[j - 1])
-            pos_assigned_gt_inds_list[i] = assign.flatten()
-            labels_list[i] = labels_list[i].flatten()
-        num_gts = num_gts.sum()  # total number of gt in the batch
-        # The unique label index of each gt in the batch
-        label_sequence = torch.arange(num_gts, device=device)
-        # Collect the average loss of each gt in each level
-        with torch.no_grad():
-            loss_levels, = multi_apply(
-                self.collect_loss_level_single,
-                losses_cls,
-                losses_bbox,
-                pos_assigned_gt_inds_list,
-                labels_seq=label_sequence)
-            # Shape: (fpn_levels, num_gts). Loss of each gt at each fpn level
-            loss_levels = torch.stack(loss_levels, dim=0)
-            # Locate the best fpn level for loss back-propagation
-            if loss_levels.numel() == 0:  # zero gt
-                argmin = loss_levels.new_empty((num_gts, ), dtype=torch.long)
-            else:
-                _, argmin = loss_levels.min(dim=0)
-
-        # Reweight the loss of each (anchor, label) pair, so that only those
-        #  at the best gt level are back-propagated.
-        losses_cls, losses_bbox, pos_inds = multi_apply(
-            self.reweight_loss_single,
-            losses_cls,
-            losses_bbox,
-            pos_assigned_gt_inds_list,
-            labels_list,
-            list(range(len(losses_cls))),
-            min_levels=argmin)
-        num_pos = torch.cat(pos_inds, 0).sum().float()
-        pos_recall = self.calculate_pos_recall(cls_scores, labels_list,
-                                               pos_inds)
-
-        if num_pos == 0:  # No gt
-            num_total_neg = sum(
-                [results.num_neg for results in sampling_results_list])
-            avg_factor = num_pos + num_total_neg
-        else:
-            avg_factor = num_pos
-        for i in range(len(losses_cls)):
-            losses_cls[i] /= avg_factor
-            losses_bbox[i] /= avg_factor
-        return dict(
-            loss_cls=losses_cls,
-            loss_bbox=losses_bbox,
-            num_pos=num_pos / batch_size,
-            pos_recall=pos_recall)
-
-    def calculate_pos_recall(self, cls_scores: List[Tensor],
-                             labels_list: List[Tensor],
-                             pos_inds: List[Tensor]) -> Tensor:
-        """Calculate positive recall with score threshold.
-
-        Args:
-            cls_scores (list[Tensor]): Classification scores at all fpn levels.
-                Each tensor is in shape (N, num_classes * num_anchors, H, W)
-            labels_list (list[Tensor]): The label that each anchor is assigned
-                to. Shape (N * H * W * num_anchors, )
-            pos_inds (list[Tensor]): List of bool tensors indicating whether
-                the anchor is assigned to a positive label.
-                Shape (N * H * W * num_anchors, )
-
-        Returns:
-            Tensor: A single float number indicating the positive recall.
-        """
-        with torch.no_grad():
-            num_class = self.num_classes
-            scores = [
-                cls.permute(0, 2, 3, 1).reshape(-1, num_class)[pos]
-                for cls, pos in zip(cls_scores, pos_inds)
-            ]
-            labels = [
-                label.reshape(-1)[pos]
-                for label, pos in zip(labels_list, pos_inds)
-            ]
-            scores = torch.cat(scores, dim=0)
-            labels = torch.cat(labels, dim=0)
-            if self.use_sigmoid_cls:
-                scores = scores.sigmoid()
-            else:
-                scores = scores.softmax(dim=1)
-
-            return accuracy(scores, labels, thresh=self.score_threshold)
-
-    def collect_loss_level_single(self, cls_loss: Tensor, reg_loss: Tensor,
-                                  assigned_gt_inds: Tensor,
-                                  labels_seq: Tensor) -> Tensor:
-        """Get the average loss in each FPN level w.r.t. each gt label.
-
-        Args:
-            cls_loss (Tensor): Classification loss of each feature map pixel,
-              shape (num_anchor, num_class)
-            reg_loss (Tensor): Regression loss of each feature map pixel,
-              shape (num_anchor, 4)
-            assigned_gt_inds (Tensor): It indicates which gt the prior is
-              assigned to (0-based, -1: no assignment). shape (num_anchor),
-            labels_seq: The rank of labels. shape (num_gt)
-
-        Returns:
-            Tensor: shape (num_gt), average loss of each gt in this level
-        """
-        if len(reg_loss.shape) == 2:  # iou loss has shape (num_prior, 4)
-            reg_loss = reg_loss.sum(dim=-1)  # sum loss in tblr dims
-        if len(cls_loss.shape) == 2:
-            cls_loss = cls_loss.sum(dim=-1)  # sum loss in class dims
-        loss = cls_loss + reg_loss
-        assert loss.size(0) == assigned_gt_inds.size(0)
-        # Default loss value is 1e6 for a layer where no anchor is positive
-        #  to ensure it will not be chosen to back-propagate gradient
-        losses_ = loss.new_full(labels_seq.shape, 1e6)
-        for i, l in enumerate(labels_seq):
-            match = assigned_gt_inds == l
-            if match.any():
-                losses_[i] = loss[match].mean()
-        return losses_,
-
-    def reweight_loss_single(self, cls_loss: Tensor, reg_loss: Tensor,
-                             assigned_gt_inds: Tensor, labels: Tensor,
-                             level: int, min_levels: Tensor) -> tuple:
-        """Reweight loss values at each level.
-
-        Reassign loss values at each level by masking those where the
-        pre-calculated loss is too large. Then return the reduced losses.
-
-        Args:
-            cls_loss (Tensor): Element-wise classification loss.
-              Shape: (num_anchors, num_classes)
-            reg_loss (Tensor): Element-wise regression loss.
-              Shape: (num_anchors, 4)
-            assigned_gt_inds (Tensor): The gt indices that each anchor bbox
-              is assigned to. -1 denotes a negative anchor, otherwise it is the
-              gt index (0-based). Shape: (num_anchors, ),
-            labels (Tensor): Label assigned to anchors. Shape: (num_anchors, ).
-            level (int): The current level index in the pyramid
-              (0-4 for RetinaNet)
-            min_levels (Tensor): The best-matching level for each gt.
-              Shape: (num_gts, ),
-
-        Returns:
-            tuple:
-
-            - cls_loss: Reduced corrected classification loss. Scalar.
-            - reg_loss: Reduced corrected regression loss. Scalar.
-            - pos_flags (Tensor): Corrected bool tensor indicating the \
-            final positive anchors. Shape: (num_anchors, ).
-        """
-        loc_weight = torch.ones_like(reg_loss)
-        cls_weight = torch.ones_like(cls_loss)
-        pos_flags = assigned_gt_inds >= 0  # positive pixel flag
-        pos_indices = torch.nonzero(pos_flags, as_tuple=False).flatten()
-
-        if pos_flags.any():  # pos pixels exist
-            pos_assigned_gt_inds = assigned_gt_inds[pos_flags]
-            zeroing_indices = (min_levels[pos_assigned_gt_inds] != level)
-            neg_indices = pos_indices[zeroing_indices]
-
-            if neg_indices.numel():
-                pos_flags[neg_indices] = 0
-                loc_weight[neg_indices] = 0
-                # Only the weight corresponding to the label is
-                #  zeroed out if not selected
-                zeroing_labels = labels[neg_indices]
-                assert (zeroing_labels >= 0).all()
-                cls_weight[neg_indices, zeroing_labels] = 0
-
-        # Weighted loss for both cls and reg loss
-        cls_loss = weight_reduce_loss(cls_loss, cls_weight, reduction='sum')
-        reg_loss = weight_reduce_loss(reg_loss, loc_weight, reduction='sum')
-
-        return cls_loss, reg_loss, pos_flags
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/roi_heads/standard_roi_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/roi_heads/standard_roi_head.py
deleted file mode 100644
index 8d168eba0fb2ccf6aa89bde5c637160f10aea83a..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/roi_heads/standard_roi_head.py
+++ /dev/null
@@ -1,419 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Optional, Tuple
-
-import torch
-from torch import Tensor
-
-from mmdet.registry import MODELS, TASK_UTILS
-from mmdet.structures import DetDataSample, SampleList
-from mmdet.structures.bbox import bbox2roi
-from mmdet.utils import ConfigType, InstanceList
-from ..task_modules.samplers import SamplingResult
-from ..utils import empty_instances, unpack_gt_instances
-from .base_roi_head import BaseRoIHead
-
-
-@MODELS.register_module()
-class StandardRoIHead(BaseRoIHead):
-    """Simplest base roi head including one bbox head and one mask head."""
-
-    def init_assigner_sampler(self) -> None:
-        """Initialize assigner and sampler."""
-        self.bbox_assigner = None
-        self.bbox_sampler = None
-        if self.train_cfg:
-            self.bbox_assigner = TASK_UTILS.build(self.train_cfg.assigner)
-            self.bbox_sampler = TASK_UTILS.build(
-                self.train_cfg.sampler, default_args=dict(context=self))
-
-    def init_bbox_head(self, bbox_roi_extractor: ConfigType,
-                       bbox_head: ConfigType) -> None:
-        """Initialize box head and box roi extractor.
-
-        Args:
-            bbox_roi_extractor (dict or ConfigDict): Config of box
-                roi extractor.
-            bbox_head (dict or ConfigDict): Config of box in box head.
-        """
-        self.bbox_roi_extractor = MODELS.build(bbox_roi_extractor)
-        self.bbox_head = MODELS.build(bbox_head)
-
-    def init_mask_head(self, mask_roi_extractor: ConfigType,
-                       mask_head: ConfigType) -> None:
-        """Initialize mask head and mask roi extractor.
-
-        Args:
-            mask_roi_extractor (dict or ConfigDict): Config of mask roi
-                extractor.
-            mask_head (dict or ConfigDict): Config of mask in mask head.
-        """
-        if mask_roi_extractor is not None:
-            self.mask_roi_extractor = MODELS.build(mask_roi_extractor)
-            self.share_roi_extractor = False
-        else:
-            self.share_roi_extractor = True
-            self.mask_roi_extractor = self.bbox_roi_extractor
-        self.mask_head = MODELS.build(mask_head)
-
-    # TODO: Need to refactor later
-    def forward(self,
-                x: Tuple[Tensor],
-                rpn_results_list: InstanceList,
-                batch_data_samples: SampleList = None) -> tuple:
-        """Network forward process. Usually includes backbone, neck and head
-        forward without any post-processing.
-
-        Args:
-            x (List[Tensor]): Multi-level features that may have different
-                resolutions.
-            rpn_results_list (list[:obj:`InstanceData`]): List of region
-                proposals.
-            batch_data_samples (list[:obj:`DetDataSample`]): Each item contains
-            the meta information of each image and corresponding
-            annotations.
-
-        Returns
-            tuple: A tuple of features from ``bbox_head`` and ``mask_head``
-            forward.
-        """
-        results = ()
-        proposals = [rpn_results.bboxes for rpn_results in rpn_results_list]
-        rois = bbox2roi(proposals)
-        # bbox head
-        if self.with_bbox:
-            bbox_results = self._bbox_forward(x, rois)
-            results = results + (bbox_results['cls_score'],
-                                 bbox_results['bbox_pred'])
-        # mask head
-        if self.with_mask:
-            mask_rois = rois[:100]
-            mask_results = self._mask_forward(x, mask_rois)
-            results = results + (mask_results['mask_preds'], )
-        return results
-
-    def loss(self, x: Tuple[Tensor], rpn_results_list: InstanceList,
-             batch_data_samples: List[DetDataSample]) -> dict:
-        """Perform forward propagation and loss calculation of the detection
-        roi on the features of the upstream network.
-
-        Args:
-            x (tuple[Tensor]): List of multi-level img features.
-            rpn_results_list (list[:obj:`InstanceData`]): List of region
-                proposals.
-            batch_data_samples (list[:obj:`DetDataSample`]): The batch
-                data samples. It usually includes information such
-                as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components
-        """
-        assert len(rpn_results_list) == len(batch_data_samples)
-        outputs = unpack_gt_instances(batch_data_samples)
-        batch_gt_instances, batch_gt_instances_ignore, _ = outputs
-
-        # assign gts and sample proposals
-        num_imgs = len(batch_data_samples)
-        sampling_results = []
-        for i in range(num_imgs):
-            # rename rpn_results.bboxes to rpn_results.priors
-            rpn_results = rpn_results_list[i]
-            rpn_results.priors = rpn_results.pop('bboxes')
-
-            assign_result = self.bbox_assigner.assign(
-                rpn_results, batch_gt_instances[i],
-                batch_gt_instances_ignore[i])
-            sampling_result = self.bbox_sampler.sample(
-                assign_result,
-                rpn_results,
-                batch_gt_instances[i],
-                feats=[lvl_feat[i][None] for lvl_feat in x])
-            sampling_results.append(sampling_result)
-
-        losses = dict()
-        # bbox head loss
-        if self.with_bbox:
-            bbox_results = self.bbox_loss(x, sampling_results)
-            losses.update(bbox_results['loss_bbox'])
-
-        # mask head forward and loss
-        if self.with_mask:
-            mask_results = self.mask_loss(x, sampling_results,
-                                          bbox_results['bbox_feats'],
-                                          batch_gt_instances)
-            losses.update(mask_results['loss_mask'])
-
-        return losses
-
-    def _bbox_forward(self, x: Tuple[Tensor], rois: Tensor) -> dict:
-        """Box head forward function used in both training and testing.
-
-        Args:
-            x (tuple[Tensor]): List of multi-level img features.
-            rois (Tensor): RoIs with the shape (n, 5) where the first
-                column indicates batch id of each RoI.
-
-        Returns:
-             dict[str, Tensor]: Usually returns a dictionary with keys:
-
-                - `cls_score` (Tensor): Classification scores.
-                - `bbox_pred` (Tensor): Box energies / deltas.
-                - `bbox_feats` (Tensor): Extract bbox RoI features.
-        """
-        # TODO: a more flexible way to decide which feature maps to use
-        bbox_feats = self.bbox_roi_extractor(
-            x[:self.bbox_roi_extractor.num_inputs], rois)
-        if self.with_shared_head:
-            bbox_feats = self.shared_head(bbox_feats)
-        cls_score, bbox_pred = self.bbox_head(bbox_feats)
-
-        bbox_results = dict(
-            cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats)
-        return bbox_results
-
-    def bbox_loss(self, x: Tuple[Tensor],
-                  sampling_results: List[SamplingResult]) -> dict:
-        """Perform forward propagation and loss calculation of the bbox head on
-        the features of the upstream network.
-
-        Args:
-            x (tuple[Tensor]): List of multi-level img features.
-            sampling_results (list["obj:`SamplingResult`]): Sampling results.
-
-        Returns:
-            dict[str, Tensor]: Usually returns a dictionary with keys:
-
-                - `cls_score` (Tensor): Classification scores.
-                - `bbox_pred` (Tensor): Box energies / deltas.
-                - `bbox_feats` (Tensor): Extract bbox RoI features.
-                - `loss_bbox` (dict): A dictionary of bbox loss components.
-        """
-        rois = bbox2roi([res.priors for res in sampling_results])
-        bbox_results = self._bbox_forward(x, rois)
-
-        bbox_loss_and_target = self.bbox_head.loss_and_target(
-            cls_score=bbox_results['cls_score'],
-            bbox_pred=bbox_results['bbox_pred'],
-            rois=rois,
-            sampling_results=sampling_results,
-            rcnn_train_cfg=self.train_cfg)
-
-        bbox_results.update(loss_bbox=bbox_loss_and_target['loss_bbox'])
-        return bbox_results
-
-    def mask_loss(self, x: Tuple[Tensor],
-                  sampling_results: List[SamplingResult], bbox_feats: Tensor,
-                  batch_gt_instances: InstanceList) -> dict:
-        """Perform forward propagation and loss calculation of the mask head on
-        the features of the upstream network.
-
-        Args:
-            x (tuple[Tensor]): Tuple of multi-level img features.
-            sampling_results (list["obj:`SamplingResult`]): Sampling results.
-            bbox_feats (Tensor): Extract bbox RoI features.
-            batch_gt_instances (list[:obj:`InstanceData`]): Batch of
-                gt_instance. It usually includes ``bboxes``, ``labels``, and
-                ``masks`` attributes.
-
-        Returns:
-            dict: Usually returns a dictionary with keys:
-
-                - `mask_preds` (Tensor): Mask prediction.
-                - `mask_feats` (Tensor): Extract mask RoI features.
-                - `mask_targets` (Tensor): Mask target of each positive\
-                    proposals in the image.
-                - `loss_mask` (dict): A dictionary of mask loss components.
-        """
-        if not self.share_roi_extractor:
-            pos_rois = bbox2roi([res.pos_priors for res in sampling_results])
-            mask_results = self._mask_forward(x, pos_rois)
-        else:
-            pos_inds = []
-            device = bbox_feats.device
-            for res in sampling_results:
-                pos_inds.append(
-                    torch.ones(
-                        res.pos_priors.shape[0],
-                        device=device,
-                        dtype=torch.uint8))
-                pos_inds.append(
-                    torch.zeros(
-                        res.neg_priors.shape[0],
-                        device=device,
-                        dtype=torch.uint8))
-            pos_inds = torch.cat(pos_inds)
-
-            mask_results = self._mask_forward(
-                x, pos_inds=pos_inds, bbox_feats=bbox_feats)
-
-        mask_loss_and_target = self.mask_head.loss_and_target(
-            mask_preds=mask_results['mask_preds'],
-            sampling_results=sampling_results,
-            batch_gt_instances=batch_gt_instances,
-            rcnn_train_cfg=self.train_cfg)
-
-        mask_results.update(loss_mask=mask_loss_and_target['loss_mask'])
-        return mask_results
-
-    def _mask_forward(self,
-                      x: Tuple[Tensor],
-                      rois: Tensor = None,
-                      pos_inds: Optional[Tensor] = None,
-                      bbox_feats: Optional[Tensor] = None) -> dict:
-        """Mask head forward function used in both training and testing.
-
-        Args:
-            x (tuple[Tensor]): Tuple of multi-level img features.
-            rois (Tensor): RoIs with the shape (n, 5) where the first
-                column indicates batch id of each RoI.
-            pos_inds (Tensor, optional): Indices of positive samples.
-                Defaults to None.
-            bbox_feats (Tensor): Extract bbox RoI features. Defaults to None.
-
-        Returns:
-            dict[str, Tensor]: Usually returns a dictionary with keys:
-
-                - `mask_preds` (Tensor): Mask prediction.
-                - `mask_feats` (Tensor): Extract mask RoI features.
-        """
-        assert ((rois is not None) ^
-                (pos_inds is not None and bbox_feats is not None))
-        if rois is not None:
-            mask_feats = self.mask_roi_extractor(
-                x[:self.mask_roi_extractor.num_inputs], rois)
-            if self.with_shared_head:
-                mask_feats = self.shared_head(mask_feats)
-        else:
-            assert bbox_feats is not None
-            mask_feats = bbox_feats[pos_inds]
-
-        mask_preds = self.mask_head(mask_feats)
-        mask_results = dict(mask_preds=mask_preds, mask_feats=mask_feats)
-        return mask_results
-
-    def predict_bbox(self,
-                     x: Tuple[Tensor],
-                     batch_img_metas: List[dict],
-                     rpn_results_list: InstanceList,
-                     rcnn_test_cfg: ConfigType,
-                     rescale: bool = False) -> InstanceList:
-        """Perform forward propagation of the bbox head and predict detection
-        results on the features of the upstream network.
-
-        Args:
-            x (tuple[Tensor]): Feature maps of all scale level.
-            batch_img_metas (list[dict]): List of image information.
-            rpn_results_list (list[:obj:`InstanceData`]): List of region
-                proposals.
-            rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of R-CNN.
-            rescale (bool): If True, return boxes in original image space.
-                Defaults to False.
-
-        Returns:
-            list[:obj:`InstanceData`]: Detection results of each image
-            after the post process.
-            Each item usually contains following keys.
-
-                - scores (Tensor): Classification scores, has a shape
-                  (num_instance, )
-                - labels (Tensor): Labels of bboxes, has a shape
-                  (num_instances, ).
-                - bboxes (Tensor): Has a shape (num_instances, 4),
-                  the last dimension 4 arrange as (x1, y1, x2, y2).
-        """
-        proposals = [res.bboxes for res in rpn_results_list]
-        rois = bbox2roi(proposals)
-
-        if rois.shape[0] == 0:
-            return empty_instances(
-                batch_img_metas,
-                rois.device,
-                task_type='bbox',
-                box_type=self.bbox_head.predict_box_type,
-                num_classes=self.bbox_head.num_classes,
-                score_per_cls=rcnn_test_cfg is None)
-
-        bbox_results = self._bbox_forward(x, rois)
-
-        # split batch bbox prediction back to each image
-        cls_scores = bbox_results['cls_score']
-        bbox_preds = bbox_results['bbox_pred']
-        num_proposals_per_img = tuple(len(p) for p in proposals)
-        rois = rois.split(num_proposals_per_img, 0)
-        cls_scores = cls_scores.split(num_proposals_per_img, 0)
-
-        # some detector with_reg is False, bbox_preds will be None
-        if bbox_preds is not None:
-            # TODO move this to a sabl_roi_head
-            # the bbox prediction of some detectors like SABL is not Tensor
-            if isinstance(bbox_preds, torch.Tensor):
-                bbox_preds = bbox_preds.split(num_proposals_per_img, 0)
-            else:
-                bbox_preds = self.bbox_head.bbox_pred_split(
-                    bbox_preds, num_proposals_per_img)
-        else:
-            bbox_preds = (None, ) * len(proposals)
-
-        result_list = self.bbox_head.predict_by_feat(
-            rois=rois,
-            cls_scores=cls_scores,
-            bbox_preds=bbox_preds,
-            batch_img_metas=batch_img_metas,
-            rcnn_test_cfg=rcnn_test_cfg,
-            rescale=rescale)
-        return result_list
-
-    def predict_mask(self,
-                     x: Tuple[Tensor],
-                     batch_img_metas: List[dict],
-                     results_list: InstanceList,
-                     rescale: bool = False) -> InstanceList:
-        """Perform forward propagation of the mask head and predict detection
-        results on the features of the upstream network.
-
-        Args:
-            x (tuple[Tensor]): Feature maps of all scale level.
-            batch_img_metas (list[dict]): List of image information.
-            results_list (list[:obj:`InstanceData`]): Detection results of
-                each image.
-            rescale (bool): If True, return boxes in original image space.
-                Defaults to False.
-
-        Returns:
-            list[:obj:`InstanceData`]: Detection results of each image
-            after the post process.
-            Each item usually contains following keys.
-
-                - scores (Tensor): Classification scores, has a shape
-                  (num_instance, )
-                - labels (Tensor): Labels of bboxes, has a shape
-                  (num_instances, ).
-                - bboxes (Tensor): Has a shape (num_instances, 4),
-                  the last dimension 4 arrange as (x1, y1, x2, y2).
-                - masks (Tensor): Has a shape (num_instances, H, W).
-        """
-        # don't need to consider aug_test.
-        bboxes = [res.bboxes for res in results_list]
-        mask_rois = bbox2roi(bboxes)
-        if mask_rois.shape[0] == 0:
-            results_list = empty_instances(
-                batch_img_metas,
-                mask_rois.device,
-                task_type='mask',
-                instance_results=results_list,
-                mask_thr_binary=self.test_cfg.mask_thr_binary)
-            return results_list
-
-        mask_results = self._mask_forward(x, mask_rois)
-        mask_preds = mask_results['mask_preds']
-        # split batch mask prediction back to each image
-        num_mask_rois_per_img = [len(res) for res in results_list]
-        mask_preds = mask_preds.split(num_mask_rois_per_img, 0)
-
-        # TODO: Handle the case where rescale is false
-        results_list = self.mask_head.predict_by_feat(
-            mask_preds=mask_preds,
-            results_list=results_list,
-            batch_img_metas=batch_img_metas,
-            rcnn_test_cfg=self.test_cfg,
-            rescale=rescale)
-        return results_list
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/seg_heads/base_semantic_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/seg_heads/base_semantic_head.py
deleted file mode 100644
index 1db71549d89766c45012517c20cef443f4760419..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/seg_heads/base_semantic_head.py
+++ /dev/null
@@ -1,113 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from abc import ABCMeta, abstractmethod
-from typing import Dict, List, Tuple, Union
-
-import torch.nn.functional as F
-from mmengine.model import BaseModule
-from torch import Tensor
-
-from mmdet.registry import MODELS
-from mmdet.structures import SampleList
-from mmdet.utils import ConfigType, OptMultiConfig
-
-
-@MODELS.register_module()
-class BaseSemanticHead(BaseModule, metaclass=ABCMeta):
-    """Base module of Semantic Head.
-
-    Args:
-        num_classes (int): the number of classes.
-        seg_rescale_factor (float): the rescale factor for ``gt_sem_seg``,
-            which equals to ``1 / output_strides``. The output_strides is
-            for ``seg_preds``. Defaults to  1 / 4.
-        init_cfg (Optional[Union[:obj:`ConfigDict`, dict]]): the initialization
-            config.
-        loss_seg (Union[:obj:`ConfigDict`, dict]): the loss of the semantic
-            head.
-    """
-
-    def __init__(self,
-                 num_classes: int,
-                 seg_rescale_factor: float = 1 / 4.,
-                 loss_seg: ConfigType = dict(
-                     type='CrossEntropyLoss',
-                     ignore_index=255,
-                     loss_weight=1.0),
-                 init_cfg: OptMultiConfig = None) -> None:
-        super().__init__(init_cfg=init_cfg)
-        self.loss_seg = MODELS.build(loss_seg)
-        self.num_classes = num_classes
-        self.seg_rescale_factor = seg_rescale_factor
-
-    @abstractmethod
-    def forward(self, x: Union[Tensor, Tuple[Tensor]]) -> Dict[str, Tensor]:
-        """Placeholder of forward function.
-
-        Args:
-            x (Tensor): Feature maps.
-
-        Returns:
-            Dict[str, Tensor]: A dictionary, including features
-                and predicted scores. Required keys: 'seg_preds'
-                and 'feats'.
-        """
-        pass
-
-    @abstractmethod
-    def loss(self, x: Union[Tensor, Tuple[Tensor]],
-             batch_data_samples: SampleList) -> Dict[str, Tensor]:
-        """
-        Args:
-            x (Union[Tensor, Tuple[Tensor]]): Feature maps.
-            batch_data_samples (list[:obj:`DetDataSample`]): The batch
-                data samples. It usually includes information such
-                as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
-
-        Args:
-            x (Tensor): Feature maps.
-
-        Returns:
-            Dict[str, Tensor]: The loss of semantic head.
-        """
-        pass
-
-    def predict(self,
-                x: Union[Tensor, Tuple[Tensor]],
-                batch_img_metas: List[dict],
-                rescale: bool = False) -> List[Tensor]:
-        """Test without Augmentation.
-
-        Args:
-            x (Union[Tensor, Tuple[Tensor]]): Feature maps.
-            batch_img_metas (List[dict]): List of image information.
-            rescale (bool): Whether to rescale the results.
-                Defaults to False.
-
-        Returns:
-            list[Tensor]: semantic segmentation logits.
-        """
-        seg_preds = self.forward(x)['seg_preds']
-        seg_preds = F.interpolate(
-            seg_preds,
-            size=batch_img_metas[0]['batch_input_shape'],
-            mode='bilinear',
-            align_corners=False)
-        seg_preds = [seg_preds[i] for i in range(len(batch_img_metas))]
-
-        if rescale:
-            seg_pred_list = []
-            for i in range(len(batch_img_metas)):
-                h, w = batch_img_metas[i]['img_shape']
-                seg_pred = seg_preds[i][:, :h, :w]
-
-                h, w = batch_img_metas[i]['ori_shape']
-                seg_pred = F.interpolate(
-                    seg_pred[None],
-                    size=(h, w),
-                    mode='bilinear',
-                    align_corners=False)[0]
-                seg_pred_list.append(seg_pred)
-        else:
-            seg_pred_list = seg_preds
-
-        return seg_pred_list
diff --git a/spaces/Kyo-Kai/Fsg-pp/sites/zerochan.py b/spaces/Kyo-Kai/Fsg-pp/sites/zerochan.py
deleted file mode 100644
index c0d3dbab039da16e912a0ddfeb2190c0dab7f5f9..0000000000000000000000000000000000000000
--- a/spaces/Kyo-Kai/Fsg-pp/sites/zerochan.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import time
-import urllib.request
-import os
-from random import randint
-from selenium.webdriver.common.by import By
-from selenium.webdriver.common.keys import Keys
-from selenium.webdriver.support.ui import WebDriverWait
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.common.exceptions import TimeoutException
-from commands.driver_instance import create_url_headers, tab_handler
-from commands.exec_path import imgList
-from commands.universal import contains_works, save_Search, continue_Search
-from ai.classifying_ai import img_classifier
-
-def getOrderedZerochanImages(driver, exec_path, user_search, num_pics, num_pages, n_likes, filters, imageControl):
-    global image_locations, image_names, ultimatium, ai_mode
-    image_names = imgList(mode=1)
-    image_locations = []
-
-    ai_mode = 1 if filters else 0
-    filters={'likes': 0 if not n_likes else n_likes}
-    searchLimit={'pagecount': num_pages,'imagecount':num_pics}
-    user_search = user_search.replace(" ","+").capitalize()
-    link = "https://www.zerochan.net/" + user_search
-
-    if not imageControl:
-        driver.get(link)
-    if imageControl:
-        continue_Search(driver, link, mode=2)
-
-    if driver.current_url == "https://www.zerochan.net/":
-        print("You continued for the first time, but there was no previous search to continue from!")
-        driver.get(driver.current_url + 'angry')
-
-    
-    is_valid_search(driver)
-    if not contains_works(driver, '//*[@id="thumbs2"]'):
-        print("No works found...")
-        return []
-
-    curr_page = driver.current_url
-    while len(image_locations) < num_pics*num_pages:
-        search_image(driver,exec_path,filters,searchLimit=searchLimit)
-        if curr_page == driver.current_url and len(image_locations) < num_pics*num_pages or image_locations[-1]==-1:
-            image_locations.pop()
-            print("Reached end of search results")
-            break
-        curr_page = driver.current_url
-    driver.quit()
-
-    return image_locations
-
-def search_image(driver, exec_path, filters, searchLimit):
-    filter_link = "https://www.zerochan.net/register"
-
-    # The main image searcher
-    for page in range(searchLimit["pagecount"]):
-        temp_img_len = len(image_locations)
-        save_Search(driver=driver, mode=2)
-        WebDriverWait(driver, timeout=11).until(EC.presence_of_element_located((By.XPATH, "//*[@id='thumbs2']")))
-        images = driver.find_elements(By.XPATH, "//*[@id='thumbs2']//li")
-        if image_locations and image_locations[-1] == -1:
-            break
-
-        for curr_iter,image in enumerate(images):
-            tempImg = image.find_element(By.XPATH,".//a").get_attribute("href")
-            if len(image_locations) >= searchLimit['imagecount']*searchLimit['pagecount'] or len(image_locations) - temp_img_len >= searchLimit['imagecount']:
-                break
-            try:
-                tempDLLink = image.find_elements(By.XPATH, ".//p//a")[0].get_attribute("href")
-                if tempDLLink.split(".")[-1] not in ["jpg","png","jpeg"]:
-                    tempDLLink = image.find_elements(By.XPATH, ".//p//a")[1].get_attribute("href")
-                tempDLAttr = tempDLLink.split("/")[-1]
-                counts = tempDLAttr.count(".")-1
-                tempDLAttr = tempDLAttr.replace(".", " ", counts).encode("ascii", "ignore").decode("ascii")
-
-                if tempImg == filter_link or tempDLAttr in image_names:
-                    print("\nImage already exists, moving to another image...")
-                    continue
-
-                rand_time = randint(0,1) + randint(0,9)/10
-                time.sleep(rand_time)     
-                if int(image.find_element(By.XPATH, './/*[@class="fav"]').get_property("text"))>filters["likes"]:
-                    urllib.request.install_opener(create_url_headers(tempImg=tempImg))
-                    urllib.request.urlretrieve(
-                        tempDLLink, f"./{exec_path.folder_path}/{tempDLAttr}"
-                    )
-                    image_locations.append(f"./{exec_path.folder_path}/{tempDLAttr}")
-                    image_names.append(f"{tempDLAttr}")
-                    print(f"\n{tempDLLink}")
-                    if ai_mode:
-                        if img_classifier(image_locations[-1]):
-                            print("AI Mode: I approve this image")
-                        else:
-                            os.remove(image_locations[-1])
-                            image_locations.pop()
-                            image_names.pop()
-                            print("AI Mode: Skipping this image")
-
-                else:
-                    image_locations.append(-1)
-
-            # In case of stale element or any other errors
-            except:
-                if driver.window_handles[-1] != driver.window_handles[0]:
-                    print("I ran into an error, closing the tab and moving on...")
-                    driver = tab_handler(driver=driver)
-                time.sleep(randint(1,3) + randint(0,9)/10)
-                continue
-
-            
-        if not valid_page(driver):
-             break
-
-def valid_page(driver):
-    try:
-        driver.get(driver.find_elements(By.XPATH, "//*[@class='pagination']//a")[-1].get_attribute("href"))
-        return True
-    except:
-        return False
-    
-def is_valid_search(driver):
-    try:
-        titles = driver.find_element(By.XPATH, "//*[@id='children']//a").get_attribute("href")
-        if titles:
-            driver.get(titles)
-    except:
-        pass
\ No newline at end of file
diff --git a/spaces/LZY123ai/anime-remove-background/app.py b/spaces/LZY123ai/anime-remove-background/app.py
deleted file mode 100644
index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000
--- a/spaces/LZY123ai/anime-remove-background/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-
-def get_mask(img, s=1024):
-    img = (img / 255).astype(np.float32)
-    h, w = h0, w0 = img.shape[:-1]
-    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
-    ph, pw = s - h, s - w
-    img_input = np.zeros([s, s, 3], dtype=np.float32)
-    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
-    img_input = np.transpose(img_input, (2, 0, 1))
-    img_input = img_input[np.newaxis, :]
-    mask = rmbg_model.run(None, {'img': img_input})[0][0]
-    mask = np.transpose(mask, (1, 2, 0))
-    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
-    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
-    return mask
-
-
-def rmbg_fn(img):
-    mask = get_mask(img)
-    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
-    mask = (mask * 255).astype(np.uint8)
-    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
-    mask = mask.repeat(3, axis=2)
-    return mask, img
-
-
-if __name__ == "__main__":
-    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-    model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
-    rmbg_model = rt.InferenceSession(model_path, providers=providers)
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# Anime Remove Background\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n"
-                    "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
-        with gr.Row():
-            with gr.Column():
-                input_img = gr.Image(label="input image")
-                examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
-                examples = gr.Dataset(components=[input_img], samples=examples_data)
-            run_btn = gr.Button(variant="primary")
-            output_mask = gr.Image(label="mask")
-            output_img = gr.Image(label="result", image_mode="RGBA")
-        examples.click(lambda x: x[0], [examples], [input_img])
-        run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
-    app.launch()
diff --git a/spaces/Lamai/LAMAIGPT/autogpt/speech/eleven_labs.py b/spaces/Lamai/LAMAIGPT/autogpt/speech/eleven_labs.py
deleted file mode 100644
index ea84efd8ca9489b40919ecd571813fe954b078e3..0000000000000000000000000000000000000000
--- a/spaces/Lamai/LAMAIGPT/autogpt/speech/eleven_labs.py
+++ /dev/null
@@ -1,86 +0,0 @@
-"""ElevenLabs speech module"""
-import os
-
-import requests
-from playsound import playsound
-
-from autogpt.config import Config
-from autogpt.speech.base import VoiceBase
-
-PLACEHOLDERS = {"your-voice-id"}
-
-
-class ElevenLabsSpeech(VoiceBase):
-    """ElevenLabs speech class"""
-
-    def _setup(self) -> None:
-        """Set up the voices, API key, etc.
-
-        Returns:
-            None: None
-        """
-
-        cfg = Config()
-        default_voices = ["ErXwobaYiN019PkySvjV", "EXAVITQu4vr4xnSDxMaL"]
-        voice_options = {
-            "Rachel": "21m00Tcm4TlvDq8ikWAM",
-            "Domi": "AZnzlk1XvdvUeBnXmlld",
-            "Bella": "EXAVITQu4vr4xnSDxMaL",
-            "Antoni": "ErXwobaYiN019PkySvjV",
-            "Elli": "MF3mGyEYCl7XYWbV9V6O",
-            "Josh": "TxGEqnHWrfWFTfGW9XjX",
-            "Arnold": "VR6AewLTigWG4xSOukaG",
-            "Adam": "pNInz6obpgDQGcFmaJgB",
-            "Sam": "yoZ06aMxZJJ28mfd3POQ",
-        }
-        self._headers = {
-            "Content-Type": "application/json",
-            "xi-api-key": cfg.elevenlabs_api_key,
-        }
-        self._voices = default_voices.copy()
-        if cfg.elevenlabs_voice_1_id in voice_options:
-            cfg.elevenlabs_voice_1_id = voice_options[cfg.elevenlabs_voice_1_id]
-        if cfg.elevenlabs_voice_2_id in voice_options:
-            cfg.elevenlabs_voice_2_id = voice_options[cfg.elevenlabs_voice_2_id]
-        self._use_custom_voice(cfg.elevenlabs_voice_1_id, 0)
-        self._use_custom_voice(cfg.elevenlabs_voice_2_id, 1)
-
-    def _use_custom_voice(self, voice, voice_index) -> None:
-        """Use a custom voice if provided and not a placeholder
-
-        Args:
-            voice (str): The voice ID
-            voice_index (int): The voice index
-
-        Returns:
-            None: None
-        """
-        # Placeholder values that should be treated as empty
-        if voice and voice not in PLACEHOLDERS:
-            self._voices[voice_index] = voice
-
-    def _speech(self, text: str, voice_index: int = 0) -> bool:
-        """Speak text using elevenlabs.io's API
-
-        Args:
-            text (str): The text to speak
-            voice_index (int, optional): The voice to use. Defaults to 0.
-
-        Returns:
-            bool: True if the request was successful, False otherwise
-        """
-        tts_url = (
-            f"https://api.elevenlabs.io/v1/text-to-speech/{self._voices[voice_index]}"
-        )
-        response = requests.post(tts_url, headers=self._headers, json={"text": text})
-
-        if response.status_code == 200:
-            with open("speech.mpeg", "wb") as f:
-                f.write(response.content)
-            playsound("speech.mpeg", True)
-            os.remove("speech.mpeg")
-            return True
-        else:
-            print("Request failed with status code:", response.status_code)
-            print("Response content:", response.content)
-            return False
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/tabs/merge.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/tabs/merge.py
deleted file mode 100644
index 77f223bcb0febafeea07b77c6dc4def77ef189ff..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/tabs/merge.py
+++ /dev/null
@@ -1,386 +0,0 @@
-import sys
-
-sys.path.append("..")
-import os
-import shutil
-
-now_dir = os.getcwd()
-import soundfile as sf
-import librosa
-from lib.tools import audioEffects
-from assets.i18n.i18n import I18nAuto
-
-i18n = I18nAuto()
-import gradio as gr
-import tabs.resources as resources
-import numpy as np
-from scipy.signal import resample
-
-def save_to_wav2(dropbox):
-    file_path = dropbox.name
-    target_path = os.path.join("assets","audios", os.path.basename(file_path))
-
-    if os.path.exists(target_path):
-        os.remove(target_path)
-        print("Replacing old dropdown file...")
-
-    shutil.move(file_path, target_path)
-    return target_path
-
-
-audio_root = "assets/audios"
-audio_others_root = "assets/audios/audio-others"
-sup_audioext = {
-    "wav",
-    "mp3",
-    "flac",
-    "ogg",
-    "opus",
-    "m4a",
-    "mp4",
-    "aac",
-    "alac",
-    "wma",
-    "aiff",
-    "webm",
-    "ac3",
-}
-audio_paths = [
-    os.path.join(root, name)
-    for root, _, files in os.walk(audio_root, topdown=False)
-    for name in files
-    if name.endswith(tuple(sup_audioext)) and root == audio_root
-]
-
-audio_others_paths = [
-    os.path.join(root, name)
-    for root, _, files in os.walk(audio_others_root, topdown=False)
-    for name in files
-    if name.endswith(tuple(sup_audioext)) and root == audio_others_root
-]
-
-
-def change_choices3():
-    audio_paths = [
-    os.path.join(root, name)
-    for root, _, files in os.walk(audio_root, topdown=False)
-    for name in files
-    if name.endswith(tuple(sup_audioext)) and root == audio_root
-    ]
-    audio_others_paths = [
-    os.path.join(root, name)
-    for root, _, files in os.walk(audio_others_root, topdown=False)
-    for name in files
-    if name.endswith(tuple(sup_audioext)) and root == audio_others_root
-    ]
-
-    return (
-        {"choices": sorted(audio_others_paths), "__type__": "update"},
-        {"choices": sorted(audio_paths), "__type__": "update"},
-    )
-
-
-def generate_output_path(output_folder, base_name, extension):
-    index = 1
-    while True:
-        output_path = os.path.join(output_folder, f"{base_name}_{index}.{extension}")
-        if not os.path.exists(output_path):
-            return output_path
-        index += 1
-
-from pydub import AudioSegment
-from pydub.silence import detect_nonsilent
-import glob
-import re
-def combine_and_save_audios(
-    audio1_path, audio2_path, output_path, volume_factor_audio1, volume_factor_audio2
-):
-    audio1 = AudioSegment.from_file(audio1_path)
-    audio2 = AudioSegment.from_file(audio2_path)
-    
-    # Verificar cuál audio tiene mayor longitud
-    if len(audio1) > len(audio2):
-        # Calcular la diferencia en duración en segundos
-        diff_duration_seconds = (len(audio1) - len(audio2)) / 1000.0  # Convertir a segundos
-        print(f"diff_duration_seconds: {diff_duration_seconds} seconds")
-        # Crear el segmento de silencio en Pydub
-        silence = AudioSegment.silent(duration=int(diff_duration_seconds))  # Convertir a milisegundos
-
-        # Agregar el silencio al audio2 para igualar la duración
-        audio2 = audio2 + silence
-    else:
-        # Calcular la diferencia en duración en segundos
-        diff_duration_seconds = (len(audio2) - len(audio1)) / 1000.0  # Convertir a segundos
-        print(f"diff_duration_seconds: {diff_duration_seconds} seconds")
-        # Crear el segmento de silencio en Pydub
-        silence = AudioSegment.silent(duration=int(diff_duration_seconds))  # Convertir a milisegundos
-
-        # Agregar el silencio al audio1 para igualar la duración
-        audio1 = audio1 + silence
-
-    # Ajustar el volumen de los audios multiplicando por el factor de ganancia
-    if volume_factor_audio1 != 1.0:
-        audio1 *= volume_factor_audio1
-    if volume_factor_audio2 != 1.0:
-        audio2 *= volume_factor_audio2
-
-    # Combinar los audios
-    combined_audio = audio1.overlay(audio2)
-
-    # Guardar el audio combinado en el archivo de salida
-    combined_audio.export(output_path, format="wav")
-
-
-def audio_combined(
-    audio1_path,
-    audio2_path,
-    volume_factor_audio1=1.0,
-    volume_factor_audio2=1.0,
-    reverb_enabled=False,
-    compressor_enabled=False,
-    noise_gate_enabled=False,
-):
-    output_folder = os.path.join(now_dir,"assets", "audios", "audio-outputs")
-    os.makedirs(output_folder, exist_ok=True)
-
-    # Generar nombres únicos para los archivos de salida
-    base_name = "combined_audio"
-    extension = "wav"
-    output_path = generate_output_path(output_folder, base_name, extension)
-    print(reverb_enabled)
-    print(compressor_enabled)
-    print(noise_gate_enabled)
-
-    if reverb_enabled or compressor_enabled or noise_gate_enabled:
-        # Procesa el primer audio con los efectos habilitados
-        base_name = "effect_audio"
-        output_path = generate_output_path(output_folder, base_name, extension)
-        processed_audio_path = audioEffects.process_audio(
-            audio2_path,
-            output_path,
-            reverb_enabled,
-            compressor_enabled,
-            noise_gate_enabled,
-        )
-        base_name = "combined_audio"
-        output_path = generate_output_path(output_folder, base_name, extension)
-        # Combina el audio procesado con el segundo audio usando audio_combined
-        combine_and_save_audios(
-            audio1_path,
-            processed_audio_path,
-            output_path,
-            volume_factor_audio1,
-            volume_factor_audio2,
-        )
-
-        return i18n("Conversion complete!"), output_path
-    else:
-        base_name = "combined_audio"
-        output_path = generate_output_path(output_folder, base_name, extension)
-        # No hay efectos habilitados, combina directamente los audios sin procesar
-        combine_and_save_audios(
-            audio1_path,
-            audio2_path,
-            output_path,
-            volume_factor_audio1,
-            volume_factor_audio2,
-        )
-
-        return i18n("Conversion complete!"), output_path
-
-def process_audio(file_path):
-    try:
-        # load audio file
-        song = AudioSegment.from_file(file_path)
-
-        print(f"Ignore the warning if you saw any...")
-
-        # set silence threshold and duration
-        silence_thresh = -70  # dB
-        min_silence_len = 750  # ms, adjust as needed
-
-        # detect nonsilent parts
-        nonsilent_parts = detect_nonsilent(song, min_silence_len=min_silence_len, silence_thresh=silence_thresh)
-
-        # Create a new directory to store chunks
-        file_dir = os.path.dirname(file_path)
-        file_name = os.path.basename(file_path).split('.')[0]
-        new_dir_path = os.path.join(file_dir, file_name)
-        os.makedirs(new_dir_path, exist_ok=True)
-
-        # Check if timestamps file exists, if so delete it
-        timestamps_file = os.path.join(file_dir, f"{file_name}_timestamps.txt")
-        if os.path.isfile(timestamps_file):
-            os.remove(timestamps_file)
-
-        # export chunks and save start times
-        segment_count = 0
-        for i, (start_i, end_i) in enumerate(nonsilent_parts):
-            chunk = song[start_i:end_i]
-            chunk_file_path = os.path.join(new_dir_path, f"chunk{i}.wav")
-            chunk.export(chunk_file_path, format="wav")
-
-            print(f"Segment {i} created!")
-            segment_count += 1
-            
-            # write start times to file
-            with open(timestamps_file, "a", encoding="utf-8") as f:
-                f.write(f"{chunk_file_path} starts at {start_i} ms\n")
-        
-        print(f"Total segments created: {segment_count}")
-        print(f"Split all chunks for {file_path} successfully!")
-
-        return "Finish", new_dir_path
-
-    except Exception as e:
-        print(f"An error occurred: {e}")
-        return "Error", None
-
-
-def merge_audio(timestamps_file):
-    try:
-        # Extract prefix from the timestamps filename
-        prefix = os.path.basename(timestamps_file).replace('_timestamps.txt', '')
-        timestamps_dir = os.path.dirname(timestamps_file)
-        print(timestamps_dir)
-        print(prefix)
-
-        # Open the timestamps file
-        with open(timestamps_file, "r", encoding="utf-8") as f:
-            lines = f.readlines()
-
-        # Initialize empty list to hold audio segments
-        audio_segments = []
-        last_end_time = 0
-
-        print(f"Processing file: {timestamps_file}")
-
-        for line in lines:
-            # Extract filename and start time from line
-            match = re.search(r"(chunk\d+.wav) starts at (\d+) ms", line)
-            if match:
-                filename, start_time = match.groups()
-                start_time = int(start_time)
-
-                # Construct the complete path to the chunk file
-                chunk_file = os.path.join(timestamps_dir, prefix, filename)
-
-                # Add silence from last_end_time to start_time
-                silence_duration = max(start_time - last_end_time, 0)
-                silence = AudioSegment.silent(duration=silence_duration)
-                audio_segments.append(silence)
-
-                # Load audio file and append to list
-                audio = AudioSegment.from_wav(chunk_file)
-                audio_segments.append(audio)
-
-                # Update last_end_time
-                last_end_time = start_time + len(audio)
-
-                print(f"Processed chunk: {chunk_file}")
-
-        # Concatenate all audio_segments and export
-        merged_filename = f"{prefix}_merged.wav"
-        merged_audio = sum(audio_segments)
-        merged_audio.export(os.path.join(timestamps_dir, "audio-outputs", merged_filename), format="wav")
-
-        print(f"Exported merged file: {merged_filename}\n")
-
-    except Exception as e:
-        print(f"An error occurred: {e}")
-
-
-
-
-def merge_audios():
-        gr.Markdown(
-            value="## " + i18n("Merge your generated audios with the instrumental")
-        )
-        with gr.Row():
-            with gr.Column():
-                dropbox = gr.File(label=i18n("Drag your audio here:"))
-                gr.Markdown(value=i18n("### Instrumental settings:"))
-                input_audio1 = gr.Dropdown(
-                    label=i18n("Choose your instrumental:"),
-                    choices=sorted(audio_others_paths),
-                    value="",
-                    interactive=True,
-                )
-                input_audio1_scale = gr.Slider(
-                    minimum=0,
-                    maximum=10,
-                    label=i18n("Volume of the instrumental audio:"),
-                    value=1.00,
-                    interactive=True,
-                )
-                gr.Markdown(value=i18n("### Audio settings:"))
-                input_audio3 = gr.Dropdown(
-                    label=i18n("Select the generated audio"),
-                    choices=sorted(audio_paths),
-                    value="",
-                    interactive=True,
-                )
-                with gr.Row():
-                    input_audio3_scale = gr.Slider(
-                        minimum=0,
-                        maximum=10,
-                        label=i18n("Volume of the generated audio:"),
-                        value=1.00,
-                        interactive=True,
-                    )
-
-                gr.Markdown(value=i18n("### Add the effects:"))
-                reverb_ = gr.Checkbox(
-                    label=i18n("Reverb"),
-                    value=False,
-                    interactive=True,
-                )
-                compressor_ = gr.Checkbox(
-                    label=i18n("Compressor"),
-                    value=False,
-                    interactive=True,
-                )
-                noise_gate_ = gr.Checkbox(
-                    label=i18n("Noise Gate"),
-                    value=False,
-                    interactive=True,
-                )
-                with gr.Row():
-                    butnone = gr.Button(i18n("Merge"), variant="primary").style(
-                        full_width=True
-                    )
-                    refresh_button = gr.Button(
-                        i18n("Refresh"), variant="primary"
-                    ).style(full_width=True)
-
-                vc_output1 = gr.Textbox(label=i18n("Output information:"))
-                vc_output2 = gr.Audio(
-                    label=i18n(
-                        "Export audio (click on the three dots in the lower right corner to download)"
-                    ),
-                    type="filepath",
-                )
-
-                dropbox.upload(
-                    fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio1]
-                )
-
-                refresh_button.click(
-                    fn=lambda: change_choices3(),
-                    inputs=[],
-                    outputs=[input_audio1, input_audio3],
-                )
-
-                butnone.click(
-                    fn=audio_combined,
-                    inputs=[
-                        input_audio1,
-                        input_audio3,
-                        input_audio1_scale,
-                        input_audio3_scale,
-                        reverb_,
-                        compressor_,
-                        noise_gate_,
-                    ],
-                    outputs=[vc_output1, vc_output2],
-                )
diff --git a/spaces/Liu-LAB/GPT-academic/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp b/spaces/Liu-LAB/GPT-academic/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp
deleted file mode 100644
index c94575903bdf2eef71ecbe66382375552446e510..0000000000000000000000000000000000000000
--- a/spaces/Liu-LAB/GPT-academic/crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp
+++ /dev/null
@@ -1,17 +0,0 @@
-#include "libipc/pool_alloc.h"
-
-#include "libipc/memory/resource.h"
-
-namespace ipc {
-namespace mem {
-
-void* pool_alloc::alloc(std::size_t size) {
-    return async_pool_alloc::alloc(size);
-}
-
-void pool_alloc::free(void* p, std::size_t size) {
-    async_pool_alloc::free(p, size);
-}
-
-} // namespace mem
-} // namespace ipc
diff --git a/spaces/Liu-LAB/GPT-academic/crazy_functions/test_project/latex/attention/parameter_attention.tex b/spaces/Liu-LAB/GPT-academic/crazy_functions/test_project/latex/attention/parameter_attention.tex
deleted file mode 100644
index 7bc4fe452dbdbfe44ff72f0cdbd37acd5c786ce6..0000000000000000000000000000000000000000
--- a/spaces/Liu-LAB/GPT-academic/crazy_functions/test_project/latex/attention/parameter_attention.tex
+++ /dev/null
@@ -1,45 +0,0 @@
-\pagebreak
-\section*{Two Feed-Forward Layers = Attention over Parameters}\label{sec:parameter_attention}
-
-In addition to attention layers, our model contains position-wise feed-forward networks (Section \ref{sec:ffn}), which consist of two linear transformations with a ReLU activation in between.  In fact, these networks too can be seen as a form of attention.  Compare the formula for such a network with the formula for a simple dot-product attention layer (biases and scaling factors omitted):
-
-\begin{align*}
-    FFN(x, W_1, W_2) = ReLU(xW_1)W_2 \\
-    A(q, K, V) = Softmax(qK^T)V
-\end{align*}
-
-Based on the similarity of these formulae, the two-layer feed-forward network can be seen as a kind of attention, where the keys and values are the rows of the trainable parameter matrices $W_1$ and $W_2$, and where we use ReLU instead of Softmax in the compatibility function.
-
-%the compatablity function is $compat(q, k_i) = ReLU(q \cdot k_i)$ instead of $Softmax(qK_T)_i$.
-
-Given this similarity, we experimented with replacing the position-wise feed-forward networks with attention layers similar to the ones we use everywhere else our model. The multi-head-attention-over-parameters sublayer is identical to the multi-head attention described in \ref{sec:multihead}, except that the "keys" and "values" inputs to each attention head are trainable model parameters, as opposed to being linear projections of a previous layer.  These parameters are scaled up by a factor of $\sqrt{d_{model}}$ in order to be more similar to activations.
-
-In our first experiment, we replaced each position-wise feed-forward network with a multi-head-attention-over-parameters sublayer with $h_p=8$ heads, key-dimensionality $d_{pk}=64$, and value-dimensionality $d_{pv}=64$, using $n_p=1536$ key-value pairs for each attention head.  The sublayer has a total of $2097152$ parameters, including the parameters in the query projection and the output projection.  This matches the number of parameters in the position-wise feed-forward network that we replaced.  While the theoretical amount of computation is also the same, in practice, the attention version caused the step times to be about 30\% longer.
-
-In our second experiment, we used $h_p=8$ heads, and $n_p=512$ key-value pairs for each attention head, again matching the total number of parameters in the base model.
-
-Results for the first experiment were slightly worse than for the base model, and results for the second experiment were slightly better, see Table~\ref{tab:parameter_attention}.
-
-\begin{table}[h]
-\caption{Replacing the position-wise feed-forward networks with multihead-attention-over-parameters produces similar results to the base model.  All metrics are on the English-to-German translation development set, newstest2013.}
-\label{tab:parameter_attention}
-\begin{center}
-\vspace{-2mm}
-%\scalebox{1.0}{
-\begin{tabular}{c|cccccc|cccc}
-\hline\rule{0pt}{2.0ex}
- & \multirow{2}{*}{$\dmodel$} & \multirow{2}{*}{$\dff$} &
-\multirow{2}{*}{$h_p$} & \multirow{2}{*}{$d_{pk}$} & \multirow{2}{*}{$d_{pv}$} &
- \multirow{2}{*}{$n_p$} &
- PPL & BLEU & params & training\\
- & & & & & &  & (dev) & (dev) & $\times10^6$ & time \\
-\hline\rule{0pt}{2.0ex}
-base & 512 & 2048 & & & & & 4.92 & 25.8 & 65 & 12 hours\\
-\hline\rule{0pt}{2.0ex}
-AOP$_1$ & 512 & & 8 & 64 & 64 & 1536 & 4.92& 25.5  & 65 & 16 hours\\
-AOP$_2$ & 512 & & 16 & 64 & 64 & 512 & \textbf{4.86} & \textbf{25.9}  & 65 & 16 hours \\
-\hline
-\end{tabular}
-%}
-\end{center}
-\end{table}
diff --git a/spaces/Locomocool/MooseOrDeer/README.md b/spaces/Locomocool/MooseOrDeer/README.md
deleted file mode 100644
index 9e8f47431cc5cc1d6f472763bc8ed590025df726..0000000000000000000000000000000000000000
--- a/spaces/Locomocool/MooseOrDeer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: MooseOrDeer
-emoji: 😻
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-pinned: false
-license: lppl-1.3c
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Marshalls/testmtd/analysis/pymo/rotation_tools.py b/spaces/Marshalls/testmtd/analysis/pymo/rotation_tools.py
deleted file mode 100644
index 5d050d0620e1dddd348dc0c048ba9e2fbc87f618..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/analysis/pymo/rotation_tools.py
+++ /dev/null
@@ -1,330 +0,0 @@
-'''
-Tools for Manipulating and Converting 3D Rotations
-
-By Omid Alemi
-Created: June 12, 2017
-
-Adapted from that matlab file...
-'''
-
-import math
-import numpy as np
-import transforms3d as t3d
-from analysis.pymo.Quaternions import Quaternions
-
-def deg2rad(x):
-    return x/180*math.pi
-
-
-def rad2deg(x):
-    return x/math.pi*180
-
-def unroll(rots):
-    return unroll_1(rots)
-
-def unroll_1(rots):
-
-    new_rots = rots.copy()
-    
-    # Compute angles and alternative rotation angles
-    angs = np.linalg.norm(rots, axis=1)
-    alt_angs=2*np.pi-angs
-
-    #find discontinuities
-    d_angs = np.diff(angs, axis=0)
-    d_angs2 = alt_angs[1:]-angs[:-1]
-    
-    # swps = np.where(np.abs(d_angs2)<np.abs(d_angs))[0] & np.where(np.abs(d_angs2)<0.2)[0]
-    swps = np.where(np.abs(d_angs2)<np.abs(d_angs))[0]
-    # print("unroll swaps", len(swps))
-    # print(np.abs(d_angs2)[swps])
-
-    #reshape into intervals where we should unroll the rotations
-    isodd = swps.shape[0] % 2 == 1
-    if isodd:
-        swps = np.append(swps, rots.shape[0]-1)
-    intv = 1+swps.reshape((swps.shape[0]//2, 2))
-    for ii in range(intv.shape[0]):
-        new_ax = -rots[intv[ii,0]:intv[ii,1],:]/np.tile(angs[intv[ii,0]:intv[ii,1], None], (1,3))
-        new_angs = alt_angs[intv[ii,0]:intv[ii,1]]
-        new_rots[intv[ii,0]:intv[ii,1],:] = new_ax*np.tile(new_angs[:, None], (1,3))
-
-    return new_rots
-
-def unroll_2(rots):
-
-    new_rots = rots.copy()
-    
-    # Compute angles and alternative rotation angles
-    angs = np.linalg.norm(rots, axis=1)
-    dotprod = np.einsum('ij,ij->i', rots[:-1,:], rots[1:,:])
-    #ax = rots/np.tile(angs[:, None], (1,3))
-    #d_ax = np.linalg.norm(np.diff(ax, axis=0), axis=1)
-    alt_angs=2*np.pi-angs
-
-    #find discontinuities
-    d_angs = np.diff(angs, axis=0)
-    d_angs2 = alt_angs[1:]-angs[:-1]
-    
-    # FIXME should check if dot product is <0 not norm d_ax
-    swps = np.where((dotprod<-1))[0]
-    #swps = np.where((np.abs(d_ax)>0.5))[0]
-    #swps = np.where(np.abs(d_angs2)<np.abs(d_angs))[0]
-
-    #reshape into intervals where we should unroll the rotations
-    isodd = swps.shape[0] % 2 == 1
-    if isodd:
-        swps=swps[:-1]
-        #swps = np.append(swps, rots.shape[0]-1)
-    intv = 1+swps.reshape((swps.shape[0]//2, 2))
-    for ii in range(intv.shape[0]):
-        new_ax = -rots[intv[ii,0]:intv[ii,1],:]/np.tile(angs[intv[ii,0]:intv[ii,1], None], (1,3))
-        new_angs = alt_angs[intv[ii,0]:intv[ii,1]]
-        new_rots[intv[ii,0]:intv[ii,1],:] = new_ax*np.tile(new_angs[:, None], (1,3))
-
-    return new_rots
-
-def euler_reorder2(rots, order='XYZ', new_order='XYZ',use_deg=False):
-    if order==new_order:
-        return rots
-    
-    if use_deg:
-        rots = np.deg2rad(rots)
-
-    quats = Quaternions.from_euler(rots, order=order.lower())
-    eul = quats.euler(order=new_order.lower())
-    
-    if use_deg:
-        eul = np.rad2deg(eul)
-
-    return eul
-
-def euler_reorder(rot, order='XYZ', new_order='XYZ',use_deg=False):
-    if order==new_order:
-        return rot
-    
-    if use_deg:
-        rot = np.deg2rad(rot)
-    print("order:" + order)
-    print("new_order:" + new_order)
-    
-    rotmat = t3d.euler.euler2mat(rot[0], rot[1], rot[2], 'r' + order.lower())
-    eul = t3d.euler.mat2euler(rotmat, 'r' + new_order.lower())
-
-    #quat = t3d.euler.euler2quat(rot[0], rot[1], rot[2], 'r' + order.lower())
-    #eul = t3d.euler.quat2euler(quat, 'r' + new_order.lower())
-    
-    if use_deg:
-        eul = np.rad2deg(eul)
-
-    return eul
-
-def offsets_inv(offset, rots, order='XYZ',use_deg=False):
-    if use_deg:
-        offset = np.deg2rad(offset)
-        rots = np.deg2rad(rots)
-
-    q0 = t3d.euler.euler2quat(rots[0], rots[1], rots[2], 'r' + order.lower())
-    q_off = t3d.euler.euler2quat(offset[0], offset[1], offset[2], 'r' + order.lower())
-    q2=t3d.euler.quat2euler(t3d.quaternions.qmult(q0,t3d.quaternions.qinverse(q_off)), 'r' + order.lower())
-    #q0=Quaternions.from_euler(rots, order=order.lower())   
-    #q_off=Quaternions.from_euler(offset, order=order.lower())
-    #q2=((-q_off)*q0).euler(order=order.lower())
-    
-    if use_deg:
-        q2 = np.rad2deg(q2)
-
-    return q2
-
-def offsets(offset, rots, order='XYZ',use_deg=False):
-    if use_deg:
-        offset = np.deg2rad(offset)
-        rots = np.deg2rad(rots)
-
-    q0 = t3d.euler.euler2quat(rots[0], rots[1], rots[2], 'r' + order.lower())
-    q_off = t3d.euler.euler2quat(offset[0], offset[1], offset[2], 'r' + order.lower())
-    q2=t3d.euler.quat2euler(t3d.quaternions.qmult(q0,q_off), 'r' + order.lower())
-    #q0=Quaternions.from_euler(rots, order=order.lower())   
-    #q_off=Quaternions.from_euler(offset, order=order.lower())
-    #q2=(q_off*q0).euler(order=order.lower())
-    
-    if use_deg:
-        q2 = np.rad2deg(q2)
-
-    return q2
-
-def euler2expmap2(rots, order='XYZ',use_deg=False):
-    if use_deg:
-        rots = np.deg2rad(rots)
-    #print("rot:" + str(rot))
-    quats=Quaternions.from_euler(rots, order=order.lower())    
-    theta, vec = quats.angle_axis()
-    return unroll(vec*np.tile(theta[:,None],(1,3)))
-
-def euler2expmap(rot, order='XYZ',use_deg=False):
-    if use_deg:
-        rot = np.deg2rad(rot)
-    #print("rot:" + str(rot))
-    vec, theta = t3d.euler.euler2axangle(rot[0], rot[1], rot[2], 'r' + order.lower())
-    return vec*theta
-
-def expmap2euler(rot, order='XYZ',use_deg=False):
-    theta = np.linalg.norm(rot)
-    if theta > 1.0e-10:
-        vector = rot / theta
-    else:
-        vector = np.array([1.,0.,0.])
-        theta=0.0
-    eul = t3d.euler.axangle2euler(vector, theta, 'r' + order.lower())
-    if use_deg:
-        return np.rad2deg(eul)
-    else:
-        return eul
-
-class Rotation():
-    def __init__(self,rot, param_type, **params):
-        self.rotmat = []
-        if param_type == 'euler':
-            self._from_euler(rot[0],rot[1],rot[2], params)
-        elif param_type == 'expmap':
-            self._from_expmap(rot[0], rot[1], rot[2], params)
-
-    def _from_euler(self, alpha, beta, gamma, params):
-        '''Expecting degress'''
-        
-        if params['from_deg']==True:
-            alpha = deg2rad(alpha)
-            beta = deg2rad(beta)
-            gamma = deg2rad(gamma)
-            
-        order = "s" + ((params['order']).lower())[::-1]
-#        Quaternions.from_euler()        
-        self.rotmat = np.transpose(t3d.euler.euler2mat(gamma, beta , alpha, axes=order))
-
-#        ca = math.cos(alpha)
-#        cb = math.cos(beta)
-#        cg = math.cos(gamma)
-#        sa = math.sin(alpha)
-#        sb = math.sin(beta)
-#        sg = math.sin(gamma)        
-#
-#        Rx = np.asarray([[1, 0, 0], 
-#              [0, ca, sa], 
-#              [0, -sa, ca]
-#              ])
-#
-#        Ry = np.asarray([[cb, 0, -sb], 
-#              [0, 1, 0],
-#              [sb, 0, cb]])
-#
-#        Rz = np.asarray([[cg, sg, 0],
-#              [-sg, cg, 0],
-#              [0, 0, 1]])
-#
-#        self.rotmat = np.eye(3)
-#        
-#        order = params['order']
-#        for i in range(0,len(order)):
-#            if order[i]=='X':
-#                self.rotmat = np.matmul(Rx, self.rotmat)
-#            elif order[i]=='Y':
-#                self.rotmat = np.matmul(Ry, self.rotmat)
-#            elif order[i]=='Z':
-#                self.rotmat = np.matmul(Rz, self.rotmat)
-#            else:
-#                print('unknown rotation axis: ' + order[i])
-#                
-#        # self.rotmat = np.matmul(np.matmul(Rz, Ry), Rx)
-#        print ("------" + "TRUE")
-#        print (self.rotmat)
-   
-    def _from_expmap(self, alpha, beta, gamma, params):
-        if (alpha == 0 and beta == 0 and gamma == 0):
-            self.rotmat = np.eye(3)
-            return
-
-        #TODO: Check exp map params
-
-        theta = np.linalg.norm([alpha, beta, gamma])
-
-        expmap = [alpha, beta, gamma] / theta
-
-        x = expmap[0]
-        y = expmap[1]
-        z = expmap[2]
-
-        s = math.sin(theta/2)
-        c = math.cos(theta/2)
-
-        self.rotmat = np.asarray([
-            [2*(x**2-1)*s**2+1,  2*x*y*s**2-2*z*c*s,  2*x*z*s**2+2*y*c*s],
-            [2*x*y*s**2+2*z*c*s,  2*(y**2-1)*s**2+1,  2*y*z*s**2-2*x*c*s],
-            [2*x*z*s**2-2*y*c*s, 2*y*z*s**2+2*x*c*s , 2*(z**2-1)*s**2+1]
-        ])
-        
-
-
-    def get_euler_axis(self):
-        R = self.rotmat
-        theta = math.acos((self.rotmat.trace() - 1) / 2)
-        axis = np.asarray([R[2,1] - R[1,2], R[0,2] - R[2,0], R[1,0] - R[0,1]])
-        axis = axis/(2*math.sin(theta))
-        return theta, axis
-
-    def to_expmap(self):
-        axis, theta = t3d.axangles.mat2axangle(self.rotmat, unit_thresh=1e-05)
-#        theta, axis = self.get_euler_axis()
-        rot_arr = theta * axis
-        if np.isnan(rot_arr).any():
-            rot_arr = [0, 0, 0]
-        return rot_arr
-    
-    def to_euler(self, use_deg=False, order='xyz'):
-        order = "s" + order.lower()
-        eulers = t3d.euler.mat2euler(np.transpose(self.rotmat), axes=order)
-        return eulers[::-1]
-        
-#        eulers = np.zeros((2, 3))
-#
-#        if np.absolute(np.absolute(self.rotmat[2, 0]) - 1) < 1e-12:
-#            #GIMBAL LOCK!
-#            print('Gimbal')
-#            if np.absolute(self.rotmat[2, 0]) - 1 < 1e-12:
-#                eulers[:,0] = math.atan2(-self.rotmat[0,1], -self.rotmat[0,2])
-#                eulers[:,1] = -math.pi/2
-#            else:
-#                eulers[:,0] = math.atan2(self.rotmat[0,1], -elf.rotmat[0,2])
-#                eulers[:,1] = math.pi/2
-#            
-#            return eulers
-#
-#        theta = - math.asin(self.rotmat[2,0])
-#        theta2 = math.pi - theta
-#
-#        # psi1, psi2
-#        eulers[0,0] = math.atan2(self.rotmat[2,1]/math.cos(theta), self.rotmat[2,2]/math.cos(theta))
-#        eulers[1,0] = math.atan2(self.rotmat[2,1]/math.cos(theta2), self.rotmat[2,2]/math.cos(theta2))
-#
-#        # theta1, theta2
-#        eulers[0,1] = theta
-#        eulers[1,1] = theta2
-#
-#        # phi1, phi2
-#        eulers[0,2] = math.atan2(self.rotmat[1,0]/math.cos(theta), self.rotmat[0,0]/math.cos(theta))
-#        eulers[1,2] = math.atan2(self.rotmat[1,0]/math.cos(theta2), self.rotmat[0,0]/math.cos(theta2))
-#
-        if use_deg:
-            eulers = rad2deg(eulers)
-
-        return eulers
-    
-    def to_quat(self):
-        #TODO
-        pass
-    
-    def __str__(self):
-        return "Rotation Matrix: \n " + self.rotmat.__str__()
-    
-
-
-
diff --git a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/data/datasets/oid.py b/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/data/datasets/oid.py
deleted file mode 100644
index 90d7f8613e4f12e942ec8967db9f17c0ec0d41f4..0000000000000000000000000000000000000000
--- a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/data/datasets/oid.py
+++ /dev/null
@@ -1,535 +0,0 @@
-# Part of the code is from https://github.com/xingyizhou/UniDet/blob/master/projects/UniDet/unidet/data/datasets/oid.py
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .register_oid import register_oid_instances
-import os
-
-categories = [
-    {'id': 1, 'name': 'Infant bed', 'freebase_id': '/m/061hd_'},
-    {'id': 2, 'name': 'Rose', 'freebase_id': '/m/06m11'},
-    {'id': 3, 'name': 'Flag', 'freebase_id': '/m/03120'},
-    {'id': 4, 'name': 'Flashlight', 'freebase_id': '/m/01kb5b'},
-    {'id': 5, 'name': 'Sea turtle', 'freebase_id': '/m/0120dh'},
-    {'id': 6, 'name': 'Camera', 'freebase_id': '/m/0dv5r'},
-    {'id': 7, 'name': 'Animal', 'freebase_id': '/m/0jbk'},
-    {'id': 8, 'name': 'Glove', 'freebase_id': '/m/0174n1'},
-    {'id': 9, 'name': 'Crocodile', 'freebase_id': '/m/09f_2'},
-    {'id': 10, 'name': 'Cattle', 'freebase_id': '/m/01xq0k1'},
-    {'id': 11, 'name': 'House', 'freebase_id': '/m/03jm5'},
-    {'id': 12, 'name': 'Guacamole', 'freebase_id': '/m/02g30s'},
-    {'id': 13, 'name': 'Penguin', 'freebase_id': '/m/05z6w'},
-    {'id': 14, 'name': 'Vehicle registration plate', 'freebase_id': '/m/01jfm_'},
-    {'id': 15, 'name': 'Bench', 'freebase_id': '/m/076lb9'},
-    {'id': 16, 'name': 'Ladybug', 'freebase_id': '/m/0gj37'},
-    {'id': 17, 'name': 'Human nose', 'freebase_id': '/m/0k0pj'},
-    {'id': 18, 'name': 'Watermelon', 'freebase_id': '/m/0kpqd'},
-    {'id': 19, 'name': 'Flute', 'freebase_id': '/m/0l14j_'},
-    {'id': 20, 'name': 'Butterfly', 'freebase_id': '/m/0cyf8'},
-    {'id': 21, 'name': 'Washing machine', 'freebase_id': '/m/0174k2'},
-    {'id': 22, 'name': 'Raccoon', 'freebase_id': '/m/0dq75'},
-    {'id': 23, 'name': 'Segway', 'freebase_id': '/m/076bq'},
-    {'id': 24, 'name': 'Taco', 'freebase_id': '/m/07crc'},
-    {'id': 25, 'name': 'Jellyfish', 'freebase_id': '/m/0d8zb'},
-    {'id': 26, 'name': 'Cake', 'freebase_id': '/m/0fszt'},
-    {'id': 27, 'name': 'Pen', 'freebase_id': '/m/0k1tl'},
-    {'id': 28, 'name': 'Cannon', 'freebase_id': '/m/020kz'},
-    {'id': 29, 'name': 'Bread', 'freebase_id': '/m/09728'},
-    {'id': 30, 'name': 'Tree', 'freebase_id': '/m/07j7r'},
-    {'id': 31, 'name': 'Shellfish', 'freebase_id': '/m/0fbdv'},
-    {'id': 32, 'name': 'Bed', 'freebase_id': '/m/03ssj5'},
-    {'id': 33, 'name': 'Hamster', 'freebase_id': '/m/03qrc'},
-    {'id': 34, 'name': 'Hat', 'freebase_id': '/m/02dl1y'},
-    {'id': 35, 'name': 'Toaster', 'freebase_id': '/m/01k6s3'},
-    {'id': 36, 'name': 'Sombrero', 'freebase_id': '/m/02jfl0'},
-    {'id': 37, 'name': 'Tiara', 'freebase_id': '/m/01krhy'},
-    {'id': 38, 'name': 'Bowl', 'freebase_id': '/m/04kkgm'},
-    {'id': 39, 'name': 'Dragonfly', 'freebase_id': '/m/0ft9s'},
-    {'id': 40, 'name': 'Moths and butterflies', 'freebase_id': '/m/0d_2m'},
-    {'id': 41, 'name': 'Antelope', 'freebase_id': '/m/0czz2'},
-    {'id': 42, 'name': 'Vegetable', 'freebase_id': '/m/0f4s2w'},
-    {'id': 43, 'name': 'Torch', 'freebase_id': '/m/07dd4'},
-    {'id': 44, 'name': 'Building', 'freebase_id': '/m/0cgh4'},
-    {'id': 45, 'name': 'Power plugs and sockets', 'freebase_id': '/m/03bbps'},
-    {'id': 46, 'name': 'Blender', 'freebase_id': '/m/02pjr4'},
-    {'id': 47, 'name': 'Billiard table', 'freebase_id': '/m/04p0qw'},
-    {'id': 48, 'name': 'Cutting board', 'freebase_id': '/m/02pdsw'},
-    {'id': 49, 'name': 'Bronze sculpture', 'freebase_id': '/m/01yx86'},
-    {'id': 50, 'name': 'Turtle', 'freebase_id': '/m/09dzg'},
-    {'id': 51, 'name': 'Broccoli', 'freebase_id': '/m/0hkxq'},
-    {'id': 52, 'name': 'Tiger', 'freebase_id': '/m/07dm6'},
-    {'id': 53, 'name': 'Mirror', 'freebase_id': '/m/054_l'},
-    {'id': 54, 'name': 'Bear', 'freebase_id': '/m/01dws'},
-    {'id': 55, 'name': 'Zucchini', 'freebase_id': '/m/027pcv'},
-    {'id': 56, 'name': 'Dress', 'freebase_id': '/m/01d40f'},
-    {'id': 57, 'name': 'Volleyball', 'freebase_id': '/m/02rgn06'},
-    {'id': 58, 'name': 'Guitar', 'freebase_id': '/m/0342h'},
-    {'id': 59, 'name': 'Reptile', 'freebase_id': '/m/06bt6'},
-    {'id': 60, 'name': 'Golf cart', 'freebase_id': '/m/0323sq'},
-    {'id': 61, 'name': 'Tart', 'freebase_id': '/m/02zvsm'},
-    {'id': 62, 'name': 'Fedora', 'freebase_id': '/m/02fq_6'},
-    {'id': 63, 'name': 'Carnivore', 'freebase_id': '/m/01lrl'},
-    {'id': 64, 'name': 'Car', 'freebase_id': '/m/0k4j'},
-    {'id': 65, 'name': 'Lighthouse', 'freebase_id': '/m/04h7h'},
-    {'id': 66, 'name': 'Coffeemaker', 'freebase_id': '/m/07xyvk'},
-    {'id': 67, 'name': 'Food processor', 'freebase_id': '/m/03y6mg'},
-    {'id': 68, 'name': 'Truck', 'freebase_id': '/m/07r04'},
-    {'id': 69, 'name': 'Bookcase', 'freebase_id': '/m/03__z0'},
-    {'id': 70, 'name': 'Surfboard', 'freebase_id': '/m/019w40'},
-    {'id': 71, 'name': 'Footwear', 'freebase_id': '/m/09j5n'},
-    {'id': 72, 'name': 'Bench', 'freebase_id': '/m/0cvnqh'},
-    {'id': 73, 'name': 'Necklace', 'freebase_id': '/m/01llwg'},
-    {'id': 74, 'name': 'Flower', 'freebase_id': '/m/0c9ph5'},
-    {'id': 75, 'name': 'Radish', 'freebase_id': '/m/015x5n'},
-    {'id': 76, 'name': 'Marine mammal', 'freebase_id': '/m/0gd2v'},
-    {'id': 77, 'name': 'Frying pan', 'freebase_id': '/m/04v6l4'},
-    {'id': 78, 'name': 'Tap', 'freebase_id': '/m/02jz0l'},
-    {'id': 79, 'name': 'Peach', 'freebase_id': '/m/0dj6p'},
-    {'id': 80, 'name': 'Knife', 'freebase_id': '/m/04ctx'},
-    {'id': 81, 'name': 'Handbag', 'freebase_id': '/m/080hkjn'},
-    {'id': 82, 'name': 'Laptop', 'freebase_id': '/m/01c648'},
-    {'id': 83, 'name': 'Tent', 'freebase_id': '/m/01j61q'},
-    {'id': 84, 'name': 'Ambulance', 'freebase_id': '/m/012n7d'},
-    {'id': 85, 'name': 'Christmas tree', 'freebase_id': '/m/025nd'},
-    {'id': 86, 'name': 'Eagle', 'freebase_id': '/m/09csl'},
-    {'id': 87, 'name': 'Limousine', 'freebase_id': '/m/01lcw4'},
-    {'id': 88, 'name': 'Kitchen & dining room table', 'freebase_id': '/m/0h8n5zk'},
-    {'id': 89, 'name': 'Polar bear', 'freebase_id': '/m/0633h'},
-    {'id': 90, 'name': 'Tower', 'freebase_id': '/m/01fdzj'},
-    {'id': 91, 'name': 'Football', 'freebase_id': '/m/01226z'},
-    {'id': 92, 'name': 'Willow', 'freebase_id': '/m/0mw_6'},
-    {'id': 93, 'name': 'Human head', 'freebase_id': '/m/04hgtk'},
-    {'id': 94, 'name': 'Stop sign', 'freebase_id': '/m/02pv19'},
-    {'id': 95, 'name': 'Banana', 'freebase_id': '/m/09qck'},
-    {'id': 96, 'name': 'Mixer', 'freebase_id': '/m/063rgb'},
-    {'id': 97, 'name': 'Binoculars', 'freebase_id': '/m/0lt4_'},
-    {'id': 98, 'name': 'Dessert', 'freebase_id': '/m/0270h'},
-    {'id': 99, 'name': 'Bee', 'freebase_id': '/m/01h3n'},
-    {'id': 100, 'name': 'Chair', 'freebase_id': '/m/01mzpv'},
-    {'id': 101, 'name': 'Wood-burning stove', 'freebase_id': '/m/04169hn'},
-    {'id': 102, 'name': 'Flowerpot', 'freebase_id': '/m/0fm3zh'},
-    {'id': 103, 'name': 'Beaker', 'freebase_id': '/m/0d20w4'},
-    {'id': 104, 'name': 'Oyster', 'freebase_id': '/m/0_cp5'},
-    {'id': 105, 'name': 'Woodpecker', 'freebase_id': '/m/01dy8n'},
-    {'id': 106, 'name': 'Harp', 'freebase_id': '/m/03m5k'},
-    {'id': 107, 'name': 'Bathtub', 'freebase_id': '/m/03dnzn'},
-    {'id': 108, 'name': 'Wall clock', 'freebase_id': '/m/0h8mzrc'},
-    {'id': 109, 'name': 'Sports uniform', 'freebase_id': '/m/0h8mhzd'},
-    {'id': 110, 'name': 'Rhinoceros', 'freebase_id': '/m/03d443'},
-    {'id': 111, 'name': 'Beehive', 'freebase_id': '/m/01gllr'},
-    {'id': 112, 'name': 'Cupboard', 'freebase_id': '/m/0642b4'},
-    {'id': 113, 'name': 'Chicken', 'freebase_id': '/m/09b5t'},
-    {'id': 114, 'name': 'Man', 'freebase_id': '/m/04yx4'},
-    {'id': 115, 'name': 'Blue jay', 'freebase_id': '/m/01f8m5'},
-    {'id': 116, 'name': 'Cucumber', 'freebase_id': '/m/015x4r'},
-    {'id': 117, 'name': 'Balloon', 'freebase_id': '/m/01j51'},
-    {'id': 118, 'name': 'Kite', 'freebase_id': '/m/02zt3'},
-    {'id': 119, 'name': 'Fireplace', 'freebase_id': '/m/03tw93'},
-    {'id': 120, 'name': 'Lantern', 'freebase_id': '/m/01jfsr'},
-    {'id': 121, 'name': 'Missile', 'freebase_id': '/m/04ylt'},
-    {'id': 122, 'name': 'Book', 'freebase_id': '/m/0bt_c3'},
-    {'id': 123, 'name': 'Spoon', 'freebase_id': '/m/0cmx8'},
-    {'id': 124, 'name': 'Grapefruit', 'freebase_id': '/m/0hqkz'},
-    {'id': 125, 'name': 'Squirrel', 'freebase_id': '/m/071qp'},
-    {'id': 126, 'name': 'Orange', 'freebase_id': '/m/0cyhj_'},
-    {'id': 127, 'name': 'Coat', 'freebase_id': '/m/01xygc'},
-    {'id': 128, 'name': 'Punching bag', 'freebase_id': '/m/0420v5'},
-    {'id': 129, 'name': 'Zebra', 'freebase_id': '/m/0898b'},
-    {'id': 130, 'name': 'Billboard', 'freebase_id': '/m/01knjb'},
-    {'id': 131, 'name': 'Bicycle', 'freebase_id': '/m/0199g'},
-    {'id': 132, 'name': 'Door handle', 'freebase_id': '/m/03c7gz'},
-    {'id': 133, 'name': 'Mechanical fan', 'freebase_id': '/m/02x984l'},
-    {'id': 134, 'name': 'Ring binder', 'freebase_id': '/m/04zwwv'},
-    {'id': 135, 'name': 'Table', 'freebase_id': '/m/04bcr3'},
-    {'id': 136, 'name': 'Parrot', 'freebase_id': '/m/0gv1x'},
-    {'id': 137, 'name': 'Sock', 'freebase_id': '/m/01nq26'},
-    {'id': 138, 'name': 'Vase', 'freebase_id': '/m/02s195'},
-    {'id': 139, 'name': 'Weapon', 'freebase_id': '/m/083kb'},
-    {'id': 140, 'name': 'Shotgun', 'freebase_id': '/m/06nrc'},
-    {'id': 141, 'name': 'Glasses', 'freebase_id': '/m/0jyfg'},
-    {'id': 142, 'name': 'Seahorse', 'freebase_id': '/m/0nybt'},
-    {'id': 143, 'name': 'Belt', 'freebase_id': '/m/0176mf'},
-    {'id': 144, 'name': 'Watercraft', 'freebase_id': '/m/01rzcn'},
-    {'id': 145, 'name': 'Window', 'freebase_id': '/m/0d4v4'},
-    {'id': 146, 'name': 'Giraffe', 'freebase_id': '/m/03bk1'},
-    {'id': 147, 'name': 'Lion', 'freebase_id': '/m/096mb'},
-    {'id': 148, 'name': 'Tire', 'freebase_id': '/m/0h9mv'},
-    {'id': 149, 'name': 'Vehicle', 'freebase_id': '/m/07yv9'},
-    {'id': 150, 'name': 'Canoe', 'freebase_id': '/m/0ph39'},
-    {'id': 151, 'name': 'Tie', 'freebase_id': '/m/01rkbr'},
-    {'id': 152, 'name': 'Shelf', 'freebase_id': '/m/0gjbg72'},
-    {'id': 153, 'name': 'Picture frame', 'freebase_id': '/m/06z37_'},
-    {'id': 154, 'name': 'Printer', 'freebase_id': '/m/01m4t'},
-    {'id': 155, 'name': 'Human leg', 'freebase_id': '/m/035r7c'},
-    {'id': 156, 'name': 'Boat', 'freebase_id': '/m/019jd'},
-    {'id': 157, 'name': 'Slow cooker', 'freebase_id': '/m/02tsc9'},
-    {'id': 158, 'name': 'Croissant', 'freebase_id': '/m/015wgc'},
-    {'id': 159, 'name': 'Candle', 'freebase_id': '/m/0c06p'},
-    {'id': 160, 'name': 'Pancake', 'freebase_id': '/m/01dwwc'},
-    {'id': 161, 'name': 'Pillow', 'freebase_id': '/m/034c16'},
-    {'id': 162, 'name': 'Coin', 'freebase_id': '/m/0242l'},
-    {'id': 163, 'name': 'Stretcher', 'freebase_id': '/m/02lbcq'},
-    {'id': 164, 'name': 'Sandal', 'freebase_id': '/m/03nfch'},
-    {'id': 165, 'name': 'Woman', 'freebase_id': '/m/03bt1vf'},
-    {'id': 166, 'name': 'Stairs', 'freebase_id': '/m/01lynh'},
-    {'id': 167, 'name': 'Harpsichord', 'freebase_id': '/m/03q5t'},
-    {'id': 168, 'name': 'Stool', 'freebase_id': '/m/0fqt361'},
-    {'id': 169, 'name': 'Bus', 'freebase_id': '/m/01bjv'},
-    {'id': 170, 'name': 'Suitcase', 'freebase_id': '/m/01s55n'},
-    {'id': 171, 'name': 'Human mouth', 'freebase_id': '/m/0283dt1'},
-    {'id': 172, 'name': 'Juice', 'freebase_id': '/m/01z1kdw'},
-    {'id': 173, 'name': 'Skull', 'freebase_id': '/m/016m2d'},
-    {'id': 174, 'name': 'Door', 'freebase_id': '/m/02dgv'},
-    {'id': 175, 'name': 'Violin', 'freebase_id': '/m/07y_7'},
-    {'id': 176, 'name': 'Chopsticks', 'freebase_id': '/m/01_5g'},
-    {'id': 177, 'name': 'Digital clock', 'freebase_id': '/m/06_72j'},
-    {'id': 178, 'name': 'Sunflower', 'freebase_id': '/m/0ftb8'},
-    {'id': 179, 'name': 'Leopard', 'freebase_id': '/m/0c29q'},
-    {'id': 180, 'name': 'Bell pepper', 'freebase_id': '/m/0jg57'},
-    {'id': 181, 'name': 'Harbor seal', 'freebase_id': '/m/02l8p9'},
-    {'id': 182, 'name': 'Snake', 'freebase_id': '/m/078jl'},
-    {'id': 183, 'name': 'Sewing machine', 'freebase_id': '/m/0llzx'},
-    {'id': 184, 'name': 'Goose', 'freebase_id': '/m/0dbvp'},
-    {'id': 185, 'name': 'Helicopter', 'freebase_id': '/m/09ct_'},
-    {'id': 186, 'name': 'Seat belt', 'freebase_id': '/m/0dkzw'},
-    {'id': 187, 'name': 'Coffee cup', 'freebase_id': '/m/02p5f1q'},
-    {'id': 188, 'name': 'Microwave oven', 'freebase_id': '/m/0fx9l'},
-    {'id': 189, 'name': 'Hot dog', 'freebase_id': '/m/01b9xk'},
-    {'id': 190, 'name': 'Countertop', 'freebase_id': '/m/0b3fp9'},
-    {'id': 191, 'name': 'Serving tray', 'freebase_id': '/m/0h8n27j'},
-    {'id': 192, 'name': 'Dog bed', 'freebase_id': '/m/0h8n6f9'},
-    {'id': 193, 'name': 'Beer', 'freebase_id': '/m/01599'},
-    {'id': 194, 'name': 'Sunglasses', 'freebase_id': '/m/017ftj'},
-    {'id': 195, 'name': 'Golf ball', 'freebase_id': '/m/044r5d'},
-    {'id': 196, 'name': 'Waffle', 'freebase_id': '/m/01dwsz'},
-    {'id': 197, 'name': 'Palm tree', 'freebase_id': '/m/0cdl1'},
-    {'id': 198, 'name': 'Trumpet', 'freebase_id': '/m/07gql'},
-    {'id': 199, 'name': 'Ruler', 'freebase_id': '/m/0hdln'},
-    {'id': 200, 'name': 'Helmet', 'freebase_id': '/m/0zvk5'},
-    {'id': 201, 'name': 'Ladder', 'freebase_id': '/m/012w5l'},
-    {'id': 202, 'name': 'Office building', 'freebase_id': '/m/021sj1'},
-    {'id': 203, 'name': 'Tablet computer', 'freebase_id': '/m/0bh9flk'},
-    {'id': 204, 'name': 'Toilet paper', 'freebase_id': '/m/09gtd'},
-    {'id': 205, 'name': 'Pomegranate', 'freebase_id': '/m/0jwn_'},
-    {'id': 206, 'name': 'Skirt', 'freebase_id': '/m/02wv6h6'},
-    {'id': 207, 'name': 'Gas stove', 'freebase_id': '/m/02wv84t'},
-    {'id': 208, 'name': 'Cookie', 'freebase_id': '/m/021mn'},
-    {'id': 209, 'name': 'Cart', 'freebase_id': '/m/018p4k'},
-    {'id': 210, 'name': 'Raven', 'freebase_id': '/m/06j2d'},
-    {'id': 211, 'name': 'Egg', 'freebase_id': '/m/033cnk'},
-    {'id': 212, 'name': 'Burrito', 'freebase_id': '/m/01j3zr'},
-    {'id': 213, 'name': 'Goat', 'freebase_id': '/m/03fwl'},
-    {'id': 214, 'name': 'Kitchen knife', 'freebase_id': '/m/058qzx'},
-    {'id': 215, 'name': 'Skateboard', 'freebase_id': '/m/06_fw'},
-    {'id': 216, 'name': 'Salt and pepper shakers', 'freebase_id': '/m/02x8cch'},
-    {'id': 217, 'name': 'Lynx', 'freebase_id': '/m/04g2r'},
-    {'id': 218, 'name': 'Boot', 'freebase_id': '/m/01b638'},
-    {'id': 219, 'name': 'Platter', 'freebase_id': '/m/099ssp'},
-    {'id': 220, 'name': 'Ski', 'freebase_id': '/m/071p9'},
-    {'id': 221, 'name': 'Swimwear', 'freebase_id': '/m/01gkx_'},
-    {'id': 222, 'name': 'Swimming pool', 'freebase_id': '/m/0b_rs'},
-    {'id': 223, 'name': 'Drinking straw', 'freebase_id': '/m/03v5tg'},
-    {'id': 224, 'name': 'Wrench', 'freebase_id': '/m/01j5ks'},
-    {'id': 225, 'name': 'Drum', 'freebase_id': '/m/026t6'},
-    {'id': 226, 'name': 'Ant', 'freebase_id': '/m/0_k2'},
-    {'id': 227, 'name': 'Human ear', 'freebase_id': '/m/039xj_'},
-    {'id': 228, 'name': 'Headphones', 'freebase_id': '/m/01b7fy'},
-    {'id': 229, 'name': 'Fountain', 'freebase_id': '/m/0220r2'},
-    {'id': 230, 'name': 'Bird', 'freebase_id': '/m/015p6'},
-    {'id': 231, 'name': 'Jeans', 'freebase_id': '/m/0fly7'},
-    {'id': 232, 'name': 'Television', 'freebase_id': '/m/07c52'},
-    {'id': 233, 'name': 'Crab', 'freebase_id': '/m/0n28_'},
-    {'id': 234, 'name': 'Microphone', 'freebase_id': '/m/0hg7b'},
-    {'id': 235, 'name': 'Home appliance', 'freebase_id': '/m/019dx1'},
-    {'id': 236, 'name': 'Snowplow', 'freebase_id': '/m/04vv5k'},
-    {'id': 237, 'name': 'Beetle', 'freebase_id': '/m/020jm'},
-    {'id': 238, 'name': 'Artichoke', 'freebase_id': '/m/047v4b'},
-    {'id': 239, 'name': 'Jet ski', 'freebase_id': '/m/01xs3r'},
-    {'id': 240, 'name': 'Stationary bicycle', 'freebase_id': '/m/03kt2w'},
-    {'id': 241, 'name': 'Human hair', 'freebase_id': '/m/03q69'},
-    {'id': 242, 'name': 'Brown bear', 'freebase_id': '/m/01dxs'},
-    {'id': 243, 'name': 'Starfish', 'freebase_id': '/m/01h8tj'},
-    {'id': 244, 'name': 'Fork', 'freebase_id': '/m/0dt3t'},
-    {'id': 245, 'name': 'Lobster', 'freebase_id': '/m/0cjq5'},
-    {'id': 246, 'name': 'Corded phone', 'freebase_id': '/m/0h8lkj8'},
-    {'id': 247, 'name': 'Drink', 'freebase_id': '/m/0271t'},
-    {'id': 248, 'name': 'Saucer', 'freebase_id': '/m/03q5c7'},
-    {'id': 249, 'name': 'Carrot', 'freebase_id': '/m/0fj52s'},
-    {'id': 250, 'name': 'Insect', 'freebase_id': '/m/03vt0'},
-    {'id': 251, 'name': 'Clock', 'freebase_id': '/m/01x3z'},
-    {'id': 252, 'name': 'Castle', 'freebase_id': '/m/0d5gx'},
-    {'id': 253, 'name': 'Tennis racket', 'freebase_id': '/m/0h8my_4'},
-    {'id': 254, 'name': 'Ceiling fan', 'freebase_id': '/m/03ldnb'},
-    {'id': 255, 'name': 'Asparagus', 'freebase_id': '/m/0cjs7'},
-    {'id': 256, 'name': 'Jaguar', 'freebase_id': '/m/0449p'},
-    {'id': 257, 'name': 'Musical instrument', 'freebase_id': '/m/04szw'},
-    {'id': 258, 'name': 'Train', 'freebase_id': '/m/07jdr'},
-    {'id': 259, 'name': 'Cat', 'freebase_id': '/m/01yrx'},
-    {'id': 260, 'name': 'Rifle', 'freebase_id': '/m/06c54'},
-    {'id': 261, 'name': 'Dumbbell', 'freebase_id': '/m/04h8sr'},
-    {'id': 262, 'name': 'Mobile phone', 'freebase_id': '/m/050k8'},
-    {'id': 263, 'name': 'Taxi', 'freebase_id': '/m/0pg52'},
-    {'id': 264, 'name': 'Shower', 'freebase_id': '/m/02f9f_'},
-    {'id': 265, 'name': 'Pitcher', 'freebase_id': '/m/054fyh'},
-    {'id': 266, 'name': 'Lemon', 'freebase_id': '/m/09k_b'},
-    {'id': 267, 'name': 'Invertebrate', 'freebase_id': '/m/03xxp'},
-    {'id': 268, 'name': 'Turkey', 'freebase_id': '/m/0jly1'},
-    {'id': 269, 'name': 'High heels', 'freebase_id': '/m/06k2mb'},
-    {'id': 270, 'name': 'Bust', 'freebase_id': '/m/04yqq2'},
-    {'id': 271, 'name': 'Elephant', 'freebase_id': '/m/0bwd_0j'},
-    {'id': 272, 'name': 'Scarf', 'freebase_id': '/m/02h19r'},
-    {'id': 273, 'name': 'Barrel', 'freebase_id': '/m/02zn6n'},
-    {'id': 274, 'name': 'Trombone', 'freebase_id': '/m/07c6l'},
-    {'id': 275, 'name': 'Pumpkin', 'freebase_id': '/m/05zsy'},
-    {'id': 276, 'name': 'Box', 'freebase_id': '/m/025dyy'},
-    {'id': 277, 'name': 'Tomato', 'freebase_id': '/m/07j87'},
-    {'id': 278, 'name': 'Frog', 'freebase_id': '/m/09ld4'},
-    {'id': 279, 'name': 'Bidet', 'freebase_id': '/m/01vbnl'},
-    {'id': 280, 'name': 'Human face', 'freebase_id': '/m/0dzct'},
-    {'id': 281, 'name': 'Houseplant', 'freebase_id': '/m/03fp41'},
-    {'id': 282, 'name': 'Van', 'freebase_id': '/m/0h2r6'},
-    {'id': 283, 'name': 'Shark', 'freebase_id': '/m/0by6g'},
-    {'id': 284, 'name': 'Ice cream', 'freebase_id': '/m/0cxn2'},
-    {'id': 285, 'name': 'Swim cap', 'freebase_id': '/m/04tn4x'},
-    {'id': 286, 'name': 'Falcon', 'freebase_id': '/m/0f6wt'},
-    {'id': 287, 'name': 'Ostrich', 'freebase_id': '/m/05n4y'},
-    {'id': 288, 'name': 'Handgun', 'freebase_id': '/m/0gxl3'},
-    {'id': 289, 'name': 'Whiteboard', 'freebase_id': '/m/02d9qx'},
-    {'id': 290, 'name': 'Lizard', 'freebase_id': '/m/04m9y'},
-    {'id': 291, 'name': 'Pasta', 'freebase_id': '/m/05z55'},
-    {'id': 292, 'name': 'Snowmobile', 'freebase_id': '/m/01x3jk'},
-    {'id': 293, 'name': 'Light bulb', 'freebase_id': '/m/0h8l4fh'},
-    {'id': 294, 'name': 'Window blind', 'freebase_id': '/m/031b6r'},
-    {'id': 295, 'name': 'Muffin', 'freebase_id': '/m/01tcjp'},
-    {'id': 296, 'name': 'Pretzel', 'freebase_id': '/m/01f91_'},
-    {'id': 297, 'name': 'Computer monitor', 'freebase_id': '/m/02522'},
-    {'id': 298, 'name': 'Horn', 'freebase_id': '/m/0319l'},
-    {'id': 299, 'name': 'Furniture', 'freebase_id': '/m/0c_jw'},
-    {'id': 300, 'name': 'Sandwich', 'freebase_id': '/m/0l515'},
-    {'id': 301, 'name': 'Fox', 'freebase_id': '/m/0306r'},
-    {'id': 302, 'name': 'Convenience store', 'freebase_id': '/m/0crjs'},
-    {'id': 303, 'name': 'Fish', 'freebase_id': '/m/0ch_cf'},
-    {'id': 304, 'name': 'Fruit', 'freebase_id': '/m/02xwb'},
-    {'id': 305, 'name': 'Earrings', 'freebase_id': '/m/01r546'},
-    {'id': 306, 'name': 'Curtain', 'freebase_id': '/m/03rszm'},
-    {'id': 307, 'name': 'Grape', 'freebase_id': '/m/0388q'},
-    {'id': 308, 'name': 'Sofa bed', 'freebase_id': '/m/03m3pdh'},
-    {'id': 309, 'name': 'Horse', 'freebase_id': '/m/03k3r'},
-    {'id': 310, 'name': 'Luggage and bags', 'freebase_id': '/m/0hf58v5'},
-    {'id': 311, 'name': 'Desk', 'freebase_id': '/m/01y9k5'},
-    {'id': 312, 'name': 'Crutch', 'freebase_id': '/m/05441v'},
-    {'id': 313, 'name': 'Bicycle helmet', 'freebase_id': '/m/03p3bw'},
-    {'id': 314, 'name': 'Tick', 'freebase_id': '/m/0175cv'},
-    {'id': 315, 'name': 'Airplane', 'freebase_id': '/m/0cmf2'},
-    {'id': 316, 'name': 'Canary', 'freebase_id': '/m/0ccs93'},
-    {'id': 317, 'name': 'Spatula', 'freebase_id': '/m/02d1br'},
-    {'id': 318, 'name': 'Watch', 'freebase_id': '/m/0gjkl'},
-    {'id': 319, 'name': 'Lily', 'freebase_id': '/m/0jqgx'},
-    {'id': 320, 'name': 'Kitchen appliance', 'freebase_id': '/m/0h99cwc'},
-    {'id': 321, 'name': 'Filing cabinet', 'freebase_id': '/m/047j0r'},
-    {'id': 322, 'name': 'Aircraft', 'freebase_id': '/m/0k5j'},
-    {'id': 323, 'name': 'Cake stand', 'freebase_id': '/m/0h8n6ft'},
-    {'id': 324, 'name': 'Candy', 'freebase_id': '/m/0gm28'},
-    {'id': 325, 'name': 'Sink', 'freebase_id': '/m/0130jx'},
-    {'id': 326, 'name': 'Mouse', 'freebase_id': '/m/04rmv'},
-    {'id': 327, 'name': 'Wine', 'freebase_id': '/m/081qc'},
-    {'id': 328, 'name': 'Wheelchair', 'freebase_id': '/m/0qmmr'},
-    {'id': 329, 'name': 'Goldfish', 'freebase_id': '/m/03fj2'},
-    {'id': 330, 'name': 'Refrigerator', 'freebase_id': '/m/040b_t'},
-    {'id': 331, 'name': 'French fries', 'freebase_id': '/m/02y6n'},
-    {'id': 332, 'name': 'Drawer', 'freebase_id': '/m/0fqfqc'},
-    {'id': 333, 'name': 'Treadmill', 'freebase_id': '/m/030610'},
-    {'id': 334, 'name': 'Picnic basket', 'freebase_id': '/m/07kng9'},
-    {'id': 335, 'name': 'Dice', 'freebase_id': '/m/029b3'},
-    {'id': 336, 'name': 'Cabbage', 'freebase_id': '/m/0fbw6'},
-    {'id': 337, 'name': 'Football helmet', 'freebase_id': '/m/07qxg_'},
-    {'id': 338, 'name': 'Pig', 'freebase_id': '/m/068zj'},
-    {'id': 339, 'name': 'Person', 'freebase_id': '/m/01g317'},
-    {'id': 340, 'name': 'Shorts', 'freebase_id': '/m/01bfm9'},
-    {'id': 341, 'name': 'Gondola', 'freebase_id': '/m/02068x'},
-    {'id': 342, 'name': 'Honeycomb', 'freebase_id': '/m/0fz0h'},
-    {'id': 343, 'name': 'Doughnut', 'freebase_id': '/m/0jy4k'},
-    {'id': 344, 'name': 'Chest of drawers', 'freebase_id': '/m/05kyg_'},
-    {'id': 345, 'name': 'Land vehicle', 'freebase_id': '/m/01prls'},
-    {'id': 346, 'name': 'Bat', 'freebase_id': '/m/01h44'},
-    {'id': 347, 'name': 'Monkey', 'freebase_id': '/m/08pbxl'},
-    {'id': 348, 'name': 'Dagger', 'freebase_id': '/m/02gzp'},
-    {'id': 349, 'name': 'Tableware', 'freebase_id': '/m/04brg2'},
-    {'id': 350, 'name': 'Human foot', 'freebase_id': '/m/031n1'},
-    {'id': 351, 'name': 'Mug', 'freebase_id': '/m/02jvh9'},
-    {'id': 352, 'name': 'Alarm clock', 'freebase_id': '/m/046dlr'},
-    {'id': 353, 'name': 'Pressure cooker', 'freebase_id': '/m/0h8ntjv'},
-    {'id': 354, 'name': 'Human hand', 'freebase_id': '/m/0k65p'},
-    {'id': 355, 'name': 'Tortoise', 'freebase_id': '/m/011k07'},
-    {'id': 356, 'name': 'Baseball glove', 'freebase_id': '/m/03grzl'},
-    {'id': 357, 'name': 'Sword', 'freebase_id': '/m/06y5r'},
-    {'id': 358, 'name': 'Pear', 'freebase_id': '/m/061_f'},
-    {'id': 359, 'name': 'Miniskirt', 'freebase_id': '/m/01cmb2'},
-    {'id': 360, 'name': 'Traffic sign', 'freebase_id': '/m/01mqdt'},
-    {'id': 361, 'name': 'Girl', 'freebase_id': '/m/05r655'},
-    {'id': 362, 'name': 'Roller skates', 'freebase_id': '/m/02p3w7d'},
-    {'id': 363, 'name': 'Dinosaur', 'freebase_id': '/m/029tx'},
-    {'id': 364, 'name': 'Porch', 'freebase_id': '/m/04m6gz'},
-    {'id': 365, 'name': 'Human beard', 'freebase_id': '/m/015h_t'},
-    {'id': 366, 'name': 'Submarine sandwich', 'freebase_id': '/m/06pcq'},
-    {'id': 367, 'name': 'Screwdriver', 'freebase_id': '/m/01bms0'},
-    {'id': 368, 'name': 'Strawberry', 'freebase_id': '/m/07fbm7'},
-    {'id': 369, 'name': 'Wine glass', 'freebase_id': '/m/09tvcd'},
-    {'id': 370, 'name': 'Seafood', 'freebase_id': '/m/06nwz'},
-    {'id': 371, 'name': 'Racket', 'freebase_id': '/m/0dv9c'},
-    {'id': 372, 'name': 'Wheel', 'freebase_id': '/m/083wq'},
-    {'id': 373, 'name': 'Sea lion', 'freebase_id': '/m/0gd36'},
-    {'id': 374, 'name': 'Toy', 'freebase_id': '/m/0138tl'},
-    {'id': 375, 'name': 'Tea', 'freebase_id': '/m/07clx'},
-    {'id': 376, 'name': 'Tennis ball', 'freebase_id': '/m/05ctyq'},
-    {'id': 377, 'name': 'Waste container', 'freebase_id': '/m/0bjyj5'},
-    {'id': 378, 'name': 'Mule', 'freebase_id': '/m/0dbzx'},
-    {'id': 379, 'name': 'Cricket ball', 'freebase_id': '/m/02ctlc'},
-    {'id': 380, 'name': 'Pineapple', 'freebase_id': '/m/0fp6w'},
-    {'id': 381, 'name': 'Coconut', 'freebase_id': '/m/0djtd'},
-    {'id': 382, 'name': 'Doll', 'freebase_id': '/m/0167gd'},
-    {'id': 383, 'name': 'Coffee table', 'freebase_id': '/m/078n6m'},
-    {'id': 384, 'name': 'Snowman', 'freebase_id': '/m/0152hh'},
-    {'id': 385, 'name': 'Lavender', 'freebase_id': '/m/04gth'},
-    {'id': 386, 'name': 'Shrimp', 'freebase_id': '/m/0ll1f78'},
-    {'id': 387, 'name': 'Maple', 'freebase_id': '/m/0cffdh'},
-    {'id': 388, 'name': 'Cowboy hat', 'freebase_id': '/m/025rp__'},
-    {'id': 389, 'name': 'Goggles', 'freebase_id': '/m/02_n6y'},
-    {'id': 390, 'name': 'Rugby ball', 'freebase_id': '/m/0wdt60w'},
-    {'id': 391, 'name': 'Caterpillar', 'freebase_id': '/m/0cydv'},
-    {'id': 392, 'name': 'Poster', 'freebase_id': '/m/01n5jq'},
-    {'id': 393, 'name': 'Rocket', 'freebase_id': '/m/09rvcxw'},
-    {'id': 394, 'name': 'Organ', 'freebase_id': '/m/013y1f'},
-    {'id': 395, 'name': 'Saxophone', 'freebase_id': '/m/06ncr'},
-    {'id': 396, 'name': 'Traffic light', 'freebase_id': '/m/015qff'},
-    {'id': 397, 'name': 'Cocktail', 'freebase_id': '/m/024g6'},
-    {'id': 398, 'name': 'Plastic bag', 'freebase_id': '/m/05gqfk'},
-    {'id': 399, 'name': 'Squash', 'freebase_id': '/m/0dv77'},
-    {'id': 400, 'name': 'Mushroom', 'freebase_id': '/m/052sf'},
-    {'id': 401, 'name': 'Hamburger', 'freebase_id': '/m/0cdn1'},
-    {'id': 402, 'name': 'Light switch', 'freebase_id': '/m/03jbxj'},
-    {'id': 403, 'name': 'Parachute', 'freebase_id': '/m/0cyfs'},
-    {'id': 404, 'name': 'Teddy bear', 'freebase_id': '/m/0kmg4'},
-    {'id': 405, 'name': 'Winter melon', 'freebase_id': '/m/02cvgx'},
-    {'id': 406, 'name': 'Deer', 'freebase_id': '/m/09kx5'},
-    {'id': 407, 'name': 'Musical keyboard', 'freebase_id': '/m/057cc'},
-    {'id': 408, 'name': 'Plumbing fixture', 'freebase_id': '/m/02pkr5'},
-    {'id': 409, 'name': 'Scoreboard', 'freebase_id': '/m/057p5t'},
-    {'id': 410, 'name': 'Baseball bat', 'freebase_id': '/m/03g8mr'},
-    {'id': 411, 'name': 'Envelope', 'freebase_id': '/m/0frqm'},
-    {'id': 412, 'name': 'Adhesive tape', 'freebase_id': '/m/03m3vtv'},
-    {'id': 413, 'name': 'Briefcase', 'freebase_id': '/m/0584n8'},
-    {'id': 414, 'name': 'Paddle', 'freebase_id': '/m/014y4n'},
-    {'id': 415, 'name': 'Bow and arrow', 'freebase_id': '/m/01g3x7'},
-    {'id': 416, 'name': 'Telephone', 'freebase_id': '/m/07cx4'},
-    {'id': 417, 'name': 'Sheep', 'freebase_id': '/m/07bgp'},
-    {'id': 418, 'name': 'Jacket', 'freebase_id': '/m/032b3c'},
-    {'id': 419, 'name': 'Boy', 'freebase_id': '/m/01bl7v'},
-    {'id': 420, 'name': 'Pizza', 'freebase_id': '/m/0663v'},
-    {'id': 421, 'name': 'Otter', 'freebase_id': '/m/0cn6p'},
-    {'id': 422, 'name': 'Office supplies', 'freebase_id': '/m/02rdsp'},
-    {'id': 423, 'name': 'Couch', 'freebase_id': '/m/02crq1'},
-    {'id': 424, 'name': 'Cello', 'freebase_id': '/m/01xqw'},
-    {'id': 425, 'name': 'Bull', 'freebase_id': '/m/0cnyhnx'},
-    {'id': 426, 'name': 'Camel', 'freebase_id': '/m/01x_v'},
-    {'id': 427, 'name': 'Ball', 'freebase_id': '/m/018xm'},
-    {'id': 428, 'name': 'Duck', 'freebase_id': '/m/09ddx'},
-    {'id': 429, 'name': 'Whale', 'freebase_id': '/m/084zz'},
-    {'id': 430, 'name': 'Shirt', 'freebase_id': '/m/01n4qj'},
-    {'id': 431, 'name': 'Tank', 'freebase_id': '/m/07cmd'},
-    {'id': 432, 'name': 'Motorcycle', 'freebase_id': '/m/04_sv'},
-    {'id': 433, 'name': 'Accordion', 'freebase_id': '/m/0mkg'},
-    {'id': 434, 'name': 'Owl', 'freebase_id': '/m/09d5_'},
-    {'id': 435, 'name': 'Porcupine', 'freebase_id': '/m/0c568'},
-    {'id': 436, 'name': 'Sun hat', 'freebase_id': '/m/02wbtzl'},
-    {'id': 437, 'name': 'Nail', 'freebase_id': '/m/05bm6'},
-    {'id': 438, 'name': 'Scissors', 'freebase_id': '/m/01lsmm'},
-    {'id': 439, 'name': 'Swan', 'freebase_id': '/m/0dftk'},
-    {'id': 440, 'name': 'Lamp', 'freebase_id': '/m/0dtln'},
-    {'id': 441, 'name': 'Crown', 'freebase_id': '/m/0nl46'},
-    {'id': 442, 'name': 'Piano', 'freebase_id': '/m/05r5c'},
-    {'id': 443, 'name': 'Sculpture', 'freebase_id': '/m/06msq'},
-    {'id': 444, 'name': 'Cheetah', 'freebase_id': '/m/0cd4d'},
-    {'id': 445, 'name': 'Oboe', 'freebase_id': '/m/05kms'},
-    {'id': 446, 'name': 'Tin can', 'freebase_id': '/m/02jnhm'},
-    {'id': 447, 'name': 'Mango', 'freebase_id': '/m/0fldg'},
-    {'id': 448, 'name': 'Tripod', 'freebase_id': '/m/073bxn'},
-    {'id': 449, 'name': 'Oven', 'freebase_id': '/m/029bxz'},
-    {'id': 450, 'name': 'Mouse', 'freebase_id': '/m/020lf'},
-    {'id': 451, 'name': 'Barge', 'freebase_id': '/m/01btn'},
-    {'id': 452, 'name': 'Coffee', 'freebase_id': '/m/02vqfm'},
-    {'id': 453, 'name': 'Snowboard', 'freebase_id': '/m/06__v'},
-    {'id': 454, 'name': 'Common fig', 'freebase_id': '/m/043nyj'},
-    {'id': 455, 'name': 'Salad', 'freebase_id': '/m/0grw1'},
-    {'id': 456, 'name': 'Marine invertebrates', 'freebase_id': '/m/03hl4l9'},
-    {'id': 457, 'name': 'Umbrella', 'freebase_id': '/m/0hnnb'},
-    {'id': 458, 'name': 'Kangaroo', 'freebase_id': '/m/04c0y'},
-    {'id': 459, 'name': 'Human arm', 'freebase_id': '/m/0dzf4'},
-    {'id': 460, 'name': 'Measuring cup', 'freebase_id': '/m/07v9_z'},
-    {'id': 461, 'name': 'Snail', 'freebase_id': '/m/0f9_l'},
-    {'id': 462, 'name': 'Loveseat', 'freebase_id': '/m/0703r8'},
-    {'id': 463, 'name': 'Suit', 'freebase_id': '/m/01xyhv'},
-    {'id': 464, 'name': 'Teapot', 'freebase_id': '/m/01fh4r'},
-    {'id': 465, 'name': 'Bottle', 'freebase_id': '/m/04dr76w'},
-    {'id': 466, 'name': 'Alpaca', 'freebase_id': '/m/0pcr'},
-    {'id': 467, 'name': 'Kettle', 'freebase_id': '/m/03s_tn'},
-    {'id': 468, 'name': 'Trousers', 'freebase_id': '/m/07mhn'},
-    {'id': 469, 'name': 'Popcorn', 'freebase_id': '/m/01hrv5'},
-    {'id': 470, 'name': 'Centipede', 'freebase_id': '/m/019h78'},
-    {'id': 471, 'name': 'Spider', 'freebase_id': '/m/09kmb'},
-    {'id': 472, 'name': 'Sparrow', 'freebase_id': '/m/0h23m'},
-    {'id': 473, 'name': 'Plate', 'freebase_id': '/m/050gv4'},
-    {'id': 474, 'name': 'Bagel', 'freebase_id': '/m/01fb_0'},
-    {'id': 475, 'name': 'Personal care', 'freebase_id': '/m/02w3_ws'},
-    {'id': 476, 'name': 'Apple', 'freebase_id': '/m/014j1m'},
-    {'id': 477, 'name': 'Brassiere', 'freebase_id': '/m/01gmv2'},
-    {'id': 478, 'name': 'Bathroom cabinet', 'freebase_id': '/m/04y4h8h'},
-    {'id': 479, 'name': 'studio couch', 'freebase_id': '/m/026qbn5'},
-    {'id': 480, 'name': 'Computer keyboard', 'freebase_id': '/m/01m2v'},
-    {'id': 481, 'name': 'Table tennis racket', 'freebase_id': '/m/05_5p_0'},
-    {'id': 482, 'name': 'Sushi', 'freebase_id': '/m/07030'},
-    {'id': 483, 'name': 'Cabinetry', 'freebase_id': '/m/01s105'},
-    {'id': 484, 'name': 'Street light', 'freebase_id': '/m/033rq4'},
-    {'id': 485, 'name': 'Towel', 'freebase_id': '/m/0162_1'},
-    {'id': 486, 'name': 'Nightstand', 'freebase_id': '/m/02z51p'},
-    {'id': 487, 'name': 'Rabbit', 'freebase_id': '/m/06mf6'},
-    {'id': 488, 'name': 'Dolphin', 'freebase_id': '/m/02hj4'},
-    {'id': 489, 'name': 'Dog', 'freebase_id': '/m/0bt9lr'},
-    {'id': 490, 'name': 'Jug', 'freebase_id': '/m/08hvt4'},
-    {'id': 491, 'name': 'Wok', 'freebase_id': '/m/084rd'},
-    {'id': 492, 'name': 'Fire hydrant', 'freebase_id': '/m/01pns0'},
-    {'id': 493, 'name': 'Human eye', 'freebase_id': '/m/014sv8'},
-    {'id': 494, 'name': 'Skyscraper', 'freebase_id': '/m/079cl'},
-    {'id': 495, 'name': 'Backpack', 'freebase_id': '/m/01940j'},
-    {'id': 496, 'name': 'Potato', 'freebase_id': '/m/05vtc'},
-    {'id': 497, 'name': 'Paper towel', 'freebase_id': '/m/02w3r3'},
-    {'id': 498, 'name': 'Lifejacket', 'freebase_id': '/m/054xkw'},
-    {'id': 499, 'name': 'Bicycle wheel', 'freebase_id': '/m/01bqk0'},
-    {'id': 500, 'name': 'Toilet', 'freebase_id': '/m/09g1w'},
-]
-
-
-def _get_builtin_metadata(cats):
-    id_to_name = {x['id']: x['name'] for x in cats}
-    thing_dataset_id_to_contiguous_id = {i + 1: i for i in range(len(cats))}
-    thing_classes = [x['name'] for x in sorted(cats, key=lambda x: x['id'])]
-    return {
-        "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id,
-        "thing_classes": thing_classes}
-
-_PREDEFINED_SPLITS_OID = {
-    # cat threshold: 500, 1500: r 170, c 151, f 179
-    "oid_train": ("oid/images/", "oid/annotations/oid_challenge_2019_train_bbox.json"),
-    # "expanded" duplicates annotations to their father classes based on the official
-    #   hierarchy. This is used in the official evaulation protocol.
-    #   https://storage.googleapis.com/openimages/web/evaluation.html
-    "oid_val_expanded": ("oid/images/validation/", "oid/annotations/oid_challenge_2019_val_expanded.json"),
-    "oid_val_expanded_rare": ("oid/images/validation/", "oid/annotations/oid_challenge_2019_val_expanded_rare.json"),
-}
-
-
-for key, (image_root, json_file) in _PREDEFINED_SPLITS_OID.items():
-    register_oid_instances(
-        key,
-        _get_builtin_metadata(categories),
-        os.path.join("datasets", json_file) if "://" not in json_file else json_file,
-        os.path.join("datasets", image_root),
-    )
\ No newline at end of file
diff --git a/spaces/Mcdimmy/Clothing-Identifier/README.md b/spaces/Mcdimmy/Clothing-Identifier/README.md
deleted file mode 100644
index c2dbc218a83a9044f009fd7528a9f25c199cc765..0000000000000000000000000000000000000000
--- a/spaces/Mcdimmy/Clothing-Identifier/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Mike
-emoji: 📉
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/MetaWabbit/Auto-GPT/autogpt/commands/file_operations.py b/spaces/MetaWabbit/Auto-GPT/autogpt/commands/file_operations.py
deleted file mode 100644
index ad145ec956dd9dafd39e09c2244d001cf5febd2f..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/autogpt/commands/file_operations.py
+++ /dev/null
@@ -1,267 +0,0 @@
-"""File operations for AutoGPT"""
-from __future__ import annotations
-
-import os
-import os.path
-from typing import Generator
-
-import requests
-from colorama import Back, Fore
-from requests.adapters import HTTPAdapter, Retry
-
-from autogpt.spinner import Spinner
-from autogpt.utils import readable_file_size
-from autogpt.workspace import WORKSPACE_PATH, path_in_workspace
-
-LOG_FILE = "file_logger.txt"
-LOG_FILE_PATH = WORKSPACE_PATH / LOG_FILE
-
-
-def check_duplicate_operation(operation: str, filename: str) -> bool:
-    """Check if the operation has already been performed on the given file
-
-    Args:
-        operation (str): The operation to check for
-        filename (str): The name of the file to check for
-
-    Returns:
-        bool: True if the operation has already been performed on the file
-    """
-    log_content = read_file(LOG_FILE)
-    log_entry = f"{operation}: {filename}\n"
-    return log_entry in log_content
-
-
-def log_operation(operation: str, filename: str) -> None:
-    """Log the file operation to the file_logger.txt
-
-    Args:
-        operation (str): The operation to log
-        filename (str): The name of the file the operation was performed on
-    """
-    log_entry = f"{operation}: {filename}\n"
-
-    # Create the log file if it doesn't exist
-    if not os.path.exists(LOG_FILE_PATH):
-        with open(LOG_FILE_PATH, "w", encoding="utf-8") as f:
-            f.write("File Operation Logger ")
-
-    append_to_file(LOG_FILE, log_entry, shouldLog=False)
-
-
-def split_file(
-    content: str, max_length: int = 4000, overlap: int = 0
-) -> Generator[str, None, None]:
-    """
-    Split text into chunks of a specified maximum length with a specified overlap
-    between chunks.
-
-    :param content: The input text to be split into chunks
-    :param max_length: The maximum length of each chunk,
-        default is 4000 (about 1k token)
-    :param overlap: The number of overlapping characters between chunks,
-        default is no overlap
-    :return: A generator yielding chunks of text
-    """
-    start = 0
-    content_length = len(content)
-
-    while start < content_length:
-        end = start + max_length
-        if end + overlap < content_length:
-            chunk = content[start : end + overlap - 1]
-        else:
-            chunk = content[start:content_length]
-
-            # Account for the case where the last chunk is shorter than the overlap, so it has already been consumed
-            if len(chunk) <= overlap:
-                break
-
-        yield chunk
-        start += max_length - overlap
-
-
-def read_file(filename: str) -> str:
-    """Read a file and return the contents
-
-    Args:
-        filename (str): The name of the file to read
-
-    Returns:
-        str: The contents of the file
-    """
-    try:
-        filepath = path_in_workspace(filename)
-        with open(filepath, "r", encoding="utf-8") as f:
-            content = f.read()
-        return content
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def ingest_file(
-    filename: str, memory, max_length: int = 4000, overlap: int = 200
-) -> None:
-    """
-    Ingest a file by reading its content, splitting it into chunks with a specified
-    maximum length and overlap, and adding the chunks to the memory storage.
-
-    :param filename: The name of the file to ingest
-    :param memory: An object with an add() method to store the chunks in memory
-    :param max_length: The maximum length of each chunk, default is 4000
-    :param overlap: The number of overlapping characters between chunks, default is 200
-    """
-    try:
-        print(f"Working with file {filename}")
-        content = read_file(filename)
-        content_length = len(content)
-        print(f"File length: {content_length} characters")
-
-        chunks = list(split_file(content, max_length=max_length, overlap=overlap))
-
-        num_chunks = len(chunks)
-        for i, chunk in enumerate(chunks):
-            print(f"Ingesting chunk {i + 1} / {num_chunks} into memory")
-            memory_to_add = (
-                f"Filename: {filename}\n" f"Content part#{i + 1}/{num_chunks}: {chunk}"
-            )
-
-            memory.add(memory_to_add)
-
-        print(f"Done ingesting {num_chunks} chunks from {filename}.")
-    except Exception as e:
-        print(f"Error while ingesting file '{filename}': {str(e)}")
-
-
-def write_to_file(filename: str, text: str) -> str:
-    """Write text to a file
-
-    Args:
-        filename (str): The name of the file to write to
-        text (str): The text to write to the file
-
-    Returns:
-        str: A message indicating success or failure
-    """
-    if check_duplicate_operation("write", filename):
-        return "Error: File has already been updated."
-    try:
-        filepath = path_in_workspace(filename)
-        directory = os.path.dirname(filepath)
-        if not os.path.exists(directory):
-            os.makedirs(directory)
-        with open(filepath, "w", encoding="utf-8") as f:
-            f.write(text)
-        log_operation("write", filename)
-        return "File written to successfully."
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def append_to_file(filename: str, text: str, shouldLog: bool = True) -> str:
-    """Append text to a file
-
-    Args:
-        filename (str): The name of the file to append to
-        text (str): The text to append to the file
-
-    Returns:
-        str: A message indicating success or failure
-    """
-    try:
-        filepath = path_in_workspace(filename)
-        with open(filepath, "a") as f:
-            f.write(text)
-
-        if shouldLog:
-            log_operation("append", filename)
-
-        return "Text appended successfully."
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def delete_file(filename: str) -> str:
-    """Delete a file
-
-    Args:
-        filename (str): The name of the file to delete
-
-    Returns:
-        str: A message indicating success or failure
-    """
-    if check_duplicate_operation("delete", filename):
-        return "Error: File has already been deleted."
-    try:
-        filepath = path_in_workspace(filename)
-        os.remove(filepath)
-        log_operation("delete", filename)
-        return "File deleted successfully."
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def search_files(directory: str) -> list[str]:
-    """Search for files in a directory
-
-    Args:
-        directory (str): The directory to search in
-
-    Returns:
-        list[str]: A list of files found in the directory
-    """
-    found_files = []
-
-    if directory in {"", "/"}:
-        search_directory = WORKSPACE_PATH
-    else:
-        search_directory = path_in_workspace(directory)
-
-    for root, _, files in os.walk(search_directory):
-        for file in files:
-            if file.startswith("."):
-                continue
-            relative_path = os.path.relpath(os.path.join(root, file), WORKSPACE_PATH)
-            found_files.append(relative_path)
-
-    return found_files
-
-
-def download_file(url, filename):
-    """Downloads a file
-    Args:
-        url (str): URL of the file to download
-        filename (str): Filename to save the file as
-    """
-    safe_filename = path_in_workspace(filename)
-    try:
-        message = f"{Fore.YELLOW}Downloading file from {Back.LIGHTBLUE_EX}{url}{Back.RESET}{Fore.RESET}"
-        with Spinner(message) as spinner:
-            session = requests.Session()
-            retry = Retry(total=3, backoff_factor=1, status_forcelist=[502, 503, 504])
-            adapter = HTTPAdapter(max_retries=retry)
-            session.mount("http://", adapter)
-            session.mount("https://", adapter)
-
-            total_size = 0
-            downloaded_size = 0
-
-            with session.get(url, allow_redirects=True, stream=True) as r:
-                r.raise_for_status()
-                total_size = int(r.headers.get("Content-Length", 0))
-                downloaded_size = 0
-
-                with open(safe_filename, "wb") as f:
-                    for chunk in r.iter_content(chunk_size=8192):
-                        f.write(chunk)
-                        downloaded_size += len(chunk)
-
-                        # Update the progress message
-                        progress = f"{readable_file_size(downloaded_size)} / {readable_file_size(total_size)}"
-                        spinner.update_message(f"{message} {progress}")
-
-            return f'Successfully downloaded and locally stored file: "{filename}"! (Size: {readable_file_size(total_size)})'
-    except requests.HTTPError as e:
-        return f"Got an HTTP Error whilst trying to download file: {e}"
-    except Exception as e:
-        return "Error: " + str(e)
diff --git a/spaces/Mileena/PIFu-Clothed-Human-Digitization/PIFu/lib/data/BaseDataset.py b/spaces/Mileena/PIFu-Clothed-Human-Digitization/PIFu/lib/data/BaseDataset.py
deleted file mode 100644
index 2d3e842341ecd51514ac96ce51a13fcaa12d1733..0000000000000000000000000000000000000000
--- a/spaces/Mileena/PIFu-Clothed-Human-Digitization/PIFu/lib/data/BaseDataset.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from torch.utils.data import Dataset
-import random
-
-
-class BaseDataset(Dataset):
-    '''
-    This is the Base Datasets.
-    Itself does nothing and is not runnable.
-    Check self.get_item function to see what it should return.
-    '''
-
-    @staticmethod
-    def modify_commandline_options(parser, is_train):
-        return parser
-
-    def __init__(self, opt, phase='train'):
-        self.opt = opt
-        self.is_train = self.phase == 'train'
-        self.projection_mode = 'orthogonal'  # Declare projection mode here
-
-    def __len__(self):
-        return 0
-
-    def get_item(self, index):
-        # In case of a missing file or IO error, switch to a random sample instead
-        try:
-            res = {
-                'name': None,  # name of this subject
-                'b_min': None,  # Bounding box (x_min, y_min, z_min) of target space
-                'b_max': None,  # Bounding box (x_max, y_max, z_max) of target space
-
-                'samples': None,  # [3, N] samples
-                'labels': None,  # [1, N] labels
-
-                'img': None,  # [num_views, C, H, W] input images
-                'calib': None,  # [num_views, 4, 4] calibration matrix
-                'extrinsic': None,  # [num_views, 4, 4] extrinsic matrix
-                'mask': None,  # [num_views, 1, H, W] segmentation masks
-            }
-            return res
-        except:
-            print("Requested index %s has missing files. Using a random sample instead." % index)
-            return self.get_item(index=random.randint(0, self.__len__() - 1))
-
-    def __getitem__(self, index):
-        return self.get_item(index)
diff --git a/spaces/MiloSobral/PortiloopDemo/portiloop/src/detection.py b/spaces/MiloSobral/PortiloopDemo/portiloop/src/detection.py
deleted file mode 100644
index cf30d6a2c37211af2ac0e26493d27dcf3c941e66..0000000000000000000000000000000000000000
--- a/spaces/MiloSobral/PortiloopDemo/portiloop/src/detection.py
+++ /dev/null
@@ -1,163 +0,0 @@
-from abc import ABC, abstractmethod
-import time
-from pathlib import Path
-from portiloop.src import ADS
-
-if ADS:
-    from pycoral.utils import edgetpu
-else:
-    import tensorflow as tf
-import numpy as np
-
-
-# Abstract interface for developers:
-
-class Detector(ABC):
-    
-    def __init__(self, threshold=None, channel=None):
-        """
-        Mandatory arguments are from the in the Portiloop GUI.
-        """
-        self.threshold = threshold
-        self.channel = channel
-
-    @abstractmethod
-    def detect(self, datapoints):
-        """
-        Takes datapoints as input and outputs a detection signal.
-        
-        Args:
-            datapoints: list of lists of n channels: may contain several datapoints.
-                A datapoint is a list of n floats, 1 for each channel.
-                In the current version of Portiloop, there is always only one datapoint per datapoints list.
-        
-        Returns:
-            signal: Object: output detection signal (for instance, the output of a neural network);
-                this output signal is the input of the Stimulator.stimulate method.
-                If you don't mean to use a Stimulator, you can simply return None.
-        """
-        raise NotImplementedError
-
-
-# Example implementation for sleep spindles:
-        
-DEFAULT_MODEL_PATH = str(Path(__file__).parent.parent / "models/portiloop_model_quant.tflite")
-# print(DEFAULT_MODEL_PATH)
-
-class SleepSpindleRealTimeDetector(Detector):
-    def __init__(self,
-                 threshold=0.5,
-                 num_models_parallel=8,
-                 window_size=54,
-                 seq_stride=42,
-                 model_path=None,
-                 verbose=False,
-                 channel=2):
-        model_path = DEFAULT_MODEL_PATH if model_path is None else model_path
-        self.verbose = verbose
-        self.num_models_parallel = num_models_parallel
-        
-        self.interpreters = []
-        for i in range(self.num_models_parallel):
-            if ADS:
-                self.interpreters.append(edgetpu.make_interpreter(model_path))
-            else:
-                self.interpreters.append(tf.lite.Interpreter(model_path=model_path))
-            self.interpreters[i].allocate_tensors()
-        self.interpreter_counter = 0
-        
-        self.input_details = self.interpreters[0].get_input_details()
-        self.output_details = self.interpreters[0].get_output_details()
-        
-        self.buffer = []
-        self.seq_stride = seq_stride
-        self.window_size = window_size 
-        
-        self.stride_counters = [np.floor((self.seq_stride / self.num_models_parallel) * (i + 1)) for i in range(self.num_models_parallel)]
-        for idx in reversed(range(1, len(self.stride_counters))):
-            self.stride_counters[idx] -= self.stride_counters[idx-1]
-        assert sum(self.stride_counters) == self.seq_stride, f"{self.stride_counters} does not sum to {self.seq_stride}"
-        
-        self.h = [np.zeros((1, 7), dtype=np.int8) for _ in range(self.num_models_parallel)]
-            
-        self.current_stride_counter = self.stride_counters[0] - 1
-        
-        super().__init__(threshold, channel)
-
-    def detect(self, datapoints):
-        """
-        Takes datapoints as input and outputs a detection signal.
-        datapoints is a list of lists of n channels: may contain several datapoints.
-        
-        The output signal is a list of tuples (is_spindle, is_train_of_spindles).
-        
-        """
-        res = []
-        for inp in datapoints:
-            result = self.add_datapoint(inp)
-            if result is not None:
-                res.append(result >= self.threshold)
-        return res
-
-    def add_datapoint(self, input_float):
-        '''
-        Add one datapoint to the buffer
-        '''
-        input_float = input_float[self.channel - 1]
-        result = None
-        # Add to current buffer
-        self.buffer.append(input_float)
-        if len(self.buffer) > self.window_size:
-            # Remove the end of the buffer
-            self.buffer = self.buffer[1:]
-            self.current_stride_counter += 1
-            if self.current_stride_counter == self.stride_counters[self.interpreter_counter]:
-                # If we have reached the next window size, we send the current buffer to the inference function and update the hidden state
-                result, self.h[self.interpreter_counter] = self.forward_tflite(self.interpreter_counter, self.buffer, self.h[self.interpreter_counter])
-                self.interpreter_counter += 1
-                self.interpreter_counter %= self.num_models_parallel
-                self.current_stride_counter = 0
-        return result
-        
-    def forward_tflite(self, idx, input_x, input_h):
-        input_details = self.interpreters[idx].get_input_details()
-        output_details = self.interpreters[idx].get_output_details()
-        
-        # convert input to int 
-        input_scale, input_zero_point = input_details[1]["quantization"]
-        input_x = np.asarray(input_x) / input_scale + input_zero_point
-        input_data_x = input_x.astype(input_details[1]["dtype"])
-        input_data_x = np.expand_dims(input_data_x, (0, 1))
-
-        # input_scale, input_zero_point = input_details[0]["quantization"]
-        # input = np.asarray(input) / input_scale + input_zero_point
-
-        # Test the model on random input data.
-        input_shape_h = input_details[0]['shape']
-        input_shape_x = input_details[1]['shape']
-
-        # input_data_h = np.array(np.random.random_sample(input_shape_h), dtype=np.int8)
-        # input_data_x = np.array(np.random.random_sample(input_shape_x), dtype=np.int8)
-        self.interpreters[idx].set_tensor(input_details[0]['index'], input_h)
-        self.interpreters[idx].set_tensor(input_details[1]['index'], input_data_x)
-        
-        if self.verbose:
-            start_time = time.time()
-
-        self.interpreters[idx].invoke()
-        
-        if self.verbose:
-            end_time = time.time()
-
-        # The function `get_tensor()` returns a copy of the tensor data.
-        # Use `tensor()` in order to get a pointer to the tensor.
-        output_data_h = self.interpreters[idx].get_tensor(output_details[0]['index'])
-        output_data_y = self.interpreters[idx].get_tensor(output_details[1]['index'])
-
-        output_scale, output_zero_point = output_details[1]["quantization"]
-        output_data_y = (int(output_data_y) - output_zero_point) * output_scale
-        
-        if self.verbose:
-            print(f"Computed output {output_data_y} in {end_time - start_time} seconds")
-
-        return output_data_y, output_data_h
diff --git a/spaces/MingGatsby/Grounding_DINO_demo/groundingdino/version.py b/spaces/MingGatsby/Grounding_DINO_demo/groundingdino/version.py
deleted file mode 100644
index b794fd409a5e3b3b65ad76a43d6a01a318877640..0000000000000000000000000000000000000000
--- a/spaces/MingGatsby/Grounding_DINO_demo/groundingdino/version.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = '0.1.0'
diff --git a/spaces/MrVicente/RA-BART/custom_tokenizer/__init__.py b/spaces/MrVicente/RA-BART/custom_tokenizer/__init__.py
deleted file mode 100644
index 5142f5314fc1c154b3b635a505286b33d485b7a8..0000000000000000000000000000000000000000
--- a/spaces/MrVicente/RA-BART/custom_tokenizer/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .bart_custom_tokenizer_fast import *
diff --git a/spaces/MrYXJ/calculate-model-flops/README.md b/spaces/MrYXJ/calculate-model-flops/README.md
deleted file mode 100644
index d752ae02eb3c5a2620f0468f3d8cb04b6b0a7283..0000000000000000000000000000000000000000
--- a/spaces/MrYXJ/calculate-model-flops/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Calculate Model Flops
-emoji: 🌖
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.43.2
-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/MultiTransformer/vision-agent-with-llava/README.md b/spaces/MultiTransformer/vision-agent-with-llava/README.md
deleted file mode 100644
index f5485d8800e789979c4bf4ff51142e65f672aca8..0000000000000000000000000000000000000000
--- a/spaces/MultiTransformer/vision-agent-with-llava/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Vision Agent With Llava
-emoji: 😻
-colorFrom: red
-colorTo: blue
-sdk: static
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/NAACL2022/papers/paper_list.py b/spaces/NAACL2022/papers/paper_list.py
deleted file mode 100644
index fc38680788ff61f493e9962aeb589d63fcc9b459..0000000000000000000000000000000000000000
--- a/spaces/NAACL2022/papers/paper_list.py
+++ /dev/null
@@ -1,102 +0,0 @@
-from __future__ import annotations
-
-import pandas as pd
-
-
-class PaperList:
-    def __init__(self):
-        self.table = pd.read_csv('papers.csv')
-        self._preprcess_table()
-
-        self.table_header = '''
-            <tr>
-                <td width="50%">Paper</td>
-                <td width="22%">Authors</td>
-                <td width="4%">pdf</td>
-                <td width="4%">category</td>
-                <td width="4%">arXiv</td>
-                <td width="4%">GitHub</td>
-                <td width="4%">HF Spaces</td>
-                <td width="4%">HF Models</td>
-                <td width="4%">HF Datasets</td>
-            </tr>'''
-
-    def _preprcess_table(self) -> None:
-        self.table['title_lowercase'] = self.table.title.str.lower()
-
-        rows = []
-        for row in self.table.itertuples():
-            paper = f'<a href="{row.url}" target="_blank">{row.title}</a>' if isinstance(
-                row.url, str) else row.title
-            pdf = f'<a href="{row.pdf}" target="_blank">pdf</a>' if isinstance(
-                row.pdf, str) else ''
-            arxiv = f'<a href="{row.arxiv}" target="_blank">arXiv</a>' if isinstance(
-                row.arxiv, str) else ''
-            github = f'<a href="{row.github}" target="_blank">GitHub</a>' if isinstance(
-                row.github, str) else ''
-            hf_space = f'<a href="{row.hf_space}" target="_blank">Space</a>' if isinstance(
-                row.hf_space, str) else ''
-            hf_model = f'<a href="{row.hf_model}" target="_blank">Model</a>' if isinstance(
-                row.hf_model, str) else ''
-            hf_dataset = f'<a href="{row.hf_dataset}" target="_blank">Dataset</a>' if isinstance(
-                row.hf_dataset, str) else ''
-            row = f'''
-                <tr>
-                    <td>{paper}</td>
-                    <td>{row.authors}</td>
-                    <td>{pdf}</td>
-                    <td>{row.category}</td>
-                    <td>{arxiv}</td>
-                    <td>{github}</td>
-                    <td>{hf_space}</td>
-                    <td>{hf_model}</td>
-                    <td>{hf_dataset}</td>
-                </tr>'''
-            rows.append(row)
-        self.table['html_table_content'] = rows
-
-    def render(self, search_query: str, case_sensitive: bool,
-               filter_names: list[str],
-               paper_categories: list[str]) -> tuple[int, str]:
-        df = self.table
-        if search_query:
-            if case_sensitive:
-                df = df[df.title.str.contains(search_query)]
-            else:
-                df = df[df.title_lowercase.str.contains(search_query.lower())]
-        has_arxiv = 'arXiv' in filter_names
-        has_github = 'GitHub' in filter_names
-        has_hf_space = 'HF Space' in filter_names
-        has_hf_model = 'HF Model' in filter_names
-        has_hf_dataset = 'HF Dataset' in filter_names
-        df = self.filter_table(df, has_arxiv, has_github, has_hf_space,
-                               has_hf_model, has_hf_dataset, paper_categories)
-        return len(df), self.to_html(df, self.table_header)
-
-    @staticmethod
-    def filter_table(df: pd.DataFrame, has_arxiv: bool, has_github: bool,
-                     has_hf_space: bool, has_hf_model: bool,
-                     has_hf_dataset: bool,
-                     paper_categories: list[str]) -> pd.DataFrame:
-        if has_arxiv:
-            df = df[~df.arxiv.isna()]
-        if has_github:
-            df = df[~df.github.isna()]
-        if has_hf_space:
-            df = df[~df.hf_space.isna()]
-        if has_hf_model:
-            df = df[~df.hf_model.isna()]
-        if has_hf_dataset:
-            df = df[~df.hf_dataset.isna()]
-        df = df[df.category.isin(set(paper_categories))]
-        return df
-
-    @staticmethod
-    def to_html(df: pd.DataFrame, table_header: str) -> str:
-        table_data = ''.join(df.html_table_content)
-        html = f'''
-        <table>
-            {table_header}
-            {table_data}
-        </table>'''
-        return html
diff --git a/spaces/NCTCMumbai/NCTC/models/official/nlp/data/classifier_data_lib.py b/spaces/NCTCMumbai/NCTC/models/official/nlp/data/classifier_data_lib.py
deleted file mode 100644
index 67c47d7874c4d3a40b23e5280e14ed8716a23176..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/nlp/data/classifier_data_lib.py
+++ /dev/null
@@ -1,1088 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""BERT library to process data for classification task."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import csv
-import importlib
-import os
-
-from absl import logging
-import tensorflow as tf
-import tensorflow_datasets as tfds
-
-from official.nlp.bert import tokenization
-
-
-class InputExample(object):
-  """A single training/test example for simple sequence classification."""
-
-  def __init__(self,
-               guid,
-               text_a,
-               text_b=None,
-               label=None,
-               weight=None,
-               int_iden=None):
-    """Constructs a InputExample.
-
-    Args:
-      guid: Unique id for the example.
-      text_a: string. The untokenized text of the first sequence. For single
-        sequence tasks, only this sequence must be specified.
-      text_b: (Optional) string. The untokenized text of the second sequence.
-        Only must be specified for sequence pair tasks.
-      label: (Optional) string. The label of the example. This should be
-        specified for train and dev examples, but not for test examples.
-      weight: (Optional) float. The weight of the example to be used during
-        training.
-      int_iden: (Optional) int. The int identification number of example in the
-        corpus.
-    """
-    self.guid = guid
-    self.text_a = text_a
-    self.text_b = text_b
-    self.label = label
-    self.weight = weight
-    self.int_iden = int_iden
-
-
-class InputFeatures(object):
-  """A single set of features of data."""
-
-  def __init__(self,
-               input_ids,
-               input_mask,
-               segment_ids,
-               label_id,
-               is_real_example=True,
-               weight=None,
-               int_iden=None):
-    self.input_ids = input_ids
-    self.input_mask = input_mask
-    self.segment_ids = segment_ids
-    self.label_id = label_id
-    self.is_real_example = is_real_example
-    self.weight = weight
-    self.int_iden = int_iden
-
-
-class DataProcessor(object):
-  """Base class for data converters for sequence classification data sets."""
-
-  def __init__(self, process_text_fn=tokenization.convert_to_unicode):
-    self.process_text_fn = process_text_fn
-
-  def get_train_examples(self, data_dir):
-    """Gets a collection of `InputExample`s for the train set."""
-    raise NotImplementedError()
-
-  def get_dev_examples(self, data_dir):
-    """Gets a collection of `InputExample`s for the dev set."""
-    raise NotImplementedError()
-
-  def get_test_examples(self, data_dir):
-    """Gets a collection of `InputExample`s for prediction."""
-    raise NotImplementedError()
-
-  def get_labels(self):
-    """Gets the list of labels for this data set."""
-    raise NotImplementedError()
-
-  @staticmethod
-  def get_processor_name():
-    """Gets the string identifier of the processor."""
-    raise NotImplementedError()
-
-  @classmethod
-  def _read_tsv(cls, input_file, quotechar=None):
-    """Reads a tab separated value file."""
-    with tf.io.gfile.GFile(input_file, "r") as f:
-      reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
-      lines = []
-      for line in reader:
-        lines.append(line)
-      return lines
-
-
-class XnliProcessor(DataProcessor):
-  """Processor for the XNLI data set."""
-  supported_languages = [
-      "ar", "bg", "de", "el", "en", "es", "fr", "hi", "ru", "sw", "th", "tr",
-      "ur", "vi", "zh"
-  ]
-
-  def __init__(self,
-               language="en",
-               process_text_fn=tokenization.convert_to_unicode):
-    super(XnliProcessor, self).__init__(process_text_fn)
-    if language == "all":
-      self.languages = XnliProcessor.supported_languages
-    elif language not in XnliProcessor.supported_languages:
-      raise ValueError("language %s is not supported for XNLI task." % language)
-    else:
-      self.languages = [language]
-
-  def get_train_examples(self, data_dir):
-    """See base class."""
-    lines = []
-    for language in self.languages:
-      # Skips the header.
-      lines.extend(
-          self._read_tsv(
-              os.path.join(data_dir, "multinli",
-                           "multinli.train.%s.tsv" % language))[1:])
-
-    examples = []
-    for (i, line) in enumerate(lines):
-      guid = "train-%d" % i
-      text_a = self.process_text_fn(line[0])
-      text_b = self.process_text_fn(line[1])
-      label = self.process_text_fn(line[2])
-      if label == self.process_text_fn("contradictory"):
-        label = self.process_text_fn("contradiction")
-      examples.append(
-          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples
-
-  def get_dev_examples(self, data_dir):
-    """See base class."""
-    lines = self._read_tsv(os.path.join(data_dir, "xnli.dev.tsv"))
-    examples = []
-    for (i, line) in enumerate(lines):
-      if i == 0:
-        continue
-      guid = "dev-%d" % i
-      text_a = self.process_text_fn(line[6])
-      text_b = self.process_text_fn(line[7])
-      label = self.process_text_fn(line[1])
-      examples.append(
-          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples
-
-  def get_test_examples(self, data_dir):
-    """See base class."""
-    lines = self._read_tsv(os.path.join(data_dir, "xnli.test.tsv"))
-    examples_by_lang = {k: [] for k in XnliProcessor.supported_languages}
-    for (i, line) in enumerate(lines):
-      if i == 0:
-        continue
-      guid = "test-%d" % i
-      language = self.process_text_fn(line[0])
-      text_a = self.process_text_fn(line[6])
-      text_b = self.process_text_fn(line[7])
-      label = self.process_text_fn(line[1])
-      examples_by_lang[language].append(
-          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples_by_lang
-
-  def get_labels(self):
-    """See base class."""
-    return ["contradiction", "entailment", "neutral"]
-
-  @staticmethod
-  def get_processor_name():
-    """See base class."""
-    return "XNLI"
-
-
-class XtremeXnliProcessor(DataProcessor):
-  """Processor for the XTREME XNLI data set."""
-  supported_languages = [
-      "ar", "bg", "de", "el", "en", "es", "fr", "hi", "ru", "sw", "th", "tr",
-      "ur", "vi", "zh"
-  ]
-
-  def get_train_examples(self, data_dir):
-    """See base class."""
-    lines = self._read_tsv(os.path.join(data_dir, "train-en.tsv"))
-
-    examples = []
-    for (i, line) in enumerate(lines):
-      guid = "train-%d" % i
-      text_a = self.process_text_fn(line[0])
-      text_b = self.process_text_fn(line[1])
-      label = self.process_text_fn(line[2])
-      examples.append(
-          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples
-
-  def get_dev_examples(self, data_dir):
-    """See base class."""
-    lines = self._read_tsv(os.path.join(data_dir, "dev-en.tsv"))
-    examples = []
-    for (i, line) in enumerate(lines):
-      guid = "dev-%d" % i
-      text_a = self.process_text_fn(line[0])
-      text_b = self.process_text_fn(line[1])
-      label = self.process_text_fn(line[2])
-      examples.append(
-          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples
-
-  def get_test_examples(self, data_dir):
-    """See base class."""
-    examples_by_lang = {k: [] for k in self.supported_languages}
-    for lang in self.supported_languages:
-      lines = self._read_tsv(os.path.join(data_dir, f"test-{lang}.tsv"))
-      for (i, line) in enumerate(lines):
-        guid = f"test-{i}"
-        text_a = self.process_text_fn(line[0])
-        text_b = self.process_text_fn(line[1])
-        label = "contradiction"
-        examples_by_lang[lang].append(
-            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples_by_lang
-
-  def get_labels(self):
-    """See base class."""
-    return ["contradiction", "entailment", "neutral"]
-
-  @staticmethod
-  def get_processor_name():
-    """See base class."""
-    return "XTREME-XNLI"
-
-
-class PawsxProcessor(DataProcessor):
-  """Processor for the PAWS-X data set."""
-  supported_languages = ["de", "en", "es", "fr", "ja", "ko", "zh"]
-
-  def __init__(self,
-               language="en",
-               process_text_fn=tokenization.convert_to_unicode):
-    super(PawsxProcessor, self).__init__(process_text_fn)
-    if language == "all":
-      self.languages = PawsxProcessor.supported_languages
-    elif language not in PawsxProcessor.supported_languages:
-      raise ValueError("language %s is not supported for PAWS-X task." %
-                       language)
-    else:
-      self.languages = [language]
-
-  def get_train_examples(self, data_dir):
-    """See base class."""
-    lines = []
-    for language in self.languages:
-      if language == "en":
-        train_tsv = "train.tsv"
-      else:
-        train_tsv = "translated_train.tsv"
-      # Skips the header.
-      lines.extend(
-          self._read_tsv(os.path.join(data_dir, language, train_tsv))[1:])
-
-    examples = []
-    for (i, line) in enumerate(lines):
-      guid = "train-%d" % i
-      text_a = self.process_text_fn(line[1])
-      text_b = self.process_text_fn(line[2])
-      label = self.process_text_fn(line[3])
-      examples.append(
-          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples
-
-  def get_dev_examples(self, data_dir):
-    """See base class."""
-    lines = []
-    for lang in PawsxProcessor.supported_languages:
-      lines.extend(self._read_tsv(os.path.join(data_dir, f"dev-{lang}.tsv")))
-
-    examples = []
-    for (i, line) in enumerate(lines):
-      guid = "dev-%d" % i
-      text_a = self.process_text_fn(line[0])
-      text_b = self.process_text_fn(line[1])
-      label = self.process_text_fn(line[2])
-      examples.append(
-          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples
-
-  def get_test_examples(self, data_dir):
-    """See base class."""
-    examples_by_lang = {k: [] for k in self.supported_languages}
-    for lang in self.supported_languages:
-      lines = self._read_tsv(os.path.join(data_dir, f"test-{lang}.tsv"))
-      for (i, line) in enumerate(lines):
-        guid = "test-%d" % i
-        text_a = self.process_text_fn(line[0])
-        text_b = self.process_text_fn(line[1])
-        label = self.process_text_fn(line[2])
-        examples_by_lang[lang].append(
-            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples_by_lang
-
-  def get_labels(self):
-    """See base class."""
-    return ["0", "1"]
-
-  @staticmethod
-  def get_processor_name():
-    """See base class."""
-    return "XTREME-PAWS-X"
-
-
-class XtremePawsxProcessor(DataProcessor):
-  """Processor for the XTREME PAWS-X data set."""
-  supported_languages = ["de", "en", "es", "fr", "ja", "ko", "zh"]
-
-  def get_train_examples(self, data_dir):
-    """See base class."""
-    lines = self._read_tsv(os.path.join(data_dir, "train-en.tsv"))
-    examples = []
-    for (i, line) in enumerate(lines):
-      guid = "train-%d" % i
-      text_a = self.process_text_fn(line[0])
-      text_b = self.process_text_fn(line[1])
-      label = self.process_text_fn(line[2])
-      examples.append(
-          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples
-
-  def get_dev_examples(self, data_dir):
-    """See base class."""
-    lines = self._read_tsv(os.path.join(data_dir, "dev-en.tsv"))
-
-    examples = []
-    for (i, line) in enumerate(lines):
-      guid = "dev-%d" % i
-      text_a = self.process_text_fn(line[0])
-      text_b = self.process_text_fn(line[1])
-      label = self.process_text_fn(line[2])
-      examples.append(
-          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples
-
-  def get_test_examples(self, data_dir):
-    """See base class."""
-    examples_by_lang = {k: [] for k in self.supported_languages}
-    for lang in self.supported_languages:
-      lines = self._read_tsv(os.path.join(data_dir, f"test-{lang}.tsv"))
-      for (i, line) in enumerate(lines):
-        guid = "test-%d" % i
-        text_a = self.process_text_fn(line[0])
-        text_b = self.process_text_fn(line[1])
-        label = "0"
-        examples_by_lang[lang].append(
-            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples_by_lang
-
-  def get_labels(self):
-    """See base class."""
-    return ["0", "1"]
-
-  @staticmethod
-  def get_processor_name():
-    """See base class."""
-    return "XTREME-PAWS-X"
-
-
-class MnliProcessor(DataProcessor):
-  """Processor for the MultiNLI data set (GLUE version)."""
-
-  def get_train_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
-
-  def get_dev_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
-        "dev_matched")
-
-  def get_test_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test")
-
-  def get_labels(self):
-    """See base class."""
-    return ["contradiction", "entailment", "neutral"]
-
-  @staticmethod
-  def get_processor_name():
-    """See base class."""
-    return "MNLI"
-
-  def _create_examples(self, lines, set_type):
-    """Creates examples for the training and dev sets."""
-    examples = []
-    for (i, line) in enumerate(lines):
-      if i == 0:
-        continue
-      guid = "%s-%s" % (set_type, self.process_text_fn(line[0]))
-      text_a = self.process_text_fn(line[8])
-      text_b = self.process_text_fn(line[9])
-      if set_type == "test":
-        label = "contradiction"
-      else:
-        label = self.process_text_fn(line[-1])
-      examples.append(
-          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples
-
-
-class MrpcProcessor(DataProcessor):
-  """Processor for the MRPC data set (GLUE version)."""
-
-  def get_train_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
-
-  def get_dev_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
-
-  def get_test_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
-
-  def get_labels(self):
-    """See base class."""
-    return ["0", "1"]
-
-  @staticmethod
-  def get_processor_name():
-    """See base class."""
-    return "MRPC"
-
-  def _create_examples(self, lines, set_type):
-    """Creates examples for the training and dev sets."""
-    examples = []
-    for (i, line) in enumerate(lines):
-      if i == 0:
-        continue
-      guid = "%s-%s" % (set_type, i)
-      text_a = self.process_text_fn(line[3])
-      text_b = self.process_text_fn(line[4])
-      if set_type == "test":
-        label = "0"
-      else:
-        label = self.process_text_fn(line[0])
-      examples.append(
-          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples
-
-
-class QqpProcessor(DataProcessor):
-  """Processor for the QQP data set (GLUE version)."""
-
-  def get_train_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
-
-  def get_dev_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
-
-  def get_test_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
-
-  def get_labels(self):
-    """See base class."""
-    return ["0", "1"]
-
-  @staticmethod
-  def get_processor_name():
-    """See base class."""
-    return "QQP"
-
-  def _create_examples(self, lines, set_type):
-    """Creates examples for the training and dev sets."""
-    examples = []
-    for (i, line) in enumerate(lines):
-      if i == 0:
-        continue
-      guid = "%s-%s" % (set_type, line[0])
-      try:
-        text_a = line[3]
-        text_b = line[4]
-        label = line[5]
-      except IndexError:
-        continue
-      examples.append(
-          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples
-
-
-class ColaProcessor(DataProcessor):
-  """Processor for the CoLA data set (GLUE version)."""
-
-  def get_train_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
-
-  def get_dev_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
-
-  def get_test_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
-
-  def get_labels(self):
-    """See base class."""
-    return ["0", "1"]
-
-  @staticmethod
-  def get_processor_name():
-    """See base class."""
-    return "COLA"
-
-  def _create_examples(self, lines, set_type):
-    """Creates examples for the training and dev sets."""
-    examples = []
-    for (i, line) in enumerate(lines):
-      # Only the test set has a header
-      if set_type == "test" and i == 0:
-        continue
-      guid = "%s-%s" % (set_type, i)
-      if set_type == "test":
-        text_a = self.process_text_fn(line[1])
-        label = "0"
-      else:
-        text_a = self.process_text_fn(line[3])
-        label = self.process_text_fn(line[1])
-      examples.append(
-          InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
-    return examples
-
-
-class RteProcessor(DataProcessor):
-  """Processor for the RTE data set (GLUE version)."""
-
-  def get_train_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
-
-  def get_dev_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
-
-  def get_test_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
-
-  def get_labels(self):
-    """See base class."""
-    # All datasets are converted to 2-class split, where for 3-class datasets we
-    # collapse neutral and contradiction into not_entailment.
-    return ["entailment", "not_entailment"]
-
-  @staticmethod
-  def get_processor_name():
-    """See base class."""
-    return "RTE"
-
-  def _create_examples(self, lines, set_type):
-    """Creates examples for the training and dev sets."""
-    examples = []
-    for i, line in enumerate(lines):
-      if i == 0:
-        continue
-      guid = "%s-%s" % (set_type, i)
-      if set_type == "test":
-        text_a = tokenization.convert_to_unicode(line[1])
-        text_b = tokenization.convert_to_unicode(line[2])
-        label = "entailment"
-      else:
-        text_a = tokenization.convert_to_unicode(line[1])
-        text_b = tokenization.convert_to_unicode(line[2])
-        label = tokenization.convert_to_unicode(line[3])
-      examples.append(
-          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples
-
-
-class SstProcessor(DataProcessor):
-  """Processor for the SST-2 data set (GLUE version)."""
-
-  def get_train_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
-
-  def get_dev_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
-
-  def get_test_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
-
-  def get_labels(self):
-    """See base class."""
-    return ["0", "1"]
-
-  @staticmethod
-  def get_processor_name():
-    """See base class."""
-    return "SST-2"
-
-  def _create_examples(self, lines, set_type):
-    """Creates examples for the training and dev sets."""
-    examples = []
-    for (i, line) in enumerate(lines):
-      if i == 0:
-        continue
-      guid = "%s-%s" % (set_type, i)
-      if set_type == "test":
-        text_a = tokenization.convert_to_unicode(line[1])
-        label = "0"
-      else:
-        text_a = tokenization.convert_to_unicode(line[0])
-        label = tokenization.convert_to_unicode(line[1])
-      examples.append(
-          InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
-    return examples
-
-
-class QnliProcessor(DataProcessor):
-  """Processor for the QNLI data set (GLUE version)."""
-
-  def get_train_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
-
-  def get_dev_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev_matched")
-
-  def get_test_examples(self, data_dir):
-    """See base class."""
-    return self._create_examples(
-        self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
-
-  def get_labels(self):
-    """See base class."""
-    return ["entailment", "not_entailment"]
-
-  @staticmethod
-  def get_processor_name():
-    """See base class."""
-    return "QNLI"
-
-  def _create_examples(self, lines, set_type):
-    """Creates examples for the training and dev sets."""
-    examples = []
-    for (i, line) in enumerate(lines):
-      if i == 0:
-        continue
-      guid = "%s-%s" % (set_type, 1)
-      if set_type == "test":
-        text_a = tokenization.convert_to_unicode(line[1])
-        text_b = tokenization.convert_to_unicode(line[2])
-        label = "entailment"
-      else:
-        text_a = tokenization.convert_to_unicode(line[1])
-        text_b = tokenization.convert_to_unicode(line[2])
-        label = tokenization.convert_to_unicode(line[-1])
-      examples.append(
-          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
-    return examples
-
-
-class TfdsProcessor(DataProcessor):
-  """Processor for generic text classification and regression TFDS data set.
-
-  The TFDS parameters are expected to be provided in the tfds_params string, in
-  a comma-separated list of parameter assignments.
-  Examples:
-    tfds_params="dataset=scicite,text_key=string"
-    tfds_params="dataset=imdb_reviews,test_split=,dev_split=test"
-    tfds_params="dataset=glue/cola,text_key=sentence"
-    tfds_params="dataset=glue/sst2,text_key=sentence"
-    tfds_params="dataset=glue/qnli,text_key=question,text_b_key=sentence"
-    tfds_params="dataset=glue/mrpc,text_key=sentence1,text_b_key=sentence2"
-    tfds_params="dataset=glue/stsb,text_key=sentence1,text_b_key=sentence2,"
-                "is_regression=true,label_type=float"
-  Possible parameters (please refer to the documentation of Tensorflow Datasets
-  (TFDS) for the meaning of individual parameters):
-    dataset: Required dataset name (potentially with subset and version number).
-    data_dir: Optional TFDS source root directory.
-    module_import: Optional Dataset module to import.
-    train_split: Name of the train split (defaults to `train`).
-    dev_split: Name of the dev split (defaults to `validation`).
-    test_split: Name of the test split (defaults to `test`).
-    text_key: Key of the text_a feature (defaults to `text`).
-    text_b_key: Key of the second text feature if available.
-    label_key: Key of the label feature (defaults to `label`).
-    test_text_key: Key of the text feature to use in test set.
-    test_text_b_key: Key of the second text feature to use in test set.
-    test_label: String to be used as the label for all test examples.
-    label_type: Type of the label key (defaults to `int`).
-    weight_key: Key of the float sample weight (is not used if not provided).
-    is_regression: Whether the task is a regression problem (defaults to False).
-  """
-
-  def __init__(self,
-               tfds_params,
-               process_text_fn=tokenization.convert_to_unicode):
-    super(TfdsProcessor, self).__init__(process_text_fn)
-    self._process_tfds_params_str(tfds_params)
-    if self.module_import:
-      importlib.import_module(self.module_import)
-
-    self.dataset, info = tfds.load(
-        self.dataset_name, data_dir=self.data_dir, with_info=True)
-    if self.is_regression:
-      self._labels = None
-    else:
-      self._labels = list(range(info.features[self.label_key].num_classes))
-
-  def _process_tfds_params_str(self, params_str):
-    """Extracts TFDS parameters from a comma-separated assignements string."""
-    dtype_map = {"int": int, "float": float}
-    cast_str_to_bool = lambda s: s.lower() not in ["false", "0"]
-
-    tuples = [x.split("=") for x in params_str.split(",")]
-    d = {k.strip(): v.strip() for k, v in tuples}
-    self.dataset_name = d["dataset"]  # Required.
-    self.data_dir = d.get("data_dir", None)
-    self.module_import = d.get("module_import", None)
-    self.train_split = d.get("train_split", "train")
-    self.dev_split = d.get("dev_split", "validation")
-    self.test_split = d.get("test_split", "test")
-    self.text_key = d.get("text_key", "text")
-    self.text_b_key = d.get("text_b_key", None)
-    self.label_key = d.get("label_key", "label")
-    self.test_text_key = d.get("test_text_key", self.text_key)
-    self.test_text_b_key = d.get("test_text_b_key", self.text_b_key)
-    self.test_label = d.get("test_label", "test_example")
-    self.label_type = dtype_map[d.get("label_type", "int")]
-    self.is_regression = cast_str_to_bool(d.get("is_regression", "False"))
-    self.weight_key = d.get("weight_key", None)
-
-  def get_train_examples(self, data_dir):
-    assert data_dir is None
-    return self._create_examples(self.train_split, "train")
-
-  def get_dev_examples(self, data_dir):
-    assert data_dir is None
-    return self._create_examples(self.dev_split, "dev")
-
-  def get_test_examples(self, data_dir):
-    assert data_dir is None
-    return self._create_examples(self.test_split, "test")
-
-  def get_labels(self):
-    return self._labels
-
-  def get_processor_name(self):
-    return "TFDS_" + self.dataset_name
-
-  def _create_examples(self, split_name, set_type):
-    """Creates examples for the training and dev sets."""
-    if split_name not in self.dataset:
-      raise ValueError("Split {} not available.".format(split_name))
-    dataset = self.dataset[split_name].as_numpy_iterator()
-    examples = []
-    text_b, weight = None, None
-    for i, example in enumerate(dataset):
-      guid = "%s-%s" % (set_type, i)
-      if set_type == "test":
-        text_a = self.process_text_fn(example[self.test_text_key])
-        if self.test_text_b_key:
-          text_b = self.process_text_fn(example[self.test_text_b_key])
-        label = self.test_label
-      else:
-        text_a = self.process_text_fn(example[self.text_key])
-        if self.text_b_key:
-          text_b = self.process_text_fn(example[self.text_b_key])
-        label = self.label_type(example[self.label_key])
-      if self.weight_key:
-        weight = float(example[self.weight_key])
-      examples.append(
-          InputExample(
-              guid=guid,
-              text_a=text_a,
-              text_b=text_b,
-              label=label,
-              weight=weight))
-    return examples
-
-
-def convert_single_example(ex_index, example, label_list, max_seq_length,
-                           tokenizer):
-  """Converts a single `InputExample` into a single `InputFeatures`."""
-  label_map = {}
-  if label_list:
-    for (i, label) in enumerate(label_list):
-      label_map[label] = i
-
-  tokens_a = tokenizer.tokenize(example.text_a)
-  tokens_b = None
-  if example.text_b:
-    tokens_b = tokenizer.tokenize(example.text_b)
-
-  if tokens_b:
-    # Modifies `tokens_a` and `tokens_b` in place so that the total
-    # length is less than the specified length.
-    # Account for [CLS], [SEP], [SEP] with "- 3"
-    _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
-  else:
-    # Account for [CLS] and [SEP] with "- 2"
-    if len(tokens_a) > max_seq_length - 2:
-      tokens_a = tokens_a[0:(max_seq_length - 2)]
-
-  # The convention in BERT is:
-  # (a) For sequence pairs:
-  #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
-  #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
-  # (b) For single sequences:
-  #  tokens:   [CLS] the dog is hairy . [SEP]
-  #  type_ids: 0     0   0   0  0     0 0
-  #
-  # Where "type_ids" are used to indicate whether this is the first
-  # sequence or the second sequence. The embedding vectors for `type=0` and
-  # `type=1` were learned during pre-training and are added to the wordpiece
-  # embedding vector (and position vector). This is not *strictly* necessary
-  # since the [SEP] token unambiguously separates the sequences, but it makes
-  # it easier for the model to learn the concept of sequences.
-  #
-  # For classification tasks, the first vector (corresponding to [CLS]) is
-  # used as the "sentence vector". Note that this only makes sense because
-  # the entire model is fine-tuned.
-  tokens = []
-  segment_ids = []
-  tokens.append("[CLS]")
-  segment_ids.append(0)
-  for token in tokens_a:
-    tokens.append(token)
-    segment_ids.append(0)
-  tokens.append("[SEP]")
-  segment_ids.append(0)
-
-  if tokens_b:
-    for token in tokens_b:
-      tokens.append(token)
-      segment_ids.append(1)
-    tokens.append("[SEP]")
-    segment_ids.append(1)
-
-  input_ids = tokenizer.convert_tokens_to_ids(tokens)
-
-  # The mask has 1 for real tokens and 0 for padding tokens. Only real
-  # tokens are attended to.
-  input_mask = [1] * len(input_ids)
-
-  # Zero-pad up to the sequence length.
-  while len(input_ids) < max_seq_length:
-    input_ids.append(0)
-    input_mask.append(0)
-    segment_ids.append(0)
-
-  assert len(input_ids) == max_seq_length
-  assert len(input_mask) == max_seq_length
-  assert len(segment_ids) == max_seq_length
-
-  label_id = label_map[example.label] if label_map else example.label
-  if ex_index < 5:
-    logging.info("*** Example ***")
-    logging.info("guid: %s", (example.guid))
-    logging.info("tokens: %s",
-                 " ".join([tokenization.printable_text(x) for x in tokens]))
-    logging.info("input_ids: %s", " ".join([str(x) for x in input_ids]))
-    logging.info("input_mask: %s", " ".join([str(x) for x in input_mask]))
-    logging.info("segment_ids: %s", " ".join([str(x) for x in segment_ids]))
-    logging.info("label: %s (id = %s)", example.label, str(label_id))
-    logging.info("weight: %s", example.weight)
-    logging.info("int_iden: %s", str(example.int_iden))
-
-  feature = InputFeatures(
-      input_ids=input_ids,
-      input_mask=input_mask,
-      segment_ids=segment_ids,
-      label_id=label_id,
-      is_real_example=True,
-      weight=example.weight,
-      int_iden=example.int_iden)
-
-  return feature
-
-
-def file_based_convert_examples_to_features(examples,
-                                            label_list,
-                                            max_seq_length,
-                                            tokenizer,
-                                            output_file,
-                                            label_type=None):
-  """Convert a set of `InputExample`s to a TFRecord file."""
-
-  tf.io.gfile.makedirs(os.path.dirname(output_file))
-  writer = tf.io.TFRecordWriter(output_file)
-
-  for (ex_index, example) in enumerate(examples):
-    if ex_index % 10000 == 0:
-      logging.info("Writing example %d of %d", ex_index, len(examples))
-
-    feature = convert_single_example(ex_index, example, label_list,
-                                     max_seq_length, tokenizer)
-
-    def create_int_feature(values):
-      f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
-      return f
-
-    def create_float_feature(values):
-      f = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
-      return f
-
-    features = collections.OrderedDict()
-    features["input_ids"] = create_int_feature(feature.input_ids)
-    features["input_mask"] = create_int_feature(feature.input_mask)
-    features["segment_ids"] = create_int_feature(feature.segment_ids)
-    if label_type is not None and label_type == float:
-      features["label_ids"] = create_float_feature([feature.label_id])
-    elif feature.label_id is not None:
-      features["label_ids"] = create_int_feature([feature.label_id])
-    features["is_real_example"] = create_int_feature(
-        [int(feature.is_real_example)])
-    if feature.weight is not None:
-      features["weight"] = create_float_feature([feature.weight])
-    if feature.int_iden is not None:
-      features["int_iden"] = create_int_feature([feature.int_iden])
-
-    tf_example = tf.train.Example(features=tf.train.Features(feature=features))
-    writer.write(tf_example.SerializeToString())
-  writer.close()
-
-
-def _truncate_seq_pair(tokens_a, tokens_b, max_length):
-  """Truncates a sequence pair in place to the maximum length."""
-
-  # This is a simple heuristic which will always truncate the longer sequence
-  # one token at a time. This makes more sense than truncating an equal percent
-  # of tokens from each, since if one sequence is very short then each token
-  # that's truncated likely contains more information than a longer sequence.
-  while True:
-    total_length = len(tokens_a) + len(tokens_b)
-    if total_length <= max_length:
-      break
-    if len(tokens_a) > len(tokens_b):
-      tokens_a.pop()
-    else:
-      tokens_b.pop()
-
-
-def generate_tf_record_from_data_file(processor,
-                                      data_dir,
-                                      tokenizer,
-                                      train_data_output_path=None,
-                                      eval_data_output_path=None,
-                                      test_data_output_path=None,
-                                      max_seq_length=128):
-  """Generates and saves training data into a tf record file.
-
-  Arguments:
-      processor: Input processor object to be used for generating data. Subclass
-        of `DataProcessor`.
-      data_dir: Directory that contains train/eval data to process. Data files
-        should be in from "dev.tsv", "test.tsv", or "train.tsv".
-      tokenizer: The tokenizer to be applied on the data.
-      train_data_output_path: Output to which processed tf record for training
-        will be saved.
-      eval_data_output_path: Output to which processed tf record for evaluation
-        will be saved.
-      test_data_output_path: Output to which processed tf record for testing
-        will be saved. Must be a pattern template with {} if processor has
-        language specific test data.
-      max_seq_length: Maximum sequence length of the to be generated
-        training/eval data.
-
-  Returns:
-      A dictionary containing input meta data.
-  """
-  assert train_data_output_path or eval_data_output_path
-
-  label_list = processor.get_labels()
-  label_type = getattr(processor, "label_type", None)
-  is_regression = getattr(processor, "is_regression", False)
-  has_sample_weights = getattr(processor, "weight_key", False)
-  assert train_data_output_path
-
-  train_input_data_examples = processor.get_train_examples(data_dir)
-  file_based_convert_examples_to_features(train_input_data_examples, label_list,
-                                          max_seq_length, tokenizer,
-                                          train_data_output_path, label_type)
-  num_training_data = len(train_input_data_examples)
-
-  if eval_data_output_path:
-    eval_input_data_examples = processor.get_dev_examples(data_dir)
-    file_based_convert_examples_to_features(eval_input_data_examples,
-                                            label_list, max_seq_length,
-                                            tokenizer, eval_data_output_path,
-                                            label_type)
-
-  if test_data_output_path:
-    test_input_data_examples = processor.get_test_examples(data_dir)
-    if isinstance(test_input_data_examples, dict):
-      for language, examples in test_input_data_examples.items():
-        file_based_convert_examples_to_features(
-            examples, label_list, max_seq_length, tokenizer,
-            test_data_output_path.format(language), label_type)
-    else:
-      file_based_convert_examples_to_features(test_input_data_examples,
-                                              label_list, max_seq_length,
-                                              tokenizer, test_data_output_path,
-                                              label_type)
-
-  meta_data = {
-      "processor_type": processor.get_processor_name(),
-      "train_data_size": num_training_data,
-      "max_seq_length": max_seq_length,
-  }
-  if is_regression:
-    meta_data["task_type"] = "bert_regression"
-    meta_data["label_type"] = {int: "int", float: "float"}[label_type]
-  else:
-    meta_data["task_type"] = "bert_classification"
-    meta_data["num_labels"] = len(processor.get_labels())
-  if has_sample_weights:
-    meta_data["has_sample_weights"] = True
-
-  if eval_data_output_path:
-    meta_data["eval_data_size"] = len(eval_input_data_examples)
-
-  if test_data_output_path:
-    test_input_data_examples = processor.get_test_examples(data_dir)
-    if isinstance(test_input_data_examples, dict):
-      for language, examples in test_input_data_examples.items():
-        meta_data["test_{}_data_size".format(language)] = len(examples)
-    else:
-      meta_data["test_data_size"] = len(test_input_data_examples)
-
-  return meta_data
diff --git a/spaces/NanoT/demo2/README.md b/spaces/NanoT/demo2/README.md
deleted file mode 100644
index 2f337173dcaf705fcf4e980967e2f5a84fd76f62..0000000000000000000000000000000000000000
--- a/spaces/NanoT/demo2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Demo2
-emoji: 🔥
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.45.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/NeuML/imagesearch/app.py b/spaces/NeuML/imagesearch/app.py
deleted file mode 100644
index 21a280def7a344d0ac5a4645ecfff620333d4e0f..0000000000000000000000000000000000000000
--- a/spaces/NeuML/imagesearch/app.py
+++ /dev/null
@@ -1,111 +0,0 @@
-"""
-Builds a similarity index for a directory of images
-"""
-
-import glob
-import os
-import sys
-import tarfile
-
-import requests
-import streamlit as st
-
-from PIL import Image
-
-from txtai.embeddings import Embeddings
-
-
-class Application:
-    """
-    Main application
-    """
-
-    def __init__(self, directory):
-        """
-        Creates a new application.
-
-        Args:
-            directory: directory of images
-        """
-
-        self.embeddings = self.build(directory)
-
-    def build(self, directory):
-        """
-        Builds an image embeddings index.
-
-        Args:
-            directory: directory with images
-
-        Returns:
-            Embeddings index
-        """
-
-        embeddings = Embeddings({"method": "sentence-transformers", "path": "clip-ViT-B-32"})
-        embeddings.index(self.images(directory))
-
-        # Update model to support multilingual queries
-        embeddings.config["path"] = "sentence-transformers/clip-ViT-B-32-multilingual-v1"
-        embeddings.model = embeddings.loadvectors()
-
-        return embeddings
-
-    def images(self, directory):
-        """
-        Generator that loops over each image in a directory.
-
-        Args:
-            directory: directory with images
-        """
-
-        for path in glob.glob(directory + "/*jpg") + glob.glob(directory + "/*png"):
-            yield (path, Image.open(path), None)
-
-    def run(self):
-        """
-        Runs a Streamlit application.
-        """
-
-        st.title("Image search")
-
-        st.markdown("This application shows how images and text can be embedded into the same space to support similarity search. ")
-        st.markdown(
-            "[sentence-transformers](https://github.com/UKPLab/sentence-transformers/tree/master/examples/applications/image-search) "
-            + "recently added support for the [OpenAI CLIP model](https://github.com/openai/CLIP). This model embeds text and images into "
-            + "the same space, enabling image similarity search. txtai can directly utilize these models."
-        )
-
-        query = st.text_input("Search query:")
-        if query:
-            index, _ = self.embeddings.search(query, 1)[0]
-            st.image(Image.open(index))
-
-
-@st.cache(allow_output_mutation=True)
-def create(directory):
-    """
-    Creates and caches a Streamlit application.
-
-    Args:
-        directory: directory of images to index
-
-    Returns:
-        Application
-    """
-
-    return Application(directory)
-
-if __name__ == "__main__":
-    os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-    files = "/tmp/txtai"
-    if not os.path.exists(files):
-        os.makedirs(files)
-
-        response = requests.get("https://github.com/neuml/txtai/releases/download/v3.5.0/tests.tar.gz", stream=True)
-        f = tarfile.open(fileobj=response.raw, mode="r|gz")
-        f.extractall(path="/tmp")
- 
-    # Create and run application
-    app = create(files)
-    app.run()
diff --git a/spaces/Norod78/Face2Doll/README.md b/spaces/Norod78/Face2Doll/README.md
deleted file mode 100644
index b984a3b7b43d90dd41c6363dcf4e8ee4459ea65d..0000000000000000000000000000000000000000
--- a/spaces/Norod78/Face2Doll/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Face2Doll (U2Net)
-emoji: 🎎
-colorFrom: pink
-colorTo: black
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/Nultx/VITS-TTS/ONNXVITS_infer.py b/spaces/Nultx/VITS-TTS/ONNXVITS_infer.py
deleted file mode 100644
index af04e614c8f1ac43faf363b1a9f6bfd667fbde21..0000000000000000000000000000000000000000
--- a/spaces/Nultx/VITS-TTS/ONNXVITS_infer.py
+++ /dev/null
@@ -1,201 +0,0 @@
-import torch
-import commons
-import models
-
-import math
-from torch import nn
-from torch.nn import functional as F
-
-import modules
-import attentions
-
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-               n_vocab,
-               out_channels,
-               hidden_channels,
-               filter_channels,
-               n_heads,
-               n_layers,
-               kernel_size,
-               p_dropout,
-               emotion_embedding):
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.emotion_embedding = emotion_embedding
-
-    if self.n_vocab != 0:
-      self.emb = nn.Embedding(n_vocab, hidden_channels)
-      if emotion_embedding:
-        self.emo_proj = nn.Linear(1024, hidden_channels)
-      nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
-
-    self.encoder = attentions.Encoder(
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, emotion_embedding=None):
-    if self.n_vocab != 0:
-      x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
-    if emotion_embedding is not None:
-      print("emotion added")
-      x = x + self.emo_proj(emotion_embedding.unsqueeze(1))
-    x = torch.transpose(x, 1, -1)  # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-    x = self.encoder(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    return x, m, logs, x_mask
-
-
-class PosteriorEncoder(nn.Module):
-  def __init__(self,
-               in_channels,
-               out_channels,
-               hidden_channels,
-               kernel_size,
-               dilation_rate,
-               n_layers,
-               gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class SynthesizerTrn(models.SynthesizerTrn):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self,
-               n_vocab,
-               spec_channels,
-               segment_size,
-               inter_channels,
-               hidden_channels,
-               filter_channels,
-               n_heads,
-               n_layers,
-               kernel_size,
-               p_dropout,
-               resblock,
-               resblock_kernel_sizes,
-               resblock_dilation_sizes,
-               upsample_rates,
-               upsample_initial_channel,
-               upsample_kernel_sizes,
-               n_speakers=0,
-               gin_channels=0,
-               use_sdp=True,
-               emotion_embedding=False,
-               ONNX_dir="./ONNX_net/",
-               **kwargs):
-
-    super().__init__(
-      n_vocab,
-      spec_channels,
-      segment_size,
-      inter_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout,
-      resblock,
-      resblock_kernel_sizes,
-      resblock_dilation_sizes,
-      upsample_rates,
-      upsample_initial_channel,
-      upsample_kernel_sizes,
-      n_speakers=n_speakers,
-      gin_channels=gin_channels,
-      use_sdp=use_sdp,
-      **kwargs
-    )
-    self.ONNX_dir = ONNX_dir
-    self.enc_p = TextEncoder(n_vocab,
-                             inter_channels,
-                             hidden_channels,
-                             filter_channels,
-                             n_heads,
-                             n_layers,
-                             kernel_size,
-                             p_dropout,
-                             emotion_embedding)
-    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-
-  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None,
-            emotion_embedding=None):
-    from ONNXVITS_utils import runonnx
-    with torch.no_grad():
-      x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emotion_embedding)
-
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-    else:
-      g = None
-
-    # logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-    logw = runonnx(f"{self.ONNX_dir}dp.onnx", x=x.numpy(), x_mask=x_mask.numpy(), g=g.numpy())
-    logw = torch.from_numpy(logw[0])
-
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)  # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1,
-                                                                             2)  # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-
-    # z = self.flow(z_p, y_mask, g=g, reverse=True)
-    z = runonnx(f"{self.ONNX_dir}flow.onnx", z_p=z_p.numpy(), y_mask=y_mask.numpy(), g=g.numpy())
-    z = torch.from_numpy(z[0])
-
-    # o = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    o = runonnx(f"{self.ONNX_dir}dec.onnx", z_in=(z * y_mask)[:, :, :max_len].numpy(), g=g.numpy())
-    o = torch.from_numpy(o[0])
-
-    return o, attn, y_mask, (z, z_p, m_p, logs_p)
\ No newline at end of file
diff --git a/spaces/Nyari/Super-Resolution-Anime-Diffusion/RealESRGANv030/scripts/pytorch2onnx.py b/spaces/Nyari/Super-Resolution-Anime-Diffusion/RealESRGANv030/scripts/pytorch2onnx.py
deleted file mode 100644
index 15bbabb8b56065a1e520c2ce31d09dbf8e324110..0000000000000000000000000000000000000000
--- a/spaces/Nyari/Super-Resolution-Anime-Diffusion/RealESRGANv030/scripts/pytorch2onnx.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import argparse
-import torch
-import torch.onnx
-from basicsr.archs.rrdbnet_arch import RRDBNet
-
-
-def main(args):
-    # An instance of the model
-    model = RRDBNet(
-        num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4
-    )
-    if args.params:
-        keyname = "params"
-    else:
-        keyname = "params_ema"
-    model.load_state_dict(torch.load(args.input)[keyname])
-    # set the train mode to false since we will only run the forward pass.
-    model.train(False)
-    model.cpu().eval()
-
-    # An example input
-    x = torch.rand(1, 3, 64, 64)
-    # Export the model
-    with torch.no_grad():
-        torch_out = torch.onnx._export(
-            model, x, args.output, opset_version=11, export_params=True
-        )
-    print(torch_out.shape)
-
-
-if __name__ == "__main__":
-    """Convert pytorch model to onnx models"""
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--input",
-        type=str,
-        default="experiments/pretrained_models/RealESRGAN_x4plus.pth",
-        help="Input model path",
-    )
-    parser.add_argument(
-        "--output", type=str, default="realesrgan-x4.onnx", help="Output onnx path"
-    )
-    parser.add_argument(
-        "--params", action="store_false", help="Use params instead of params_ema"
-    )
-    args = parser.parse_args()
-
-    main(args)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lang.sh b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lang.sh
deleted file mode 100644
index e9a80001eb47d5af863d6aab11a59362a59cef61..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lang.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/bin/bash
-
-sil_prob=0.5
-num_sil_states=3
-num_nonsil_states=1
-
-. ./cmd.sh
-. ./path.sh
-. parse_options.sh
-
-set -eux
-
-dict=$1
-data_dir=$2
-
-dict_dir=$data_dir/local/dict
-tmplm_dir=$data_dir/local/lang_tmp
-lm_dir=$data_dir/lang
-
-mkdir -p $dict_dir $tmplm_dir $lm_dir
-
-# prepare dict
-echo "SIL" > $dict_dir/silence_phones.txt
-echo "SIL" > $dict_dir/optional_silence.txt
-awk '{print $1}' $dict > $dict_dir/nonsilence_phones.txt
-
-echo "SIL SIL" > $dict_dir/lexicon.txt
-echo "<UNK> SIL" >> $dict_dir/lexicon.txt
-awk '{print $1" "$1}' $dict >> $dict_dir/lexicon.txt
-
-echo "SIL" > $dict_dir/extra_questions.txt
-awk '{printf $1" "} END {printf "\n"}' $dict >> $dict_dir/extra_questions.txt
-
-# prepare lang
-utils/prepare_lang.sh --sil-prob $sil_prob --position-dependent-phones false \
-  --num_sil_states $num_sil_states --num_nonsil_states $num_nonsil_states \
-  $dict_dir "<UNK>" $tmplm_dir $lm_dir
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/scripts/vads.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/scripts/vads.py
deleted file mode 100644
index 2398da97d8c44b8f3f270b22d5508a003482b4d6..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wav2vec/unsupervised/scripts/vads.py
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/usr/bin/env python3 -u
-# 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
-import sys
-
-from copy import deepcopy
-from scipy.signal import lfilter
-
-import numpy as np
-from tqdm import tqdm
-import soundfile as sf
-import os.path as osp
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(description="compute vad segments")
-    parser.add_argument(
-        "--rvad-home",
-        "-r",
-        help="path to rvad home (see https://github.com/zhenghuatan/rVADfast)",
-        required=True,
-    )
-
-    return parser
-
-
-def rvad(speechproc, path):
-    winlen, ovrlen, pre_coef, nfilter, nftt = 0.025, 0.01, 0.97, 20, 512
-    ftThres = 0.5
-    vadThres = 0.4
-    opts = 1
-
-    data, fs = sf.read(path)
-    assert fs == 16_000, "sample rate must be 16khz"
-    ft, flen, fsh10, nfr10 = speechproc.sflux(data, fs, winlen, ovrlen, nftt)
-
-    # --spectral flatness --
-    pv01 = np.zeros(ft.shape[0])
-    pv01[np.less_equal(ft, ftThres)] = 1
-    pitch = deepcopy(ft)
-
-    pvblk = speechproc.pitchblockdetect(pv01, pitch, nfr10, opts)
-
-    # --filtering--
-    ENERGYFLOOR = np.exp(-50)
-    b = np.array([0.9770, -0.9770])
-    a = np.array([1.0000, -0.9540])
-    fdata = lfilter(b, a, data, axis=0)
-
-    # --pass 1--
-    noise_samp, noise_seg, n_noise_samp = speechproc.snre_highenergy(
-        fdata, nfr10, flen, fsh10, ENERGYFLOOR, pv01, pvblk
-    )
-
-    # sets noisy segments to zero
-    for j in range(n_noise_samp):
-        fdata[range(int(noise_samp[j, 0]), int(noise_samp[j, 1]) + 1)] = 0
-
-    vad_seg = speechproc.snre_vad(
-        fdata, nfr10, flen, fsh10, ENERGYFLOOR, pv01, pvblk, vadThres
-    )
-    return vad_seg, data
-
-
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-
-    sys.path.append(args.rvad_home)
-    import speechproc
-
-    stride = 160
-    lines = sys.stdin.readlines()
-    root = lines[0].rstrip()
-    for fpath in tqdm(lines[1:]):
-        path = osp.join(root, fpath.split()[0])
-        vads, wav = rvad(speechproc, path)
-
-        start = None
-        vad_segs = []
-        for i, v in enumerate(vads):
-            if start is None and v == 1:
-                start = i * stride
-            elif start is not None and v == 0:
-                vad_segs.append((start, i * stride))
-                start = None
-        if start is not None:
-            vad_segs.append((start, len(wav)))
-
-        print(" ".join(f"{v[0]}:{v[1]}" for v in vad_segs))
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/huggingface/hf_gpt2.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/huggingface/hf_gpt2.py
deleted file mode 100644
index 3a8eb78198f5808557092f814e92f1c9d72933ec..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/models/huggingface/hf_gpt2.py
+++ /dev/null
@@ -1,168 +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
-from typing import Dict, List, Optional
-
-import torch
-from fairseq.models import (
-    FairseqIncrementalDecoder,
-    FairseqLanguageModel,
-    register_model,
-    register_model_architecture,
-)
-
-
-logger = logging.getLogger(__name__)
-
-
-DEFAULT_MAX_TARGET_POSITIONS = 1024
-
-
-@register_model("hf_gpt2")
-class HuggingFaceGPT2LanguageModel(FairseqLanguageModel):
-    def __init__(self, decoder):
-        super().__init__(decoder)
-
-    @staticmethod
-    def add_args(parser):
-        """Add model-specific arguments to the parser."""
-        # fmt: off
-        parser.add_argument('--embed-dim', type=int, metavar='N',
-                            help='embedding dimension')
-        parser.add_argument('--num-attention-heads', type=int, metavar='N',
-                            help='num attention heads')
-        parser.add_argument('--num-layers', type=int, metavar='N',
-                            help='num layers')
-        parser.add_argument('--dropout', type=float, metavar='D',
-                            help='dropout probability for all fully connected layers '
-                                 'in the embeddings, encoder, and pooler')
-        parser.add_argument('--attention-dropout', type=float, metavar='D',
-                            help='dropout probability for attention weights')
-        # fmt: on
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-        default_architecture(args)
-        return cls(HuggingFaceGPT2Decoder(args, task))
-
-
-class HuggingFaceGPT2Decoder(FairseqIncrementalDecoder):
-    def __init__(self, args, task):
-        try:
-            from transformers import GPT2Config, GPT2LMHeadModel
-        except ImportError:
-            raise ImportError(
-                "\n\nPlease install huggingface/transformers with:"
-                "\n\n  pip install transformers"
-            )
-
-        super().__init__(task.target_dictionary)
-
-        config = GPT2Config(
-            vocab_size=len(task.target_dictionary),
-            n_positions=args.max_target_positions + 1,
-            n_ctx=args.max_target_positions,
-            n_embd=args.embed_dim,
-            n_layer=args.num_layers,
-            n_head=args.num_attention_heads,
-            resid_pdrop=args.dropout,
-            embd_pdrop=args.dropout,
-            attn_pdrop=args.attention_dropout,
-            layer_norm_epsilon=1e-6,
-        )
-        self.model = GPT2LMHeadModel(config)
-
-        # set zero embedding for padding symbol
-        self.pad_idx = task.target_dictionary.pad()
-        self.model.transformer.wte.weight.data[self.pad_idx].zero_()
-        self.model.transformer.wpe.weight.data[0].zero_()
-
-    def forward(
-        self,
-        prev_output_tokens,
-        src_lengths=None,
-        incremental_state: Optional[Dict[str, List[torch.Tensor]]] = None,
-        encoder_out=None,
-    ):
-        features = self.extract_features(prev_output_tokens, incremental_state)
-        lm_logits = self.model.lm_head(features)
-        return (lm_logits,)
-
-    def extract_features(
-        self,
-        prev_output_tokens,
-        incremental_state: Optional[Dict[str, List[torch.Tensor]]] = None,
-    ):
-        if incremental_state:
-            past = self.get_incremental_state("past")
-        else:
-            past = None
-
-        # don't attend to padding symbols
-        attention_mask = prev_output_tokens.ne(self.pad_idx).int()
-
-        # set position ids to exclude padding symbols
-        position_ids = attention_mask * (
-            torch.arange(1, 1 + prev_output_tokens.size(1))
-            .to(prev_output_tokens)
-            .repeat(prev_output_tokens.size(0), 1)
-        )
-
-        outputs = self.model.transformer(
-            input_ids=prev_output_tokens,
-            past=past,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-        )
-        last_hidden_states = outputs[0]
-
-        if incremental_state:
-            self.set_incremental_state(incremental_state, "past", outputs[1])
-
-        return last_hidden_states
-
-    def max_positions(self):
-        return self.model.config.n_positions - 1
-
-
-@register_model_architecture("hf_gpt2", "hf_gpt2")
-def default_architecture(args):
-    if getattr(args, "max_target_positions", None) is None:
-        args.max_target_positions = getattr(
-            args, "tokens_per_sample", DEFAULT_MAX_TARGET_POSITIONS
-        )
-    args.embed_dim = getattr(args, "embed_dim", 768)
-    args.num_attention_heads = getattr(args, "num_attention_heads", 12)
-    args.num_layers = getattr(args, "num_layers", 12)
-    args.dropout = getattr(args, "dropout", 0.1)
-    args.attention_dropout = getattr(args, "attention_dropout", 0.1)
-
-
-@register_model_architecture("hf_gpt2", "hf_gpt2_medium")
-def hf_gpt2_medium(args):
-    args.embed_dim = getattr(args, "embed_dim", 1024)
-    args.num_attention_heads = getattr(args, "num_attention_heads", 16)
-    args.num_layers = getattr(args, "num_layers", 24)
-    default_architecture(args)
-
-
-@register_model_architecture("hf_gpt2", "hf_gpt2_large")
-def hf_gpt2_large(args):
-    args.embed_dim = getattr(args, "embed_dim", 1280)
-    args.num_attention_heads = getattr(args, "num_attention_heads", 20)
-    args.num_layers = getattr(args, "num_layers", 36)
-    default_architecture(args)
-
-
-@register_model_architecture("hf_gpt2", "hf_gpt2_xl")
-def hf_gpt2_xl(args):
-    args.embed_dim = getattr(args, "embed_dim", 1600)
-    args.num_attention_heads = getattr(args, "num_attention_heads", 25)
-    args.num_layers = getattr(args, "num_layers", 48)
-    default_architecture(args)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_iopath.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_iopath.py
deleted file mode 100644
index 908261a6619806f7ef9b5dd1beb5d6817b249a6e..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/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/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py
deleted file mode 100644
index 4e13b38a5d3fb44dd3969e6afcb8f202274ee3b7..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_synthesis/preprocessing/denoise_and_vad_audio.py
+++ /dev/null
@@ -1,204 +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
-import logging
-import os
-import csv
-import tempfile
-from collections import defaultdict
-from pathlib import Path
-
-import torchaudio
-try:
-    import webrtcvad
-except ImportError:
-    raise ImportError("Please install py-webrtcvad: pip install webrtcvad")
-import pandas as pd
-from tqdm import tqdm
-
-from examples.speech_synthesis.preprocessing.denoiser.pretrained import master64
-import examples.speech_synthesis.preprocessing.denoiser.utils as utils
-from examples.speech_synthesis.preprocessing.vad import (
-    frame_generator, vad_collector, read_wave, write_wave, FS_MS, THRESHOLD,
-    SCALE
-)
-from examples.speech_to_text.data_utils import save_df_to_tsv
-
-
-log = logging.getLogger(__name__)
-
-PATHS = ["after_denoise", "after_vad"]
-MIN_T = 0.05
-
-
-def generate_tmp_filename(extension="txt"):
-    return tempfile._get_default_tempdir() + "/" + \
-           next(tempfile._get_candidate_names()) + "." + extension
-
-
-def convert_sr(inpath, sr, output_path=None):
-    if not output_path:
-        output_path = generate_tmp_filename("wav")
-    cmd = f"sox {inpath} -r {sr} {output_path}"
-    os.system(cmd)
-    return output_path
-
-
-def apply_vad(vad, inpath):
-    audio, sample_rate = read_wave(inpath)
-    frames = frame_generator(FS_MS, audio, sample_rate)
-    frames = list(frames)
-    segments = vad_collector(sample_rate, FS_MS, 300, vad, frames)
-    merge_segments = list()
-    timestamp_start = 0.0
-    timestamp_end = 0.0
-    # removing start, end, and long sequences of sils
-    for i, segment in enumerate(segments):
-        merge_segments.append(segment[0])
-        if i and timestamp_start:
-            sil_duration = segment[1] - timestamp_end
-            if sil_duration > THRESHOLD:
-                merge_segments.append(int(THRESHOLD / SCALE) * (b'\x00'))
-            else:
-                merge_segments.append(int((sil_duration / SCALE)) * (b'\x00'))
-        timestamp_start = segment[1]
-        timestamp_end = segment[2]
-    segment = b''.join(merge_segments)
-    return segment, sample_rate
-
-
-def write(wav, filename, sr=16_000):
-    # Normalize audio if it prevents clipping
-    wav = wav / max(wav.abs().max().item(), 1)
-    torchaudio.save(filename, wav.cpu(), sr, encoding="PCM_S",
-                    bits_per_sample=16)
-
-
-def process(args):
-    # making sure we are requested either denoise or vad
-    if not args.denoise and not args.vad:
-        log.error("No denoise or vad is requested.")
-        return
-
-    log.info("Creating out directories...")
-    if args.denoise:
-        out_denoise = Path(args.output_dir).absolute().joinpath(PATHS[0])
-        out_denoise.mkdir(parents=True, exist_ok=True)
-    if args.vad:
-        out_vad = Path(args.output_dir).absolute().joinpath(PATHS[1])
-        out_vad.mkdir(parents=True, exist_ok=True)
-
-    log.info("Loading pre-trained speech enhancement model...")
-    model = master64().to(args.device)
-
-    log.info("Building the VAD model...")
-    vad = webrtcvad.Vad(int(args.vad_agg_level))
-
-    # preparing the output dict
-    output_dict = defaultdict(list)
-
-    log.info(f"Parsing input manifest: {args.audio_manifest}")
-    with open(args.audio_manifest, "r") as f:
-        manifest_dict = csv.DictReader(f, delimiter="\t")
-        for row in tqdm(manifest_dict):
-            filename = str(row["audio"])
-
-            final_output = filename
-            keep_sample = True
-            n_frames = row["n_frames"]
-            snr = -1
-            if args.denoise:
-                output_path_denoise = out_denoise.joinpath(Path(filename).name)
-                # convert to 16khz in case we use a differet sr
-                tmp_path = convert_sr(final_output, 16000)
-
-                # loading audio file and generating the enhanced version
-                out, sr = torchaudio.load(tmp_path)
-                out = out.to(args.device)
-                estimate = model(out)
-                estimate = (1 - args.dry_wet) * estimate + args.dry_wet * out
-                write(estimate[0], str(output_path_denoise), sr)
-
-                snr = utils.cal_snr(out, estimate)
-                snr = snr.cpu().detach().numpy()[0][0]
-                final_output = str(output_path_denoise)
-
-            if args.vad:
-                output_path_vad = out_vad.joinpath(Path(filename).name)
-                sr = torchaudio.info(final_output).sample_rate
-                if sr in [16000, 32000, 48000]:
-                    tmp_path = final_output
-                elif sr < 16000:
-                    tmp_path = convert_sr(final_output, 16000)
-                elif sr < 32000:
-                    tmp_path = convert_sr(final_output, 32000)
-                else:
-                    tmp_path = convert_sr(final_output, 48000)
-                # apply VAD
-                segment, sample_rate = apply_vad(vad, tmp_path)
-                if len(segment) < sample_rate * MIN_T:
-                    keep_sample = False
-                    print((
-                        f"WARNING: skip {filename} because it is too short "
-                        f"after VAD ({len(segment) / sample_rate} < {MIN_T})"
-                    ))
-                else:
-                    if sample_rate != sr:
-                        tmp_path = generate_tmp_filename("wav")
-                        write_wave(tmp_path, segment, sample_rate)
-                        convert_sr(tmp_path, sr,
-                                   output_path=str(output_path_vad))
-                    else:
-                        write_wave(str(output_path_vad), segment, sample_rate)
-                    final_output = str(output_path_vad)
-                    segment, _ = torchaudio.load(final_output)
-                    n_frames = segment.size(1)
-
-            if keep_sample:
-                output_dict["id"].append(row["id"])
-                output_dict["audio"].append(final_output)
-                output_dict["n_frames"].append(n_frames)
-                output_dict["tgt_text"].append(row["tgt_text"])
-                output_dict["speaker"].append(row["speaker"])
-                output_dict["src_text"].append(row["src_text"])
-                output_dict["snr"].append(snr)
-
-        out_tsv_path = Path(args.output_dir) / Path(args.audio_manifest).name
-        log.info(f"Saving manifest to {out_tsv_path.as_posix()}")
-        save_df_to_tsv(pd.DataFrame.from_dict(output_dict), out_tsv_path)
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--audio-manifest", "-i", required=True,
-                        type=str, help="path to the input manifest.")
-    parser.add_argument(
-        "--output-dir", "-o", required=True, type=str,
-        help="path to the output dir. it will contain files after denoising and"
-             " vad"
-    )
-    parser.add_argument("--vad-agg-level", "-a", type=int, default=2,
-                        help="the aggresive level of the vad [0-3].")
-    parser.add_argument(
-        "--dry-wet", "-dw", type=float, default=0.01,
-        help="the level of linear interpolation between noisy and enhanced "
-             "files."
-    )
-    parser.add_argument(
-        "--device", "-d", type=str, default="cpu",
-        help="the device to be used for the speech enhancement model: "
-             "cpu | cuda."
-    )
-    parser.add_argument("--denoise", action="store_true",
-                        help="apply a denoising")
-    parser.add_argument("--vad", action="store_true", help="apply a VAD")
-    args = parser.parse_args()
-
-    process(args)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/iterative_refinement_generator.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/iterative_refinement_generator.py
deleted file mode 100644
index 4fb0946f499329ceb130761b59675d761df1c158..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/iterative_refinement_generator.py
+++ /dev/null
@@ -1,359 +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 collections import namedtuple
-
-import numpy as np
-import torch
-from fairseq import utils
-
-
-DecoderOut = namedtuple(
-    "IterativeRefinementDecoderOut",
-    ["output_tokens", "output_scores", "attn", "step", "max_step", "history"],
-)
-
-
-class IterativeRefinementGenerator(object):
-    def __init__(
-        self,
-        tgt_dict,
-        models=None,
-        eos_penalty=0.0,
-        max_iter=10,
-        max_ratio=2,
-        beam_size=1,
-        decoding_format=None,
-        retain_dropout=False,
-        adaptive=True,
-        retain_history=False,
-        reranking=False,
-    ):
-        """
-        Generates translations based on iterative refinement.
-
-        Args:
-            tgt_dict: target dictionary
-            eos_penalty: if > 0.0, it penalized early-stopping in decoding
-            max_iter: maximum number of refinement iterations
-            max_ratio: generate sequences of maximum length ax, where x is the source length
-            decoding_format: decoding mode in {'unigram', 'ensemble', 'vote', 'dp', 'bs'}
-            retain_dropout: retaining dropout in the inference
-            adaptive: decoding with early stop
-        """
-        self.bos = tgt_dict.bos()
-        self.pad = tgt_dict.pad()
-        self.unk = tgt_dict.unk()
-        self.eos = tgt_dict.eos()
-        self.vocab_size = len(tgt_dict)
-        self.eos_penalty = eos_penalty
-        self.max_iter = max_iter
-        self.max_ratio = max_ratio
-        self.beam_size = beam_size
-        self.reranking = reranking
-        self.decoding_format = decoding_format
-        self.retain_dropout = retain_dropout
-        self.retain_history = retain_history
-        self.adaptive = adaptive
-        self.models = models
-
-    def generate_batched_itr(
-        self,
-        data_itr,
-        maxlen_a=None,
-        maxlen_b=None,
-        cuda=False,
-        timer=None,
-        prefix_size=0,
-    ):
-        """Iterate over a batched dataset and yield individual translations.
-
-        Args:
-            maxlen_a/b: generate sequences of maximum length ax + b,
-                where x is the source sentence length.
-            cuda: use GPU for generation
-            timer: StopwatchMeter for timing generations.
-        """
-
-        for sample in data_itr:
-            if "net_input" not in sample:
-                continue
-            if timer is not None:
-                timer.start()
-            with torch.no_grad():
-                hypos = self.generate(
-                    self.models,
-                    sample,
-                    prefix_tokens=sample["target"][:, :prefix_size]
-                    if prefix_size > 0
-                    else None,
-                )
-            if timer is not None:
-                timer.stop(sample["ntokens"])
-            for i, id in enumerate(sample["id"]):
-                # remove padding
-                src = utils.strip_pad(sample["net_input"]["src_tokens"][i, :], self.pad)
-                ref = utils.strip_pad(sample["target"][i, :], self.pad)
-                yield id, src, ref, hypos[i]
-
-    @torch.no_grad()
-    def generate(self, models, sample, prefix_tokens=None, constraints=None):
-        if constraints is not None:
-            raise NotImplementedError(
-                "Constrained decoding with the IterativeRefinementGenerator is not supported"
-            )
-
-        # TODO: iterative refinement generator does not support ensemble for now.
-        if not self.retain_dropout:
-            for model in models:
-                model.eval()
-
-        model, reranker = models[0], None
-        if self.reranking:
-            assert len(models) > 1, "Assuming the last checkpoint is the reranker"
-            assert (
-                self.beam_size > 1
-            ), "Reranking requires multiple translation for each example"
-
-            reranker = models[-1]
-            models = models[:-1]
-
-        if len(models) > 1 and hasattr(model, "enable_ensemble"):
-            assert model.allow_ensemble, "{} does not support ensembling".format(
-                model.__class__.__name__
-            )
-            model.enable_ensemble(models)
-
-        # TODO: better encoder inputs?
-        src_tokens = sample["net_input"]["src_tokens"]
-        src_lengths = sample["net_input"]["src_lengths"]
-        bsz, src_len = src_tokens.size()
-
-        # initialize
-        encoder_out = model.forward_encoder([src_tokens, src_lengths])
-        prev_decoder_out = model.initialize_output_tokens(encoder_out, src_tokens)
-
-        if self.beam_size > 1:
-            assert (
-                model.allow_length_beam
-            ), "{} does not support decoding with length beam.".format(
-                model.__class__.__name__
-            )
-
-            # regenerate data based on length-beam
-            length_beam_order = (
-                utils.new_arange(src_tokens, self.beam_size, bsz).t().reshape(-1)
-            )
-            encoder_out = model.encoder.reorder_encoder_out(
-                encoder_out, length_beam_order
-            )
-            prev_decoder_out = model.regenerate_length_beam(
-                prev_decoder_out, self.beam_size
-            )
-            bsz = bsz * self.beam_size
-
-        sent_idxs = torch.arange(bsz)
-        prev_output_tokens = prev_decoder_out.output_tokens.clone()
-
-        if self.retain_history:
-            prev_decoder_out = prev_decoder_out._replace(history=[prev_output_tokens])
-
-        finalized = [[] for _ in range(bsz)]
-
-        def is_a_loop(x, y, s, a):
-            b, l_x, l_y = x.size(0), x.size(1), y.size(1)
-            if l_x > l_y:
-                y = torch.cat([y, x.new_zeros(b, l_x - l_y).fill_(self.pad)], 1)
-                s = torch.cat([s, s.new_zeros(b, l_x - l_y)], 1)
-                if a is not None:
-                    a = torch.cat([a, a.new_zeros(b, l_x - l_y, a.size(2))], 1)
-            elif l_x < l_y:
-                x = torch.cat([x, y.new_zeros(b, l_y - l_x).fill_(self.pad)], 1)
-            return (x == y).all(1), y, s, a
-
-        def finalized_hypos(step, prev_out_token, prev_out_score, prev_out_attn):
-            cutoff = prev_out_token.ne(self.pad)
-            tokens = prev_out_token[cutoff]
-            if prev_out_score is None:
-                scores, score = None, None
-            else:
-                scores = prev_out_score[cutoff]
-                score = scores.mean()
-
-            if prev_out_attn is None:
-                hypo_attn, alignment = None, None
-            else:
-                hypo_attn = prev_out_attn[cutoff]
-                alignment = hypo_attn.max(dim=1)[1]
-            return {
-                "steps": step,
-                "tokens": tokens,
-                "positional_scores": scores,
-                "score": score,
-                "hypo_attn": hypo_attn,
-                "alignment": alignment,
-            }
-
-        for step in range(self.max_iter + 1):
-
-            decoder_options = {
-                "eos_penalty": self.eos_penalty,
-                "max_ratio": self.max_ratio,
-                "decoding_format": self.decoding_format,
-            }
-            prev_decoder_out = prev_decoder_out._replace(
-                step=step,
-                max_step=self.max_iter + 1,
-            )
-
-            decoder_out = model.forward_decoder(
-                prev_decoder_out, encoder_out, **decoder_options
-            )
-
-            if self.adaptive:
-                # terminate if there is a loop
-                terminated, out_tokens, out_scores, out_attn = is_a_loop(
-                    prev_output_tokens,
-                    decoder_out.output_tokens,
-                    decoder_out.output_scores,
-                    decoder_out.attn,
-                )
-                decoder_out = decoder_out._replace(
-                    output_tokens=out_tokens,
-                    output_scores=out_scores,
-                    attn=out_attn,
-                )
-
-            else:
-                terminated = decoder_out.output_tokens.new_zeros(
-                    decoder_out.output_tokens.size(0)
-                ).bool()
-
-            if step == self.max_iter:  # reach last iteration, terminate
-                terminated.fill_(1)
-
-            # collect finalized sentences
-            finalized_idxs = sent_idxs[terminated]
-            finalized_tokens = decoder_out.output_tokens[terminated]
-            finalized_scores = decoder_out.output_scores[terminated]
-            finalized_attn = (
-                None
-                if (decoder_out.attn is None or decoder_out.attn.size(0) == 0)
-                else decoder_out.attn[terminated]
-            )
-
-            if self.retain_history:
-                finalized_history_tokens = [h[terminated] for h in decoder_out.history]
-
-            for i in range(finalized_idxs.size(0)):
-                finalized[finalized_idxs[i]] = [
-                    finalized_hypos(
-                        step,
-                        finalized_tokens[i],
-                        finalized_scores[i],
-                        None if finalized_attn is None else finalized_attn[i],
-                    )
-                ]
-
-                if self.retain_history:
-                    finalized[finalized_idxs[i]][0]["history"] = []
-                    for j in range(len(finalized_history_tokens)):
-                        finalized[finalized_idxs[i]][0]["history"].append(
-                            finalized_hypos(
-                                step, finalized_history_tokens[j][i], None, None
-                            )
-                        )
-
-            # check if all terminated
-            if terminated.sum() == terminated.size(0):
-                break
-
-            # for next step
-            not_terminated = ~terminated
-            prev_decoder_out = decoder_out._replace(
-                output_tokens=decoder_out.output_tokens[not_terminated],
-                output_scores=decoder_out.output_scores[not_terminated],
-                attn=decoder_out.attn[not_terminated]
-                if (decoder_out.attn is not None and decoder_out.attn.size(0) > 0)
-                else None,
-                history=[h[not_terminated] for h in decoder_out.history]
-                if decoder_out.history is not None
-                else None,
-            )
-            encoder_out = model.encoder.reorder_encoder_out(
-                encoder_out, not_terminated.nonzero(as_tuple=False).squeeze()
-            )
-            sent_idxs = sent_idxs[not_terminated]
-            prev_output_tokens = prev_decoder_out.output_tokens.clone()
-
-        if self.beam_size > 1:
-            if reranker is not None:
-                finalized = self.rerank(
-                    reranker, finalized, [src_tokens, src_lengths], self.beam_size
-                )
-
-            # aggregate information from length beam
-            finalized = [
-                finalized[
-                    np.argmax(
-                        [
-                            finalized[self.beam_size * i + j][0]["score"]
-                            for j in range(self.beam_size)
-                        ]
-                    )
-                    + self.beam_size * i
-                ]
-                for i in range(len(finalized) // self.beam_size)
-            ]
-
-        return finalized
-
-    def rerank(self, reranker, finalized, encoder_input, beam_size):
-        def rebuild_batch(finalized):
-            finalized_tokens = [f[0]["tokens"] for f in finalized]
-            finalized_maxlen = max(f.size(0) for f in finalized_tokens)
-            final_output_tokens = (
-                finalized_tokens[0]
-                .new_zeros(len(finalized_tokens), finalized_maxlen)
-                .fill_(self.pad)
-            )
-            for i, f in enumerate(finalized_tokens):
-                final_output_tokens[i, : f.size(0)] = f
-            return final_output_tokens
-
-        final_output_tokens = rebuild_batch(finalized)
-        final_output_tokens[
-            :, 0
-        ] = self.eos  # autoregressive model assumes starting with EOS
-
-        reranker_encoder_out = reranker.encoder(*encoder_input)
-        length_beam_order = (
-            utils.new_arange(
-                final_output_tokens, beam_size, reranker_encoder_out.encoder_out.size(1)
-            )
-            .t()
-            .reshape(-1)
-        )
-        reranker_encoder_out = reranker.encoder.reorder_encoder_out(
-            reranker_encoder_out, length_beam_order
-        )
-        reranking_scores = reranker.get_normalized_probs(
-            reranker.decoder(final_output_tokens[:, :-1], reranker_encoder_out),
-            True,
-            None,
-        )
-        reranking_scores = reranking_scores.gather(2, final_output_tokens[:, 1:, None])
-        reranking_masks = final_output_tokens[:, 1:].ne(self.pad)
-        reranking_scores = (
-            reranking_scores[:, :, 0].masked_fill_(~reranking_masks, 0).sum(1)
-        )
-        reranking_scores = reranking_scores / reranking_masks.sum(1).type_as(
-            reranking_scores
-        )
-
-        for i in range(len(finalized)):
-            finalized[i][0]["score"] = reranking_scores[i]
-
-        return finalized
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/models/bart/model.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/models/bart/model.py
deleted file mode 100644
index 71d0b27cd2c0655fe3b00479b672d6d042a4d5ed..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/models/bart/model.py
+++ /dev/null
@@ -1,384 +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.
-"""
-BART: Denoising Sequence-to-Sequence Pre-training for
-Natural Language Generation, Translation, and Comprehension
-"""
-from typing import Optional
-
-import logging
-
-import torch
-import torch.nn as nn
-from fairseq import utils
-from fairseq.models import register_model, register_model_architecture
-from fairseq.models.transformer import TransformerModel
-from fairseq.modules.transformer_sentence_encoder import init_bert_params
-
-from .hub_interface import BARTHubInterface
-
-
-logger = logging.getLogger(__name__)
-
-
-@register_model("bart")
-class BARTModel(TransformerModel):
-    __jit_unused_properties__ = ["supported_targets"]
-
-    @classmethod
-    def hub_models(cls):
-        return {
-            "bart.base": "http://dl.fbaipublicfiles.com/fairseq/models/bart.base.tar.gz",
-            "bart.large": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz",
-            "bart.large.mnli": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.mnli.tar.gz",
-            "bart.large.cnn": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz",
-            "bart.large.xsum": "http://dl.fbaipublicfiles.com/fairseq/models/bart.large.xsum.tar.gz",
-        }
-
-    def __init__(self, args, encoder, decoder):
-        super().__init__(args, encoder, decoder)
-
-        # We follow BERT's random weight initialization
-        self.apply(init_bert_params)
-
-        self.classification_heads = nn.ModuleDict()
-        if hasattr(self.encoder, "dictionary"):
-            self.eos: int = self.encoder.dictionary.eos()
-
-    @staticmethod
-    def add_args(parser):
-        super(BARTModel, BARTModel).add_args(parser)
-        parser.add_argument(
-            "--pooler-dropout",
-            type=float,
-            metavar="D",
-            help="dropout probability in the masked_lm pooler layers",
-        )
-        parser.add_argument(
-            "--pooler-activation-fn",
-            choices=utils.get_available_activation_fns(),
-            help="activation function to use for pooler layer",
-        )
-        parser.add_argument(
-            "--spectral-norm-classification-head",
-            action="store_true",
-            help="Apply spectral normalization on the classification head",
-        )
-
-    @property
-    def supported_targets(self):
-        return {"self"}
-
-    def forward(
-        self,
-        src_tokens,
-        src_lengths,
-        prev_output_tokens,
-        features_only: bool = False,
-        classification_head_name: Optional[str] = None,
-        token_embeddings: Optional[torch.Tensor] = None,
-        return_all_hiddens: bool = True,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-    ):
-        if classification_head_name is not None:
-            features_only = True
-
-        encoder_out = self.encoder(
-            src_tokens,
-            src_lengths=src_lengths,
-            token_embeddings=token_embeddings,
-            return_all_hiddens=return_all_hiddens
-        )
-        x, extra = self.decoder(
-            prev_output_tokens,
-            encoder_out=encoder_out,
-            features_only=features_only,
-            alignment_layer=alignment_layer,
-            alignment_heads=alignment_heads,
-            src_lengths=src_lengths,
-            return_all_hiddens=return_all_hiddens,
-        )
-        eos: int = self.eos
-        if classification_head_name is not None:
-            sentence_representation = x[
-                src_tokens.eq(eos), :
-            ].view(x.size(0), -1, x.size(-1))[:, -1, :]
-            for k, head in self.classification_heads.items():
-                # for torch script only supports iteration
-                if k == classification_head_name:
-                    x = head(sentence_representation)
-                    break
-        return x, extra
-
-    @classmethod
-    def from_pretrained(
-        cls,
-        model_name_or_path,
-        checkpoint_file="model.pt",
-        data_name_or_path=".",
-        bpe="gpt2",
-        sample_break_mode="eos",
-        **kwargs,
-    ):
-        from fairseq import hub_utils
-
-        x = hub_utils.from_pretrained(
-            model_name_or_path,
-            checkpoint_file,
-            data_name_or_path,
-            archive_map=cls.hub_models(),
-            bpe=bpe,
-            load_checkpoint_heads=True,
-            sample_break_mode=sample_break_mode,
-            **kwargs,
-        )
-        return BARTHubInterface(x["args"], x["task"], x["models"][0])
-
-    def register_classification_head(
-        self, name, num_classes=None, inner_dim=None, **kwargs
-    ):
-        """Register a classification head."""
-        logger.info("Registering classification head: {0}".format(name))
-        if name in self.classification_heads:
-            prev_num_classes = self.classification_heads[name].out_proj.out_features
-            prev_inner_dim = self.classification_heads[name].dense.out_features
-            if num_classes != prev_num_classes or inner_dim != prev_inner_dim:
-                logger.warning(
-                    're-registering head "{}" with num_classes {} (prev: {}) '
-                    "and inner_dim {} (prev: {})".format(
-                        name, num_classes, prev_num_classes, inner_dim, prev_inner_dim
-                    )
-                )
-        self.classification_heads[name] = BARTClassificationHead(
-            input_dim=self.args.encoder_embed_dim,
-            inner_dim=inner_dim or self.args.encoder_embed_dim,
-            num_classes=num_classes,
-            activation_fn=self.args.pooler_activation_fn,
-            pooler_dropout=self.args.pooler_dropout,
-            do_spectral_norm=getattr(
-                self.args, "spectral_norm_classification_head", False
-            ),
-        )
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        super().upgrade_state_dict_named(state_dict, name)
-
-        prefix = name + "." if name != "" else ""
-        current_head_names = (
-            []
-            if not hasattr(self, "classification_heads")
-            else self.classification_heads.keys()
-        )
-
-        # Handle new classification heads present in the state dict.
-        keys_to_delete = []
-        for k in state_dict.keys():
-            if not k.startswith(prefix + "classification_heads."):
-                continue
-
-            head_name = k[len(prefix + "classification_heads.") :].split(".")[0]
-            num_classes = state_dict[
-                prefix + "classification_heads." + head_name + ".out_proj.weight"
-            ].size(0)
-            inner_dim = state_dict[
-                prefix + "classification_heads." + head_name + ".dense.weight"
-            ].size(0)
-
-            if getattr(self.args, "load_checkpoint_heads", False):
-                if head_name not in current_head_names:
-                    self.register_classification_head(head_name, num_classes, inner_dim)
-            else:
-                if head_name not in current_head_names:
-                    logger.warning(
-                        "deleting classification head ({}) from checkpoint "
-                        "not present in current model: {}".format(head_name, k)
-                    )
-                    keys_to_delete.append(k)
-                elif (
-                    num_classes
-                    != self.classification_heads[head_name].out_proj.out_features
-                    or inner_dim
-                    != self.classification_heads[head_name].dense.out_features
-                ):
-                    logger.warning(
-                        "deleting classification head ({}) from checkpoint "
-                        "with different dimensions than current model: {}".format(
-                            head_name, k
-                        )
-                    )
-                    keys_to_delete.append(k)
-        for k in keys_to_delete:
-            del state_dict[k]
-
-        def truncate_emb(key):
-            if key in state_dict:
-                state_dict[key] = state_dict[key][:-1, :]
-
-        # When finetuning on translation task, remove last row of
-        # embedding matrix that corresponds to mask_idx token.
-        loaded_dict_size = state_dict["encoder.embed_tokens.weight"].size(0)
-        if (
-            loaded_dict_size == len(self.encoder.dictionary) + 1
-            and "<mask>" not in self.encoder.dictionary
-        ):
-            truncate_emb("encoder.embed_tokens.weight")
-            truncate_emb("decoder.embed_tokens.weight")
-            truncate_emb("encoder.output_projection.weight")
-            truncate_emb("decoder.output_projection.weight")
-
-        # When continued pretraining on new set of languages for mbart,
-        # add extra lang embeddings at the end of embed_tokens.
-        # Note: newly added languages are assumed to have been added at the end.
-        if self.args.task == "multilingual_denoising" and loaded_dict_size < len(
-            self.encoder.dictionary
-        ):
-            logger.info(
-                "Adding extra language embeddings not found in pretrained model for "
-                "continued pretraining of MBART on new set of languages."
-            )
-            loaded_mask_token_embedding = state_dict["encoder.embed_tokens.weight"][
-                -1, :
-            ]
-
-            num_langids_to_add = len(self.encoder.dictionary) - loaded_dict_size
-            embed_dim = state_dict["encoder.embed_tokens.weight"].size(1)
-
-            new_lang_embed_to_add = torch.zeros(num_langids_to_add, embed_dim)
-            nn.init.normal_(new_lang_embed_to_add, mean=0, std=embed_dim ** -0.5)
-            new_lang_embed_to_add = new_lang_embed_to_add.to(
-                dtype=state_dict["encoder.embed_tokens.weight"].dtype,
-            )
-
-            state_dict["encoder.embed_tokens.weight"] = torch.cat(
-                [
-                    state_dict["encoder.embed_tokens.weight"][
-                        : loaded_dict_size - 1, :
-                    ],
-                    new_lang_embed_to_add,
-                    loaded_mask_token_embedding.unsqueeze(0),
-                ]
-            )
-            state_dict["decoder.embed_tokens.weight"] = torch.cat(
-                [
-                    state_dict["decoder.embed_tokens.weight"][
-                        : loaded_dict_size - 1, :
-                    ],
-                    new_lang_embed_to_add,
-                    loaded_mask_token_embedding.unsqueeze(0),
-                ]
-            )
-
-        # Copy any newly-added classification heads into the state dict
-        # with their current weights.
-        if hasattr(self, "classification_heads"):
-            cur_state = self.classification_heads.state_dict()
-            for k, v in cur_state.items():
-                if prefix + "classification_heads." + k not in state_dict:
-                    logger.info("Overwriting " + prefix + "classification_heads." + k)
-                    state_dict[prefix + "classification_heads." + k] = v
-
-
-class BARTClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(
-        self,
-        input_dim,
-        inner_dim,
-        num_classes,
-        activation_fn,
-        pooler_dropout,
-        do_spectral_norm=False,
-    ):
-        super().__init__()
-        self.dense = nn.Linear(input_dim, inner_dim)
-        self.activation_fn = utils.get_activation_fn(activation_fn)
-        self.dropout = nn.Dropout(p=pooler_dropout)
-        self.out_proj = nn.Linear(inner_dim, num_classes)
-
-        if do_spectral_norm:
-            self.out_proj = torch.nn.utils.spectral_norm(self.out_proj)
-
-    def forward(self, features, **kwargs):
-        x = features
-        x = self.dropout(x)
-        x = self.dense(x)
-        x = self.activation_fn(x)
-        x = self.dropout(x)
-        x = self.out_proj(x)
-        return x
-
-
-@register_model_architecture("bart", "bart_large")
-def bart_large_architecture(args):
-    args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 1024)
-    args.encoder_layers = getattr(args, "encoder_layers", 12)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
-    args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
-    args.encoder_learned_pos = getattr(args, "encoder_learned_pos", True)
-    args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
-    args.decoder_ffn_embed_dim = getattr(
-        args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
-    )
-    args.decoder_layers = getattr(args, "decoder_layers", 12)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
-    args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
-    args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True)
-    args.attention_dropout = getattr(args, "attention_dropout", 0.0)
-    args.relu_dropout = getattr(args, "relu_dropout", 0.0)
-    args.dropout = getattr(args, "dropout", 0.1)
-    args.max_target_positions = getattr(args, "max_target_positions", 1024)
-    args.max_source_positions = getattr(args, "max_source_positions", 1024)
-    args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
-    args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
-    args.share_decoder_input_output_embed = getattr(
-        args, "share_decoder_input_output_embed", True
-    )
-    args.share_all_embeddings = getattr(args, "share_all_embeddings", True)
-
-    args.decoder_output_dim = getattr(
-        args, "decoder_output_dim", args.decoder_embed_dim
-    )
-    args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
-
-    args.no_scale_embedding = getattr(args, "no_scale_embedding", True)
-    args.layernorm_embedding = getattr(args, "layernorm_embedding", True)
-
-    args.activation_fn = getattr(args, "activation_fn", "gelu")
-    args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh")
-    args.pooler_dropout = getattr(args, "pooler_dropout", 0.0)
-
-
-@register_model_architecture("bart", "bart_base")
-def bart_base_architecture(args):
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 768)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * 768)
-    args.encoder_layers = getattr(args, "encoder_layers", 6)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 12)
-    args.decoder_layers = getattr(args, "decoder_layers", 6)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 12)
-    bart_large_architecture(args)
-
-
-@register_model_architecture("bart", "mbart_large")
-def mbart_large_architecture(args):
-    args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
-    bart_large_architecture(args)
-
-
-@register_model_architecture("bart", "mbart_base")
-def mbart_base_architecture(args):
-    args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
-    bart_base_architecture(args)
-
-
-@register_model_architecture("bart", "mbart_base_wmt20")
-def mbart_base_wmt20_architecture(args):
-    args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
-    mbart_base_architecture(args)
diff --git a/spaces/OFA-Sys/OFA-vqa/checkpoints.md b/spaces/OFA-Sys/OFA-vqa/checkpoints.md
deleted file mode 100644
index e887af2f7dbc198661b6224d5eab1b7f2bdd5b77..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/checkpoints.md
+++ /dev/null
@@ -1,13 +0,0 @@
-# Checkpoints
-
-We provide links for you to download our checkpoints. We will release all the checkpoints including pretrained and finetuned models on different tasks. 
-
-## Pretraining
-* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/ofa_large.pt"> Pre-trained checkpoint (OFA-Large) </a>
-
-## Finetuning
-
-* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_large_best_clean.pt"> Finetuned checkpoint for Caption on COCO </a>
-* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcoco_large_best.pt"> Finetuned checkpoint for RefCOCO </a>
-* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocoplus_large_best.pt"> Finetuned checkpoint for RefCOCO+ </a>
-* <a href="https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/refcocog_large_best.pt"> Finetuned checkpoint for RefCOCOg </a>
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/round_robin_zip_datasets.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/round_robin_zip_datasets.py
deleted file mode 100644
index 2cb7447ea955a7c3ae7372f09ee426c08acd430e..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/round_robin_zip_datasets.py
+++ /dev/null
@@ -1,160 +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
-from collections import OrderedDict
-from typing import Dict, Sequence
-
-import numpy as np
-
-from . import FairseqDataset, LanguagePairDataset
-
-logger = logging.getLogger(__name__)
-
-
-class RoundRobinZipDatasets(FairseqDataset):
-    """Zip multiple :class:`~fairseq.data.FairseqDataset` instances together.
-
-    Shorter datasets are repeated in a round-robin fashion to match the length
-    of the longest one.
-
-    Args:
-        datasets (Dict[~fairseq.data.FairseqDataset]): a dictionary of
-            :class:`~fairseq.data.FairseqDataset` instances.
-        eval_key (str, optional): a key used at evaluation time that causes
-            this instance to pass-through batches from *datasets[eval_key]*.
-    """
-
-    def __init__(self, datasets, eval_key=None):
-        super().__init__()
-        if isinstance(datasets, dict):
-            datasets = OrderedDict(datasets)
-        assert isinstance(datasets, OrderedDict)
-        assert datasets, "Can't make a RoundRobinZipDatasets out of nothing"
-        for dataset in datasets.values():
-            assert isinstance(dataset, FairseqDataset)
-
-        self.datasets = datasets
-        self.eval_key = eval_key
-
-        self.longest_dataset_key = max(datasets, key=lambda k: len(datasets[k]))
-        self.longest_dataset = datasets[self.longest_dataset_key]
-        self._ordered_indices: Dict[str, Sequence[int]] = None
-
-    def _map_index(self, key, index):
-        assert (
-            self._ordered_indices is not None
-        ), "Must call RoundRobinZipDatasets.ordered_indices() first"
-        o = self._ordered_indices[key]
-        return o[index % len(o)]
-
-    def __getitem__(self, index):
-        if self.eval_key is None:
-            return OrderedDict(
-                [
-                    (key, dataset[self._map_index(key, index)])
-                    for key, dataset in self.datasets.items()
-                ]
-            )
-        else:
-            # at evaluation time it's useful to pass-through batches from a single key
-            return self.datasets[self.eval_key][self._map_index(self.eval_key, index)]
-
-    def __len__(self):
-        if self._ordered_indices is not None:
-            return len(self._ordered_indices[self.longest_dataset_key])
-        return len(self.longest_dataset)
-
-    def collater(self, samples):
-        """Merge a list of samples to form a mini-batch."""
-        if len(samples) == 0:
-            return None
-        if self.eval_key is None:
-            return OrderedDict(
-                [
-                    (key, dataset.collater([sample[key] for sample in samples]))
-                    for key, dataset in self.datasets.items()
-                ]
-            )
-        else:
-            # at evaluation time it's useful to pass-through batches from a single key
-            return self.datasets[self.eval_key].collater(samples)
-
-    def num_tokens(self, index):
-        """Return an example's length (number of tokens), used for batching."""
-        # TODO make it configurable whether to use max() or sum() here
-        return max(
-            dataset.num_tokens(self._map_index(key, index))
-            for key, dataset in self.datasets.items()
-        )
-
-    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 {
-            key: dataset.size(self._map_index(key, index))
-            for key, dataset in self.datasets.items()
-        }
-
-    def ordered_indices(self):
-        """Ordered indices for batching."""
-        if self._ordered_indices is None:
-            # Call the underlying dataset's ordered_indices() here, so that we
-            # get the same random ordering as we would have from using the
-            # underlying sub-datasets directly.
-            self._ordered_indices = OrderedDict(
-                [
-                    (key, dataset.ordered_indices())
-                    for key, dataset in self.datasets.items()
-                ]
-            )
-        return np.arange(len(self))
-
-    def filter_indices_by_size(self, indices, max_positions=None):
-        """
-        Filter each sub-dataset independently, then update the round robin to work
-        on the filtered sub-datasets.
-        """
-
-        def _deep_until_language_pair(dataset):
-            if isinstance(dataset, LanguagePairDataset):
-                return dataset
-            if hasattr(dataset, "tgt_dataset"):
-                return _deep_until_language_pair(dataset.tgt_dataset)
-            if hasattr(dataset, "dataset"):
-                return _deep_until_language_pair(dataset.dataset)
-            raise Exception(f"Don't know how to unwrap this dataset: {dataset}")
-
-        if not isinstance(max_positions, dict):
-            max_positions = {k: max_positions for k in self.datasets.keys()}
-        ignored_some = False
-        for key, dataset in self.datasets.items():
-            dataset = _deep_until_language_pair(dataset)
-            self._ordered_indices[key], ignored = dataset.filter_indices_by_size(
-                self._ordered_indices[key], max_positions[key]
-            )
-            if len(ignored) > 0:
-                ignored_some = True
-                logger.warning(
-                    f"{len(ignored)} samples from {key} have invalid sizes and will be skipped, "
-                    f"max_positions={max_positions[key]}, first few sample ids={ignored[:10]}"
-                )
-        # Since we are modifying in place the _ordered_indices,
-        # it's not possible anymore to return valid ignored indices.
-        # Hopefully the extra debug information print above should be enough to debug.
-        # Ideally we would receive ignore_invalid_inputs so that we could have
-        # a proper error message.
-        return (np.arange(len(self)), [0] if ignored_some else [])
-
-    @property
-    def supports_prefetch(self):
-        return all(
-            getattr(dataset, "supports_prefetch", False)
-            for dataset in self.datasets.values()
-        )
-
-    def prefetch(self, indices):
-        for key, dataset in self.datasets.items():
-            dataset.prefetch([self._map_index(key, index) for index in indices])
diff --git a/spaces/Omnibus/Video-Diffusion-WebUI/video_diffusion/tuneavideo/util.py b/spaces/Omnibus/Video-Diffusion-WebUI/video_diffusion/tuneavideo/util.py
deleted file mode 100644
index 77a5ebbf1f6d7ca002de3bede9d8d98fc24946d2..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/Video-Diffusion-WebUI/video_diffusion/tuneavideo/util.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import os
-from typing import Union
-
-import imageio
-import numpy as np
-import torch
-import torchvision
-from einops import rearrange
-from tqdm import tqdm
-
-
-def save_videos_grid(
-    videos: torch.Tensor, save_path: str = "output", path: str = "output.gif", rescale=False, n_rows=4, fps=3
-):
-    videos = rearrange(videos, "b c t h w -> t b c h w")
-    outputs = []
-    for x in videos:
-        x = torchvision.utils.make_grid(x, nrow=n_rows)
-        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
-        if rescale:
-            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
-        x = (x * 255).numpy().astype(np.uint8)
-        outputs.append(x)
-
-    if not os.path.exists(save_path):
-        os.makedirs(save_path)
-
-    imageio.mimsave(os.path.join(save_path, path), outputs, fps=fps)
-    return os.path.join(save_path, path)
-
-
-# DDIM Inversion
-@torch.no_grad()
-def init_prompt(prompt, pipeline):
-    uncond_input = pipeline.tokenizer(
-        [""], padding="max_length", max_length=pipeline.tokenizer.model_max_length, return_tensors="pt"
-    )
-    uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0]
-    text_input = pipeline.tokenizer(
-        [prompt],
-        padding="max_length",
-        max_length=pipeline.tokenizer.model_max_length,
-        truncation=True,
-        return_tensors="pt",
-    )
-    text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0]
-    context = torch.cat([uncond_embeddings, text_embeddings])
-
-    return context
-
-
-def next_step(
-    model_output: Union[torch.FloatTensor, np.ndarray],
-    timestep: int,
-    sample: Union[torch.FloatTensor, np.ndarray],
-    ddim_scheduler,
-):
-    timestep, next_timestep = (
-        min(timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999),
-        timestep,
-    )
-    alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod
-    alpha_prod_t_next = ddim_scheduler.alphas_cumprod[next_timestep]
-    beta_prod_t = 1 - alpha_prod_t
-    next_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
-    next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
-    next_sample = alpha_prod_t_next**0.5 * next_original_sample + next_sample_direction
-    return next_sample
-
-
-def get_noise_pred_single(latents, t, context, unet):
-    noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"]
-    return noise_pred
-
-
-@torch.no_grad()
-def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt):
-    context = init_prompt(prompt, pipeline)
-    uncond_embeddings, cond_embeddings = context.chunk(2)
-    all_latent = [latent]
-    latent = latent.clone().detach()
-    for i in tqdm(range(num_inv_steps)):
-        t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1]
-        noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet)
-        latent = next_step(noise_pred, t, latent, ddim_scheduler)
-        all_latent.append(latent)
-    return all_latent
-
-
-@torch.no_grad()
-def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""):
-    ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt)
-    return ddim_latents
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/configs/common/models/mask_rcnn_c4.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/configs/common/models/mask_rcnn_c4.py
deleted file mode 100644
index a3dcf8be42a39c6e5f6e76e3ab23adeccb33085d..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/configs/common/models/mask_rcnn_c4.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from detectron2.config import LazyCall as L
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.meta_arch import GeneralizedRCNN
-from detectron2.modeling.anchor_generator import DefaultAnchorGenerator
-from detectron2.modeling.backbone import BasicStem, BottleneckBlock, ResNet
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.modeling.matcher import Matcher
-from detectron2.modeling.poolers import ROIPooler
-from detectron2.modeling.proposal_generator import RPN, StandardRPNHead
-from detectron2.modeling.roi_heads import (
-    FastRCNNOutputLayers,
-    MaskRCNNConvUpsampleHead,
-    Res5ROIHeads,
-)
-
-model = L(GeneralizedRCNN)(
-    backbone=L(ResNet)(
-        stem=L(BasicStem)(in_channels=3, out_channels=64, norm="FrozenBN"),
-        stages=L(ResNet.make_default_stages)(
-            depth=50,
-            stride_in_1x1=True,
-            norm="FrozenBN",
-        ),
-        out_features=["res4"],
-    ),
-    proposal_generator=L(RPN)(
-        in_features=["res4"],
-        head=L(StandardRPNHead)(in_channels=1024, num_anchors=15),
-        anchor_generator=L(DefaultAnchorGenerator)(
-            sizes=[[32, 64, 128, 256, 512]],
-            aspect_ratios=[0.5, 1.0, 2.0],
-            strides=[16],
-            offset=0.0,
-        ),
-        anchor_matcher=L(Matcher)(
-            thresholds=[0.3, 0.7], labels=[0, -1, 1], allow_low_quality_matches=True
-        ),
-        box2box_transform=L(Box2BoxTransform)(weights=[1.0, 1.0, 1.0, 1.0]),
-        batch_size_per_image=256,
-        positive_fraction=0.5,
-        pre_nms_topk=(12000, 6000),
-        post_nms_topk=(2000, 1000),
-        nms_thresh=0.7,
-    ),
-    roi_heads=L(Res5ROIHeads)(
-        num_classes=80,
-        batch_size_per_image=512,
-        positive_fraction=0.25,
-        proposal_matcher=L(Matcher)(
-            thresholds=[0.5], labels=[0, 1], allow_low_quality_matches=False
-        ),
-        in_features=["res4"],
-        pooler=L(ROIPooler)(
-            output_size=14,
-            scales=(1.0 / 16,),
-            sampling_ratio=0,
-            pooler_type="ROIAlignV2",
-        ),
-        res5=L(ResNet.make_stage)(
-            block_class=BottleneckBlock,
-            num_blocks=3,
-            stride_per_block=[2, 1, 1],
-            in_channels=1024,
-            bottleneck_channels=512,
-            out_channels=2048,
-            norm="FrozenBN",
-            stride_in_1x1=True,
-        ),
-        box_predictor=L(FastRCNNOutputLayers)(
-            input_shape=L(ShapeSpec)(channels="${...res5.out_channels}", height=1, width=1),
-            test_score_thresh=0.05,
-            box2box_transform=L(Box2BoxTransform)(weights=(10, 10, 5, 5)),
-            num_classes="${..num_classes}",
-        ),
-        mask_head=L(MaskRCNNConvUpsampleHead)(
-            input_shape=L(ShapeSpec)(
-                channels="${...res5.out_channels}",
-                width="${...pooler.output_size}",
-                height="${...pooler.output_size}",
-            ),
-            num_classes="${..num_classes}",
-            conv_dims=[256],
-        ),
-    ),
-    pixel_mean=[103.530, 116.280, 123.675],
-    pixel_std=[1.0, 1.0, 1.0],
-    input_format="BGR",
-)
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/inpainting.py b/spaces/OpenGVLab/InternGPT/iGPT/models/inpainting.py
deleted file mode 100644
index 0584eca603b17290add571ff3cbd43bc1a245a8d..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/inpainting.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import os
-import sys
-import cv2
-import numpy as np
-import torch
-from PIL import Image
-from .utils import gen_new_name, prompts
-import torch
-from omegaconf import OmegaConf
-import numpy as np
-import wget
-from .inpainting_src.ldm_inpainting.ldm.models.diffusion.ddim import DDIMSampler
-from .inpainting_src.ldm_inpainting.ldm.util import instantiate_from_config
-from .utils import cal_dilate_factor, dilate_mask
-
-
-def make_batch(image, mask, device):
-    image = image.astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image)
-
-    mask = mask.astype(np.float32) / 255.0
-    mask = mask[None, None]
-    mask[mask < 0.5] = 0
-    mask[mask >= 0.5] = 1
-    mask = torch.from_numpy(mask)
-        
-    masked_image = (1 - mask) * image
-
-    batch = {"image": image, "mask": mask, "masked_image": masked_image}
-    for k in batch:
-        batch[k] = batch[k].to(device=device)
-        batch[k] = batch[k] * 2.0 - 1.0
-    return batch
-
-
-class LDMInpainting:
-    def __init__(self, device):
-        self.model_checkpoint_path = 'model_zoo/ldm_inpainting_big.ckpt'
-        config = './iGPT/models/inpainting_src/ldm_inpainting/config.yaml'
-        self.ddim_steps = 50
-        self.device = device
-        config = OmegaConf.load(config)
-        model = instantiate_from_config(config.model)
-        self.download_parameters()
-        model.load_state_dict(torch.load(self.model_checkpoint_path)["state_dict"], strict=False)
-        self.model = model.to(device=device)
-        self.sampler = DDIMSampler(model)
-    
-    def download_parameters(self):
-        url = 'https://heibox.uni-heidelberg.de/f/4d9ac7ea40c64582b7c9/?dl=1'
-        if not os.path.exists(self.model_checkpoint_path):
-            wget.download(url, out=self.model_checkpoint_path)
-
-    @prompts(name="Remove the Masked Object",
-             description="useful when you want to remove an object by masking the region in the image. "
-                         "like: remove masked object or inpaint the masked region.. "
-                         "The input to this tool should be a comma separated string of two, "
-                         "representing the image_path and mask_path")
-    @torch.no_grad()
-    def inference(self, inputs):
-        print(f'inputs: {inputs}')
-        # image, mask, device
-        img_path, mask_path = inputs.split(',')[0], inputs.split(',')[1]
-        img_path = img_path.strip()
-        mask_path = mask_path.strip()
-        image = Image.open(img_path)
-        mask = Image.open(mask_path).convert('L')
-        w, h = image.size
-        image = image.resize((512, 512))
-        mask = mask.resize((512, 512))
-        image = np.array(image)
-        mask = np.array(mask)
-        dilate_factor = cal_dilate_factor(mask.astype(np.uint8))
-        mask = dilate_mask(mask, dilate_factor)
-        
-        with self.model.ema_scope():
-            batch = make_batch(image, mask, device=self.device)
-            # encode masked image and concat downsampled mask
-            c = self.model.cond_stage_model.encode(batch["masked_image"])
-            cc = torch.nn.functional.interpolate(batch["mask"],
-                                                 size=c.shape[-2:])
-            c = torch.cat((c, cc), dim=1)
-
-            shape = (c.shape[1] - 1,) + c.shape[2:]
-            samples_ddim, _ = self.sampler.sample(S=self.ddim_steps,
-                                                    conditioning=c,
-                                                    batch_size=c.shape[0],
-                                                    shape=shape,
-                                                    verbose=False)
-            x_samples_ddim = self.model.decode_first_stage(samples_ddim)
-
-            image = torch.clamp((batch["image"] + 1.0) / 2.0,
-                                min=0.0, max=1.0)
-            mask = torch.clamp((batch["mask"] + 1.0) / 2.0,
-                               min=0.0, max=1.0)
-            predicted_image = torch.clamp((x_samples_ddim + 1.0) / 2.0,
-                                          min=0.0, max=1.0)
-
-            inpainted = (1 - mask) * image + mask * predicted_image
-            inpainted = inpainted.cpu().numpy().transpose(0, 2, 3, 1)[0] * 255
-        
-        # print(type(inpainted))
-        inpainted = inpainted.astype(np.uint8)
-        new_img_name = gen_new_name(img_path, 'LDMInpainter')
-        new_img = Image.fromarray(inpainted)
-        new_img = new_img.resize((w, h))
-        new_img.save(new_img_name)
-        print(
-            f"\nProcessed LDMInpainting, Inputs: {inputs}, "
-            f"Output Image: {new_img_name}")
-        return new_img_name
-        # return inpainted
-
-'''
-if __name__ == '__main__':
-    painting = LDMInpainting('cuda:0')
-    res = painting.inference(f'image/82e612_fe54ca_raw.png,image/04a785_fe54ca_mask.png.')
-    print(res)
-'''
-
-    
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/assign_score_withk.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/assign_score_withk.py
deleted file mode 100644
index 4906adaa2cffd1b46912fbe7d4f87ef2f9fa0012..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/assign_score_withk.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
-    '_ext', ['assign_score_withk_forward', 'assign_score_withk_backward'])
-
-
-class AssignScoreWithK(Function):
-    r"""Perform weighted sum to generate output features according to scores.
-    Modified from `PAConv <https://github.com/CVMI-Lab/PAConv/tree/main/
-    scene_seg/lib/paconv_lib/src/gpu>`_.
-
-    This is a memory-efficient CUDA implementation of assign_scores operation,
-    which first transform all point features with weight bank, then assemble
-    neighbor features with ``knn_idx`` and perform weighted sum of ``scores``.
-
-    See the `paper <https://arxiv.org/pdf/2103.14635.pdf>`_ appendix Sec. D for
-        more detailed descriptions.
-
-    Note:
-        This implementation assumes using ``neighbor`` kernel input, which is
-            (point_features - center_features, point_features).
-        See https://github.com/CVMI-Lab/PAConv/blob/main/scene_seg/model/
-        pointnet2/paconv.py#L128 for more details.
-    """
-
-    @staticmethod
-    def forward(ctx,
-                scores,
-                point_features,
-                center_features,
-                knn_idx,
-                aggregate='sum'):
-        """
-        Args:
-            scores (torch.Tensor): (B, npoint, K, M), predicted scores to
-                aggregate weight matrices in the weight bank.
-                ``npoint`` is the number of sampled centers.
-                ``K`` is the number of queried neighbors.
-                ``M`` is the number of weight matrices in the weight bank.
-            point_features (torch.Tensor): (B, N, M, out_dim)
-                Pre-computed point features to be aggregated.
-            center_features (torch.Tensor): (B, N, M, out_dim)
-                Pre-computed center features to be aggregated.
-            knn_idx (torch.Tensor): (B, npoint, K), index of sampled kNN.
-                We assume the first idx in each row is the idx of the center.
-            aggregate (str, optional): Aggregation method.
-                Can be 'sum', 'avg' or 'max'. Defaults: 'sum'.
-
-        Returns:
-            torch.Tensor: (B, out_dim, npoint, K), the aggregated features.
-        """
-        agg = {'sum': 0, 'avg': 1, 'max': 2}
-
-        B, N, M, out_dim = point_features.size()
-        _, npoint, K, _ = scores.size()
-
-        output = point_features.new_zeros((B, out_dim, npoint, K))
-        ext_module.assign_score_withk_forward(
-            point_features.contiguous(),
-            center_features.contiguous(),
-            scores.contiguous(),
-            knn_idx.contiguous(),
-            output,
-            B=B,
-            N0=N,
-            N1=npoint,
-            M=M,
-            K=K,
-            O=out_dim,
-            aggregate=agg[aggregate])
-
-        ctx.save_for_backward(output, point_features, center_features, scores,
-                              knn_idx)
-        ctx.agg = agg[aggregate]
-
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_out):
-        """
-        Args:
-            grad_out (torch.Tensor): (B, out_dim, npoint, K)
-
-        Returns:
-            grad_scores (torch.Tensor): (B, npoint, K, M)
-            grad_point_features (torch.Tensor): (B, N, M, out_dim)
-            grad_center_features (torch.Tensor): (B, N, M, out_dim)
-        """
-        _, point_features, center_features, scores, knn_idx = ctx.saved_tensors
-
-        agg = ctx.agg
-
-        B, N, M, out_dim = point_features.size()
-        _, npoint, K, _ = scores.size()
-
-        grad_point_features = point_features.new_zeros(point_features.shape)
-        grad_center_features = center_features.new_zeros(center_features.shape)
-        grad_scores = scores.new_zeros(scores.shape)
-
-        ext_module.assign_score_withk_backward(
-            grad_out.contiguous(),
-            point_features.contiguous(),
-            center_features.contiguous(),
-            scores.contiguous(),
-            knn_idx.contiguous(),
-            grad_point_features,
-            grad_center_features,
-            grad_scores,
-            B=B,
-            N0=N,
-            N1=npoint,
-            M=M,
-            K=K,
-            O=out_dim,
-            aggregate=agg)
-
-        return grad_scores, grad_point_features, \
-            grad_center_features, None, None
-
-
-assign_score_withk = AssignScoreWithK.apply
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/datasets/pipelines/transforms.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/datasets/pipelines/transforms.py
deleted file mode 100644
index 94e869b252ef6d8b43604add2bbc02f034614bfb..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/datasets/pipelines/transforms.py
+++ /dev/null
@@ -1,889 +0,0 @@
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-from annotator.uniformer.mmcv.utils import deprecated_api_warning, is_tuple_of
-from numpy import random
-
-from ..builder import PIPELINES
-
-
-@PIPELINES.register_module()
-class Resize(object):
-    """Resize images & seg.
-
-    This transform resizes the input image to some scale. If the input dict
-    contains the key "scale", then the scale in the input dict is used,
-    otherwise the specified scale in the init method is used.
-
-    ``img_scale`` can be None, a tuple (single-scale) or a list of tuple
-    (multi-scale). There are 4 multiscale modes:
-
-    - ``ratio_range is not None``:
-    1. When img_scale is None, img_scale is the shape of image in results
-        (img_scale = results['img'].shape[:2]) and the image is resized based
-        on the original size. (mode 1)
-    2. When img_scale is a tuple (single-scale), randomly sample a ratio from
-        the ratio range and multiply it with the image scale. (mode 2)
-
-    - ``ratio_range is None and multiscale_mode == "range"``: randomly sample a
-    scale from the a range. (mode 3)
-
-    - ``ratio_range is None and multiscale_mode == "value"``: randomly sample a
-    scale from multiple scales. (mode 4)
-
-    Args:
-        img_scale (tuple or list[tuple]): Images scales for resizing.
-        multiscale_mode (str): Either "range" or "value".
-        ratio_range (tuple[float]): (min_ratio, max_ratio)
-        keep_ratio (bool): Whether to keep the aspect ratio when resizing the
-            image.
-    """
-
-    def __init__(self,
-                 img_scale=None,
-                 multiscale_mode='range',
-                 ratio_range=None,
-                 keep_ratio=True):
-        if img_scale is None:
-            self.img_scale = None
-        else:
-            if isinstance(img_scale, list):
-                self.img_scale = img_scale
-            else:
-                self.img_scale = [img_scale]
-            assert mmcv.is_list_of(self.img_scale, tuple)
-
-        if ratio_range is not None:
-            # mode 1: given img_scale=None and a range of image ratio
-            # mode 2: given a scale and a range of image ratio
-            assert self.img_scale is None or len(self.img_scale) == 1
-        else:
-            # mode 3 and 4: given multiple scales or a range of scales
-            assert multiscale_mode in ['value', 'range']
-
-        self.multiscale_mode = multiscale_mode
-        self.ratio_range = ratio_range
-        self.keep_ratio = keep_ratio
-
-    @staticmethod
-    def random_select(img_scales):
-        """Randomly select an img_scale from given candidates.
-
-        Args:
-            img_scales (list[tuple]): Images scales for selection.
-
-        Returns:
-            (tuple, int): Returns a tuple ``(img_scale, scale_dix)``,
-                where ``img_scale`` is the selected image scale and
-                ``scale_idx`` is the selected index in the given candidates.
-        """
-
-        assert mmcv.is_list_of(img_scales, tuple)
-        scale_idx = np.random.randint(len(img_scales))
-        img_scale = img_scales[scale_idx]
-        return img_scale, scale_idx
-
-    @staticmethod
-    def random_sample(img_scales):
-        """Randomly sample an img_scale when ``multiscale_mode=='range'``.
-
-        Args:
-            img_scales (list[tuple]): Images scale range for sampling.
-                There must be two tuples in img_scales, which specify the lower
-                and upper bound of image scales.
-
-        Returns:
-            (tuple, None): Returns a tuple ``(img_scale, None)``, where
-                ``img_scale`` is sampled scale and None is just a placeholder
-                to be consistent with :func:`random_select`.
-        """
-
-        assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
-        img_scale_long = [max(s) for s in img_scales]
-        img_scale_short = [min(s) for s in img_scales]
-        long_edge = np.random.randint(
-            min(img_scale_long),
-            max(img_scale_long) + 1)
-        short_edge = np.random.randint(
-            min(img_scale_short),
-            max(img_scale_short) + 1)
-        img_scale = (long_edge, short_edge)
-        return img_scale, None
-
-    @staticmethod
-    def random_sample_ratio(img_scale, ratio_range):
-        """Randomly sample an img_scale when ``ratio_range`` is specified.
-
-        A ratio will be randomly sampled from the range specified by
-        ``ratio_range``. Then it would be multiplied with ``img_scale`` to
-        generate sampled scale.
-
-        Args:
-            img_scale (tuple): Images scale base to multiply with ratio.
-            ratio_range (tuple[float]): The minimum and maximum ratio to scale
-                the ``img_scale``.
-
-        Returns:
-            (tuple, None): Returns a tuple ``(scale, None)``, where
-                ``scale`` is sampled ratio multiplied with ``img_scale`` and
-                None is just a placeholder to be consistent with
-                :func:`random_select`.
-        """
-
-        assert isinstance(img_scale, tuple) and len(img_scale) == 2
-        min_ratio, max_ratio = ratio_range
-        assert min_ratio <= max_ratio
-        ratio = np.random.random_sample() * (max_ratio - min_ratio) + min_ratio
-        scale = int(img_scale[0] * ratio), int(img_scale[1] * ratio)
-        return scale, None
-
-    def _random_scale(self, results):
-        """Randomly sample an img_scale according to ``ratio_range`` and
-        ``multiscale_mode``.
-
-        If ``ratio_range`` is specified, a ratio will be sampled and be
-        multiplied with ``img_scale``.
-        If multiple scales are specified by ``img_scale``, a scale will be
-        sampled according to ``multiscale_mode``.
-        Otherwise, single scale will be used.
-
-        Args:
-            results (dict): Result dict from :obj:`dataset`.
-
-        Returns:
-            dict: Two new keys 'scale` and 'scale_idx` are added into
-                ``results``, which would be used by subsequent pipelines.
-        """
-
-        if self.ratio_range is not None:
-            if self.img_scale is None:
-                h, w = results['img'].shape[:2]
-                scale, scale_idx = self.random_sample_ratio((w, h),
-                                                            self.ratio_range)
-            else:
-                scale, scale_idx = self.random_sample_ratio(
-                    self.img_scale[0], self.ratio_range)
-        elif len(self.img_scale) == 1:
-            scale, scale_idx = self.img_scale[0], 0
-        elif self.multiscale_mode == 'range':
-            scale, scale_idx = self.random_sample(self.img_scale)
-        elif self.multiscale_mode == 'value':
-            scale, scale_idx = self.random_select(self.img_scale)
-        else:
-            raise NotImplementedError
-
-        results['scale'] = scale
-        results['scale_idx'] = scale_idx
-
-    def _resize_img(self, results):
-        """Resize images with ``results['scale']``."""
-        if self.keep_ratio:
-            img, scale_factor = mmcv.imrescale(
-                results['img'], results['scale'], return_scale=True)
-            # the w_scale and h_scale has minor difference
-            # a real fix should be done in the mmcv.imrescale in the future
-            new_h, new_w = img.shape[:2]
-            h, w = results['img'].shape[:2]
-            w_scale = new_w / w
-            h_scale = new_h / h
-        else:
-            img, w_scale, h_scale = mmcv.imresize(
-                results['img'], results['scale'], return_scale=True)
-        scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
-                                dtype=np.float32)
-        results['img'] = img
-        results['img_shape'] = img.shape
-        results['pad_shape'] = img.shape  # in case that there is no padding
-        results['scale_factor'] = scale_factor
-        results['keep_ratio'] = self.keep_ratio
-
-    def _resize_seg(self, results):
-        """Resize semantic segmentation map with ``results['scale']``."""
-        for key in results.get('seg_fields', []):
-            if self.keep_ratio:
-                gt_seg = mmcv.imrescale(
-                    results[key], results['scale'], interpolation='nearest')
-            else:
-                gt_seg = mmcv.imresize(
-                    results[key], results['scale'], interpolation='nearest')
-            results[key] = gt_seg
-
-    def __call__(self, results):
-        """Call function to resize images, bounding boxes, masks, semantic
-        segmentation map.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor',
-                'keep_ratio' keys are added into result dict.
-        """
-
-        if 'scale' not in results:
-            self._random_scale(results)
-        self._resize_img(results)
-        self._resize_seg(results)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += (f'(img_scale={self.img_scale}, '
-                     f'multiscale_mode={self.multiscale_mode}, '
-                     f'ratio_range={self.ratio_range}, '
-                     f'keep_ratio={self.keep_ratio})')
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RandomFlip(object):
-    """Flip the image & seg.
-
-    If the input dict contains the key "flip", then the flag will be used,
-    otherwise it will be randomly decided by a ratio specified in the init
-    method.
-
-    Args:
-        prob (float, optional): The flipping probability. Default: None.
-        direction(str, optional): The flipping direction. Options are
-            'horizontal' and 'vertical'. Default: 'horizontal'.
-    """
-
-    @deprecated_api_warning({'flip_ratio': 'prob'}, cls_name='RandomFlip')
-    def __init__(self, prob=None, direction='horizontal'):
-        self.prob = prob
-        self.direction = direction
-        if prob is not None:
-            assert prob >= 0 and prob <= 1
-        assert direction in ['horizontal', 'vertical']
-
-    def __call__(self, results):
-        """Call function to flip bounding boxes, masks, semantic segmentation
-        maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Flipped results, 'flip', 'flip_direction' keys are added into
-                result dict.
-        """
-
-        if 'flip' not in results:
-            flip = True if np.random.rand() < self.prob else False
-            results['flip'] = flip
-        if 'flip_direction' not in results:
-            results['flip_direction'] = self.direction
-        if results['flip']:
-            # flip image
-            results['img'] = mmcv.imflip(
-                results['img'], direction=results['flip_direction'])
-
-            # flip segs
-            for key in results.get('seg_fields', []):
-                # use copy() to make numpy stride positive
-                results[key] = mmcv.imflip(
-                    results[key], direction=results['flip_direction']).copy()
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(prob={self.prob})'
-
-
-@PIPELINES.register_module()
-class Pad(object):
-    """Pad the image & mask.
-
-    There are two padding modes: (1) pad to a fixed size and (2) pad to the
-    minimum size that is divisible by some number.
-    Added keys are "pad_shape", "pad_fixed_size", "pad_size_divisor",
-
-    Args:
-        size (tuple, optional): Fixed padding size.
-        size_divisor (int, optional): The divisor of padded size.
-        pad_val (float, optional): Padding value. Default: 0.
-        seg_pad_val (float, optional): Padding value of segmentation map.
-            Default: 255.
-    """
-
-    def __init__(self,
-                 size=None,
-                 size_divisor=None,
-                 pad_val=0,
-                 seg_pad_val=255):
-        self.size = size
-        self.size_divisor = size_divisor
-        self.pad_val = pad_val
-        self.seg_pad_val = seg_pad_val
-        # only one of size and size_divisor should be valid
-        assert size is not None or size_divisor is not None
-        assert size is None or size_divisor is None
-
-    def _pad_img(self, results):
-        """Pad images according to ``self.size``."""
-        if self.size is not None:
-            padded_img = mmcv.impad(
-                results['img'], shape=self.size, pad_val=self.pad_val)
-        elif self.size_divisor is not None:
-            padded_img = mmcv.impad_to_multiple(
-                results['img'], self.size_divisor, pad_val=self.pad_val)
-        results['img'] = padded_img
-        results['pad_shape'] = padded_img.shape
-        results['pad_fixed_size'] = self.size
-        results['pad_size_divisor'] = self.size_divisor
-
-    def _pad_seg(self, results):
-        """Pad masks according to ``results['pad_shape']``."""
-        for key in results.get('seg_fields', []):
-            results[key] = mmcv.impad(
-                results[key],
-                shape=results['pad_shape'][:2],
-                pad_val=self.seg_pad_val)
-
-    def __call__(self, results):
-        """Call function to pad images, masks, semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Updated result dict.
-        """
-
-        self._pad_img(results)
-        self._pad_seg(results)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(size={self.size}, size_divisor={self.size_divisor}, ' \
-                    f'pad_val={self.pad_val})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Normalize(object):
-    """Normalize the image.
-
-    Added key is "img_norm_cfg".
-
-    Args:
-        mean (sequence): Mean values of 3 channels.
-        std (sequence): Std values of 3 channels.
-        to_rgb (bool): Whether to convert the image from BGR to RGB,
-            default is true.
-    """
-
-    def __init__(self, mean, std, to_rgb=True):
-        self.mean = np.array(mean, dtype=np.float32)
-        self.std = np.array(std, dtype=np.float32)
-        self.to_rgb = to_rgb
-
-    def __call__(self, results):
-        """Call function to normalize images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Normalized results, 'img_norm_cfg' key is added into
-                result dict.
-        """
-
-        results['img'] = mmcv.imnormalize(results['img'], self.mean, self.std,
-                                          self.to_rgb)
-        results['img_norm_cfg'] = dict(
-            mean=self.mean, std=self.std, to_rgb=self.to_rgb)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(mean={self.mean}, std={self.std}, to_rgb=' \
-                    f'{self.to_rgb})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Rerange(object):
-    """Rerange the image pixel value.
-
-    Args:
-        min_value (float or int): Minimum value of the reranged image.
-            Default: 0.
-        max_value (float or int): Maximum value of the reranged image.
-            Default: 255.
-    """
-
-    def __init__(self, min_value=0, max_value=255):
-        assert isinstance(min_value, float) or isinstance(min_value, int)
-        assert isinstance(max_value, float) or isinstance(max_value, int)
-        assert min_value < max_value
-        self.min_value = min_value
-        self.max_value = max_value
-
-    def __call__(self, results):
-        """Call function to rerange images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-        Returns:
-            dict: Reranged results.
-        """
-
-        img = results['img']
-        img_min_value = np.min(img)
-        img_max_value = np.max(img)
-
-        assert img_min_value < img_max_value
-        # rerange to [0, 1]
-        img = (img - img_min_value) / (img_max_value - img_min_value)
-        # rerange to [min_value, max_value]
-        img = img * (self.max_value - self.min_value) + self.min_value
-        results['img'] = img
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(min_value={self.min_value}, max_value={self.max_value})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class CLAHE(object):
-    """Use CLAHE method to process the image.
-
-    See `ZUIDERVELD,K. Contrast Limited Adaptive Histogram Equalization[J].
-    Graphics Gems, 1994:474-485.` for more information.
-
-    Args:
-        clip_limit (float): Threshold for contrast limiting. Default: 40.0.
-        tile_grid_size (tuple[int]): Size of grid for histogram equalization.
-            Input image will be divided into equally sized rectangular tiles.
-            It defines the number of tiles in row and column. Default: (8, 8).
-    """
-
-    def __init__(self, clip_limit=40.0, tile_grid_size=(8, 8)):
-        assert isinstance(clip_limit, (float, int))
-        self.clip_limit = clip_limit
-        assert is_tuple_of(tile_grid_size, int)
-        assert len(tile_grid_size) == 2
-        self.tile_grid_size = tile_grid_size
-
-    def __call__(self, results):
-        """Call function to Use CLAHE method process images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Processed results.
-        """
-
-        for i in range(results['img'].shape[2]):
-            results['img'][:, :, i] = mmcv.clahe(
-                np.array(results['img'][:, :, i], dtype=np.uint8),
-                self.clip_limit, self.tile_grid_size)
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(clip_limit={self.clip_limit}, '\
-                    f'tile_grid_size={self.tile_grid_size})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RandomCrop(object):
-    """Random crop the image & seg.
-
-    Args:
-        crop_size (tuple): Expected size after cropping, (h, w).
-        cat_max_ratio (float): The maximum ratio that single category could
-            occupy.
-    """
-
-    def __init__(self, crop_size, cat_max_ratio=1., ignore_index=255):
-        assert crop_size[0] > 0 and crop_size[1] > 0
-        self.crop_size = crop_size
-        self.cat_max_ratio = cat_max_ratio
-        self.ignore_index = ignore_index
-
-    def get_crop_bbox(self, img):
-        """Randomly get a crop bounding box."""
-        margin_h = max(img.shape[0] - self.crop_size[0], 0)
-        margin_w = max(img.shape[1] - self.crop_size[1], 0)
-        offset_h = np.random.randint(0, margin_h + 1)
-        offset_w = np.random.randint(0, margin_w + 1)
-        crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0]
-        crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1]
-
-        return crop_y1, crop_y2, crop_x1, crop_x2
-
-    def crop(self, img, crop_bbox):
-        """Crop from ``img``"""
-        crop_y1, crop_y2, crop_x1, crop_x2 = crop_bbox
-        img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
-        return img
-
-    def __call__(self, results):
-        """Call function to randomly crop images, semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Randomly cropped results, 'img_shape' key in result dict is
-                updated according to crop size.
-        """
-
-        img = results['img']
-        crop_bbox = self.get_crop_bbox(img)
-        if self.cat_max_ratio < 1.:
-            # Repeat 10 times
-            for _ in range(10):
-                seg_temp = self.crop(results['gt_semantic_seg'], crop_bbox)
-                labels, cnt = np.unique(seg_temp, return_counts=True)
-                cnt = cnt[labels != self.ignore_index]
-                if len(cnt) > 1 and np.max(cnt) / np.sum(
-                        cnt) < self.cat_max_ratio:
-                    break
-                crop_bbox = self.get_crop_bbox(img)
-
-        # crop the image
-        img = self.crop(img, crop_bbox)
-        img_shape = img.shape
-        results['img'] = img
-        results['img_shape'] = img_shape
-
-        # crop semantic seg
-        for key in results.get('seg_fields', []):
-            results[key] = self.crop(results[key], crop_bbox)
-
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(crop_size={self.crop_size})'
-
-
-@PIPELINES.register_module()
-class RandomRotate(object):
-    """Rotate the image & seg.
-
-    Args:
-        prob (float): The rotation probability.
-        degree (float, tuple[float]): Range of degrees to select from. If
-            degree is a number instead of tuple like (min, max),
-            the range of degree will be (``-degree``, ``+degree``)
-        pad_val (float, optional): Padding value of image. Default: 0.
-        seg_pad_val (float, optional): Padding value of segmentation map.
-            Default: 255.
-        center (tuple[float], optional): Center point (w, h) of the rotation in
-            the source image. If not specified, the center of the image will be
-            used. Default: None.
-        auto_bound (bool): Whether to adjust the image size to cover the whole
-            rotated image. Default: False
-    """
-
-    def __init__(self,
-                 prob,
-                 degree,
-                 pad_val=0,
-                 seg_pad_val=255,
-                 center=None,
-                 auto_bound=False):
-        self.prob = prob
-        assert prob >= 0 and prob <= 1
-        if isinstance(degree, (float, int)):
-            assert degree > 0, f'degree {degree} should be positive'
-            self.degree = (-degree, degree)
-        else:
-            self.degree = degree
-        assert len(self.degree) == 2, f'degree {self.degree} should be a ' \
-                                      f'tuple of (min, max)'
-        self.pal_val = pad_val
-        self.seg_pad_val = seg_pad_val
-        self.center = center
-        self.auto_bound = auto_bound
-
-    def __call__(self, results):
-        """Call function to rotate image, semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Rotated results.
-        """
-
-        rotate = True if np.random.rand() < self.prob else False
-        degree = np.random.uniform(min(*self.degree), max(*self.degree))
-        if rotate:
-            # rotate image
-            results['img'] = mmcv.imrotate(
-                results['img'],
-                angle=degree,
-                border_value=self.pal_val,
-                center=self.center,
-                auto_bound=self.auto_bound)
-
-            # rotate segs
-            for key in results.get('seg_fields', []):
-                results[key] = mmcv.imrotate(
-                    results[key],
-                    angle=degree,
-                    border_value=self.seg_pad_val,
-                    center=self.center,
-                    auto_bound=self.auto_bound,
-                    interpolation='nearest')
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(prob={self.prob}, ' \
-                    f'degree={self.degree}, ' \
-                    f'pad_val={self.pal_val}, ' \
-                    f'seg_pad_val={self.seg_pad_val}, ' \
-                    f'center={self.center}, ' \
-                    f'auto_bound={self.auto_bound})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RGB2Gray(object):
-    """Convert RGB image to grayscale image.
-
-    This transform calculate the weighted mean of input image channels with
-    ``weights`` and then expand the channels to ``out_channels``. When
-    ``out_channels`` is None, the number of output channels is the same as
-    input channels.
-
-    Args:
-        out_channels (int): Expected number of output channels after
-            transforming. Default: None.
-        weights (tuple[float]): The weights to calculate the weighted mean.
-            Default: (0.299, 0.587, 0.114).
-    """
-
-    def __init__(self, out_channels=None, weights=(0.299, 0.587, 0.114)):
-        assert out_channels is None or out_channels > 0
-        self.out_channels = out_channels
-        assert isinstance(weights, tuple)
-        for item in weights:
-            assert isinstance(item, (float, int))
-        self.weights = weights
-
-    def __call__(self, results):
-        """Call function to convert RGB image to grayscale image.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with grayscale image.
-        """
-        img = results['img']
-        assert len(img.shape) == 3
-        assert img.shape[2] == len(self.weights)
-        weights = np.array(self.weights).reshape((1, 1, -1))
-        img = (img * weights).sum(2, keepdims=True)
-        if self.out_channels is None:
-            img = img.repeat(weights.shape[2], axis=2)
-        else:
-            img = img.repeat(self.out_channels, axis=2)
-
-        results['img'] = img
-        results['img_shape'] = img.shape
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(out_channels={self.out_channels}, ' \
-                    f'weights={self.weights})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class AdjustGamma(object):
-    """Using gamma correction to process the image.
-
-    Args:
-        gamma (float or int): Gamma value used in gamma correction.
-            Default: 1.0.
-    """
-
-    def __init__(self, gamma=1.0):
-        assert isinstance(gamma, float) or isinstance(gamma, int)
-        assert gamma > 0
-        self.gamma = gamma
-        inv_gamma = 1.0 / gamma
-        self.table = np.array([(i / 255.0)**inv_gamma * 255
-                               for i in np.arange(256)]).astype('uint8')
-
-    def __call__(self, results):
-        """Call function to process the image with gamma correction.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Processed results.
-        """
-
-        results['img'] = mmcv.lut_transform(
-            np.array(results['img'], dtype=np.uint8), self.table)
-
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(gamma={self.gamma})'
-
-
-@PIPELINES.register_module()
-class SegRescale(object):
-    """Rescale semantic segmentation maps.
-
-    Args:
-        scale_factor (float): The scale factor of the final output.
-    """
-
-    def __init__(self, scale_factor=1):
-        self.scale_factor = scale_factor
-
-    def __call__(self, results):
-        """Call function to scale the semantic segmentation map.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with semantic segmentation map scaled.
-        """
-        for key in results.get('seg_fields', []):
-            if self.scale_factor != 1:
-                results[key] = mmcv.imrescale(
-                    results[key], self.scale_factor, interpolation='nearest')
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(scale_factor={self.scale_factor})'
-
-
-@PIPELINES.register_module()
-class PhotoMetricDistortion(object):
-    """Apply photometric distortion to image sequentially, every transformation
-    is applied with a probability of 0.5. The position of random contrast is in
-    second or second to last.
-
-    1. random brightness
-    2. random contrast (mode 0)
-    3. convert color from BGR to HSV
-    4. random saturation
-    5. random hue
-    6. convert color from HSV to BGR
-    7. random contrast (mode 1)
-
-    Args:
-        brightness_delta (int): delta of brightness.
-        contrast_range (tuple): range of contrast.
-        saturation_range (tuple): range of saturation.
-        hue_delta (int): delta of hue.
-    """
-
-    def __init__(self,
-                 brightness_delta=32,
-                 contrast_range=(0.5, 1.5),
-                 saturation_range=(0.5, 1.5),
-                 hue_delta=18):
-        self.brightness_delta = brightness_delta
-        self.contrast_lower, self.contrast_upper = contrast_range
-        self.saturation_lower, self.saturation_upper = saturation_range
-        self.hue_delta = hue_delta
-
-    def convert(self, img, alpha=1, beta=0):
-        """Multiple with alpha and add beat with clip."""
-        img = img.astype(np.float32) * alpha + beta
-        img = np.clip(img, 0, 255)
-        return img.astype(np.uint8)
-
-    def brightness(self, img):
-        """Brightness distortion."""
-        if random.randint(2):
-            return self.convert(
-                img,
-                beta=random.uniform(-self.brightness_delta,
-                                    self.brightness_delta))
-        return img
-
-    def contrast(self, img):
-        """Contrast distortion."""
-        if random.randint(2):
-            return self.convert(
-                img,
-                alpha=random.uniform(self.contrast_lower, self.contrast_upper))
-        return img
-
-    def saturation(self, img):
-        """Saturation distortion."""
-        if random.randint(2):
-            img = mmcv.bgr2hsv(img)
-            img[:, :, 1] = self.convert(
-                img[:, :, 1],
-                alpha=random.uniform(self.saturation_lower,
-                                     self.saturation_upper))
-            img = mmcv.hsv2bgr(img)
-        return img
-
-    def hue(self, img):
-        """Hue distortion."""
-        if random.randint(2):
-            img = mmcv.bgr2hsv(img)
-            img[:, :,
-                0] = (img[:, :, 0].astype(int) +
-                      random.randint(-self.hue_delta, self.hue_delta)) % 180
-            img = mmcv.hsv2bgr(img)
-        return img
-
-    def __call__(self, results):
-        """Call function to perform photometric distortion on images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with images distorted.
-        """
-
-        img = results['img']
-        # random brightness
-        img = self.brightness(img)
-
-        # mode == 0 --> do random contrast first
-        # mode == 1 --> do random contrast last
-        mode = random.randint(2)
-        if mode == 1:
-            img = self.contrast(img)
-
-        # random saturation
-        img = self.saturation(img)
-
-        # random hue
-        img = self.hue(img)
-
-        # random contrast
-        if mode == 0:
-            img = self.contrast(img)
-
-        results['img'] = img
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += (f'(brightness_delta={self.brightness_delta}, '
-                     f'contrast_range=({self.contrast_lower}, '
-                     f'{self.contrast_upper}), '
-                     f'saturation_range=({self.saturation_lower}, '
-                     f'{self.saturation_upper}), '
-                     f'hue_delta={self.hue_delta})')
-        return repr_str
diff --git a/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/deblurring/image_deblur.py b/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/deblurring/image_deblur.py
deleted file mode 100644
index d03a635abd1262155047570de19dd59fb312533c..0000000000000000000000000000000000000000
--- a/spaces/PSLD/PSLD/diffusion-posterior-sampling/bkse/models/deblurring/image_deblur.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import torch
-import torch.nn as nn
-import utils.util as util
-from models.dips import ImageDIP, KernelDIP
-from models.kernel_encoding.kernel_wizard import KernelWizard
-from models.losses.hyper_laplacian_penalty import HyperLaplacianPenalty
-from models.losses.perceptual_loss import PerceptualLoss
-from models.losses.ssim_loss import SSIM
-from torch.optim.lr_scheduler import StepLR
-from tqdm import tqdm
-
-
-class ImageDeblur:
-    def __init__(self, opt):
-        self.opt = opt
-
-        # losses
-        self.ssim_loss = SSIM().cuda()
-        self.mse = nn.MSELoss().cuda()
-        self.perceptual_loss = PerceptualLoss().cuda()
-        self.laplace_penalty = HyperLaplacianPenalty(3, 0.66).cuda()
-
-        self.kernel_wizard = KernelWizard(opt["KernelWizard"]).cuda()
-        self.kernel_wizard.load_state_dict(torch.load(opt["KernelWizard"]["pretrained"]))
-
-        for k, v in self.kernel_wizard.named_parameters():
-            v.requires_grad = False
-
-    def reset_optimizers(self):
-        self.x_optimizer = torch.optim.Adam(self.x_dip.parameters(), lr=self.opt["x_lr"])
-        self.k_optimizer = torch.optim.Adam(self.k_dip.parameters(), lr=self.opt["k_lr"])
-
-        self.x_scheduler = StepLR(self.x_optimizer, step_size=self.opt["num_iters"] // 5, gamma=0.7)
-
-        self.k_scheduler = StepLR(self.k_optimizer, step_size=self.opt["num_iters"] // 5, gamma=0.7)
-
-    def prepare_DIPs(self):
-        # x is stand for the sharp image, k is stand for the kernel
-        self.x_dip = ImageDIP(self.opt["ImageDIP"]).cuda()
-        self.k_dip = KernelDIP(self.opt["KernelDIP"]).cuda()
-
-        # fixed input vectors of DIPs
-        # zk and zx are the length of the corresponding vectors
-        self.dip_zk = util.get_noise(64, "noise", (64, 64)).cuda()
-        self.dip_zx = util.get_noise(8, "noise", self.opt["img_size"]).cuda()
-
-    def warmup(self, warmup_x, warmup_k):
-        # Input vector of DIPs is sampled from N(z, I)
-        reg_noise_std = self.opt["reg_noise_std"]
-
-        for step in tqdm(range(self.opt["num_warmup_iters"])):
-            self.x_optimizer.zero_grad()
-            dip_zx_rand = self.dip_zx + reg_noise_std * torch.randn_like(self.dip_zx).cuda()
-            x = self.x_dip(dip_zx_rand)
-
-            loss = self.mse(x, warmup_x)
-            loss.backward()
-            self.x_optimizer.step()
-
-        print("Warming up k DIP")
-        for step in tqdm(range(self.opt["num_warmup_iters"])):
-            self.k_optimizer.zero_grad()
-            dip_zk_rand = self.dip_zk + reg_noise_std * torch.randn_like(self.dip_zk).cuda()
-            k = self.k_dip(dip_zk_rand)
-
-            loss = self.mse(k, warmup_k)
-            loss.backward()
-            self.k_optimizer.step()
-
-    def deblur(self, img):
-        pass
diff --git a/spaces/PSLD/PSLD/stable-diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py b/spaces/PSLD/PSLD/stable-diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py
deleted file mode 100644
index bdb64e0c78cc3520f92d79db3124c85fc3cfb9b4..0000000000000000000000000000000000000000
--- a/spaces/PSLD/PSLD/stable-diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py
+++ /dev/null
@@ -1,1184 +0,0 @@
-import torch
-import torch.nn.functional as F
-import math
-
-
-class NoiseScheduleVP:
-    def __init__(
-            self,
-            schedule='discrete',
-            betas=None,
-            alphas_cumprod=None,
-            continuous_beta_0=0.1,
-            continuous_beta_1=20.,
-        ):
-        """Create a wrapper class for the forward SDE (VP type).
-
-        ***
-        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
-                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
-        ***
-
-        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
-        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
-        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
-
-            log_alpha_t = self.marginal_log_mean_coeff(t)
-            sigma_t = self.marginal_std(t)
-            lambda_t = self.marginal_lambda(t)
-
-        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
-
-            t = self.inverse_lambda(lambda_t)
-
-        ===============================================================
-
-        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
-
-        1. For discrete-time DPMs:
-
-            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
-                t_i = (i + 1) / N
-            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
-            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
-
-            Args:
-                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
-                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
-
-            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
-
-            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
-                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
-                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
-                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
-                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
-                and
-                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
-
-
-        2. For continuous-time DPMs:
-
-            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
-            schedule are the default settings in DDPM and improved-DDPM:
-
-            Args:
-                beta_min: A `float` number. The smallest beta for the linear schedule.
-                beta_max: A `float` number. The largest beta for the linear schedule.
-                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
-                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
-                T: A `float` number. The ending time of the forward process.
-
-        ===============================================================
-
-        Args:
-            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
-                    'linear' or 'cosine' for continuous-time DPMs.
-        Returns:
-            A wrapper object of the forward SDE (VP type).
-        
-        ===============================================================
-
-        Example:
-
-        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', betas=betas)
-
-        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
-
-        # For continuous-time DPMs (VPSDE), linear schedule:
-        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
-
-        """
-
-        if schedule not in ['discrete', 'linear', 'cosine']:
-            raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
-
-        self.schedule = schedule
-        if schedule == 'discrete':
-            if betas is not None:
-                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
-            else:
-                assert alphas_cumprod is not None
-                log_alphas = 0.5 * torch.log(alphas_cumprod)
-            self.total_N = len(log_alphas)
-            self.T = 1.
-            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
-            self.log_alpha_array = log_alphas.reshape((1, -1,))
-        else:
-            self.total_N = 1000
-            self.beta_0 = continuous_beta_0
-            self.beta_1 = continuous_beta_1
-            self.cosine_s = 0.008
-            self.cosine_beta_max = 999.
-            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
-            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
-            self.schedule = schedule
-            if schedule == 'cosine':
-                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
-                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
-                self.T = 0.9946
-            else:
-                self.T = 1.
-
-    def marginal_log_mean_coeff(self, t):
-        """
-        Compute log(alpha_t) of a given continuous-time label t in [0, T].
-        """
-        if self.schedule == 'discrete':
-            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))
-        elif self.schedule == 'linear':
-            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
-        elif self.schedule == 'cosine':
-            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
-            log_alpha_t =  log_alpha_fn(t) - self.cosine_log_alpha_0
-            return log_alpha_t
-
-    def marginal_alpha(self, t):
-        """
-        Compute alpha_t of a given continuous-time label t in [0, T].
-        """
-        return torch.exp(self.marginal_log_mean_coeff(t))
-
-    def marginal_std(self, t):
-        """
-        Compute sigma_t of a given continuous-time label t in [0, T].
-        """
-        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
-
-    def marginal_lambda(self, t):
-        """
-        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
-        """
-        log_mean_coeff = self.marginal_log_mean_coeff(t)
-        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
-        return log_mean_coeff - log_std
-
-    def inverse_lambda(self, lamb):
-        """
-        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
-        """
-        if self.schedule == 'linear':
-            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            Delta = self.beta_0**2 + tmp
-            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
-        elif self.schedule == 'discrete':
-            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
-            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1]))
-            return t.reshape((-1,))
-        else:
-            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
-            t = t_fn(log_alpha)
-            return t
-
-
-def model_wrapper(
-    model,
-    noise_schedule,
-    model_type="noise",
-    model_kwargs={},
-    guidance_type="uncond",
-    condition=None,
-    unconditional_condition=None,
-    guidance_scale=1.,
-    classifier_fn=None,
-    classifier_kwargs={},
-):
-    """Create a wrapper function for the noise prediction model.
-
-    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
-    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
-
-    We support four types of the diffusion model by setting `model_type`:
-
-        1. "noise": noise prediction model. (Trained by predicting noise).
-
-        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
-
-        3. "v": velocity prediction model. (Trained by predicting the velocity).
-            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
-
-            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
-                arXiv preprint arXiv:2202.00512 (2022).
-            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
-                arXiv preprint arXiv:2210.02303 (2022).
-    
-        4. "score": marginal score function. (Trained by denoising score matching).
-            Note that the score function and the noise prediction model follows a simple relationship:
-            ```
-                noise(x_t, t) = -sigma_t * score(x_t, t)
-            ```
-
-    We support three types of guided sampling by DPMs by setting `guidance_type`:
-        1. "uncond": unconditional sampling by DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-
-        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            `` 
-
-            The input `classifier_fn` has the following format:
-            ``
-                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
-            ``
-
-            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
-                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
-
-        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
-            `` 
-            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
-
-            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
-                arXiv preprint arXiv:2207.12598 (2022).
-        
-
-    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
-    or continuous-time labels (i.e. epsilon to T).
-
-    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
-    ``
-        def model_fn(x, t_continuous) -> noise:
-            t_input = get_model_input_time(t_continuous)
-            return noise_pred(model, x, t_input, **model_kwargs)         
-    ``
-    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
-
-    ===============================================================
-
-    Args:
-        model: A diffusion model with the corresponding format described above.
-        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-        model_type: A `str`. The parameterization type of the diffusion model.
-                    "noise" or "x_start" or "v" or "score".
-        model_kwargs: A `dict`. A dict for the other inputs of the model function.
-        guidance_type: A `str`. The type of the guidance for sampling.
-                    "uncond" or "classifier" or "classifier-free".
-        condition: A pytorch tensor. The condition for the guided sampling.
-                    Only used for "classifier" or "classifier-free" guidance type.
-        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
-                    Only used for "classifier-free" guidance type.
-        guidance_scale: A `float`. The scale for the guided sampling.
-        classifier_fn: A classifier function. Only used for the classifier guidance.
-        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
-    Returns:
-        A noise prediction model that accepts the noised data and the continuous time as the inputs.
-    """
-
-    def get_model_input_time(t_continuous):
-        """
-        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
-        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
-        For continuous-time DPMs, we just use `t_continuous`.
-        """
-        if noise_schedule.schedule == 'discrete':
-            return (t_continuous - 1. / noise_schedule.total_N) * 1000.
-        else:
-            return t_continuous
-
-    def noise_pred_fn(x, t_continuous, cond=None):
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        t_input = get_model_input_time(t_continuous)
-        if cond is None:
-            output = model(x, t_input, **model_kwargs)
-        else:
-            output = model(x, t_input, cond, **model_kwargs)
-        if model_type == "noise":
-            return output
-        elif model_type == "x_start":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
-        elif model_type == "v":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
-        elif model_type == "score":
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return -expand_dims(sigma_t, dims) * output
-
-    def cond_grad_fn(x, t_input):
-        """
-        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
-        """
-        with torch.enable_grad():
-            x_in = x.detach().requires_grad_(True)
-            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
-            return torch.autograd.grad(log_prob.sum(), x_in)[0]
-
-    def model_fn(x, t_continuous):
-        """
-        The noise predicition model function that is used for DPM-Solver.
-        """
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        if guidance_type == "uncond":
-            return noise_pred_fn(x, t_continuous)
-        elif guidance_type == "classifier":
-            assert classifier_fn is not None
-            t_input = get_model_input_time(t_continuous)
-            cond_grad = cond_grad_fn(x, t_input)
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            noise = noise_pred_fn(x, t_continuous)
-            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
-        elif guidance_type == "classifier-free":
-            if guidance_scale == 1. or unconditional_condition is None:
-                return noise_pred_fn(x, t_continuous, cond=condition)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t_continuous] * 2)
-                c_in = torch.cat([unconditional_condition, condition])
-                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
-                return noise_uncond + guidance_scale * (noise - noise_uncond)
-
-    assert model_type in ["noise", "x_start", "v"]
-    assert guidance_type in ["uncond", "classifier", "classifier-free"]
-    return model_fn
-
-
-class DPM_Solver:
-    def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
-        """Construct a DPM-Solver. 
-
-        We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
-        If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
-        If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
-            In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
-            The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
-
-        Args:
-            model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
-                ``
-                def model_fn(x, t_continuous):
-                    return noise
-                ``
-            noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-            predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
-            thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
-            max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
-        
-        [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
-        """
-        self.model = model_fn
-        self.noise_schedule = noise_schedule
-        self.predict_x0 = predict_x0
-        self.thresholding = thresholding
-        self.max_val = max_val
-
-    def noise_prediction_fn(self, x, t):
-        """
-        Return the noise prediction model.
-        """
-        return self.model(x, t)
-
-    def data_prediction_fn(self, x, t):
-        """
-        Return the data prediction model (with thresholding).
-        """
-        noise = self.noise_prediction_fn(x, t)
-        dims = x.dim()
-        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
-        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
-        if self.thresholding:
-            p = 0.995   # A hyperparameter in the paper of "Imagen" [1].
-            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
-            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
-            x0 = torch.clamp(x0, -s, s) / s
-        return x0
-
-    def model_fn(self, x, t):
-        """
-        Convert the model to the noise prediction model or the data prediction model. 
-        """
-        if self.predict_x0:
-            return self.data_prediction_fn(x, t)
-        else:
-            return self.noise_prediction_fn(x, t)
-
-    def get_time_steps(self, skip_type, t_T, t_0, N, device):
-        """Compute the intermediate time steps for sampling.
-
-        Args:
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            N: A `int`. The total number of the spacing of the time steps.
-            device: A torch device.
-        Returns:
-            A pytorch tensor of the time steps, with the shape (N + 1,).
-        """
-        if skip_type == 'logSNR':
-            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
-            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
-            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
-            return self.noise_schedule.inverse_lambda(logSNR_steps)
-        elif skip_type == 'time_uniform':
-            return torch.linspace(t_T, t_0, N + 1).to(device)
-        elif skip_type == 'time_quadratic':
-            t_order = 2
-            t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
-            return t
-        else:
-            raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
-
-    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
-        """
-        Get the order of each step for sampling by the singlestep DPM-Solver.
-
-        We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
-        Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
-            - If order == 1:
-                We take `steps` of DPM-Solver-1 (i.e. DDIM).
-            - If order == 2:
-                - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
-                - If steps % 2 == 0, we use K steps of DPM-Solver-2.
-                - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
-            - If order == 3:
-                - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
-
-        ============================================
-        Args:
-            order: A `int`. The max order for the solver (2 or 3).
-            steps: A `int`. The total number of function evaluations (NFE).
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            device: A torch device.
-        Returns:
-            orders: A list of the solver order of each step.
-        """
-        if order == 3:
-            K = steps // 3 + 1
-            if steps % 3 == 0:
-                orders = [3,] * (K - 2) + [2, 1]
-            elif steps % 3 == 1:
-                orders = [3,] * (K - 1) + [1]
-            else:
-                orders = [3,] * (K - 1) + [2]
-        elif order == 2:
-            if steps % 2 == 0:
-                K = steps // 2
-                orders = [2,] * K
-            else:
-                K = steps // 2 + 1
-                orders = [2,] * (K - 1) + [1]
-        elif order == 1:
-            K = 1
-            orders = [1,] * steps
-        else:
-            raise ValueError("'order' must be '1' or '2' or '3'.")
-        if skip_type == 'logSNR':
-            # To reproduce the results in DPM-Solver paper
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
-        else:
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders)).to(device)]
-        return timesteps_outer, orders
-
-    def denoise_to_zero_fn(self, x, s):
-        """
-        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. 
-        """
-        return self.data_prediction_fn(x, s)
-
-    def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
-        """
-        DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_1 = torch.expm1(-h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                expand_dims(sigma_t / sigma_s, dims) * x
-                - expand_dims(alpha_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-        else:
-            phi_1 = torch.expm1(h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                - expand_dims(sigma_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-
-    def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False, solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-2 from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the second-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 0.5
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
-        alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_1 = torch.expm1(-h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                expand_dims(sigma_s1 / sigma_s, dims) * x
-                - expand_dims(alpha_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-                    - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-                    + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (model_s1 - model_s)
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_1 = torch.expm1(h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                - expand_dims(sigma_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-                    - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-                    - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
-                )
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1}
-        else:
-            return x_t
-
-    def singlestep_dpm_solver_third_update(self, x, s, t, r1=1./3., r2=2./3., model_s=None, model_s1=None, return_intermediate=False, solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-3 from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
-                If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 1. / 3.
-        if r2 is None:
-            r2 = 2. / 3.
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        lambda_s2 = lambda_s + r2 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        s2 = ns.inverse_lambda(lambda_s2)
-        log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(s2), ns.marginal_std(t)
-        alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_12 = torch.expm1(-r2 * h)
-            phi_1 = torch.expm1(-h)
-            phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
-            phi_2 = phi_1 / h + 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                    expand_dims(sigma_s1 / sigma_s, dims) * x
-                    - expand_dims(alpha_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                expand_dims(sigma_s2 / sigma_s, dims) * x
-                - expand_dims(alpha_s2 * phi_12, dims) * model_s
-                + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-                    + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-                    + expand_dims(alpha_t * phi_2, dims) * D1
-                    - expand_dims(alpha_t * phi_3, dims) * D2
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_12 = torch.expm1(r2 * h)
-            phi_1 = torch.expm1(h)
-            phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
-            phi_2 = phi_1 / h - 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                    expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                    - expand_dims(sigma_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
-                - expand_dims(sigma_s2 * phi_12, dims) * model_s
-                - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-                    - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-                    - expand_dims(sigma_t * phi_2, dims) * D1
-                    - expand_dims(sigma_t * phi_3, dims) * D2
-                )
-
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
-        else:
-            return x_t
-
-    def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
-        """
-        Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_1, model_prev_0 = model_prev_list
-        t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0 = h_0 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        if self.predict_x0:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(sigma_t / sigma_prev_0, dims) * x
-                    - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                    - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                    expand_dims(sigma_t / sigma_prev_0, dims) * x
-                    - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                    + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
-                )
-        else:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                    - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                    - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                    - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                    - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
-                )
-        return x_t
-
-    def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
-        """
-        Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_2, model_prev_1, model_prev_0 = model_prev_list
-        t_prev_2, t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_1 = lambda_prev_1 - lambda_prev_2
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0, r1 = h_0 / h, h_1 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
-        D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
-        D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
-        if self.predict_x0:
-            x_t = (
-                expand_dims(sigma_t / sigma_prev_0, dims) * x
-                - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
-                - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h**2 - 0.5), dims) * D2
-            )
-        else:
-            x_t = (
-                expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
-                - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h**2 - 0.5), dims) * D2
-            )
-        return x_t
-
-    def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None, r2=None):
-        """
-        Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-            r1: A `float`. The hyperparameter of the second-order or third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
-        elif order == 2:
-            return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1)
-        elif order == 3:
-            return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate, solver_type=solver_type, r1=r1, r2=r2)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
-        """
-        Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
-        elif order == 2:
-            return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        elif order == 3:
-            return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5, solver_type='dpm_solver'):
-        """
-        The adaptive step size solver based on singlestep DPM-Solver.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `t_T`.
-            order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            h_init: A `float`. The initial step size (for logSNR).
-            atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
-            rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
-            theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
-            t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the 
-                current time and `t_0` is less than `t_err`. The default setting is 1e-5.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_0: A pytorch tensor. The approximated solution at time `t_0`.
-
-        [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
-        """
-        ns = self.noise_schedule
-        s = t_T * torch.ones((x.shape[0],)).to(x)
-        lambda_s = ns.marginal_lambda(s)
-        lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
-        h = h_init * torch.ones_like(s).to(x)
-        x_prev = x
-        nfe = 0
-        if order == 2:
-            r1 = 0.5
-            lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, solver_type=solver_type, **kwargs)
-        elif order == 3:
-            r1, r2 = 1. / 3., 2. / 3.
-            lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1, return_intermediate=True, solver_type=solver_type)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2, solver_type=solver_type, **kwargs)
-        else:
-            raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
-        while torch.abs((s - t_0)).mean() > t_err:
-            t = ns.inverse_lambda(lambda_s + h)
-            x_lower, lower_noise_kwargs = lower_update(x, s, t)
-            x_higher = higher_update(x, s, t, **lower_noise_kwargs)
-            delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
-            norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
-            E = norm_fn((x_higher - x_lower) / delta).max()
-            if torch.all(E <= 1.):
-                x = x_higher
-                s = t
-                x_prev = x_lower
-                lambda_s = ns.marginal_lambda(s)
-            h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
-            nfe += order
-        print('adaptive solver nfe', nfe)
-        return x
-
-    def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
-        method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
-        atol=0.0078, rtol=0.05,
-    ):
-        """
-        Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
-
-        =====================================================
-
-        We support the following algorithms for both noise prediction model and data prediction model:
-            - 'singlestep':
-                Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver. 
-                We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
-                The total number of function evaluations (NFE) == `steps`.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    - If `order` == 1:
-                        - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
-                        - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
-                        - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                    - If `order` == 3:
-                        - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                        - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
-            - 'multistep':
-                Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
-                We initialize the first `order` values by lower order multistep solvers.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    Denote K = steps.
-                    - If `order` == 1:
-                        - We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
-                    - If `order` == 3:
-                        - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
-            - 'singlestep_fixed':
-                Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
-                We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
-            - 'adaptive':
-                Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
-                We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
-                You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
-                (NFE) and the sample quality.
-                    - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
-                    - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
-
-        =====================================================
-
-        Some advices for choosing the algorithm:
-            - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
-                Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
-                            skip_type='time_uniform', method='singlestep')
-            - For **guided sampling with large guidance scale** by DPMs:
-                Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
-                            skip_type='time_uniform', method='multistep')
-
-        We support three types of `skip_type`:
-            - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
-            - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
-            - 'time_quadratic': quadratic time for the time steps.
-
-        =====================================================
-        Args:
-            x: A pytorch tensor. The initial value at time `t_start`
-                e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
-            steps: A `int`. The total number of function evaluations (NFE).
-            t_start: A `float`. The starting time of the sampling.
-                If `T` is None, we use self.noise_schedule.T (default is 1.0).
-            t_end: A `float`. The ending time of the sampling.
-                If `t_end` is None, we use 1. / self.noise_schedule.total_N.
-                e.g. if total_N == 1000, we have `t_end` == 1e-3.
-                For discrete-time DPMs:
-                    - We recommend `t_end` == 1. / self.noise_schedule.total_N.
-                For continuous-time DPMs:
-                    - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
-            order: A `int`. The order of DPM-Solver.
-            skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
-            method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
-            denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
-                Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
-
-                This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
-                score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
-                for diffusion models sampling by diffusion SDEs for low-resolutional images
-                (such as CIFAR-10). However, we observed that such trick does not matter for
-                high-resolutional images. As it needs an additional NFE, we do not recommend
-                it for high-resolutional images.
-            lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
-                Only valid for `method=multistep` and `steps < 15`. We empirically find that
-                this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
-                (especially for steps <= 10). So we recommend to set it to be `True`.
-            solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
-            atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-            rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-        Returns:
-            x_end: A pytorch tensor. The approximated solution at time `t_end`.
-
-        """
-        t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
-        t_T = self.noise_schedule.T if t_start is None else t_start
-        device = x.device
-        if method == 'adaptive':
-            with torch.no_grad():
-                x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol, solver_type=solver_type)
-        elif method == 'multistep':
-            assert steps >= order
-            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
-            assert timesteps.shape[0] - 1 == steps
-            with torch.no_grad():
-                vec_t = timesteps[0].expand((x.shape[0]))
-                model_prev_list = [self.model_fn(x, vec_t)]
-                t_prev_list = [vec_t]
-                # Init the first `order` values by lower order multistep DPM-Solver.
-                for init_order in range(1, order):
-                    vec_t = timesteps[init_order].expand(x.shape[0])
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order, solver_type=solver_type)
-                    model_prev_list.append(self.model_fn(x, vec_t))
-                    t_prev_list.append(vec_t)
-                # Compute the remaining values by `order`-th order multistep DPM-Solver.
-                for step in range(order, steps + 1):
-                    vec_t = timesteps[step].expand(x.shape[0])
-                    if lower_order_final and steps < 15:
-                        step_order = min(order, steps + 1 - step)
-                    else:
-                        step_order = order
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order, solver_type=solver_type)
-                    for i in range(order - 1):
-                        t_prev_list[i] = t_prev_list[i + 1]
-                        model_prev_list[i] = model_prev_list[i + 1]
-                    t_prev_list[-1] = vec_t
-                    # We do not need to evaluate the final model value.
-                    if step < steps:
-                        model_prev_list[-1] = self.model_fn(x, vec_t)
-        elif method in ['singlestep', 'singlestep_fixed']:
-            if method == 'singlestep':
-                timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order, skip_type=skip_type, t_T=t_T, t_0=t_0, device=device)
-            elif method == 'singlestep_fixed':
-                K = steps // order
-                orders = [order,] * K
-                timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
-            for i, order in enumerate(orders):
-                t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
-                timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(), N=order, device=device)
-                lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
-                vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
-                h = lambda_inner[-1] - lambda_inner[0]
-                r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
-                r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
-                x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
-        if denoise_to_zero:
-            x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
-        return x
-
-
-
-#############################################################
-# other utility functions
-#############################################################
-
-def interpolate_fn(x, xp, yp):
-    """
-    A piecewise linear function y = f(x), using xp and yp as keypoints.
-    We implement f(x) in a differentiable way (i.e. applicable for autograd).
-    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
-
-    Args:
-        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
-        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
-        yp: PyTorch tensor with shape [C, K].
-    Returns:
-        The function values f(x), with shape [N, C].
-    """
-    N, K = x.shape[0], xp.shape[1]
-    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
-    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
-    x_idx = torch.argmin(x_indices, dim=2)
-    cand_start_idx = x_idx - 1
-    start_idx = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(1, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
-    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
-    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
-    start_idx2 = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(0, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
-    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
-    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
-    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
-    return cand
-
-
-def expand_dims(v, dims):
-    """
-    Expand the tensor `v` to the dim `dims`.
-
-    Args:
-        `v`: a PyTorch tensor with shape [N].
-        `dim`: a `int`.
-    Returns:
-        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
-    """
-    return v[(...,) + (None,)*(dims - 1)]
\ No newline at end of file
diff --git a/spaces/PaulHilders/CLIPGroundingExplainability/clip_grounding/evaluation/clip_on_png.py b/spaces/PaulHilders/CLIPGroundingExplainability/clip_grounding/evaluation/clip_on_png.py
deleted file mode 100644
index 8121362eed3cf6072c053ad832140ae954a4322c..0000000000000000000000000000000000000000
--- a/spaces/PaulHilders/CLIPGroundingExplainability/clip_grounding/evaluation/clip_on_png.py
+++ /dev/null
@@ -1,362 +0,0 @@
-"""Evaluates cross-modal correspondence of CLIP on PNG images."""
-
-import os
-import sys
-from os.path import join, exists
-
-import warnings
-warnings.filterwarnings('ignore')
-
-from clip_grounding.utils.paths import REPO_PATH
-sys.path.append(join(REPO_PATH, "CLIP_explainability/Transformer-MM-Explainability/"))
-
-import torch
-import CLIP.clip as clip
-from PIL import Image
-import numpy as np
-import cv2
-import matplotlib.pyplot as plt
-from captum.attr import visualization
-from torchmetrics import JaccardIndex
-from collections import defaultdict
-from IPython.core.display import display, HTML
-from skimage import filters
-
-from CLIP_explainability.utils import interpret, show_img_heatmap, show_txt_heatmap, color, _tokenizer
-from clip_grounding.datasets.png import PNG
-from clip_grounding.utils.image import pad_to_square
-from clip_grounding.utils.visualize import show_grid_of_images
-from clip_grounding.utils.log import tqdm_iterator, print_update
-
-
-# global usage
-# specify device
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# load CLIP model
-model, preprocess = clip.load("ViT-B/32", device=device, jit=False)
-
-
-def show_cam(mask):
-    heatmap = cv2.applyColorMap(np.uint8(255 * mask), cv2.COLORMAP_JET)
-    heatmap = np.float32(heatmap) / 255
-    cam = heatmap
-    cam = cam / np.max(cam)
-    return cam
-
-
-def interpret_and_generate(model, img, texts, orig_image, return_outputs=False, show=True):
-    text = clip.tokenize(texts).to(device)
-    R_text, R_image = interpret(model=model, image=img, texts=text, device=device)
-    batch_size = text.shape[0]
-    
-    outputs = []
-    for i in range(batch_size):
-        text_scores, text_tokens_decoded = show_txt_heatmap(texts[i], text[i], R_text[i], show=show)
-        image_relevance = show_img_heatmap(R_image[i], img, orig_image=orig_image, device=device, show=show)
-        plt.show()
-        outputs.append({"text_scores": text_scores, "image_relevance": image_relevance, "tokens_decoded": text_tokens_decoded})
-    
-    if return_outputs:
-        return outputs
-
-
-def process_entry_text_to_image(entry, unimodal=False):
-    image = entry['image']
-    text_mask = entry['text_mask']
-    text = entry['text']
-    orig_image = pad_to_square(image)
-    
-    img = preprocess(orig_image).unsqueeze(0).to(device)
-    text_index = text_mask.argmax()
-    texts = [text[text_index]] if not unimodal else ['']
-    
-    return img, texts, orig_image
-
-
-def preprocess_ground_truth_mask(mask, resize_shape):
-    mask = Image.fromarray(mask.astype(np.uint8) * 255)
-    mask = pad_to_square(mask, color=0)
-    mask = mask.resize(resize_shape)
-    mask = np.asarray(mask) / 255.
-    return mask
-
-
-def apply_otsu_threshold(relevance_map):
-    threshold = filters.threshold_otsu(relevance_map)
-    otsu_map = (relevance_map > threshold).astype(np.uint8)
-    return otsu_map
-
-
-def evaluate_text_to_image(method, dataset, debug=False):
-
-    instance_level_metrics = defaultdict(list)
-    entry_level_metrics = defaultdict(list)
-    
-    jaccard = JaccardIndex(num_classes=2)
-    jaccard = jaccard.to(device)
-
-    num_iter = len(dataset)
-    if debug:
-        num_iter = 100
-
-    iterator = tqdm_iterator(range(num_iter), desc=f"Evaluating on {type(dataset).__name__} dataset")
-    for idx in iterator:
-        instance = dataset[idx]
-        
-        instance_iou = 0.
-        for entry in instance:
-            
-            # preprocess the image and text
-            unimodal = True if method == "clip-unimodal" else False
-            test_img, test_texts, orig_image = process_entry_text_to_image(entry, unimodal=unimodal)
-
-            if method in ["clip", "clip-unimodal"]:
-                
-                # compute the relevance scores 
-                outputs = interpret_and_generate(model, test_img, test_texts, orig_image, return_outputs=True, show=False)
-                
-                # use the image relevance score to compute IoU w.r.t. ground truth segmentation masks
-
-                # NOTE: since we pass single entry (1-sized batch), outputs[0] contains our reqd outputs
-                relevance_map = outputs[0]["image_relevance"]
-            elif method == "random":
-                relevance_map = np.random.uniform(low=0., high=1., size=tuple(test_img.shape[2:]))
-                
-            otsu_relevance_map = apply_otsu_threshold(relevance_map)
-            
-            ground_truth_mask = entry["image_mask"]
-            ground_truth_mask = preprocess_ground_truth_mask(ground_truth_mask, relevance_map.shape)
-            
-            entry_iou = jaccard(
-                torch.from_numpy(otsu_relevance_map).to(device),
-                torch.from_numpy(ground_truth_mask.astype(np.uint8)).to(device),
-            )
-            entry_iou = entry_iou.item()
-            instance_iou += (entry_iou / len(entry))
-            
-            entry_level_metrics["iou"].append(entry_iou)
-        
-        # capture instance (image-sentence pair) level IoU
-        instance_level_metrics["iou"].append(instance_iou)
-    
-    average_metrics = {k: np.mean(v) for k, v in entry_level_metrics.items()}
-    
-    return (
-        average_metrics,
-        instance_level_metrics,
-        entry_level_metrics
-    )
-
-
-def process_entry_image_to_text(entry, unimodal=False):
-    
-    if not unimodal:
-        if len(np.asarray(entry["image"]).shape) == 3:
-            mask = np.repeat(np.expand_dims(entry['image_mask'], -1), 3, axis=-1)
-        else:
-            mask = np.asarray(entry['image_mask'])
-
-        masked_image = (mask * np.asarray(entry['image'])).astype(np.uint8)
-        masked_image = Image.fromarray(masked_image)
-        orig_image = pad_to_square(masked_image)
-        img = preprocess(orig_image).unsqueeze(0).to(device)
-    else:
-        orig_image_shape = max(np.asarray(entry['image']).shape[:2])
-        orig_image = Image.fromarray(np.zeros((orig_image_shape, orig_image_shape, 3), dtype=np.uint8))
-        # orig_image = Image.fromarray(np.random.randint(0, 256, (orig_image_shape, orig_image_shape, 3), dtype=np.uint8))
-        img = preprocess(orig_image).unsqueeze(0).to(device)
-    
-    texts = [' '.join(entry['text'])]
-
-    return img, texts, orig_image
-
-
-def process_text_mask(text, text_mask, tokens):
-
-    token_level_mask = np.zeros(len(tokens))
-
-    for label, subtext in zip(text_mask, text):
-
-        subtext_tokens=_tokenizer.encode(subtext)
-        subtext_tokens_decoded=[_tokenizer.decode([a]) for a in subtext_tokens]
-
-        if label == 1:
-            start = tokens.index(subtext_tokens_decoded[0])
-            end = tokens.index(subtext_tokens_decoded[-1])
-            token_level_mask[start:end + 1] = 1
-
-    return token_level_mask
-
-
-def evaluate_image_to_text(method, dataset, debug=False, clamp_sentence_len=70):
-
-    instance_level_metrics = defaultdict(list)
-    entry_level_metrics = defaultdict(list)
-    
-    # skipped if text length > 77 which is CLIP limit
-    num_entries_skipped = 0
-    num_total_entries = 0
-    
-    num_iter = len(dataset)
-    if debug:
-        num_iter = 100
-    
-    jaccard_image_to_text = JaccardIndex(num_classes=2).to(device)
-
-    iterator = tqdm_iterator(range(num_iter), desc=f"Evaluating on {type(dataset).__name__} dataset")
-    for idx in iterator:
-        instance = dataset[idx]
-        
-        instance_iou = 0.
-        for entry in instance:
-            num_total_entries += 1
-            
-            # preprocess the image and text
-            unimodal = True if method == "clip-unimodal" else False
-            img, texts, orig_image = process_entry_image_to_text(entry, unimodal=unimodal)
-
-            appx_total_sent_len = np.sum([len(x.split(" ")) for x in texts])
-            if appx_total_sent_len > clamp_sentence_len:
-                # print(f"Skipping an entry since it's text has appx"\
-                # " {appx_total_sent_len} while CLIP cannot process beyond {clamp_sentence_len}")
-                num_entries_skipped += 1
-                continue
-            
-            # compute the relevance scores 
-            if method in ["clip", "clip-unimodal"]:
-                try:
-                    outputs = interpret_and_generate(model, img, texts, orig_image, return_outputs=True, show=False)
-                except:
-                    num_entries_skipped += 1
-                    continue
-            elif method == "random":
-                text = texts[0]
-                text_tokens = _tokenizer.encode(text)
-                text_tokens_decoded=[_tokenizer.decode([a]) for a in text_tokens]
-                outputs = [
-                    {
-                        "text_scores": np.random.uniform(low=0., high=1., size=len(text_tokens_decoded)),
-                        "tokens_decoded": text_tokens_decoded,
-                    }
-                ]
-            
-            # use the text relevance score to compute IoU w.r.t. ground truth text masks
-            # NOTE: since we pass single entry (1-sized batch), outputs[0] contains our reqd outputs
-            token_relevance_scores = outputs[0]["text_scores"]
-            if isinstance(token_relevance_scores, torch.Tensor):
-                token_relevance_scores = token_relevance_scores.cpu().numpy()
-            token_relevance_scores = apply_otsu_threshold(token_relevance_scores)
-            token_ground_truth_mask = process_text_mask(entry["text"], entry["text_mask"], outputs[0]["tokens_decoded"])
-            
-            entry_iou = jaccard_image_to_text(
-                torch.from_numpy(token_relevance_scores).to(device),
-                torch.from_numpy(token_ground_truth_mask.astype(np.uint8)).to(device),
-            )
-            entry_iou = entry_iou.item()
-
-            instance_iou += (entry_iou / len(entry))
-            entry_level_metrics["iou"].append(entry_iou)
-        
-        # capture instance (image-sentence pair) level IoU
-        instance_level_metrics["iou"].append(instance_iou)
-    
-    print(f"CAUTION: Skipped {(num_entries_skipped / num_total_entries) * 100} % since these had length > 77 (CLIP limit).")
-    average_metrics = {k: np.mean(v) for k, v in entry_level_metrics.items()}
-    
-    return (
-        average_metrics,
-        instance_level_metrics,
-        entry_level_metrics
-    )
-
-
-if __name__ == "__main__":
-    
-    import argparse
-    parser = argparse.ArgumentParser("Evaluate Image-to-Text & Text-to-Image model")
-    parser.add_argument(
-        "--eval_method", type=str, default="clip",
-        choices=["clip", "random", "clip-unimodal"],
-        help="Evaluation method to use",
-    )
-    parser.add_argument(
-        "--ignore_cache", action="store_true",
-        help="Ignore cache and force re-generation of the results",
-    )
-    parser.add_argument(
-        "--debug", action="store_true",
-        help="Run evaluation on a small subset of the dataset",
-    )
-    args = parser.parse_args()
-    
-    print_update("Using evaluation method: {}".format(args.eval_method))
-    
-    
-    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",
-    }
-    
-    # specify device
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    
-    # load CLIP model
-    print_update("Loading CLIP model...")
-    model, preprocess = clip.load("ViT-B/32", device=device, jit=False)
-    print()
-    
-    # load PNG dataset
-    print_update("Loading PNG dataset...")
-    dataset = PNG(dataset_root=join(REPO_PATH, "data", "panoptic_narrative_grounding"), split="val2017")
-    print()
-    
-    # evaluate
-
-    # save metrics
-    metrics_dir = join(REPO_PATH, "outputs")
-    os.makedirs(metrics_dir, exist_ok=True)
-
-    metrics_path = join(metrics_dir, f"{args.eval_method}_on_{type(dataset).__name__}_text2image_metrics.pt")
-    if (not exists(metrics_path)) or args.ignore_cache:
-        print_update("Computing metrics for text-to-image grounding")
-        average_metrics, instance_level_metrics, entry_level_metrics = evaluate_text_to_image(
-            args.eval_method, dataset, debug=args.debug,
-        )
-        metrics = {
-            "average_metrics": average_metrics,
-            "instance_level_metrics":instance_level_metrics,
-            "entry_level_metrics": entry_level_metrics
-        }
-
-        torch.save(metrics, metrics_path)
-        print("TEXT2IMAGE METRICS SAVED TO:", metrics_path)
-    else:
-        print(f"Metrics already exist at: {metrics_path}. Loading cached metrics.")
-        metrics = torch.load(metrics_path)
-        average_metrics = metrics["average_metrics"]
-    print("TEXT2IMAGE METRICS:", np.round(average_metrics["iou"], 4))
-
-    print()
-    
-    metrics_path = join(metrics_dir, f"{args.eval_method}_on_{type(dataset).__name__}_image2text_metrics.pt")
-    if (not exists(metrics_path)) or args.ignore_cache:
-        print_update("Computing metrics for image-to-text grounding")
-        average_metrics, instance_level_metrics, entry_level_metrics = evaluate_image_to_text(
-            args.eval_method, dataset, debug=args.debug,
-        )
-        
-        torch.save(
-            {
-                "average_metrics": average_metrics,
-                "instance_level_metrics":instance_level_metrics,
-                "entry_level_metrics": entry_level_metrics
-            },
-            metrics_path,
-        )
-        print("IMAGE2TEXT METRICS SAVED TO:", metrics_path)
-    else:
-        print(f"Metrics already exist at: {metrics_path}. Loading cached metrics.")
-        metrics = torch.load(metrics_path)
-        average_metrics = metrics["average_metrics"]
-    print("IMAGE2TEXT METRICS:", np.round(average_metrics["iou"], 4))
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/priority.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/priority.py
deleted file mode 100644
index 64cc4e3a05f8d5b89ab6eb32461e6e80f1d62e67..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/priority.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from enum import Enum
-
-
-class Priority(Enum):
-    """Hook priority levels.
-
-    +--------------+------------+
-    | Level        | Value      |
-    +==============+============+
-    | HIGHEST      | 0          |
-    +--------------+------------+
-    | VERY_HIGH    | 10         |
-    +--------------+------------+
-    | HIGH         | 30         |
-    +--------------+------------+
-    | ABOVE_NORMAL | 40         |
-    +--------------+------------+
-    | NORMAL       | 50         |
-    +--------------+------------+
-    | BELOW_NORMAL | 60         |
-    +--------------+------------+
-    | LOW          | 70         |
-    +--------------+------------+
-    | VERY_LOW     | 90         |
-    +--------------+------------+
-    | LOWEST       | 100        |
-    +--------------+------------+
-    """
-
-    HIGHEST = 0
-    VERY_HIGH = 10
-    HIGH = 30
-    ABOVE_NORMAL = 40
-    NORMAL = 50
-    BELOW_NORMAL = 60
-    LOW = 70
-    VERY_LOW = 90
-    LOWEST = 100
-
-
-def get_priority(priority):
-    """Get priority value.
-
-    Args:
-        priority (int or str or :obj:`Priority`): Priority.
-
-    Returns:
-        int: The priority value.
-    """
-    if isinstance(priority, int):
-        if priority < 0 or priority > 100:
-            raise ValueError('priority must be between 0 and 100')
-        return priority
-    elif isinstance(priority, Priority):
-        return priority.value
-    elif isinstance(priority, str):
-        return Priority[priority.upper()].value
-    else:
-        raise TypeError('priority must be an integer or Priority enum value')
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/evaluation/od_eval.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/evaluation/od_eval.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Plurigrid/LifeSim/src/components/ui/dropdown-menu.tsx b/spaces/Plurigrid/LifeSim/src/components/ui/dropdown-menu.tsx
deleted file mode 100644
index 5803489a1d197a9db5018e413e63abe84b2efb8e..0000000000000000000000000000000000000000
--- a/spaces/Plurigrid/LifeSim/src/components/ui/dropdown-menu.tsx
+++ /dev/null
@@ -1,200 +0,0 @@
-"use client"
-
-import * as React from "react"
-import * as DropdownMenuPrimitive from "@radix-ui/react-dropdown-menu"
-import { Check, ChevronRight, Circle } from "lucide-react"
-
-import { cn } from "@/lib/utils"
-
-const DropdownMenu = DropdownMenuPrimitive.Root
-
-const DropdownMenuTrigger = DropdownMenuPrimitive.Trigger
-
-const DropdownMenuGroup = DropdownMenuPrimitive.Group
-
-const DropdownMenuPortal = DropdownMenuPrimitive.Portal
-
-const DropdownMenuSub = DropdownMenuPrimitive.Sub
-
-const DropdownMenuRadioGroup = DropdownMenuPrimitive.RadioGroup
-
-const DropdownMenuSubTrigger = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.SubTrigger>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.SubTrigger> & {
-    inset?: boolean
-  }
->(({ className, inset, children, ...props }, ref) => (
-  <DropdownMenuPrimitive.SubTrigger
-    ref={ref}
-    className={cn(
-      "flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none focus:bg-stone-100 data-[state=open]:bg-stone-100 dark:focus:bg-stone-800 dark:data-[state=open]:bg-stone-800",
-      inset && "pl-8",
-      className
-    )}
-    {...props}
-  >
-    {children}
-    <ChevronRight className="ml-auto h-4 w-4" />
-  </DropdownMenuPrimitive.SubTrigger>
-))
-DropdownMenuSubTrigger.displayName =
-  DropdownMenuPrimitive.SubTrigger.displayName
-
-const DropdownMenuSubContent = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.SubContent>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.SubContent>
->(({ className, ...props }, ref) => (
-  <DropdownMenuPrimitive.SubContent
-    ref={ref}
-    className={cn(
-      "z-50 min-w-[8rem] overflow-hidden rounded-md border border-stone-200 bg-white p-1 text-stone-950 shadow-lg data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 dark:border-stone-800 dark:bg-stone-950 dark:text-stone-50",
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuSubContent.displayName =
-  DropdownMenuPrimitive.SubContent.displayName
-
-const DropdownMenuContent = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Content>
->(({ className, sideOffset = 4, ...props }, ref) => (
-  <DropdownMenuPrimitive.Portal>
-    <DropdownMenuPrimitive.Content
-      ref={ref}
-      sideOffset={sideOffset}
-      className={cn(
-        "z-50 min-w-[8rem] overflow-hidden rounded-md border border-stone-200 bg-white p-1 text-stone-950 shadow-md data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 dark:border-stone-800 dark:bg-stone-950 dark:text-stone-50",
-        className
-      )}
-      {...props}
-    />
-  </DropdownMenuPrimitive.Portal>
-))
-DropdownMenuContent.displayName = DropdownMenuPrimitive.Content.displayName
-
-const DropdownMenuItem = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Item>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Item> & {
-    inset?: boolean
-  }
->(({ className, inset, ...props }, ref) => (
-  <DropdownMenuPrimitive.Item
-    ref={ref}
-    className={cn(
-      "relative flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none transition-colors focus:bg-stone-100 focus:text-stone-900 data-[disabled]:pointer-events-none data-[disabled]:opacity-50 dark:focus:bg-stone-800 dark:focus:text-stone-50",
-      inset && "pl-8",
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuItem.displayName = DropdownMenuPrimitive.Item.displayName
-
-const DropdownMenuCheckboxItem = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.CheckboxItem>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.CheckboxItem>
->(({ className, children, checked, ...props }, ref) => (
-  <DropdownMenuPrimitive.CheckboxItem
-    ref={ref}
-    className={cn(
-      "relative flex cursor-default select-none items-center rounded-sm py-1.5 pl-8 pr-2 text-sm outline-none transition-colors focus:bg-stone-100 focus:text-stone-900 data-[disabled]:pointer-events-none data-[disabled]:opacity-50 dark:focus:bg-stone-800 dark:focus:text-stone-50",
-      className
-    )}
-    checked={checked}
-    {...props}
-  >
-    <span className="absolute left-2 flex h-3.5 w-3.5 items-center justify-center">
-      <DropdownMenuPrimitive.ItemIndicator>
-        <Check className="h-4 w-4" />
-      </DropdownMenuPrimitive.ItemIndicator>
-    </span>
-    {children}
-  </DropdownMenuPrimitive.CheckboxItem>
-))
-DropdownMenuCheckboxItem.displayName =
-  DropdownMenuPrimitive.CheckboxItem.displayName
-
-const DropdownMenuRadioItem = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.RadioItem>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.RadioItem>
->(({ className, children, ...props }, ref) => (
-  <DropdownMenuPrimitive.RadioItem
-    ref={ref}
-    className={cn(
-      "relative flex cursor-default select-none items-center rounded-sm py-1.5 pl-8 pr-2 text-sm outline-none transition-colors focus:bg-stone-100 focus:text-stone-900 data-[disabled]:pointer-events-none data-[disabled]:opacity-50 dark:focus:bg-stone-800 dark:focus:text-stone-50",
-      className
-    )}
-    {...props}
-  >
-    <span className="absolute left-2 flex h-3.5 w-3.5 items-center justify-center">
-      <DropdownMenuPrimitive.ItemIndicator>
-        <Circle className="h-2 w-2 fill-current" />
-      </DropdownMenuPrimitive.ItemIndicator>
-    </span>
-    {children}
-  </DropdownMenuPrimitive.RadioItem>
-))
-DropdownMenuRadioItem.displayName = DropdownMenuPrimitive.RadioItem.displayName
-
-const DropdownMenuLabel = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Label>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Label> & {
-    inset?: boolean
-  }
->(({ className, inset, ...props }, ref) => (
-  <DropdownMenuPrimitive.Label
-    ref={ref}
-    className={cn(
-      "px-2 py-1.5 text-sm font-semibold",
-      inset && "pl-8",
-      className
-    )}
-    {...props}
-  />
-))
-DropdownMenuLabel.displayName = DropdownMenuPrimitive.Label.displayName
-
-const DropdownMenuSeparator = React.forwardRef<
-  React.ElementRef<typeof DropdownMenuPrimitive.Separator>,
-  React.ComponentPropsWithoutRef<typeof DropdownMenuPrimitive.Separator>
->(({ className, ...props }, ref) => (
-  <DropdownMenuPrimitive.Separator
-    ref={ref}
-    className={cn("-mx-1 my-1 h-px bg-stone-100 dark:bg-stone-800", className)}
-    {...props}
-  />
-))
-DropdownMenuSeparator.displayName = DropdownMenuPrimitive.Separator.displayName
-
-const DropdownMenuShortcut = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLSpanElement>) => {
-  return (
-    <span
-      className={cn("ml-auto text-xs tracking-widest opacity-60", className)}
-      {...props}
-    />
-  )
-}
-DropdownMenuShortcut.displayName = "DropdownMenuShortcut"
-
-export {
-  DropdownMenu,
-  DropdownMenuTrigger,
-  DropdownMenuContent,
-  DropdownMenuItem,
-  DropdownMenuCheckboxItem,
-  DropdownMenuRadioItem,
-  DropdownMenuLabel,
-  DropdownMenuSeparator,
-  DropdownMenuShortcut,
-  DropdownMenuGroup,
-  DropdownMenuPortal,
-  DropdownMenuSub,
-  DropdownMenuSubContent,
-  DropdownMenuSubTrigger,
-  DropdownMenuRadioGroup,
-}
diff --git a/spaces/Rayzggz/illi-Bert-VITS2/text/english_bert_mock.py b/spaces/Rayzggz/illi-Bert-VITS2/text/english_bert_mock.py
deleted file mode 100644
index 3b894ced5b6d619a18d6bdd7d7606ba9e6532050..0000000000000000000000000000000000000000
--- a/spaces/Rayzggz/illi-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/Rbrq/DeticChatGPT/detic/modeling/roi_heads/detic_fast_rcnn.py b/spaces/Rbrq/DeticChatGPT/detic/modeling/roi_heads/detic_fast_rcnn.py
deleted file mode 100644
index 186822dd8f67ef9d991ee79101b3bf1243a722a5..0000000000000000000000000000000000000000
--- a/spaces/Rbrq/DeticChatGPT/detic/modeling/roi_heads/detic_fast_rcnn.py
+++ /dev/null
@@ -1,595 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import math
-import json
-import numpy as np
-from typing import Dict, Union
-import torch
-from fvcore.nn import giou_loss, smooth_l1_loss
-from torch import nn
-from torch.nn import functional as F
-import fvcore.nn.weight_init as weight_init
-import detectron2.utils.comm as comm
-from detectron2.config import configurable
-from detectron2.layers import ShapeSpec, batched_nms, cat, cross_entropy, nonzero_tuple
-from detectron2.structures import Boxes, Instances
-from detectron2.utils.events import get_event_storage
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
-from detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference
-from detectron2.modeling.roi_heads.fast_rcnn import _log_classification_stats
-
-from torch.cuda.amp import autocast
-from ..utils import load_class_freq, get_fed_loss_inds
-from .zero_shot_classifier import ZeroShotClassifier
-
-__all__ = ["DeticFastRCNNOutputLayers"]
-
-
-class DeticFastRCNNOutputLayers(FastRCNNOutputLayers):
-    @configurable
-    def __init__(
-        self, 
-        input_shape: ShapeSpec,
-        *,
-        mult_proposal_score=False,
-        cls_score=None,
-        sync_caption_batch = False,
-        use_sigmoid_ce = False,
-        use_fed_loss = False,
-        ignore_zero_cats = False,
-        fed_loss_num_cat = 50,
-        dynamic_classifier = False,
-        image_label_loss = '',
-        use_zeroshot_cls = False,
-        image_loss_weight = 0.1,
-        with_softmax_prop = False,
-        caption_weight = 1.0,
-        neg_cap_weight = 1.0,
-        add_image_box = False,
-        debug = False,
-        prior_prob = 0.01,
-        cat_freq_path = '',
-        fed_loss_freq_weight = 0.5,
-        softmax_weak_loss = False,
-        **kwargs,
-    ):
-        super().__init__(
-            input_shape=input_shape, 
-            **kwargs,
-        )
-        self.mult_proposal_score = mult_proposal_score
-        self.sync_caption_batch = sync_caption_batch
-        self.use_sigmoid_ce = use_sigmoid_ce
-        self.use_fed_loss = use_fed_loss
-        self.ignore_zero_cats = ignore_zero_cats
-        self.fed_loss_num_cat = fed_loss_num_cat
-        self.dynamic_classifier = dynamic_classifier
-        self.image_label_loss = image_label_loss
-        self.use_zeroshot_cls = use_zeroshot_cls
-        self.image_loss_weight = image_loss_weight
-        self.with_softmax_prop = with_softmax_prop
-        self.caption_weight = caption_weight
-        self.neg_cap_weight = neg_cap_weight
-        self.add_image_box = add_image_box
-        self.softmax_weak_loss = softmax_weak_loss
-        self.debug = debug
-
-        if softmax_weak_loss:
-            assert image_label_loss in ['max_size'] 
-
-        if self.use_sigmoid_ce:
-            bias_value = -math.log((1 - prior_prob) / prior_prob)
-            nn.init.constant_(self.cls_score.bias, bias_value)
-        
-        if self.use_fed_loss or self.ignore_zero_cats:
-            freq_weight = load_class_freq(cat_freq_path, fed_loss_freq_weight)
-            self.register_buffer('freq_weight', freq_weight)
-        else:
-            self.freq_weight = None
-
-        if self.use_fed_loss and len(self.freq_weight) < self.num_classes:
-            # assert self.num_classes == 11493
-            print('Extending federated loss weight')
-            self.freq_weight = torch.cat(
-                [self.freq_weight, 
-                self.freq_weight.new_zeros(
-                    self.num_classes - len(self.freq_weight))]
-            )
-
-        assert (not self.dynamic_classifier) or (not self.use_fed_loss)
-        input_size = input_shape.channels * \
-            (input_shape.width or 1) * (input_shape.height or 1)
-        
-        if self.use_zeroshot_cls:
-            del self.cls_score
-            del self.bbox_pred
-            assert cls_score is not None
-            self.cls_score = cls_score
-            self.bbox_pred = nn.Sequential(
-                nn.Linear(input_size, input_size),
-                nn.ReLU(inplace=True),
-                nn.Linear(input_size, 4)
-            )
-            weight_init.c2_xavier_fill(self.bbox_pred[0])
-            nn.init.normal_(self.bbox_pred[-1].weight, std=0.001)
-            nn.init.constant_(self.bbox_pred[-1].bias, 0)
-
-        if self.with_softmax_prop:
-            self.prop_score = nn.Sequential(
-                nn.Linear(input_size, input_size),
-                nn.ReLU(inplace=True),
-                nn.Linear(input_size, self.num_classes + 1),
-            )
-            weight_init.c2_xavier_fill(self.prop_score[0])
-            nn.init.normal_(self.prop_score[-1].weight, mean=0, std=0.001)
-            nn.init.constant_(self.prop_score[-1].bias, 0)
-
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        ret = super().from_config(cfg, input_shape)
-        ret.update({
-            'mult_proposal_score': cfg.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE,
-            'sync_caption_batch': cfg.MODEL.SYNC_CAPTION_BATCH,
-            'use_sigmoid_ce': cfg.MODEL.ROI_BOX_HEAD.USE_SIGMOID_CE,
-            'use_fed_loss': cfg.MODEL.ROI_BOX_HEAD.USE_FED_LOSS,
-            'ignore_zero_cats': cfg.MODEL.ROI_BOX_HEAD.IGNORE_ZERO_CATS,
-            'fed_loss_num_cat': cfg.MODEL.ROI_BOX_HEAD.FED_LOSS_NUM_CAT,
-            'dynamic_classifier': cfg.MODEL.DYNAMIC_CLASSIFIER,
-            'image_label_loss': cfg.MODEL.ROI_BOX_HEAD.IMAGE_LABEL_LOSS,
-            'use_zeroshot_cls': cfg.MODEL.ROI_BOX_HEAD.USE_ZEROSHOT_CLS,
-            'image_loss_weight': cfg.MODEL.ROI_BOX_HEAD.IMAGE_LOSS_WEIGHT,
-            'with_softmax_prop': cfg.MODEL.ROI_BOX_HEAD.WITH_SOFTMAX_PROP,
-            'caption_weight': cfg.MODEL.ROI_BOX_HEAD.CAPTION_WEIGHT,
-            'neg_cap_weight': cfg.MODEL.ROI_BOX_HEAD.NEG_CAP_WEIGHT,
-            'add_image_box': cfg.MODEL.ROI_BOX_HEAD.ADD_IMAGE_BOX,
-            'debug': cfg.DEBUG or cfg.SAVE_DEBUG or cfg.IS_DEBUG,
-            'prior_prob': cfg.MODEL.ROI_BOX_HEAD.PRIOR_PROB,
-            'cat_freq_path': cfg.MODEL.ROI_BOX_HEAD.CAT_FREQ_PATH,
-            'fed_loss_freq_weight': cfg.MODEL.ROI_BOX_HEAD.FED_LOSS_FREQ_WEIGHT,
-            'softmax_weak_loss': cfg.MODEL.ROI_BOX_HEAD.SOFTMAX_WEAK_LOSS,
-        })
-        if ret['use_zeroshot_cls']:
-            ret['cls_score'] = ZeroShotClassifier(cfg, input_shape)
-        return ret
-
-    def losses(self, predictions, proposals, \
-        use_advanced_loss=True,
-        classifier_info=(None,None,None)):
-        """
-        enable advanced loss
-        """
-        scores, proposal_deltas = predictions
-        gt_classes = (
-            cat([p.gt_classes for p in proposals], dim=0) if len(proposals) else torch.empty(0)
-        )
-        num_classes = self.num_classes
-        if self.dynamic_classifier:
-            _, cls_id_map = classifier_info[1]
-            gt_classes = cls_id_map[gt_classes]
-            num_classes = scores.shape[1] - 1
-            assert cls_id_map[self.num_classes] == num_classes
-        _log_classification_stats(scores, gt_classes)
-
-        if len(proposals):
-            proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0)  # Nx4
-            assert not proposal_boxes.requires_grad, "Proposals should not require gradients!"
-            gt_boxes = cat(
-                [(p.gt_boxes if p.has("gt_boxes") else p.proposal_boxes).tensor for p in proposals],
-                dim=0,
-            )
-        else:
-            proposal_boxes = gt_boxes = torch.empty((0, 4), device=proposal_deltas.device)
-
-        if self.use_sigmoid_ce:
-            loss_cls = self.sigmoid_cross_entropy_loss(scores, gt_classes)
-        else:
-            loss_cls = self.softmax_cross_entropy_loss(scores, gt_classes)
-        return {
-            "loss_cls": loss_cls, 
-            "loss_box_reg": self.box_reg_loss(
-                proposal_boxes, gt_boxes, proposal_deltas, gt_classes, 
-                num_classes=num_classes)
-        }
-
-
-    def sigmoid_cross_entropy_loss(self, pred_class_logits, gt_classes):
-        if pred_class_logits.numel() == 0:
-            return pred_class_logits.new_zeros([1])[0] # This is more robust than .sum() * 0.
-
-        B = pred_class_logits.shape[0]
-        C = pred_class_logits.shape[1] - 1
-
-        target = pred_class_logits.new_zeros(B, C + 1)
-        target[range(len(gt_classes)), gt_classes] = 1 # B x (C + 1)
-        target = target[:, :C] # B x C
-
-        weight = 1
- 
-        if self.use_fed_loss and (self.freq_weight is not None): # fedloss
-            appeared = get_fed_loss_inds(
-                gt_classes, 
-                num_sample_cats=self.fed_loss_num_cat,
-                C=C,
-                weight=self.freq_weight)
-            appeared_mask = appeared.new_zeros(C + 1)
-            appeared_mask[appeared] = 1 # C + 1
-            appeared_mask = appeared_mask[:C]
-            fed_w = appeared_mask.view(1, C).expand(B, C)
-            weight = weight * fed_w.float()
-        if self.ignore_zero_cats and (self.freq_weight is not None):
-            w = (self.freq_weight.view(-1) > 1e-4).float()
-            weight = weight * w.view(1, C).expand(B, C)
-            # import pdb; pdb.set_trace()
-
-        cls_loss = F.binary_cross_entropy_with_logits(
-            pred_class_logits[:, :-1], target, reduction='none') # B x C
-        loss =  torch.sum(cls_loss * weight) / B  
-        return loss
-        
-    
-    def softmax_cross_entropy_loss(self, pred_class_logits, gt_classes):
-        """
-        change _no_instance handling
-        """
-        if pred_class_logits.numel() == 0:
-            return pred_class_logits.new_zeros([1])[0]
-
-        if self.ignore_zero_cats and (self.freq_weight is not None):
-            zero_weight = torch.cat([
-                (self.freq_weight.view(-1) > 1e-4).float(),
-                self.freq_weight.new_ones(1)]) # C + 1
-            loss = F.cross_entropy(
-                pred_class_logits, gt_classes, 
-                weight=zero_weight, reduction="mean")
-        elif self.use_fed_loss and (self.freq_weight is not None): # fedloss
-            C = pred_class_logits.shape[1] - 1
-            appeared = get_fed_loss_inds(
-                gt_classes, 
-                num_sample_cats=self.fed_loss_num_cat,
-                C=C,
-                weight=self.freq_weight)
-            appeared_mask = appeared.new_zeros(C + 1).float()
-            appeared_mask[appeared] = 1. # C + 1
-            appeared_mask[C] = 1.
-            loss = F.cross_entropy(
-                pred_class_logits, gt_classes, 
-                weight=appeared_mask, reduction="mean")        
-        else:
-            loss = F.cross_entropy(
-                pred_class_logits, gt_classes, reduction="mean")                  
-        return loss
-
-
-    def box_reg_loss(
-        self, proposal_boxes, gt_boxes, pred_deltas, gt_classes, 
-        num_classes=-1):
-        """
-        Allow custom background index
-        """
-        num_classes = num_classes if num_classes > 0 else self.num_classes
-        box_dim = proposal_boxes.shape[1]  # 4 or 5
-        fg_inds = nonzero_tuple((gt_classes >= 0) & (gt_classes < num_classes))[0]
-        if pred_deltas.shape[1] == box_dim:  # cls-agnostic regression
-            fg_pred_deltas = pred_deltas[fg_inds]
-        else:
-            fg_pred_deltas = pred_deltas.view(-1, self.num_classes, box_dim)[
-                fg_inds, gt_classes[fg_inds]
-            ]
-
-        if self.box_reg_loss_type == "smooth_l1":
-            gt_pred_deltas = self.box2box_transform.get_deltas(
-                proposal_boxes[fg_inds],
-                gt_boxes[fg_inds],
-            )
-            loss_box_reg = smooth_l1_loss(
-                fg_pred_deltas, gt_pred_deltas, self.smooth_l1_beta, reduction="sum"
-            )
-        elif self.box_reg_loss_type == "giou":
-            fg_pred_boxes = self.box2box_transform.apply_deltas(
-                fg_pred_deltas, proposal_boxes[fg_inds]
-            )
-            loss_box_reg = giou_loss(fg_pred_boxes, gt_boxes[fg_inds], reduction="sum")
-        else:
-            raise ValueError(f"Invalid bbox reg loss type '{self.box_reg_loss_type}'")
-        return loss_box_reg / max(gt_classes.numel(), 1.0)
-
-    def inference(self, predictions, proposals):
-        """
-        enable use proposal boxes
-        """
-        predictions = (predictions[0], predictions[1])
-        boxes = self.predict_boxes(predictions, proposals)
-        scores = self.predict_probs(predictions, proposals)
-        if self.mult_proposal_score:
-            proposal_scores = [p.get('objectness_logits') for p in proposals]
-            scores = [(s * ps[:, None]) ** 0.5 \
-                for s, ps in zip(scores, proposal_scores)]
-        image_shapes = [x.image_size for x in proposals]
-        return fast_rcnn_inference(
-            boxes,
-            scores,
-            image_shapes,
-            self.test_score_thresh,
-            self.test_nms_thresh,
-            self.test_topk_per_image,
-        )
-
-
-    def predict_probs(self, predictions, proposals):
-        """
-        support sigmoid
-        """
-        # scores, _ = predictions
-        scores = predictions[0]
-        num_inst_per_image = [len(p) for p in proposals]
-        if self.use_sigmoid_ce:
-            probs = scores.sigmoid()
-        else:
-            probs = F.softmax(scores, dim=-1)
-        return probs.split(num_inst_per_image, dim=0)
-
-
-    def image_label_losses(self, predictions, proposals, image_labels, \
-        classifier_info=(None,None,None), ann_type='image'):
-        '''
-        Inputs:
-            scores: N x (C + 1)
-            image_labels B x 1
-        '''
-        num_inst_per_image = [len(p) for p in proposals]
-        scores = predictions[0]
-        scores = scores.split(num_inst_per_image, dim=0) # B x n x (C + 1)
-        if self.with_softmax_prop:
-            prop_scores = predictions[2].split(num_inst_per_image, dim=0)
-        else:
-            prop_scores = [None for _ in num_inst_per_image]
-        B = len(scores)
-        img_box_count = 0
-        select_size_count = 0
-        select_x_count = 0
-        select_y_count = 0
-        max_score_count = 0
-        storage = get_event_storage()
-        loss = scores[0].new_zeros([1])[0]
-        caption_loss = scores[0].new_zeros([1])[0]
-        for idx, (score, labels, prop_score, p) in enumerate(zip(
-            scores, image_labels, prop_scores, proposals)):
-            if score.shape[0] == 0:
-                loss += score.new_zeros([1])[0]
-                continue
-            if 'caption' in ann_type:
-                score, caption_loss_img = self._caption_loss(
-                    score, classifier_info, idx, B)
-                caption_loss += self.caption_weight * caption_loss_img
-                if ann_type == 'caption':
-                    continue
-
-            if self.debug:
-                p.selected = score.new_zeros(
-                    (len(p),), dtype=torch.long) - 1
-            for i_l, label in enumerate(labels):
-                if self.dynamic_classifier:
-                    if idx == 0 and i_l == 0 and comm.is_main_process():
-                        storage.put_scalar('stats_label', label)
-                    label = classifier_info[1][1][label]
-                    assert label < score.shape[1]
-                if self.image_label_loss in ['wsod', 'wsddn']: 
-                    loss_i, ind = self._wsddn_loss(score, prop_score, label)
-                elif self.image_label_loss == 'max_score':
-                    loss_i, ind = self._max_score_loss(score, label)
-                elif self.image_label_loss == 'max_size':
-                    loss_i, ind = self._max_size_loss(score, label, p)
-                elif self.image_label_loss == 'first':
-                    loss_i, ind = self._first_loss(score, label)
-                elif self.image_label_loss == 'image':
-                    loss_i, ind = self._image_loss(score, label)
-                elif self.image_label_loss == 'min_loss':
-                    loss_i, ind = self._min_loss_loss(score, label)
-                else:
-                    assert 0
-                loss += loss_i / len(labels)
-                if type(ind) == type([]):
-                    img_box_count = sum(ind) / len(ind)
-                    if self.debug:
-                        for ind_i in ind:
-                            p.selected[ind_i] = label
-                else:
-                    img_box_count = ind
-                    select_size_count = p[ind].proposal_boxes.area() / \
-                        (p.image_size[0] * p.image_size[1])
-                    max_score_count = score[ind, label].sigmoid()
-                    select_x_count = (p.proposal_boxes.tensor[ind, 0] + \
-                        p.proposal_boxes.tensor[ind, 2]) / 2 / p.image_size[1]
-                    select_y_count = (p.proposal_boxes.tensor[ind, 1] + \
-                        p.proposal_boxes.tensor[ind, 3]) / 2 / p.image_size[0]
-                    if self.debug:
-                        p.selected[ind] = label
-
-        loss = loss / B
-        storage.put_scalar('stats_l_image', loss.item())
-        if 'caption' in ann_type:
-            caption_loss = caption_loss / B
-            loss = loss + caption_loss
-            storage.put_scalar('stats_l_caption', caption_loss.item())
-        if comm.is_main_process():
-            storage.put_scalar('pool_stats', img_box_count)
-            storage.put_scalar('stats_select_size', select_size_count)
-            storage.put_scalar('stats_select_x', select_x_count)
-            storage.put_scalar('stats_select_y', select_y_count)
-            storage.put_scalar('stats_max_label_score', max_score_count)
-
-        return {
-            'image_loss': loss * self.image_loss_weight,
-            'loss_cls': score.new_zeros([1])[0],
-            'loss_box_reg': score.new_zeros([1])[0]}
-
-
-    def forward(self, x, classifier_info=(None,None,None)):
-        """
-        enable classifier_info
-        """
-        if x.dim() > 2:
-            x = torch.flatten(x, start_dim=1)
-        scores = []
-   
-        if classifier_info[0] is not None:
-            cls_scores = self.cls_score(x, classifier=classifier_info[0])
-            scores.append(cls_scores)
-        else:
-            cls_scores = self.cls_score(x)
-            scores.append(cls_scores)
-
-        if classifier_info[2] is not None:
-            cap_cls = classifier_info[2]
-            if self.sync_caption_batch:
-                caption_scores = self.cls_score(x, classifier=cap_cls[:, :-1]) 
-            else:
-                caption_scores = self.cls_score(x, classifier=cap_cls)
-            scores.append(caption_scores)
-        scores = torch.cat(scores, dim=1) # B x C' or B x N or B x (C'+N)
-
-        proposal_deltas = self.bbox_pred(x)
-        if self.with_softmax_prop:
-            prop_score = self.prop_score(x)
-            return scores, proposal_deltas, prop_score
-        else:
-            return scores, proposal_deltas
-
-
-    def _caption_loss(self, score, classifier_info, idx, B):
-        assert (classifier_info[2] is not None)
-        assert self.add_image_box
-        cls_and_cap_num = score.shape[1]
-        cap_num = classifier_info[2].shape[0]
-        score, caption_score = score.split(
-            [cls_and_cap_num - cap_num, cap_num], dim=1)
-        # n x (C + 1), n x B
-        caption_score = caption_score[-1:] # 1 x B # -1: image level box
-        caption_target = caption_score.new_zeros(
-            caption_score.shape) # 1 x B or 1 x MB, M: num machines
-        if self.sync_caption_batch:
-            # caption_target: 1 x MB
-            rank = comm.get_rank()
-            global_idx = B * rank + idx
-            assert (classifier_info[2][
-                global_idx, -1] - rank) ** 2 < 1e-8, \
-                    '{} {} {} {} {}'.format(
-                        rank, global_idx, 
-                        classifier_info[2][global_idx, -1],
-                        classifier_info[2].shape, 
-                        classifier_info[2][:, -1])
-            caption_target[:, global_idx] = 1.
-        else:
-            assert caption_score.shape[1] == B
-            caption_target[:, idx] = 1.
-        caption_loss_img = F.binary_cross_entropy_with_logits(
-                caption_score, caption_target, reduction='none')
-        if self.sync_caption_batch:
-            fg_mask = (caption_target > 0.5).float()
-            assert (fg_mask.sum().item() - 1.) ** 2 < 1e-8, '{} {}'.format(
-                fg_mask.shape, fg_mask)
-            pos_loss = (caption_loss_img * fg_mask).sum()
-            neg_loss = (caption_loss_img * (1. - fg_mask)).sum()
-            caption_loss_img = pos_loss + self.neg_cap_weight * neg_loss
-        else:
-            caption_loss_img = caption_loss_img.sum()
-        return score, caption_loss_img
-
-
-    def _wsddn_loss(self, score, prop_score, label):
-        assert prop_score is not None
-        loss = 0
-        final_score = score.sigmoid() * \
-            F.softmax(prop_score, dim=0) # B x (C + 1)
-        img_score = torch.clamp(
-            torch.sum(final_score, dim=0), 
-            min=1e-10, max=1-1e-10) # (C + 1)
-        target = img_score.new_zeros(img_score.shape) # (C + 1)
-        target[label] = 1.
-        loss += F.binary_cross_entropy(img_score, target)
-        ind = final_score[:, label].argmax()
-        return loss, ind
-
-
-    def _max_score_loss(self, score, label):
-        loss = 0
-        target = score.new_zeros(score.shape[1])
-        target[label] = 1.
-        ind = score[:, label].argmax().item()
-        loss += F.binary_cross_entropy_with_logits(
-            score[ind], target, reduction='sum')
-        return loss, ind
-
-
-    def _min_loss_loss(self, score, label):
-        loss = 0
-        target = score.new_zeros(score.shape)
-        target[:, label] = 1.
-        with torch.no_grad():
-            x = F.binary_cross_entropy_with_logits(
-                score, target, reduction='none').sum(dim=1) # n
-        ind = x.argmin().item()
-        loss += F.binary_cross_entropy_with_logits(
-            score[ind], target[0], reduction='sum')
-        return loss, ind
-
-
-    def _first_loss(self, score, label):
-        loss = 0
-        target = score.new_zeros(score.shape[1])
-        target[label] = 1.
-        ind = 0
-        loss += F.binary_cross_entropy_with_logits(
-            score[ind], target, reduction='sum')
-        return loss, ind
-
-
-    def _image_loss(self, score, label):
-        assert self.add_image_box
-        target = score.new_zeros(score.shape[1])
-        target[label] = 1.
-        ind = score.shape[0] - 1
-        loss = F.binary_cross_entropy_with_logits(
-            score[ind], target, reduction='sum')
-        return loss, ind
-
-
-    def _max_size_loss(self, score, label, p):
-        loss = 0
-        target = score.new_zeros(score.shape[1])
-        target[label] = 1.
-        sizes = p.proposal_boxes.area()
-        ind = sizes[:-1].argmax().item() if len(sizes) > 1 else 0
-        if self.softmax_weak_loss:
-            loss += F.cross_entropy(
-                score[ind:ind+1], 
-                score.new_tensor(label, dtype=torch.long).view(1), 
-                reduction='sum')
-        else:
-            loss += F.binary_cross_entropy_with_logits(
-                score[ind], target, reduction='sum')
-        return loss, ind
-
-
-
-def put_label_distribution(storage, hist_name, hist_counts, num_classes):
-    """
-    """
-    ht_min, ht_max = 0, num_classes
-    hist_edges = torch.linspace(
-        start=ht_min, end=ht_max, steps=num_classes + 1, dtype=torch.float32)
-
-    hist_params = dict(
-        tag=hist_name,
-        min=ht_min,
-        max=ht_max,
-        num=float(hist_counts.sum()),
-        sum=float((hist_counts * torch.arange(len(hist_counts))).sum()),
-        sum_squares=float(((hist_counts * torch.arange(len(hist_counts))) ** 2).sum()),
-        bucket_limits=hist_edges[1:].tolist(),
-        bucket_counts=hist_counts.tolist(),
-        global_step=storage._iter,
-    )
-    storage._histograms.append(hist_params)
\ No newline at end of file
diff --git a/spaces/Realcat/image-matching-webui/third_party/SOLD2/sold2/dataset/__init__.py b/spaces/Realcat/image-matching-webui/third_party/SOLD2/sold2/dataset/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/RegalHyperus/rvc-anime-game/infer_pack/transforms.py b/spaces/RegalHyperus/rvc-anime-game/infer_pack/transforms.py
deleted file mode 100644
index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000
--- a/spaces/RegalHyperus/rvc-anime-game/infer_pack/transforms.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails=None,
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
-
-    outputs, logabsdet = spline_fn(
-        inputs=inputs,
-        unnormalized_widths=unnormalized_widths,
-        unnormalized_heights=unnormalized_heights,
-        unnormalized_derivatives=unnormalized_derivatives,
-        inverse=inverse,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-        **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
-
-
-def unconstrained_rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails="linear",
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == "linear":
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError("{} tails are not implemented.".format(tails))
-
-    (
-        outputs[inside_interval_mask],
-        logabsdet[inside_interval_mask],
-    ) = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound,
-        right=tail_bound,
-        bottom=-tail_bound,
-        top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-    )
-
-    return outputs, logabsdet
-
-
-def rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    left=0.0,
-    right=1.0,
-    bottom=0.0,
-    top=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError("Input to a transform is not within its domain")
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError("Minimal bin width too large for the number of bins")
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError("Minimal bin height too large for the number of bins")
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        ) + input_heights * (input_delta - input_derivatives)
-        b = input_heights * input_derivatives - (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        )
-        c = -input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * root.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - root).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (
-            input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
-        )
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * theta.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - theta).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/ReneGuo/cat_or_dog/app.py b/spaces/ReneGuo/cat_or_dog/app.py
deleted file mode 100644
index 1dd3bc98a9c96bf5590c92a00f0a1504d3f33a4c..0000000000000000000000000000000000000000
--- a/spaces/ReneGuo/cat_or_dog/app.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from weakref import proxy
-import gradio as gr
-from fastai.vision.all import *
-
-my_learn_inf = load_learner('model.pkl')
-
-
-def cat_dog_classifier(input):
-    catg, idx, probs = my_learn_inf.predict(input)
-    print(f'This pictrue is {catg}, and prob is {probs[idx] * 100:{2}.{4}} %')
-    return dict(zip(my_learn_inf.dls.vocab, map(float, probs)))
-
-examples = ['dog.jpg', 'cat.jpg', 'lion.jpg']
-
-iface = gr.Interface(fn=cat_dog_classifier, inputs="image", outputs="label", examples=examples)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/RobLi/ControlNet-v1-1/preprocessor.py b/spaces/RobLi/ControlNet-v1-1/preprocessor.py
deleted file mode 100644
index 14619804b8ad4cfff1e0e10a1b1a8372937cbc19..0000000000000000000000000000000000000000
--- a/spaces/RobLi/ControlNet-v1-1/preprocessor.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import gc
-
-import numpy as np
-import PIL.Image
-import torch
-from controlnet_aux import (CannyDetector, ContentShuffleDetector, HEDdetector,
-                            LineartAnimeDetector, LineartDetector,
-                            MidasDetector, MLSDdetector, NormalBaeDetector,
-                            OpenposeDetector, PidiNetDetector)
-from controlnet_aux.util import HWC3
-
-from cv_utils import resize_image
-from depth_estimator import DepthEstimator
-from image_segmentor import ImageSegmentor
-
-
-class Preprocessor:
-    MODEL_ID = 'lllyasviel/Annotators'
-
-    def __init__(self):
-        self.model = None
-        self.name = ''
-
-    def load(self, name: str) -> None:
-        if name == self.name:
-            return
-        if name == 'HED':
-            self.model = HEDdetector.from_pretrained(self.MODEL_ID)
-        elif name == 'Midas':
-            self.model = MidasDetector.from_pretrained(self.MODEL_ID)
-        elif name == 'MLSD':
-            self.model = MLSDdetector.from_pretrained(self.MODEL_ID)
-        elif name == 'Openpose':
-            self.model = OpenposeDetector.from_pretrained(self.MODEL_ID)
-        elif name == 'PidiNet':
-            self.model = PidiNetDetector.from_pretrained(self.MODEL_ID)
-        elif name == 'NormalBae':
-            self.model = NormalBaeDetector.from_pretrained(self.MODEL_ID)
-        elif name == 'Lineart':
-            self.model = LineartDetector.from_pretrained(self.MODEL_ID)
-        elif name == 'LineartAnime':
-            self.model = LineartAnimeDetector.from_pretrained(self.MODEL_ID)
-        elif name == 'Canny':
-            self.model = CannyDetector()
-        elif name == 'ContentShuffle':
-            self.model = ContentShuffleDetector()
-        elif name == 'DPT':
-            self.model = DepthEstimator()
-        elif name == 'UPerNet':
-            self.model = ImageSegmentor()
-        else:
-            raise ValueError
-        torch.cuda.empty_cache()
-        gc.collect()
-        self.name = name
-
-    def __call__(self, image: PIL.Image.Image, **kwargs) -> PIL.Image.Image:
-        if self.name == 'Canny':
-            if 'detect_resolution' in kwargs:
-                detect_resolution = kwargs.pop('detect_resolution')
-                image = np.array(image)
-                image = HWC3(image)
-                image = resize_image(image, resolution=detect_resolution)
-            image = self.model(image, **kwargs)
-            return PIL.Image.fromarray(image)
-        elif self.name == 'Midas':
-            detect_resolution = kwargs.pop('detect_resolution', 512)
-            image_resolution = kwargs.pop('image_resolution', 512)
-            image = np.array(image)
-            image = HWC3(image)
-            image = resize_image(image, resolution=detect_resolution)
-            image = self.model(image, **kwargs)
-            image = HWC3(image)
-            image = resize_image(image, resolution=image_resolution)
-            return PIL.Image.fromarray(image)
-        else:
-            return self.model(image, **kwargs)
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/ops/points_sampler.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/ops/points_sampler.py
deleted file mode 100644
index a802a74fd6c3610d9ae178e6201f47423eca7ad1..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmcv/ops/points_sampler.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from typing import List
-
-import torch
-from torch import nn as nn
-
-from annotator.uniformer.mmcv.runner import force_fp32
-from .furthest_point_sample import (furthest_point_sample,
-                                    furthest_point_sample_with_dist)
-
-
-def calc_square_dist(point_feat_a, point_feat_b, norm=True):
-    """Calculating square distance between a and b.
-
-    Args:
-        point_feat_a (Tensor): (B, N, C) Feature vector of each point.
-        point_feat_b (Tensor): (B, M, C) Feature vector of each point.
-        norm (Bool, optional): Whether to normalize the distance.
-            Default: True.
-
-    Returns:
-        Tensor: (B, N, M) Distance between each pair points.
-    """
-    num_channel = point_feat_a.shape[-1]
-    # [bs, n, 1]
-    a_square = torch.sum(point_feat_a.unsqueeze(dim=2).pow(2), dim=-1)
-    # [bs, 1, m]
-    b_square = torch.sum(point_feat_b.unsqueeze(dim=1).pow(2), dim=-1)
-
-    corr_matrix = torch.matmul(point_feat_a, point_feat_b.transpose(1, 2))
-
-    dist = a_square + b_square - 2 * corr_matrix
-    if norm:
-        dist = torch.sqrt(dist) / num_channel
-    return dist
-
-
-def get_sampler_cls(sampler_type):
-    """Get the type and mode of points sampler.
-
-    Args:
-        sampler_type (str): The type of points sampler.
-            The valid value are "D-FPS", "F-FPS", or "FS".
-
-    Returns:
-        class: Points sampler type.
-    """
-    sampler_mappings = {
-        'D-FPS': DFPSSampler,
-        'F-FPS': FFPSSampler,
-        'FS': FSSampler,
-    }
-    try:
-        return sampler_mappings[sampler_type]
-    except KeyError:
-        raise KeyError(
-            f'Supported `sampler_type` are {sampler_mappings.keys()}, but got \
-                {sampler_type}')
-
-
-class PointsSampler(nn.Module):
-    """Points sampling.
-
-    Args:
-        num_point (list[int]): Number of sample points.
-        fps_mod_list (list[str], optional): Type of FPS method, valid mod
-            ['F-FPS', 'D-FPS', 'FS'], Default: ['D-FPS'].
-            F-FPS: using feature distances for FPS.
-            D-FPS: using Euclidean distances of points for FPS.
-            FS: using F-FPS and D-FPS simultaneously.
-        fps_sample_range_list (list[int], optional):
-            Range of points to apply FPS. Default: [-1].
-    """
-
-    def __init__(self,
-                 num_point: List[int],
-                 fps_mod_list: List[str] = ['D-FPS'],
-                 fps_sample_range_list: List[int] = [-1]):
-        super().__init__()
-        # FPS would be applied to different fps_mod in the list,
-        # so the length of the num_point should be equal to
-        # fps_mod_list and fps_sample_range_list.
-        assert len(num_point) == len(fps_mod_list) == len(
-            fps_sample_range_list)
-        self.num_point = num_point
-        self.fps_sample_range_list = fps_sample_range_list
-        self.samplers = nn.ModuleList()
-        for fps_mod in fps_mod_list:
-            self.samplers.append(get_sampler_cls(fps_mod)())
-        self.fp16_enabled = False
-
-    @force_fp32()
-    def forward(self, points_xyz, features):
-        """
-        Args:
-            points_xyz (Tensor): (B, N, 3) xyz coordinates of the features.
-            features (Tensor): (B, C, N) Descriptors of the features.
-
-        Returns:
-            Tensor: (B, npoint, sample_num) Indices of sampled points.
-        """
-        indices = []
-        last_fps_end_index = 0
-
-        for fps_sample_range, sampler, npoint in zip(
-                self.fps_sample_range_list, self.samplers, self.num_point):
-            assert fps_sample_range < points_xyz.shape[1]
-
-            if fps_sample_range == -1:
-                sample_points_xyz = points_xyz[:, last_fps_end_index:]
-                if features is not None:
-                    sample_features = features[:, :, last_fps_end_index:]
-                else:
-                    sample_features = None
-            else:
-                sample_points_xyz = \
-                    points_xyz[:, last_fps_end_index:fps_sample_range]
-                if features is not None:
-                    sample_features = features[:, :, last_fps_end_index:
-                                               fps_sample_range]
-                else:
-                    sample_features = None
-
-            fps_idx = sampler(sample_points_xyz.contiguous(), sample_features,
-                              npoint)
-
-            indices.append(fps_idx + last_fps_end_index)
-            last_fps_end_index += fps_sample_range
-        indices = torch.cat(indices, dim=1)
-
-        return indices
-
-
-class DFPSSampler(nn.Module):
-    """Using Euclidean distances of points for FPS."""
-
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, points, features, npoint):
-        """Sampling points with D-FPS."""
-        fps_idx = furthest_point_sample(points.contiguous(), npoint)
-        return fps_idx
-
-
-class FFPSSampler(nn.Module):
-    """Using feature distances for FPS."""
-
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, points, features, npoint):
-        """Sampling points with F-FPS."""
-        assert features is not None, \
-            'feature input to FFPS_Sampler should not be None'
-        features_for_fps = torch.cat([points, features.transpose(1, 2)], dim=2)
-        features_dist = calc_square_dist(
-            features_for_fps, features_for_fps, norm=False)
-        fps_idx = furthest_point_sample_with_dist(features_dist, npoint)
-        return fps_idx
-
-
-class FSSampler(nn.Module):
-    """Using F-FPS and D-FPS simultaneously."""
-
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, points, features, npoint):
-        """Sampling points with FS_Sampling."""
-        assert features is not None, \
-            'feature input to FS_Sampler should not be None'
-        ffps_sampler = FFPSSampler()
-        dfps_sampler = DFPSSampler()
-        fps_idx_ffps = ffps_sampler(points, features, npoint)
-        fps_idx_dfps = dfps_sampler(points, features, npoint)
-        fps_idx = torch.cat([fps_idx_ffps, fps_idx_dfps], dim=1)
-        return fps_idx
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/core/mask/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/core/mask/__init__.py
deleted file mode 100644
index ab1e88bc686d5c2fe72b3114cb2b3e372e73a0f8..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/core/mask/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from .mask_target import mask_target
-from .structures import BaseInstanceMasks, BitmapMasks, PolygonMasks
-from .utils import encode_mask_results, split_combined_polys
-
-__all__ = [
-    'split_combined_polys', 'mask_target', 'BaseInstanceMasks', 'BitmapMasks',
-    'PolygonMasks', 'encode_mask_results'
-]
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/gfl_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/gfl_head.py
deleted file mode 100644
index 961bc92237663ad5343d3d08eb9c0e4e811ada05..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/gfl_head.py
+++ /dev/null
@@ -1,647 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
-from mmcv.runner import force_fp32
-
-from mmdet.core import (anchor_inside_flags, bbox2distance, bbox_overlaps,
-                        build_assigner, build_sampler, distance2bbox,
-                        images_to_levels, multi_apply, multiclass_nms,
-                        reduce_mean, unmap)
-from ..builder import HEADS, build_loss
-from .anchor_head import AnchorHead
-
-
-class Integral(nn.Module):
-    """A fixed layer for calculating integral result from distribution.
-
-    This layer calculates the target location by :math: `sum{P(y_i) * y_i}`,
-    P(y_i) denotes the softmax vector that represents the discrete distribution
-    y_i denotes the discrete set, usually {0, 1, 2, ..., reg_max}
-
-    Args:
-        reg_max (int): The maximal value of the discrete set. Default: 16. You
-            may want to reset it according to your new dataset or related
-            settings.
-    """
-
-    def __init__(self, reg_max=16):
-        super(Integral, self).__init__()
-        self.reg_max = reg_max
-        self.register_buffer('project',
-                             torch.linspace(0, self.reg_max, self.reg_max + 1))
-
-    def forward(self, x):
-        """Forward feature from the regression head to get integral result of
-        bounding box location.
-
-        Args:
-            x (Tensor): Features of the regression head, shape (N, 4*(n+1)),
-                n is self.reg_max.
-
-        Returns:
-            x (Tensor): Integral result of box locations, i.e., distance
-                offsets from the box center in four directions, shape (N, 4).
-        """
-        x = F.softmax(x.reshape(-1, self.reg_max + 1), dim=1)
-        x = F.linear(x, self.project.type_as(x)).reshape(-1, 4)
-        return x
-
-
-@HEADS.register_module()
-class GFLHead(AnchorHead):
-    """Generalized Focal Loss: Learning Qualified and Distributed Bounding
-    Boxes for Dense Object Detection.
-
-    GFL head structure is similar with ATSS, however GFL uses
-    1) joint representation for classification and localization quality, and
-    2) flexible General distribution for bounding box locations,
-    which are supervised by
-    Quality Focal Loss (QFL) and Distribution Focal Loss (DFL), respectively
-
-    https://arxiv.org/abs/2006.04388
-
-    Args:
-        num_classes (int): Number of categories excluding the background
-            category.
-        in_channels (int): Number of channels in the input feature map.
-        stacked_convs (int): Number of conv layers in cls and reg tower.
-            Default: 4.
-        conv_cfg (dict): dictionary to construct and config conv layer.
-            Default: None.
-        norm_cfg (dict): dictionary to construct and config norm layer.
-            Default: dict(type='GN', num_groups=32, requires_grad=True).
-        loss_qfl (dict): Config of Quality Focal Loss (QFL).
-        reg_max (int): Max value of integral set :math: `{0, ..., reg_max}`
-            in QFL setting. Default: 16.
-    Example:
-        >>> self = GFLHead(11, 7)
-        >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
-        >>> cls_quality_score, bbox_pred = self.forward(feats)
-        >>> assert len(cls_quality_score) == len(self.scales)
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 stacked_convs=4,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
-                 loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
-                 reg_max=16,
-                 **kwargs):
-        self.stacked_convs = stacked_convs
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.reg_max = reg_max
-        super(GFLHead, self).__init__(num_classes, in_channels, **kwargs)
-
-        self.sampling = False
-        if self.train_cfg:
-            self.assigner = build_assigner(self.train_cfg.assigner)
-            # SSD sampling=False so use PseudoSampler
-            sampler_cfg = dict(type='PseudoSampler')
-            self.sampler = build_sampler(sampler_cfg, context=self)
-
-        self.integral = Integral(self.reg_max)
-        self.loss_dfl = build_loss(loss_dfl)
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.relu = nn.ReLU(inplace=True)
-        self.cls_convs = nn.ModuleList()
-        self.reg_convs = nn.ModuleList()
-        for i in range(self.stacked_convs):
-            chn = self.in_channels if i == 0 else self.feat_channels
-            self.cls_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-            self.reg_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-        assert self.num_anchors == 1, 'anchor free version'
-        self.gfl_cls = nn.Conv2d(
-            self.feat_channels, self.cls_out_channels, 3, padding=1)
-        self.gfl_reg = nn.Conv2d(
-            self.feat_channels, 4 * (self.reg_max + 1), 3, padding=1)
-        self.scales = nn.ModuleList(
-            [Scale(1.0) for _ in self.anchor_generator.strides])
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.cls_convs:
-            normal_init(m.conv, std=0.01)
-        for m in self.reg_convs:
-            normal_init(m.conv, std=0.01)
-        bias_cls = bias_init_with_prob(0.01)
-        normal_init(self.gfl_cls, std=0.01, bias=bias_cls)
-        normal_init(self.gfl_reg, std=0.01)
-
-    def forward(self, feats):
-        """Forward features from the upstream network.
-
-        Args:
-            feats (tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-
-        Returns:
-            tuple: Usually a tuple of classification scores and bbox prediction
-                cls_scores (list[Tensor]): Classification and quality (IoU)
-                    joint scores for all scale levels, each is a 4D-tensor,
-                    the channel number is num_classes.
-                bbox_preds (list[Tensor]): Box distribution logits for all
-                    scale levels, each is a 4D-tensor, the channel number is
-                    4*(n+1), n is max value of integral set.
-        """
-        return multi_apply(self.forward_single, feats, self.scales)
-
-    def forward_single(self, x, scale):
-        """Forward feature of a single scale level.
-
-        Args:
-            x (Tensor): Features of a single scale level.
-            scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
-                the bbox prediction.
-
-        Returns:
-            tuple:
-                cls_score (Tensor): Cls and quality joint scores for a single
-                    scale level the channel number is num_classes.
-                bbox_pred (Tensor): Box distribution logits for a single scale
-                    level, the channel number is 4*(n+1), n is max value of
-                    integral set.
-        """
-        cls_feat = x
-        reg_feat = x
-        for cls_conv in self.cls_convs:
-            cls_feat = cls_conv(cls_feat)
-        for reg_conv in self.reg_convs:
-            reg_feat = reg_conv(reg_feat)
-        cls_score = self.gfl_cls(cls_feat)
-        bbox_pred = scale(self.gfl_reg(reg_feat)).float()
-        return cls_score, bbox_pred
-
-    def anchor_center(self, anchors):
-        """Get anchor centers from anchors.
-
-        Args:
-            anchors (Tensor): Anchor list with shape (N, 4), "xyxy" format.
-
-        Returns:
-            Tensor: Anchor centers with shape (N, 2), "xy" format.
-        """
-        anchors_cx = (anchors[..., 2] + anchors[..., 0]) / 2
-        anchors_cy = (anchors[..., 3] + anchors[..., 1]) / 2
-        return torch.stack([anchors_cx, anchors_cy], dim=-1)
-
-    def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights,
-                    bbox_targets, stride, num_total_samples):
-        """Compute loss of a single scale level.
-
-        Args:
-            anchors (Tensor): Box reference for each scale level with shape
-                (N, num_total_anchors, 4).
-            cls_score (Tensor): Cls and quality joint scores for each scale
-                level has shape (N, num_classes, H, W).
-            bbox_pred (Tensor): Box distribution logits for each scale
-                level with shape (N, 4*(n+1), H, W), n is max value of integral
-                set.
-            labels (Tensor): Labels of each anchors with shape
-                (N, num_total_anchors).
-            label_weights (Tensor): Label weights of each anchor with shape
-                (N, num_total_anchors)
-            bbox_targets (Tensor): BBox regression targets of each anchor wight
-                shape (N, num_total_anchors, 4).
-            stride (tuple): Stride in this scale level.
-            num_total_samples (int): Number of positive samples that is
-                reduced over all GPUs.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        assert stride[0] == stride[1], 'h stride is not equal to w stride!'
-        anchors = anchors.reshape(-1, 4)
-        cls_score = cls_score.permute(0, 2, 3,
-                                      1).reshape(-1, self.cls_out_channels)
-        bbox_pred = bbox_pred.permute(0, 2, 3,
-                                      1).reshape(-1, 4 * (self.reg_max + 1))
-        bbox_targets = bbox_targets.reshape(-1, 4)
-        labels = labels.reshape(-1)
-        label_weights = label_weights.reshape(-1)
-
-        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-        bg_class_ind = self.num_classes
-        pos_inds = ((labels >= 0)
-                    & (labels < bg_class_ind)).nonzero().squeeze(1)
-        score = label_weights.new_zeros(labels.shape)
-
-        if len(pos_inds) > 0:
-            pos_bbox_targets = bbox_targets[pos_inds]
-            pos_bbox_pred = bbox_pred[pos_inds]
-            pos_anchors = anchors[pos_inds]
-            pos_anchor_centers = self.anchor_center(pos_anchors) / stride[0]
-
-            weight_targets = cls_score.detach().sigmoid()
-            weight_targets = weight_targets.max(dim=1)[0][pos_inds]
-            pos_bbox_pred_corners = self.integral(pos_bbox_pred)
-            pos_decode_bbox_pred = distance2bbox(pos_anchor_centers,
-                                                 pos_bbox_pred_corners)
-            pos_decode_bbox_targets = pos_bbox_targets / stride[0]
-            score[pos_inds] = bbox_overlaps(
-                pos_decode_bbox_pred.detach(),
-                pos_decode_bbox_targets,
-                is_aligned=True)
-            pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1)
-            target_corners = bbox2distance(pos_anchor_centers,
-                                           pos_decode_bbox_targets,
-                                           self.reg_max).reshape(-1)
-
-            # regression loss
-            loss_bbox = self.loss_bbox(
-                pos_decode_bbox_pred,
-                pos_decode_bbox_targets,
-                weight=weight_targets,
-                avg_factor=1.0)
-
-            # dfl loss
-            loss_dfl = self.loss_dfl(
-                pred_corners,
-                target_corners,
-                weight=weight_targets[:, None].expand(-1, 4).reshape(-1),
-                avg_factor=4.0)
-        else:
-            loss_bbox = bbox_pred.sum() * 0
-            loss_dfl = bbox_pred.sum() * 0
-            weight_targets = bbox_pred.new_tensor(0)
-
-        # cls (qfl) loss
-        loss_cls = self.loss_cls(
-            cls_score, (labels, score),
-            weight=label_weights,
-            avg_factor=num_total_samples)
-
-        return loss_cls, loss_bbox, loss_dfl, weight_targets.sum()
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
-    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]): Cls and quality scores for each scale
-                level has shape (N, num_classes, H, W).
-            bbox_preds (list[Tensor]): Box distribution logits for each scale
-                level with shape (N, 4*(n+1), H, W), n is max value of integral
-                set.
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (list[Tensor] | None): specify which bounding
-                boxes can be ignored when computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-
-        device = cls_scores[0].device
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels)
-        if cls_reg_targets is None:
-            return None
-
-        (anchor_list, labels_list, label_weights_list, bbox_targets_list,
-         bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
-
-        num_total_samples = reduce_mean(
-            torch.tensor(num_total_pos, dtype=torch.float,
-                         device=device)).item()
-        num_total_samples = max(num_total_samples, 1.0)
-
-        losses_cls, losses_bbox, losses_dfl,\
-            avg_factor = multi_apply(
-                self.loss_single,
-                anchor_list,
-                cls_scores,
-                bbox_preds,
-                labels_list,
-                label_weights_list,
-                bbox_targets_list,
-                self.anchor_generator.strides,
-                num_total_samples=num_total_samples)
-
-        avg_factor = sum(avg_factor)
-        avg_factor = reduce_mean(avg_factor).item()
-        losses_bbox = list(map(lambda x: x / avg_factor, losses_bbox))
-        losses_dfl = list(map(lambda x: x / avg_factor, losses_dfl))
-        return dict(
-            loss_cls=losses_cls, loss_bbox=losses_bbox, loss_dfl=losses_dfl)
-
-    def _get_bboxes(self,
-                    cls_scores,
-                    bbox_preds,
-                    mlvl_anchors,
-                    img_shapes,
-                    scale_factors,
-                    cfg,
-                    rescale=False,
-                    with_nms=True):
-        """Transform outputs for a single batch item into labeled boxes.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for a single scale level
-                has shape (N, num_classes, H, W).
-            bbox_preds (list[Tensor]): Box distribution logits for a single
-                scale level with shape (N, 4*(n+1), H, W), n is max value of
-                integral set.
-            mlvl_anchors (list[Tensor]): Box reference for a single scale level
-                with shape (num_total_anchors, 4).
-            img_shapes (list[tuple[int]]): Shape of the input image,
-                list[(height, width, 3)].
-            scale_factors (list[ndarray]): Scale factor of the image arange as
-                (w_scale, h_scale, w_scale, h_scale).
-            cfg (mmcv.Config | None): Test / postprocessing configuration,
-                if None, test_cfg would be used.
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-            with_nms (bool): If True, do nms before return boxes.
-                Default: True.
-
-        Returns:
-            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
-                The first item is an (n, 5) tensor, where 5 represent
-                (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
-                The shape of the second tensor in the tuple is (n,), and
-                each element represents the class label of the corresponding
-                box.
-        """
-        cfg = self.test_cfg if cfg is None else cfg
-        assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
-        batch_size = cls_scores[0].shape[0]
-
-        mlvl_bboxes = []
-        mlvl_scores = []
-        for cls_score, bbox_pred, stride, anchors in zip(
-                cls_scores, bbox_preds, self.anchor_generator.strides,
-                mlvl_anchors):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-            assert stride[0] == stride[1]
-            scores = cls_score.permute(0, 2, 3, 1).reshape(
-                batch_size, -1, self.cls_out_channels).sigmoid()
-            bbox_pred = bbox_pred.permute(0, 2, 3, 1)
-
-            bbox_pred = self.integral(bbox_pred) * stride[0]
-            bbox_pred = bbox_pred.reshape(batch_size, -1, 4)
-
-            nms_pre = cfg.get('nms_pre', -1)
-            if nms_pre > 0 and scores.shape[1] > nms_pre:
-                max_scores, _ = scores.max(-1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                batch_inds = torch.arange(batch_size).view(
-                    -1, 1).expand_as(topk_inds).long()
-                anchors = anchors[topk_inds, :]
-                bbox_pred = bbox_pred[batch_inds, topk_inds, :]
-                scores = scores[batch_inds, topk_inds, :]
-            else:
-                anchors = anchors.expand_as(bbox_pred)
-
-            bboxes = distance2bbox(
-                self.anchor_center(anchors), bbox_pred, max_shape=img_shapes)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-
-        batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1)
-        if rescale:
-            batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor(
-                scale_factors).unsqueeze(1)
-
-        batch_mlvl_scores = torch.cat(mlvl_scores, dim=1)
-        # Add a dummy background class to the backend when using sigmoid
-        # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
-        # BG cat_id: num_class
-        padding = batch_mlvl_scores.new_zeros(batch_size,
-                                              batch_mlvl_scores.shape[1], 1)
-        batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1)
-
-        if with_nms:
-            det_results = []
-            for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes,
-                                                  batch_mlvl_scores):
-                det_bbox, det_label = multiclass_nms(mlvl_bboxes, mlvl_scores,
-                                                     cfg.score_thr, cfg.nms,
-                                                     cfg.max_per_img)
-                det_results.append(tuple([det_bbox, det_label]))
-        else:
-            det_results = [
-                tuple(mlvl_bs)
-                for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores)
-            ]
-        return det_results
-
-    def get_targets(self,
-                    anchor_list,
-                    valid_flag_list,
-                    gt_bboxes_list,
-                    img_metas,
-                    gt_bboxes_ignore_list=None,
-                    gt_labels_list=None,
-                    label_channels=1,
-                    unmap_outputs=True):
-        """Get targets for GFL head.
-
-        This method is almost the same as `AnchorHead.get_targets()`. Besides
-        returning the targets as the parent method does, it also returns the
-        anchors as the first element of the returned tuple.
-        """
-        num_imgs = len(img_metas)
-        assert len(anchor_list) == len(valid_flag_list) == num_imgs
-
-        # anchor number of multi levels
-        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-        num_level_anchors_list = [num_level_anchors] * num_imgs
-
-        # concat all level anchors and flags to a single tensor
-        for i in range(num_imgs):
-            assert len(anchor_list[i]) == len(valid_flag_list[i])
-            anchor_list[i] = torch.cat(anchor_list[i])
-            valid_flag_list[i] = torch.cat(valid_flag_list[i])
-
-        # compute targets for each image
-        if gt_bboxes_ignore_list is None:
-            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
-        if gt_labels_list is None:
-            gt_labels_list = [None for _ in range(num_imgs)]
-        (all_anchors, all_labels, all_label_weights, all_bbox_targets,
-         all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
-             self._get_target_single,
-             anchor_list,
-             valid_flag_list,
-             num_level_anchors_list,
-             gt_bboxes_list,
-             gt_bboxes_ignore_list,
-             gt_labels_list,
-             img_metas,
-             label_channels=label_channels,
-             unmap_outputs=unmap_outputs)
-        # no valid anchors
-        if any([labels is None for labels in all_labels]):
-            return None
-        # sampled anchors of all images
-        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
-        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
-        # split targets to a list w.r.t. multiple levels
-        anchors_list = images_to_levels(all_anchors, num_level_anchors)
-        labels_list = images_to_levels(all_labels, num_level_anchors)
-        label_weights_list = images_to_levels(all_label_weights,
-                                              num_level_anchors)
-        bbox_targets_list = images_to_levels(all_bbox_targets,
-                                             num_level_anchors)
-        bbox_weights_list = images_to_levels(all_bbox_weights,
-                                             num_level_anchors)
-        return (anchors_list, labels_list, label_weights_list,
-                bbox_targets_list, bbox_weights_list, num_total_pos,
-                num_total_neg)
-
-    def _get_target_single(self,
-                           flat_anchors,
-                           valid_flags,
-                           num_level_anchors,
-                           gt_bboxes,
-                           gt_bboxes_ignore,
-                           gt_labels,
-                           img_meta,
-                           label_channels=1,
-                           unmap_outputs=True):
-        """Compute regression, classification targets for anchors in a single
-        image.
-
-        Args:
-            flat_anchors (Tensor): Multi-level anchors of the image, which are
-                concatenated into a single tensor of shape (num_anchors, 4)
-            valid_flags (Tensor): Multi level valid flags of the image,
-                which are concatenated into a single tensor of
-                    shape (num_anchors,).
-            num_level_anchors Tensor): Number of anchors of each scale level.
-            gt_bboxes (Tensor): Ground truth bboxes of the image,
-                shape (num_gts, 4).
-            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
-                ignored, shape (num_ignored_gts, 4).
-            gt_labels (Tensor): Ground truth labels of each box,
-                shape (num_gts,).
-            img_meta (dict): Meta info of the image.
-            label_channels (int): Channel of label.
-            unmap_outputs (bool): Whether to map outputs back to the original
-                set of anchors.
-
-        Returns:
-            tuple: N is the number of total anchors in the image.
-                anchors (Tensor): All anchors in the image with shape (N, 4).
-                labels (Tensor): Labels of all anchors in the image with shape
-                    (N,).
-                label_weights (Tensor): Label weights of all anchor in the
-                    image with shape (N,).
-                bbox_targets (Tensor): BBox targets of all anchors in the
-                    image with shape (N, 4).
-                bbox_weights (Tensor): BBox weights of all anchors in the
-                    image with shape (N, 4).
-                pos_inds (Tensor): Indices of positive anchor with shape
-                    (num_pos,).
-                neg_inds (Tensor): Indices of negative anchor with shape
-                    (num_neg,).
-        """
-        inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
-                                           img_meta['img_shape'][:2],
-                                           self.train_cfg.allowed_border)
-        if not inside_flags.any():
-            return (None, ) * 7
-        # assign gt and sample anchors
-        anchors = flat_anchors[inside_flags, :]
-
-        num_level_anchors_inside = self.get_num_level_anchors_inside(
-            num_level_anchors, inside_flags)
-        assign_result = self.assigner.assign(anchors, num_level_anchors_inside,
-                                             gt_bboxes, gt_bboxes_ignore,
-                                             gt_labels)
-
-        sampling_result = self.sampler.sample(assign_result, anchors,
-                                              gt_bboxes)
-
-        num_valid_anchors = anchors.shape[0]
-        bbox_targets = torch.zeros_like(anchors)
-        bbox_weights = torch.zeros_like(anchors)
-        labels = anchors.new_full((num_valid_anchors, ),
-                                  self.num_classes,
-                                  dtype=torch.long)
-        label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
-
-        pos_inds = sampling_result.pos_inds
-        neg_inds = sampling_result.neg_inds
-        if len(pos_inds) > 0:
-            pos_bbox_targets = sampling_result.pos_gt_bboxes
-            bbox_targets[pos_inds, :] = pos_bbox_targets
-            bbox_weights[pos_inds, :] = 1.0
-            if gt_labels is None:
-                # Only rpn gives gt_labels as None
-                # Foreground is the first class
-                labels[pos_inds] = 0
-            else:
-                labels[pos_inds] = gt_labels[
-                    sampling_result.pos_assigned_gt_inds]
-            if self.train_cfg.pos_weight <= 0:
-                label_weights[pos_inds] = 1.0
-            else:
-                label_weights[pos_inds] = self.train_cfg.pos_weight
-        if len(neg_inds) > 0:
-            label_weights[neg_inds] = 1.0
-
-        # map up to original set of anchors
-        if unmap_outputs:
-            num_total_anchors = flat_anchors.size(0)
-            anchors = unmap(anchors, num_total_anchors, inside_flags)
-            labels = unmap(
-                labels, num_total_anchors, inside_flags, fill=self.num_classes)
-            label_weights = unmap(label_weights, num_total_anchors,
-                                  inside_flags)
-            bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
-            bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
-
-        return (anchors, labels, label_weights, bbox_targets, bbox_weights,
-                pos_inds, neg_inds)
-
-    def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):
-        split_inside_flags = torch.split(inside_flags, num_level_anchors)
-        num_level_anchors_inside = [
-            int(flags.sum()) for flags in split_inside_flags
-        ]
-        return num_level_anchors_inside
diff --git a/spaces/RobinWZQ/CCLAP/hist_loss.py b/spaces/RobinWZQ/CCLAP/hist_loss.py
deleted file mode 100644
index d7cc88266cc36514356f6d5bd8b02133876371b3..0000000000000000000000000000000000000000
--- a/spaces/RobinWZQ/CCLAP/hist_loss.py
+++ /dev/null
@@ -1,208 +0,0 @@
-"""
-Copyright 2021 Mahmoud Afifi.
- Mahmoud Afifi, Marcus A. Brubaker, and Michael S. Brown. "HistoGAN: 
- Controlling Colors of GAN-Generated and Real Images via Color Histograms." 
- In CVPR, 2021.
-
- @inproceedings{afifi2021histogan,
-  title={Histo{GAN}: Controlling Colors of {GAN}-Generated and Real Images via 
-  Color Histograms},
-  author={Afifi, Mahmoud and Brubaker, Marcus A. and Brown, Michael S.},
-  booktitle={CVPR},
-  year={2021}
-}
-"""
-
-import torch
-import torch.nn as nn
-from PIL import Image
-import matplotlib.pyplot as plt
-import torch.nn.functional as F
-import torchvision.transforms as transforms
-import numpy as np
-
-EPS = 1e-6
-
-class RGBuvHistBlock(nn.Module):
-  def __init__(self, h=64, insz=150, resizing='interpolation',
-               method='inverse-quadratic', sigma=0.02, intensity_scale=True,
-               device='cuda'):
-    """ Computes the RGB-uv histogram feature of a given image.
-    Args:
-      h: histogram dimension size (scalar). The default value is 64.
-      insz: maximum size of the input image; if it is larger than this size, the 
-        image will be resized (scalar). Default value is 150 (i.e., 150 x 150 
-        pixels).
-      resizing: resizing method if applicable. Options are: 'interpolation' or 
-        'sampling'. Default is 'interpolation'.
-      method: the method used to count the number of pixels for each bin in the 
-        histogram feature. Options are: 'thresholding', 'RBF' (radial basis 
-        function), or 'inverse-quadratic'. Default value is 'inverse-quadratic'.
-      sigma: if the method value is 'RBF' or 'inverse-quadratic', then this is 
-        the sigma parameter of the kernel function. The default value is 0.02.
-      intensity_scale: boolean variable to use the intensity scale (I_y in 
-        Equation 2). Default value is True.
-
-    Methods:
-      forward: accepts input image and returns its histogram feature. Note that 
-        unless the method is 'thresholding', this is a differentiable function 
-        and can be easily integrated with the loss function. As mentioned in the
-         paper, the 'inverse-quadratic' was found more stable than 'RBF' in our 
-         training.
-    """
-    super(RGBuvHistBlock, self).__init__()
-    self.h = h
-    self.insz = insz
-    self.device = device
-    self.resizing = resizing
-    self.method = method
-    self.intensity_scale = intensity_scale
-    if self.method == 'thresholding':
-      self.eps = 6.0 / h
-    else:
-      self.sigma = sigma
-
-  def forward(self, x):
-    x = torch.clamp(x, 0, 1)
-    if x.shape[2] > self.insz or x.shape[3] > self.insz:
-      if self.resizing == 'interpolation':
-        x_sampled = F.interpolate(x, size=(self.insz, self.insz),
-                                  mode='bilinear', align_corners=False)
-      elif self.resizing == 'sampling':
-        inds_1 = torch.LongTensor(
-          np.linspace(0, x.shape[2], self.h, endpoint=False)).to(
-          device=self.device)
-        inds_2 = torch.LongTensor(
-          np.linspace(0, x.shape[3], self.h, endpoint=False)).to(
-          device=self.device)
-        x_sampled = x.index_select(2, inds_1)
-        x_sampled = x_sampled.index_select(3, inds_2)
-      else:
-        raise Exception(
-          f'Wrong resizing method. It should be: interpolation or sampling. '
-          f'But the given value is {self.resizing}.')
-    else:
-      x_sampled = x
-
-    L = x_sampled.shape[0]  # size of mini-batch
-    if x_sampled.shape[1] > 3:
-      x_sampled = x_sampled[:, :3, :, :]
-    X = torch.unbind(x_sampled, dim=0)
-    hists = torch.zeros((x_sampled.shape[0], 3, self.h, self.h)).to(
-      device=self.device)
-    for l in range(L):
-      I = torch.t(torch.reshape(X[l], (3, -1)))
-      II = torch.pow(I, 2)
-      if self.intensity_scale:
-        Iy = torch.unsqueeze(torch.sqrt(II[:, 0] + II[:, 1] + II[:, 2] + EPS), 
-                             dim=1)
-      else:
-        Iy = 1
-
-      Iu0 = torch.unsqueeze(torch.log(I[:, 0] + EPS) - torch.log(I[:, 1] + EPS),
-                            dim=1)
-      Iv0 = torch.unsqueeze(torch.log(I[:, 0] + EPS) - torch.log(I[:, 2] + EPS),
-                            dim=1)
-      diff_u0 = abs(
-        Iu0 - torch.unsqueeze(torch.tensor(np.linspace(-3, 3, num=self.h)),
-                              dim=0).to(self.device))
-      diff_v0 = abs(
-        Iv0 - torch.unsqueeze(torch.tensor(np.linspace(-3, 3, num=self.h)),
-                              dim=0).to(self.device))
-      if self.method == 'thresholding':
-        diff_u0 = torch.reshape(diff_u0, (-1, self.h)) <= self.eps / 2
-        diff_v0 = torch.reshape(diff_v0, (-1, self.h)) <= self.eps / 2
-      elif self.method == 'RBF':
-        diff_u0 = torch.pow(torch.reshape(diff_u0, (-1, self.h)),
-                            2) / self.sigma ** 2
-        diff_v0 = torch.pow(torch.reshape(diff_v0, (-1, self.h)),
-                            2) / self.sigma ** 2
-        diff_u0 = torch.exp(-diff_u0)  # Radial basis function
-        diff_v0 = torch.exp(-diff_v0)
-      elif self.method == 'inverse-quadratic':
-        diff_u0 = torch.pow(torch.reshape(diff_u0, (-1, self.h)),
-                            2) / self.sigma ** 2
-        diff_v0 = torch.pow(torch.reshape(diff_v0, (-1, self.h)),
-                            2) / self.sigma ** 2
-        diff_u0 = 1 / (1 + diff_u0)  # Inverse quadratic
-        diff_v0 = 1 / (1 + diff_v0)
-      else:
-        raise Exception(
-          f'Wrong kernel method. It should be either thresholding, RBF,' 
-          f' inverse-quadratic. But the given value is {self.method}.')
-      diff_u0 = diff_u0.type(torch.float32)
-      diff_v0 = diff_v0.type(torch.float32)
-      a = torch.t(Iy * diff_u0)
-      hists[l, 0, :, :] = torch.mm(a, diff_v0)
-
-      Iu1 = torch.unsqueeze(torch.log(I[:, 1] + EPS) - torch.log(I[:, 0] + EPS),
-                            dim=1)
-      Iv1 = torch.unsqueeze(torch.log(I[:, 1] + EPS) - torch.log(I[:, 2] + EPS),
-                            dim=1)
-      diff_u1 = abs(
-        Iu1 - torch.unsqueeze(torch.tensor(np.linspace(-3, 3, num=self.h)),
-                              dim=0).to(self.device))
-      diff_v1 = abs(
-        Iv1 - torch.unsqueeze(torch.tensor(np.linspace(-3, 3, num=self.h)),
-                              dim=0).to(self.device))
-
-      if self.method == 'thresholding':
-        diff_u1 = torch.reshape(diff_u1, (-1, self.h)) <= self.eps / 2
-        diff_v1 = torch.reshape(diff_v1, (-1, self.h)) <= self.eps / 2
-      elif self.method == 'RBF':
-        diff_u1 = torch.pow(torch.reshape(diff_u1, (-1, self.h)),
-                            2) / self.sigma ** 2
-        diff_v1 = torch.pow(torch.reshape(diff_v1, (-1, self.h)),
-                            2) / self.sigma ** 2
-        diff_u1 = torch.exp(-diff_u1)  # Gaussian
-        diff_v1 = torch.exp(-diff_v1)
-      elif self.method == 'inverse-quadratic':
-        diff_u1 = torch.pow(torch.reshape(diff_u1, (-1, self.h)),
-                            2) / self.sigma ** 2
-        diff_v1 = torch.pow(torch.reshape(diff_v1, (-1, self.h)),
-                            2) / self.sigma ** 2
-        diff_u1 = 1 / (1 + diff_u1)  # Inverse quadratic
-        diff_v1 = 1 / (1 + diff_v1)
-
-      diff_u1 = diff_u1.type(torch.float32)
-      diff_v1 = diff_v1.type(torch.float32)
-      a = torch.t(Iy * diff_u1)
-      hists[l, 1, :, :] = torch.mm(a, diff_v1)
-
-      Iu2 = torch.unsqueeze(torch.log(I[:, 2] + EPS) - torch.log(I[:, 0] + EPS),
-                            dim=1)
-      Iv2 = torch.unsqueeze(torch.log(I[:, 2] + EPS) - torch.log(I[:, 1] + EPS),
-                            dim=1)
-      diff_u2 = abs(
-        Iu2 - torch.unsqueeze(torch.tensor(np.linspace(-3, 3, num=self.h)),
-                              dim=0).to(self.device))
-      diff_v2 = abs(
-        Iv2 - torch.unsqueeze(torch.tensor(np.linspace(-3, 3, num=self.h)),
-                              dim=0).to(self.device))
-      if self.method == 'thresholding':
-        diff_u2 = torch.reshape(diff_u2, (-1, self.h)) <= self.eps / 2
-        diff_v2 = torch.reshape(diff_v2, (-1, self.h)) <= self.eps / 2
-      elif self.method == 'RBF':
-        diff_u2 = torch.pow(torch.reshape(diff_u2, (-1, self.h)),
-                            2) / self.sigma ** 2
-        diff_v2 = torch.pow(torch.reshape(diff_v2, (-1, self.h)),
-                            2) / self.sigma ** 2
-        diff_u2 = torch.exp(-diff_u2)  # Gaussian
-        diff_v2 = torch.exp(-diff_v2)
-      elif self.method == 'inverse-quadratic':
-        diff_u2 = torch.pow(torch.reshape(diff_u2, (-1, self.h)),
-                            2) / self.sigma ** 2
-        diff_v2 = torch.pow(torch.reshape(diff_v2, (-1, self.h)),
-                            2) / self.sigma ** 2
-        diff_u2 = 1 / (1 + diff_u2)  # Inverse quadratic
-        diff_v2 = 1 / (1 + diff_v2)
-      diff_u2 = diff_u2.type(torch.float32)
-      diff_v2 = diff_v2.type(torch.float32)
-      a = torch.t(Iy * diff_u2)
-      hists[l, 2, :, :] = torch.mm(a, diff_v2)
-
-    # normalization
-    hists_normalized = hists / (
-        ((hists.sum(dim=1)).sum(dim=1)).sum(dim=1).view(-1, 1, 1, 1) + EPS)
-
-    return hists_normalized
\ No newline at end of file
diff --git a/spaces/Rules99/YouRadiologist/app.py b/spaces/Rules99/YouRadiologist/app.py
deleted file mode 100644
index a3e39606cb7d6a358f341a694f65245060822b54..0000000000000000000000000000000000000000
--- a/spaces/Rules99/YouRadiologist/app.py
+++ /dev/null
@@ -1,576 +0,0 @@
-### FRAMEWORKS AND DEPENDENCIES
-import copy
-import os
-import sys
-from collections import OrderedDict
-from pathlib import Path
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib.cm as mpl_color_map
-from PIL import Image, ImageFilter
-from collections import OrderedDict
-import matplotlib as mpl
-import torch
-import torch.nn as nn
-from torchvision import datasets, models, transforms
-import torchxrayvision as xrv
-from pytorch_grad_cam import GradCAM
-# Other methods available: ScoreCAM, GradCAMPlusPlus, AblationCAM, XGradCAM, EigenCAM
-from pytorch_grad_cam.utils.image import show_cam_on_image
-from skimage.io import imread
-import pydicom as dicom
-import operator
-import mols2grid
-import streamlit.components.v1 as components
-from rdkit import Chem
-from rdkit.Chem.Descriptors import ExactMolWt
-from chembl_webresource_client.new_client import new_client
-import streamlit as st
-
-####UTILS.PY
-model_names = ['densenet121-res224-mimic_nb', 'densenet121-res224-mimic_ch']
-
-#### FUNCTIONS FOR STREAMLIT
-### Cache Drugs (Get Compounds found)
-@st.cache(allow_output_mutation=True)
-def getdrugs(name,phase):
-    drug_indication = new_client.drug_indication
-    molecules = new_client.molecule
-    obj = drug_indication.filter(efo_term__icontains=name)   
-    appdrugs = molecules.filter(molecule_chembl_id__in=[x['molecule_chembl_id'] for x in obj])
-    
-
-    if phase!=[]:
-        temp = None
-        for ph in phase:
-            dftemp = pd.DataFrame.from_dict(appdrugs.filter(max_phase=int(ph)))
-            dftemp["phase"] = int(ph)
-            if isinstance(temp,pd.DataFrame):
-                temp= pd.concat([temp,dftemp],axis=0)
-            else:
-                temp = dftemp
-
-        df = temp
-    else:
-        df = pd.DataFrame.from_dict(appdrugs)
-            
-    try:
-                df.dropna(subset=["molecule_properties","molecule_structures"],inplace=True)
-                
-                df["smiles"] = df.molecule_structures.apply(lambda x:x["canonical_smiles"])
-                df["Acceptors"] = df.molecule_properties.apply(lambda x :x["hba"])
-                df["Donnors"] = df.molecule_properties.apply(lambda x :x["hbd"])
-                df["mol_weight"] = df.molecule_properties.apply(lambda x :x["mw_freebase"])
-                df["Logp"] = df.molecule_properties.apply(lambda x :x["cx_logp"])
-
-                subs = ["pref_name","smiles","Acceptors","Donnors","mol_weight","Logp"]
-                df.dropna(subset=subs,inplace=True)
-                df["Acceptors"] =  df["Acceptors"].astype(int)
-                df["Donnors"] = df["Donnors"].astype(int)
-                df["mol_weight"] =  df["mol_weight"].astype(float)
-                df["Logp"] = df["Logp"] .astype(float)
-                
-                return df.loc[:,subs]
-    except:
-                return None
-### Title 
-def header():
-    
-        st.markdown("<h1 style='text-align: center;'>Chest Anomaly Identifier</h1>",unsafe_allow_html=True)
-        ### Description
-        st.markdown("""<p style='text-align: center;'>This is a pocket application that is mainly focused on aiding medical 
-professionals on their diagnostics and treatments for chest anomalies based on chest X-Rays. On this application, users 
-can upload a chest X-Ray image and a deep learning model will output the probability of 14 different anomalies taking 
- place on that image</p>""",unsafe_allow_html=True)
-
-        ### Image
-        st.image("doctors.jpg")
-### Controllers 
-def controllers2(model_probs):
-    
-    
-
-
-    # Select the anomaly to detect 
-    st.sidebar.markdown("<h1 style='text-align: center;'>Anomaly detection</h1>",unsafe_allow_html=True)
-    option_anomaly = st.sidebar.selectbox('Select Anomaly to detect',['Atelectasis', 'Consolidation', 'Pneumothorax','Edema', 'Effusion', 'Pneumonia', 'Cardiomegaly'],help='Select the anomaly you want to detect')
-    # Filtering anomalies
-    st.sidebar.markdown('''
-                    <h4 style='text-align: center;'>This controller is used to filter anomaly detection </h4>
-                    
-                    - N                : Select the number of most likely anomalies you want to detect
-                    - Threshold        : It measures how strict you are with the threshold
-                    - Colors           : For color intensity of anomaly detection
-                    - Obscureness      : For darker or lighter colors
-                    
-                  
-                    ''',unsafe_allow_html=True)
-
-    N      = st.sidebar.slider(label="N",min_value=1,max_value=5,value=3,step=1,help="Select the number of most likely anomalies you want to detect")      
-    threshold      = st.sidebar.slider(label="Threshold",min_value=0.0,max_value=1.0,value=0.3,step=0.1,help="Select the degree of confidence you want to detect. The more is the value the more strict you are in your detection")
-    colors      = st.sidebar.slider("Intense Colors",min_value=0.0,max_value=1.0,value=0.6,step=0.1,help="Select the color intensity you want to display at the time on detecting an anomaly. The higuer the value, the more intense the color")
-    obscureness      = st.sidebar.slider("Obscureness",min_value=0.0,max_value=1.0,value=0.8,step=0.1,help="Select the obscureness you want your colors have. The higuer the value, the more obscure is the color")
-    
-    
-    # Select the treatment
-
-    st.sidebar.markdown("<h1 style='text-align: center;'>Anomaly Treatment</h1>",unsafe_allow_html=True)
-    option = st.sidebar.selectbox('Select the anomaly for treatment',list(model_probs[model_names[0]].keys()),help='Select the anomaly you want to treat')
-    
-    
-
-    #### Filtering treatments
-    st.sidebar.markdown("<h1 style='text-align: center;'>Compound's filter</h1>",unsafe_allow_html=True)
-    ## Write the compound 
-    st.sidebar.markdown('''
-                    <h4 style='text-align: center;'>This controller sidebar is used to filter the compounds by the following features</h4>
-                    
-                    - Molecular weight : is the weight of a compound in grame per mol
-                    - LogP             : it measures how hydrophilic or hydrophobic  a compound is
-                    - NumDonnors       : number of chemical components that are able to deliver electrons to other chemical components
-                    - NumAcceptors     : number of chemical components that are able to accept electrons to other chemical components
-                    - MaxPhase         : select the phase in which the compound is stablished
-                    ''',unsafe_allow_html=True)
-    weight_cutoff = st.sidebar.slider(
-        label="Molecular weight",
-        min_value=0,
-        max_value=1000,
-        value=500,
-        step=10,
-        help="Look for compounds that have less or equal molecular weight than the value selected"
-    )
-    logp_cutoff = st.sidebar.slider(
-        label="LogP",
-        min_value=-10,
-        max_value=10,
-        value=5,
-        step=1,
-        help="Look for compounds that have less or equal logp than the value selected"
-    )
-    NumHDonors_cutoff = st.sidebar.slider(
-        label="NumHDonors",
-        min_value=0,
-        max_value=15,
-        value=5,
-        step=1,
-        help="Look for compounds that have less or equal donors weight than the value selected"
-    )
-    NumHAcceptors_cutoff = st.sidebar.slider(
-        label="NumHAcceptors",
-        min_value=0,
-        max_value=20,
-        value=10,
-        step=1,
-        help="Look for compounds that have less or equal acceptors weight than the value selected"
-    )
-    max_phase = st.sidebar.multiselect("Select Phase of the compound",
-            ['1','2', '3', '4'],
-            help=""" 
-            - Phase 1 : Phase I   of the compound in progress
-            - Phase 2 : Phase II  of the compound in progress
-            - Phase 3 : Phase III of the compound in progress
-            - Phase 4 : Approved compound          """
-        )
-    
-    return option_anomaly,threshold,colors,obscureness,option,weight_cutoff,logp_cutoff,NumHDonors_cutoff,NumHAcceptors_cutoff,max_phase,N
-
-    
-
-### MODEL.PY
-
-def takemodel(models:OrderedDict,cams:OrderedDict,weights="mimic_ch"):
-    """
-        Define models and cams of each model; tools useful for heatmap
-    Args:
-        models (OrderedDict[xrv.models.DenseNet]): the CNN of the model
-        cams (OrderedDict[GradCam]): Useful tool to make the heatmap
-        weights (str): Name of the pretrained model weights
-    """
-    models[weights] = xrv.models.DenseNet(weights=weights)
-    models[weights].eval()
-    target_layer = models[weights].features[-2]
-    cams[weights] = GradCAM(models[weights], target_layer, use_cuda=False)
-    return models,cams
-#### Read the image | Normalize
-def normalize(sample, maxval):
-    """
-    Scales images to be roughly [-1024 1024].
-     Args:
-        image (dicom,jp,png): image
-        maxval (int): maxvalue of the dicom image
-        
-    From torchxrayvision
-    """
-    
-    if sample.max() > maxval:
-        raise Exception("max image value ({}) higher than expected bound ({}).".format(sample.max(), maxval))
-    
-    sample = (2 * (sample.astype(np.float32) / maxval) - 1.) * 1024
-    #sample = sample / np.std(sample)
-    return sample
-
-def extensionimages(image_path):
-    """
-    Read Image of jpg dicom or png if it does not find the image returns skimage.io.imread(imgpath)
-    Args:
-    image_path (str): path of the image
-
-    """
-
-    if (str(image_path).find("jpg")!=-1) or (str(image_path).find("png")!=-1):
-
-        # sample = Image.open("JPG_test/0c4eb1e1-b801903c-bcebe8a4-3da9cd3c-3b94a27c.jpg")
-          sample = Image.open(image_path)
-          return np.array(sample)
-    if str(image_path).find("dcm")!=-1:
-        img = dicom.dcmread(image_path).pixel_array
-       
-        return img
-    else:
-        return imread(image_path)
-
-
-def read_image(img, tr=None,visualize=True):
-    """
-    Scales images to be roughly [-1024 1024].
-     Args:
-        image_path (str): path of the image    
-    From torchxrayvision
-    """
-    # img = extensionimages(image_path)
-    ### If black image has 3 dim get just one channel
-    
-
-    try:
-        img = img[:, :, 0]
-    ### Otherwise we take 2 channels
-    except IndexError:
-        pass
-    # Another option will be equalizing the image
-    # img = cv2.equalizeHist(img.astype(np.uint8))
-    img = ((img-img.min())/(img.max()-img.min())*255)
-    ### Normalize to values -1024 1024
-    img = normalize(img, 255)
-    # print(img.min(),img.max())
-    # Add color channel
-    img = img[None, :, :]
-    if tr is not None:                    
-        img = tr(img)
-    else:
-        raise Exception("You should pass a transformer to downsample the images")
-    return img
-
-#### Applly colormap on image
-def apply_colormap_on_image(org_im, activation, colormap_name, threshold=0.3,alpha=0.6):
-    """
-        Apply heatmap on image
-    Args:
-        org_img (PIL img): Original image (224x224)
-        activation_map (numpy arr): Activation map (grayscale) 0-255 (224x224)
-        colormap_name (str): Name of the colormap (colormap_name)
-        threshold (float): threshold at which to overlay heatmap (threshold that anomaly must surpass in terms of probability)
-        alpha (float): adjust the intense in which the model predicts
-    Original source: https://github.com/utkuozbulak/pytorch-cnn-visualizations
-
-    Added thresholding to activations.
-    """
-    ### Grayscale_cam
-    grayscale_cam = copy.deepcopy(activation)
-    # Get colormap just color type
-    color_map = mpl_color_map.get_cmap(colormap_name)
-    # Like map the activation function to the color map
-    
-    no_trans_heatmap = color_map(activation)
-    ### Not_trans_heatmap output (224x224x4 channels) (HSV-alpha channels)
-    ### H --> channel 0 H --> channel 1 H --> channel 2 alpha --> channel 3
-    
-    # Change alpha channel in colormap to make sure original image is displayed deepcopy
-    alpha_channel = 3
-    heatmap = copy.copy(no_trans_heatmap)
-    heatmap[:, :, alpha_channel] = alpha
-
-    # set to fully transparent if there is a very low activation (if the activation map is lower than the threshold)
-    idx = (grayscale_cam <= threshold)
-    # convert to a 3d index the shape of the image (expand the image by arrays) 
-    # Input shape 224x244 --- Output Shape 224x224x1
-    ignore_idx = np.expand_dims(np.zeros(grayscale_cam.shape, dtype=bool), 2)
- 
-    ### Idx is the four fimenation of the heatmap concatenate 224x224x3 with 224x224x1 ---> 224x224x4
-    idx = np.concatenate([ignore_idx]*3 + [np.expand_dims(idx, 2)], axis=2)
-    
-    
-    heatmap[idx] = 0
-    ### Inputs 224x224x4 
-    ### Scale to a 255 integer and map to PIL image
-    heatmap = Image.fromarray((heatmap*255).astype(np.uint8))
-    ### Color map activation scale to 255 PIL image
-    no_trans_heatmap = Image.fromarray((no_trans_heatmap*255).astype(np.uint8))
-    
-    # Apply heatmap on image
-    ### Create and RGBA image
-    heatmap_on_image = Image.new("RGBA", org_im.size)
-    ### org_im PIL converted onto RGBA and overlapped with heatmap on image
-    heatmap_on_image = Image.alpha_composite(heatmap_on_image, org_im.convert('RGBA'))
-    ### heatmap_on_image overlap with heatmap
-    heatmap_on_image = Image.alpha_composite(heatmap_on_image, heatmap)
-    return no_trans_heatmap, heatmap_on_image   
-
-
-
-def heatmap_core(image:np.array,pathologies:list,target:str,model_cmaps:list,threshold = 0.3, alpha = 0.8,obscureness = 0.8,fontsize=14)->plt:
-    """
-    Returns the heatmap of the image
-     Args:
-        image (np.array): Numpy Array Image (224x224)
-        target (str): Pathology to select
-        model_cmaps (list): colors to heatmap
-        pathologies(list): List of pathologies
-        threshold (float): Threshold to be more exigent or less exigent with the zone in which you are looking for
-        alpha (float): the higher this value, the more intense is the colormaps
-        obscureness (float) : the mhigher is this value the darker are the color maps
-        fontsize (float): adjust the fontsize of the plot
-    Original source: https://github.com/utkuozbulak/pytorch-cnn-visualizations
-    Modifications by : ### TeamMIMICIV  
-
-    Added thresholding to activations.
-    """
-    
-    #### Initializing models 
-    models = OrderedDict()
-    cams = OrderedDict()
-    for model_name in ['densenet121-res224-mimic_nb', 'densenet121-res224-mimic_ch']:
-      #### Adding the models and cams to the OrderedDict structure
-      models,cams = takemodel(models,cams,weights=model_name)
-    ### Get an image
-    input_tensor = torch.from_numpy(image).unsqueeze(0)
-
-    img = input_tensor.numpy()[0, 0, :, :]
-    img = (img / 1024.0 / 2.0) + 0.5
-    img = np.clip(img, 0, 1)
-    img = Image.fromarray(np.uint8(img * 255) , 'L')
-
-    # using the variable axs for multiple Axes
-    plt.figure(figsize=(10, 8))
-    
-    i = 0
-    for model_name, model in models.items():
-      # get our model performance
-      with torch.no_grad():
-          out = model(input_tensor).cpu()
-        
-      # reshape the dataset labels to match our model
-      # xrv.datasets.relabel_dataset(model.pathologies, d_pc)
-
-      # finds the index of the target based on the model pathologies
-      assert target  in pathologies,"Pathology input not in pathology maps"
-      target_category = model.pathologies.index(target)
-      grayscale_cam = cams[model_name](input_tensor=input_tensor, target_category=target_category)
-      # In this example grayscale_cam has only one image in the batch:
-      grayscale_cam = grayscale_cam[0, :]
-
-      _, img = apply_colormap_on_image(img, grayscale_cam, model_cmaps[i].name, threshold=threshold,alpha=alpha)
-
-      # add plot to add the color to the axis
-      plt.plot(0, 0, '-', lw=6, color=model_cmaps[i](0.7), label=model_name)
-
-      # what did we predict?
-      prob = np.round(out[0].detach().numpy()[target_category], 4)
-  
-      i += 1
-
-    plt.legend(fontsize=fontsize)
-    plt.imshow(img, cmap='bone')
-    plt.axis('off')
-    # plt.show()
-    return plt
-
-
-def heatmap(img,target,threshold = 0.3, alpha = 0.8,obscureness = 0.8,fontsize=14):
-    """
-    Returns the heatmap of the image
-     Args:
-        imgpath (str): Name of the image path 
-        target (str): Pathology to select
-       
-        threshold (float): Threshold to be more exigent or less exigent with the zone in which you are looking for
-        alpha (float): the higher this value, the more intense is the colormaps
-        obscureness (float) : the mhigher is this value the darker are the color maps
-        fontsize (float): adjust the fontsize of the plot
-    Original source: https://github.com/utkuozbulak/pytorch-cnn-visualizations
-    Modifications by : ### TeamMIMICIV  
-    Added thresholding to activations.
-    """
-    pathologies = ['Atelectasis', 'Consolidation', 'Pneumothorax','Edema', 'Effusion', 'Pneumonia', 'Cardiomegaly']
-    model_cmaps = [mpl_color_map.Purples, mpl_color_map.Greens_r]
-    tr = transforms.Compose(
-        [xrv.datasets.XRayCenterCrop(), xrv.datasets.XRayResizer(224, engine='cv2')]
-    )
-    image = read_image(img,tr=tr)
-    return heatmap_core(image,pathologies,target,model_cmaps,threshold = threshold, alpha = alpha,obscureness = obscureness,fontsize=fontsize)
-
-
-#### Initializing models 
-def probtemp(image:np.array)->dict:
-    """
-    Returns the output probabilities of two models
-     Args:
-        image (np.array): Numpy already scaled 
-    """
-    #### Initializing models 
-    models = OrderedDict()
-    cams = OrderedDict()
-    
-    for model_name in ['densenet121-res224-mimic_nb', 'densenet121-res224-mimic_ch']:
-      #### Adding the models and cams to the OrderedDict structure
-      models,cams = takemodel(models,cams,weights=model_name)
-    ### Get an image
-    input_tensor = torch.from_numpy(image).unsqueeze(0)
-
-    img = input_tensor.numpy()[0, 0, :, :]
-    img = (img / 1024.0 / 2.0) + 0.5
-    img = np.clip(img, 0, 1)
-    img = Image.fromarray(np.uint8(img * 255) , 'L')
-
-    model_dics = {}
-    for model_name, model in models.items():
-      # get our model performance
-      with torch.no_grad():
-          out = model(input_tensor).cpu()
-          model_dics[model_name] = {key:value  for (key,value)  in zip(model.pathologies, out.detach().numpy()[0]) if len(key)>2}
-    return model_dics
-def getprobs(img):
-    """
-    Returns the heatmap of the image
-     Args:
-        imgpath (str): Name of the image path 
-        target (str): Pathology to select
-       
-        threshold (float): Threshold to be more exigent or less exigent with the zone in which you are looking for
-        alpha (float): the higher this value, the more intense is the colormaps
-        obscureness (float) : the mhigher is this value the darker are the color maps
-        fontsize (float): adjust the fontsize of the plot
-    Original source: https://github.com/utkuozbulak/pytorch-cnn-visualizations
-    Modifications by : ### TeamMIMICIV  
-    Added thresholding to activations.
-    """
-    pathologies = ['Atelectasis', 'Consolidation', 'Pneumothorax','Edema', 'Effusion', 'Pneumonia', 'Cardiomegaly']
-    tr = transforms.Compose(
-        [xrv.datasets.XRayCenterCrop(), xrv.datasets.XRayResizer(224, engine='cv2')]
-    )
-    image = read_image(img,tr=tr)
-    return probtemp(image)
-
-
-
-
-#### MORE FUNCTIONS.PY
-### Get the probability of models
-def sortedmodels(probs,model_name):
-            """
-            Sorts the probability model 
-            Args:
-            probs (dict) : dictionary of model probabilities
-            model_name (str) : name of the model
-            """
-            ### Probability of the model
-            promodels = probs[model_name]
-            # Sort results by the descending probability order 
-            return dict(sorted(promodels.items(), key=operator.itemgetter(1),reverse=True))
-def disprobs(model_probs,model_name,N):
-    """        
-            Displays the probability models and Sorts the probability model 
-            Args:
-            model_probs (dict) : dictionary of model probabilities
-            model_name (str) : name of the model
-            """
-    exp1 = st.expander(f"Probabilities for {model_name}")
-    pr = sortedmodels(model_probs,model_name)
-    for cnt,(key,value) in enumerate(pr.items()):
-         if cnt==N:
-             break
-         exp1.metric(label=key, value=str(cnt+1), delta=str(value))
-
-def getfile(uploaded_file=None):
-    """
-    Get the file uploaded
-    """
-    if uploaded_file is not None:
-        return extensionimages(uploaded_file)
-    return extensionimages("example.dcm")
-### Error in case we do not find compounds
-def error(option):
-    option = str(option).replace(" ","%20")
-    par3 = f'https://www.ebi.ac.uk/chembl/g/#search_results/all/query={option})'
-    par2 =  "<a href = {} >".format(par3)
-    par =par2 +"ChEBML" + "</a>"
-    
-    st.markdown("<p style='text-align: center;'>We have not found compounds for this illness; for more information visit this link: {}</p>".format(par), unsafe_allow_html=True)
-
-def main():
-
-            sys.path.insert(0,"..")
-            ### Title
-            st.set_page_config(layout="wide")
-            header()
-            ### Uploader  
-            uploaded_file = st.file_uploader("Choose an X-Ray image to detect anomalies of the chest (the file must be a dicom extension or jpg)",)
-            #### Get the image
-
-            imgdef = getfile(uploaded_file)
-            __,col4,_,col5,_,col6,__ = st.columns((0.1,1,0.2,2.5,0.2,1,0.1)) 
-            col5.markdown("<h3 style='text-align: center;'>Input Image</h3>",unsafe_allow_html=True)
-            with col5:
-                ### Plot the input image
-                fig, ax = plt.subplots()
-                ax.imshow(imgdef,cmap="gray")
-                st.pyplot(fig=fig)
-            # Printing the possibility of having anomalies
-
-            __,col1,_,col3,_,col2,__ = st.columns((0.1,1,0.2,2.5,0.2,1,0.1)) 
-            col3.markdown("<h3 style='text-align: center;'>Anomaly Detection</h3>",unsafe_allow_html=True)
-            model_probs  = getprobs(imgdef)
-            option_anomaly,threshold,colors,obscureness,option,weight_cutoff,logp_cutoff,NumHDonors_cutoff,NumHAcceptors_cutoff,max_phase,N = controllers2(model_probs)
-            ### MODEL 1
-            with col1:
-                disprobs(model_probs,model_names[0],N)
-            ### MODEL_2
-            with col2:
-                disprobs(model_probs,model_names[1],N)
-
-            ### ANOMALY HEATMAP
-            with col3:
-                plot = heatmap(imgdef,option_anomaly,threshold,colors,obscureness,14)
-                st.pyplot(plot)
-            df = getdrugs(option,max_phase)
-
-            st.markdown("<h3 style='text-align: center;'>Compounds for {}</h3>".format(option),unsafe_allow_html=True)
-            __,col10,col11,_,_,col12,__ = st.columns((0.1,0.8,2.5,0.2,0.2,1,0.1)) 
-
-            ### TREATMENT FILTERING
-            if df is not None:
-                        
-                        #### Filter dataframe by controllers
-                        df_result = df[df["mol_weight"] < weight_cutoff]
-                        df_result2 = df_result[df_result["Logp"] < logp_cutoff]
-                        df_result3 = df_result2[df_result2["Donnors"] < NumHDonors_cutoff]
-                        df_result4 = df_result3[df_result3["Acceptors"] < NumHAcceptors_cutoff]
-                    
-
-                        
-                        if len(df_result4)==0:
-                            
-                            error(option)
-                        else:
-                                raw_html = mols2grid.display(df_result,  mapping={"smiles": "SMILES","pref_name":"Name","Acceptors":"Acceptors","Donnors":"Donnors","Logp":"Logp","mol_weight":"mol_weight"},
-                                subset=["img","Name"],tooltip=["Name","Acceptors","Donnors","Logp","mol_weight"],tooltip_placement="top",tooltip_trigger="click hover")._repr_html_()
-                                with col11:
-
-                                    components.html(raw_html, width=900, height=900, scrolling=True)
-            #### We do not find compounds for the anomaly 
-            else:
-                
-                error(option)
-
-if __name__=="__main__":
-    main()
\ No newline at end of file
diff --git a/spaces/RustX/CSV-ChatBot/modules/chatbot.py b/spaces/RustX/CSV-ChatBot/modules/chatbot.py
deleted file mode 100644
index 14ea2ace983e25b53c36ed6c82238b5b2e63b615..0000000000000000000000000000000000000000
--- a/spaces/RustX/CSV-ChatBot/modules/chatbot.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import streamlit as st
-from langchain.chat_models import ChatOpenAI
-from langchain.chains import ConversationalRetrievalChain
-from langchain.prompts.prompt import PromptTemplate
-
-
-class Chatbot:
-    _template = """다음 대화와 후속 질문이 주어지면 후속 질문을 독립형 질문으로 바꾸십시오.
-    질문이 CSV 파일의 정보에 관한 것이라고 가정할 수 있습니다.
-    Chat History:
-    {chat_history}
-    Follow-up entry: {question}
-    Standalone question:"""
-
-    CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
-
-    qa_template = """"csv 파일의 정보를 기반으로 질문에 답하는 AI 대화 비서입니다.
-    csv 파일의 데이터와 질문이 제공되며 사용자가 필요한 정보를 찾도록 도와야 합니다. 
-    알고 있는 정보에 대해서만 응답하십시오. 답을 지어내려고 하지 마세요.
-    귀하의 답변은 짧고 친근하며 동일한 언어로 작성되어야 합니다.
-    question: {question}
-    =========
-    {context}
-    =======
-    """
-
-    QA_PROMPT = PromptTemplate(template=qa_template, input_variables=["question", "context"])
-
-    def __init__(self, model_name, temperature, vectors):
-        self.model_name = model_name
-        self.temperature = temperature
-        self.vectors = vectors
-
-    def conversational_chat(self, query):
-        """
-        Starts a conversational chat with a model via Langchain
-        """
-
-        chain = ConversationalRetrievalChain.from_llm(
-            llm=ChatOpenAI(model_name=self.model_name, temperature=self.temperature),
-            condense_question_prompt=self.CONDENSE_QUESTION_PROMPT,
-            qa_prompt=self.QA_PROMPT,
-            retriever=self.vectors.as_retriever(),
-        )
-        result = chain({"question": query, "chat_history": st.session_state["history"]})
-
-        st.session_state["history"].append((query, result["answer"]))
-
-        return result["answer"]
\ No newline at end of file
diff --git a/spaces/RustX/CSV-ChatBot/modules/embedder.py b/spaces/RustX/CSV-ChatBot/modules/embedder.py
deleted file mode 100644
index 308695fadb03cbe4f608aa7738a4ea9c90fac5b5..0000000000000000000000000000000000000000
--- a/spaces/RustX/CSV-ChatBot/modules/embedder.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import os
-import pickle
-import tempfile
-from langchain.document_loaders.csv_loader import CSVLoader
-from langchain.vectorstores import FAISS
-from langchain.embeddings.openai import OpenAIEmbeddings
-
-
-class Embedder:
-    def __init__(self):
-        self.PATH = "embeddings"
-        self.createEmbeddingsDir()
-
-    def createEmbeddingsDir(self):
-        """
-        Creates a directory to store the embeddings vectors
-        """
-        if not os.path.exists(self.PATH):
-            os.mkdir(self.PATH)
-
-    def storeDocEmbeds(self, file, filename):
-        """
-        Stores document embeddings using Langchain and FAISS
-        """
-        # Write the uploaded file to a temporary file
-        with tempfile.NamedTemporaryFile(mode="wb", delete=False) as tmp_file:
-            tmp_file.write(file)
-            tmp_file_path = tmp_file.name
-
-        # Load the data from the file using Langchain
-        loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8")
-        data = loader.load_and_split()
-
-        # Create an embeddings object using Langchain
-        embeddings = OpenAIEmbeddings()
-
-        # Store the embeddings vectors using FAISS
-        vectors = FAISS.from_documents(data, embeddings)
-        os.remove(tmp_file_path)
-
-        # Save the vectors to a pickle file
-        with open(f"{self.PATH}/{filename}.pkl", "wb") as f:
-            pickle.dump(vectors, f)
-
-    def getDocEmbeds(self, file, filename):
-        """
-        Retrieves document embeddings
-        """
-        # Check if embeddings vectors have already been stored in a pickle file
-        if not os.path.isfile(f"{self.PATH}/{filename}.pkl"):
-            # If not, store the vectors using the storeDocEmbeds function
-            self.storeDocEmbeds(file, filename)
-
-        # Load the vectors from the pickle file
-        with open(f"{self.PATH}/{filename}.pkl", "rb") as f:
-            vectors = pickle.load(f)
-
-        return vectors
\ No newline at end of file
diff --git a/spaces/Sa-m/Vehicles-Detection-Custom-YoloV7/models/experimental.py b/spaces/Sa-m/Vehicles-Detection-Custom-YoloV7/models/experimental.py
deleted file mode 100644
index 3fa5c12e314ce4569a1df1a624059722b07846f0..0000000000000000000000000000000000000000
--- a/spaces/Sa-m/Vehicles-Detection-Custom-YoloV7/models/experimental.py
+++ /dev/null
@@ -1,262 +0,0 @@
-import numpy as np
-import random
-import torch
-import torch.nn as nn
-
-from models.common import Conv, DWConv
-from utils.google_utils import attempt_download
-
-
-class CrossConv(nn.Module):
-    # Cross Convolution Downsample
-    def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
-        # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
-        super(CrossConv, self).__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, (1, k), (1, s))
-        self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
-        self.add = shortcut and c1 == c2
-
-    def forward(self, x):
-        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class Sum(nn.Module):
-    # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
-    def __init__(self, n, weight=False):  # n: number of inputs
-        super(Sum, self).__init__()
-        self.weight = weight  # apply weights boolean
-        self.iter = range(n - 1)  # iter object
-        if weight:
-            self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True)  # layer weights
-
-    def forward(self, x):
-        y = x[0]  # no weight
-        if self.weight:
-            w = torch.sigmoid(self.w) * 2
-            for i in self.iter:
-                y = y + x[i + 1] * w[i]
-        else:
-            for i in self.iter:
-                y = y + x[i + 1]
-        return y
-
-
-class MixConv2d(nn.Module):
-    # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
-    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
-        super(MixConv2d, self).__init__()
-        groups = len(k)
-        if equal_ch:  # equal c_ per group
-            i = torch.linspace(0, groups - 1E-6, c2).floor()  # c2 indices
-            c_ = [(i == g).sum() for g in range(groups)]  # intermediate channels
-        else:  # equal weight.numel() per group
-            b = [c2] + [0] * groups
-            a = np.eye(groups + 1, groups, k=-1)
-            a -= np.roll(a, 1, axis=1)
-            a *= np.array(k) ** 2
-            a[0] = 1
-            c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b
-
-        self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
-        self.bn = nn.BatchNorm2d(c2)
-        self.act = nn.LeakyReLU(0.1, inplace=True)
-
-    def forward(self, x):
-        return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
-
-
-class Ensemble(nn.ModuleList):
-    # Ensemble of models
-    def __init__(self):
-        super(Ensemble, self).__init__()
-
-    def forward(self, x, augment=False):
-        y = []
-        for module in self:
-            y.append(module(x, augment)[0])
-        # y = torch.stack(y).max(0)[0]  # max ensemble
-        # y = torch.stack(y).mean(0)  # mean ensemble
-        y = torch.cat(y, 1)  # nms ensemble
-        return y, None  # inference, train output
-
-
-
-
-
-class ORT_NMS(torch.autograd.Function):
-    '''ONNX-Runtime NMS operation'''
-    @staticmethod
-    def forward(ctx,
-                boxes,
-                scores,
-                max_output_boxes_per_class=torch.tensor([100]),
-                iou_threshold=torch.tensor([0.45]),
-                score_threshold=torch.tensor([0.25])):
-        device = boxes.device
-        batch = scores.shape[0]
-        num_det = random.randint(0, 100)
-        batches = torch.randint(0, batch, (num_det,)).sort()[0].to(device)
-        idxs = torch.arange(100, 100 + num_det).to(device)
-        zeros = torch.zeros((num_det,), dtype=torch.int64).to(device)
-        selected_indices = torch.cat([batches[None], zeros[None], idxs[None]], 0).T.contiguous()
-        selected_indices = selected_indices.to(torch.int64)
-        return selected_indices
-
-    @staticmethod
-    def symbolic(g, boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold):
-        return g.op("NonMaxSuppression", boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold)
-
-
-class TRT_NMS(torch.autograd.Function):
-    '''TensorRT NMS operation'''
-    @staticmethod
-    def forward(
-        ctx,
-        boxes,
-        scores,
-        background_class=-1,
-        box_coding=1,
-        iou_threshold=0.45,
-        max_output_boxes=100,
-        plugin_version="1",
-        score_activation=0,
-        score_threshold=0.25,
-    ):
-        batch_size, num_boxes, num_classes = scores.shape
-        num_det = torch.randint(0, max_output_boxes, (batch_size, 1), dtype=torch.int32)
-        det_boxes = torch.randn(batch_size, max_output_boxes, 4)
-        det_scores = torch.randn(batch_size, max_output_boxes)
-        det_classes = torch.randint(0, num_classes, (batch_size, max_output_boxes), dtype=torch.int32)
-        return num_det, det_boxes, det_scores, det_classes
-
-    @staticmethod
-    def symbolic(g,
-                 boxes,
-                 scores,
-                 background_class=-1,
-                 box_coding=1,
-                 iou_threshold=0.45,
-                 max_output_boxes=100,
-                 plugin_version="1",
-                 score_activation=0,
-                 score_threshold=0.25):
-        out = g.op("TRT::EfficientNMS_TRT",
-                   boxes,
-                   scores,
-                   background_class_i=background_class,
-                   box_coding_i=box_coding,
-                   iou_threshold_f=iou_threshold,
-                   max_output_boxes_i=max_output_boxes,
-                   plugin_version_s=plugin_version,
-                   score_activation_i=score_activation,
-                   score_threshold_f=score_threshold,
-                   outputs=4)
-        nums, boxes, scores, classes = out
-        return nums, boxes, scores, classes
-
-
-class ONNX_ORT(nn.Module):
-    '''onnx module with ONNX-Runtime NMS operation.'''
-    def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=640, device=None):
-        super().__init__()
-        self.device = device if device else torch.device("cpu")
-        self.max_obj = torch.tensor([max_obj]).to(device)
-        self.iou_threshold = torch.tensor([iou_thres]).to(device)
-        self.score_threshold = torch.tensor([score_thres]).to(device)
-        self.max_wh = max_wh # if max_wh != 0 : non-agnostic else : agnostic
-        self.convert_matrix = torch.tensor([[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]],
-                                           dtype=torch.float32,
-                                           device=self.device)
-
-    def forward(self, x):
-        boxes = x[:, :, :4]
-        conf = x[:, :, 4:5]
-        scores = x[:, :, 5:]
-        scores *= conf
-        boxes @= self.convert_matrix
-        max_score, category_id = scores.max(2, keepdim=True)
-        dis = category_id.float() * self.max_wh
-        nmsbox = boxes + dis
-        max_score_tp = max_score.transpose(1, 2).contiguous()
-        selected_indices = ORT_NMS.apply(nmsbox, max_score_tp, self.max_obj, self.iou_threshold, self.score_threshold)
-        X, Y = selected_indices[:, 0], selected_indices[:, 2]
-        selected_boxes = boxes[X, Y, :]
-        selected_categories = category_id[X, Y, :].float()
-        selected_scores = max_score[X, Y, :]
-        X = X.unsqueeze(1).float()
-        return torch.cat([X, selected_boxes, selected_categories, selected_scores], 1)
-
-class ONNX_TRT(nn.Module):
-    '''onnx module with TensorRT NMS operation.'''
-    def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None ,device=None):
-        super().__init__()
-        assert max_wh is None
-        self.device = device if device else torch.device('cpu')
-        self.background_class = -1,
-        self.box_coding = 1,
-        self.iou_threshold = iou_thres
-        self.max_obj = max_obj
-        self.plugin_version = '1'
-        self.score_activation = 0
-        self.score_threshold = score_thres
-
-    def forward(self, x):
-        boxes = x[:, :, :4]
-        conf = x[:, :, 4:5]
-        scores = x[:, :, 5:]
-        scores *= conf
-        num_det, det_boxes, det_scores, det_classes = TRT_NMS.apply(boxes, scores, self.background_class, self.box_coding,
-                                                                    self.iou_threshold, self.max_obj,
-                                                                    self.plugin_version, self.score_activation,
-                                                                    self.score_threshold)
-        return num_det, det_boxes, det_scores, det_classes
-
-
-class End2End(nn.Module):
-    '''export onnx or tensorrt model with NMS operation.'''
-    def __init__(self, model, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None, device=None):
-        super().__init__()
-        device = device if device else torch.device('cpu')
-        assert isinstance(max_wh,(int)) or max_wh is None
-        self.model = model.to(device)
-        self.model.model[-1].end2end = True
-        self.patch_model = ONNX_TRT if max_wh is None else ONNX_ORT
-        self.end2end = self.patch_model(max_obj, iou_thres, score_thres, max_wh, device)
-        self.end2end.eval()
-
-    def forward(self, x):
-        x = self.model(x)
-        x = self.end2end(x)
-        return x
-
-
-
-
-
-def attempt_load(weights, map_location=None):
-    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
-    model = Ensemble()
-    for w in weights if isinstance(weights, list) else [weights]:
-        attempt_download(w)
-        ckpt = torch.load(w, map_location=map_location)  # load
-        model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval())  # FP32 model
-    
-    # Compatibility updates
-    for m in model.modules():
-        if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
-            m.inplace = True  # pytorch 1.7.0 compatibility
-        elif type(m) is nn.Upsample:
-            m.recompute_scale_factor = None  # torch 1.11.0 compatibility
-        elif type(m) is Conv:
-            m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
-    
-    if len(model) == 1:
-        return model[-1]  # return model
-    else:
-        print('Ensemble created with %s\n' % weights)
-        for k in ['names', 'stride']:
-            setattr(model, k, getattr(model[-1], k))
-        return model  # return ensemble
-
-
diff --git a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/opt.py b/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/opt.py
deleted file mode 100644
index 192ed325af080361fcd32b891ba5ab04e90e5712..0000000000000000000000000000000000000000
--- a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/opt.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import argparse
-
-parser = argparse.ArgumentParser(description='PyTorch AlphaPose Training')
-
-"----------------------------- General options -----------------------------"
-parser.add_argument('--expID', default='default', type=str,
-                    help='Experiment ID')
-parser.add_argument('--dataset', default='coco', type=str,
-                    help='Dataset choice: mpii | coco')
-parser.add_argument('--nThreads', default=30, type=int,
-                    help='Number of data loading threads')
-parser.add_argument('--debug', default=False, type=bool,
-                    help='Print the debug information')
-parser.add_argument('--snapshot', default=1, type=int,
-                    help='How often to take a snapshot of the model (0 = never)')
-
-"----------------------------- AlphaPose options -----------------------------"
-parser.add_argument('--addDPG', default=False, type=bool,
-                    help='Train with data augmentation')
-parser.add_argument('--sp', default=False, action='store_true',
-                    help='Use single process for pytorch')
-parser.add_argument('--profile', default=False, action='store_true',
-                    help='add speed profiling at screen output')
-
-"----------------------------- Model options -----------------------------"
-parser.add_argument('--netType', default='hgPRM', type=str,
-                    help='Options: hgPRM | resnext')
-parser.add_argument('--loadModel', default=None, type=str,
-                    help='Provide full path to a previously trained model')
-parser.add_argument('--Continue', default=False, type=bool,
-                    help='Pick up where an experiment left off')
-parser.add_argument('--nFeats', default=256, type=int,
-                    help='Number of features in the hourglass')
-parser.add_argument('--nClasses', default=33, type=int,
-                    help='Number of output channel')
-parser.add_argument('--nStack', default=4, type=int,
-                    help='Number of hourglasses to stack')
-
-"----------------------------- Hyperparameter options -----------------------------"
-parser.add_argument('--fast_inference', default=True, type=bool,
-                    help='Fast inference')
-parser.add_argument('--use_pyranet', default=True, type=bool,
-                    help='use pyranet')
-
-"----------------------------- Hyperparameter options -----------------------------"
-parser.add_argument('--LR', default=2.5e-4, type=float,
-                    help='Learning rate')
-parser.add_argument('--momentum', default=0, type=float,
-                    help='Momentum')
-parser.add_argument('--weightDecay', default=0, type=float,
-                    help='Weight decay')
-parser.add_argument('--crit', default='MSE', type=str,
-                    help='Criterion type')
-parser.add_argument('--optMethod', default='rmsprop', type=str,
-                    help='Optimization method: rmsprop | sgd | nag | adadelta')
-
-"----------------------------- Training options -----------------------------"
-parser.add_argument('--nEpochs', default=50, type=int,
-                    help='Number of hourglasses to stack')
-parser.add_argument('--epoch', default=0, type=int,
-                    help='Current epoch')
-parser.add_argument('--trainBatch', default=40, type=int,
-                    help='Train-batch size')
-parser.add_argument('--validBatch', default=20, type=int,
-                    help='Valid-batch size')
-parser.add_argument('--trainIters', default=0, type=int,
-                    help='Total train iters')
-parser.add_argument('--valIters', default=0, type=int,
-                    help='Total valid iters')
-parser.add_argument('--init', default=None, type=str,
-                    help='Initialization')
-
-"----------------------------- Data options -----------------------------"
-parser.add_argument('--inputResH', default=320, type=int,
-                    help='Input image height')
-parser.add_argument('--inputResW', default=256, type=int,
-                    help='Input image width')
-parser.add_argument('--outputResH', default=80, type=int,
-                    help='Output heatmap height')
-parser.add_argument('--outputResW', default=64, type=int,
-                    help='Output heatmap width')
-parser.add_argument('--scale', default=0.25, type=float,
-                    help='Degree of scale augmentation')
-parser.add_argument('--rotate', default=30, type=float,
-                    help='Degree of rotation augmentation')
-parser.add_argument('--hmGauss', default=1, type=int,
-                    help='Heatmap gaussian size')
-
-"----------------------------- PyraNet options -----------------------------"
-parser.add_argument('--baseWidth', default=9, type=int,
-                    help='Heatmap gaussian size')
-parser.add_argument('--cardinality', default=5, type=int,
-                    help='Heatmap gaussian size')
-parser.add_argument('--nResidual', default=1, type=int,
-                    help='Number of residual modules at each location in the pyranet')
-
-"----------------------------- Distribution options -----------------------------"
-parser.add_argument('--dist', dest='dist', type=int, default=1,
-                    help='distributed training or not')
-parser.add_argument('--backend', dest='backend', type=str, default='gloo',
-                    help='backend for distributed training')
-parser.add_argument('--port', dest='port',
-                    help='port of server')
-
-"----------------------------- Detection options -----------------------------"
-parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]',
-                    default='res152')
-parser.add_argument('--indir', dest='inputpath',
-                    help='image-directory', default="")
-parser.add_argument('--list', dest='inputlist',
-                    help='image-list', default="")
-parser.add_argument('--mode', dest='mode',
-                    help='detection mode, fast/normal/accurate', default="normal")
-parser.add_argument('--outdir', dest='outputpath',
-                    help='output-directory', default="examples/res/")
-parser.add_argument('--inp_dim', dest='inp_dim', type=str, default='608',
-                    help='inpdim')
-parser.add_argument('--conf', dest='confidence', type=float, default=0.05,
-                    help='bounding box confidence threshold')
-parser.add_argument('--nms', dest='nms_thesh', type=float, default=0.6,
-                    help='bounding box nms threshold')
-parser.add_argument('--save_img', default=False, action='store_true',
-                    help='save result as image')
-parser.add_argument('--vis', default=False, action='store_true',
-                    help='visualize image')
-parser.add_argument('--matching', default=False, action='store_true',
-                    help='use best matching')
-parser.add_argument('--format', type=str,
-                    help='save in the format of cmu or coco or openpose, option: coco/cmu/open')
-parser.add_argument('--detbatch', type=int, default=1,
-                    help='detection batch size')
-parser.add_argument('--posebatch', type=int, default=80,
-                    help='pose estimation maximum batch size')
-
-"----------------------------- Video options -----------------------------"
-parser.add_argument('--video', dest='video',
-                    help='video-name', default="")
-parser.add_argument('--webcam', dest='webcam', type=str,
-                    help='webcam number', default='0')
-parser.add_argument('--save_video', dest='save_video',
-                    help='whether to save rendered video', default=False, action='store_true')
-parser.add_argument('--vis_fast', dest='vis_fast',
-                    help='use fast rendering', action='store_true', default=False)
-opt = parser.parse_args()
-
-opt.num_classes = 80
diff --git a/spaces/SegevC/bf_predictor/app.py b/spaces/SegevC/bf_predictor/app.py
deleted file mode 100644
index 003993184b2d807e174f199fc03c1775a217e962..0000000000000000000000000000000000000000
--- a/spaces/SegevC/bf_predictor/app.py
+++ /dev/null
@@ -1,89 +0,0 @@
-from fastai import *
-from fastcore.all import *
-from fastai.vision.all import *
-import pandas as pd 
-import re
-import gradio as gr
-
-
-def get_x(data_set):
-  return Path(data_set['image_path'])
-
-def get_y(data_set):
-  return data_set['bf_est']
-
-def new_splitter(df):
-  # Get the unique values in the 'id' column
-  unique_ids = df['id'].unique()
-
-  # Shuffle the unique values
-  np.random.seed(42)
-  np.random.shuffle(unique_ids)
-
-  # Calculate the number of unique values to be included in the first dataframe
-  num_unique_in_test = int(np.ceil(len(unique_ids) * 0.8))
-
-  # Get the first 'num_unique_in_df1' unique values
-  test_ids = unique_ids[:num_unique_in_test]
-
-  # Get the rows of the original dataframe that contain the 'df1_ids'
-  test = df.index[df['id'].isin(test_ids)].tolist()
-
-  # Get the rest of the rows from the original dataframe
-  valid = df.index[~df['id'].isin(test_ids)].tolist()
-  
-  return test, valid
-
-title = "Body Fat Predictor"
-description = "A Body Fat Predictor trained on the subreddit \"guessmybf\". \
-For best preformence upload a front facing photo. \n The Range button allows \
-you to adjust the range of the predicted body fat percentage. \
-Choosing a higher range results in a less specific prediction but a more ecurate estimate, \
-while a lower range provides a less precise estimate. \n The Examples section \
-provides visual examples of different body fat percentages and how they appear \
-on an individual's body. It is important to note that two individuals can have \
-the same body fat percentage but look different due to differences in body composition \
-(such as muscle mass)."
-
-learner = load_learner("bf_model.pkl")
-
-# def predict_bf(img):
-#   return round(float(learner.predict(img)[1]),2)
-
-def predict_bf(img, range=0):
-  if range not in [0,1,2,3,4]:
-    range = 1
-  prediction = math.floor(float(learner.predict(img)[1]))
-  if range ==0:
-    return f"{str(prediction)}%"
-  else:
-     return  f"{str(round(prediction) - range)}-{str(round(prediction) + range)}%" 
-
-
-examples=[
-        ['./exp/6-8.jpg', '6-8%'],  
-        ['./exp/7-9.jpg', '7-9%'], 
-        ['./exp/9-11.jpg', '9-11%'],
-        ['./exp/9-11v2.jpg', '9-11%'],
-        ['./exp/10-12.jpg', '10-12%'],
-        ['./exp/10-12v2.jpg', '10-12%'],
-        ['./exp/11-13.jpg', '11-13%'],
-        ['./exp/13-15.jpg', '13-15%'],
-        ['./exp/13-15v2.jpg', '13-15%'],
-        ['./exp/15-17.jpg', '15-17%'],
-        ['./exp/15-17v2.jpg', '15-17%'],
-        ['./exp/15-17v3.jpg', '15-17%'],
-        ['./exp/17-19.jpg', '17-19%'],
-        ['./exp/19-20.jpg', '19-20%'],
-        ['./exp/19-20v2.jpg', '19-20%'],
-        ['./exp/21-23.jpg', '21-23%'],
-        ['./exp/22-24.jpg', '22-24%'],
-        ['./exp/23-25.jpg', '23-25%'],
-        ['./exp/24-26.jpg', '24-26%']
-        ]
-
-
-image = gr.Image(shape=(192,192))
-intf = gr.Interface(fn =predict_bf, inputs = [image, gr.Radio([0,1,2,3,4])], outputs = gr.outputs.Textbox(), 
-                    title = title, description = description, examples=examples)
-intf.launch(inline=True, share=False)
diff --git a/spaces/ServerX/PorcoDiaz/go-tensorboard.bat b/spaces/ServerX/PorcoDiaz/go-tensorboard.bat
deleted file mode 100644
index cb81c17d3865513adec8eb0b832b7888cd1e4078..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/go-tensorboard.bat
+++ /dev/null
@@ -1,2 +0,0 @@
-python fixes/tensor-launch.py
-pause
\ No newline at end of file
diff --git a/spaces/SmartPoint7/TwitterPRO/README.md b/spaces/SmartPoint7/TwitterPRO/README.md
deleted file mode 100644
index 968ce617a09c7b46165a9e94b0781266db581ffc..0000000000000000000000000000000000000000
--- a/spaces/SmartPoint7/TwitterPRO/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: TwitterPRO
-emoji: 🐢
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: afl-3.0
-duplicated_from: typesdigital/TwitterPRO
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SmilingWolf/wd-v1-4-tags/README.md b/spaces/SmilingWolf/wd-v1-4-tags/README.md
deleted file mode 100644
index ffe9df97178c9dc49c2e22b18a4e60a6201c0abe..0000000000000000000000000000000000000000
--- a/spaces/SmilingWolf/wd-v1-4-tags/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
----
-title: WaifuDiffusion v1.4 Tags
-emoji: 💬
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.16.2
-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/Songj/DotaHeroClassifier/README.md b/spaces/Songj/DotaHeroClassifier/README.md
deleted file mode 100644
index 2daf6431495cffe5912ad692cbe8696c89f47510..0000000000000000000000000000000000000000
--- a/spaces/Songj/DotaHeroClassifier/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: DotaHeroClassifier
-emoji: 📚
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.20.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Sonnt/Fracture_Webapp/backup/081222/Homepage.py b/spaces/Sonnt/Fracture_Webapp/backup/081222/Homepage.py
deleted file mode 100644
index 00acbe48100c70632bd4defea7fd01128641e97d..0000000000000000000000000000000000000000
--- a/spaces/Sonnt/Fracture_Webapp/backup/081222/Homepage.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import streamlit as st
-from Antuns.page_setting import page_intro
-
-#Streamlit dashboard------------------------------------------------------------------------------------------
-page_intro()
-st.sidebar.success("")
-st.markdown("""
-            :flag-vn:
-            """
-            ,unsafe_allow_html=False)
\ No newline at end of file
diff --git a/spaces/Sresti/sharma/README.md b/spaces/Sresti/sharma/README.md
deleted file mode 100644
index 59b508e082df3fa24c1acdd03c4e44fd4f4a924b..0000000000000000000000000000000000000000
--- a/spaces/Sresti/sharma/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Sharma
-emoji: 🔥
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/grids/diffusion/_explorers.py b/spaces/SuYuanS/AudioCraft_Plus/audiocraft/grids/diffusion/_explorers.py
deleted file mode 100644
index 0bf4ca57b63f5f9308bd1178ddbde5d8f06748e5..0000000000000000000000000000000000000000
--- a/spaces/SuYuanS/AudioCraft_Plus/audiocraft/grids/diffusion/_explorers.py
+++ /dev/null
@@ -1,66 +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 treetable as tt
-
-from .._base_explorers import BaseExplorer
-
-
-class DiffusionExplorer(BaseExplorer):
-    eval_metrics = ["sisnr", "visqol"]
-
-    def stages(self):
-        return ["train", "valid", "valid_ema", "evaluate", "evaluate_ema"]
-
-    def get_grid_meta(self):
-        """Returns the list of Meta information to display for each XP/job.
-        """
-        return [
-            tt.leaf("index", align=">"),
-            tt.leaf("name", wrap=140),
-            tt.leaf("state"),
-            tt.leaf("sig", align=">"),
-        ]
-
-    def get_grid_metrics(self):
-        """Return the metrics that should be displayed in the tracking table.
-        """
-        return [
-            tt.group(
-                "train",
-                [
-                    tt.leaf("epoch"),
-                    tt.leaf("loss", ".3%"),
-                ],
-                align=">",
-            ),
-            tt.group(
-                "valid",
-                [
-                    tt.leaf("loss", ".3%"),
-                    # tt.leaf("loss_0", ".3%"),
-                ],
-                align=">",
-            ),
-            tt.group(
-                "valid_ema",
-                [
-                    tt.leaf("loss", ".3%"),
-                    # tt.leaf("loss_0", ".3%"),
-                ],
-                align=">",
-            ),
-            tt.group(
-                "evaluate", [tt.leaf("rvm", ".4f"), tt.leaf("rvm_0", ".4f"),
-                             tt.leaf("rvm_1", ".4f"), tt.leaf("rvm_2", ".4f"),
-                             tt.leaf("rvm_3", ".4f"), ], align=">"
-            ),
-            tt.group(
-                "evaluate_ema", [tt.leaf("rvm", ".4f"), tt.leaf("rvm_0", ".4f"),
-                                 tt.leaf("rvm_1", ".4f"), tt.leaf("rvm_2", ".4f"),
-                                 tt.leaf("rvm_3", ".4f")], align=">"
-            ),
-        ]
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/PdfImagePlugin.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/PdfImagePlugin.py
deleted file mode 100644
index c41f8aee0044799050dbcd2d7a01a7726511fae4..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/PIL/PdfImagePlugin.py
+++ /dev/null
@@ -1,284 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# PDF (Acrobat) file handling
-#
-# History:
-# 1996-07-16 fl   Created
-# 1997-01-18 fl   Fixed header
-# 2004-02-21 fl   Fixes for 1/L/CMYK images, etc.
-# 2004-02-24 fl   Fixes for 1 and P images.
-#
-# Copyright (c) 1997-2004 by Secret Labs AB.  All rights reserved.
-# Copyright (c) 1996-1997 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-##
-# Image plugin for PDF images (output only).
-##
-
-import io
-import math
-import os
-import time
-
-from . import Image, ImageFile, ImageSequence, PdfParser, __version__, features
-
-#
-# --------------------------------------------------------------------
-
-# object ids:
-#  1. catalogue
-#  2. pages
-#  3. image
-#  4. page
-#  5. page contents
-
-
-def _save_all(im, fp, filename):
-    _save(im, fp, filename, save_all=True)
-
-
-##
-# (Internal) Image save plugin for the PDF format.
-
-
-def _save(im, fp, filename, save_all=False):
-    is_appending = im.encoderinfo.get("append", False)
-    if is_appending:
-        existing_pdf = PdfParser.PdfParser(f=fp, filename=filename, mode="r+b")
-    else:
-        existing_pdf = PdfParser.PdfParser(f=fp, filename=filename, mode="w+b")
-
-    dpi = im.encoderinfo.get("dpi")
-    if dpi:
-        x_resolution = dpi[0]
-        y_resolution = dpi[1]
-    else:
-        x_resolution = y_resolution = im.encoderinfo.get("resolution", 72.0)
-
-    info = {
-        "title": None
-        if is_appending
-        else os.path.splitext(os.path.basename(filename))[0],
-        "author": None,
-        "subject": None,
-        "keywords": None,
-        "creator": None,
-        "producer": None,
-        "creationDate": None if is_appending else time.gmtime(),
-        "modDate": None if is_appending else time.gmtime(),
-    }
-    for k, default in info.items():
-        v = im.encoderinfo.get(k) if k in im.encoderinfo else default
-        if v:
-            existing_pdf.info[k[0].upper() + k[1:]] = v
-
-    #
-    # make sure image data is available
-    im.load()
-
-    existing_pdf.start_writing()
-    existing_pdf.write_header()
-    existing_pdf.write_comment(f"created by Pillow {__version__} PDF driver")
-
-    #
-    # pages
-    ims = [im]
-    if save_all:
-        append_images = im.encoderinfo.get("append_images", [])
-        for append_im in append_images:
-            append_im.encoderinfo = im.encoderinfo.copy()
-            ims.append(append_im)
-    number_of_pages = 0
-    image_refs = []
-    page_refs = []
-    contents_refs = []
-    for im in ims:
-        im_number_of_pages = 1
-        if save_all:
-            try:
-                im_number_of_pages = im.n_frames
-            except AttributeError:
-                # Image format does not have n_frames.
-                # It is a single frame image
-                pass
-        number_of_pages += im_number_of_pages
-        for i in range(im_number_of_pages):
-            image_refs.append(existing_pdf.next_object_id(0))
-            page_refs.append(existing_pdf.next_object_id(0))
-            contents_refs.append(existing_pdf.next_object_id(0))
-            existing_pdf.pages.append(page_refs[-1])
-
-    #
-    # catalog and list of pages
-    existing_pdf.write_catalog()
-
-    page_number = 0
-    for im_sequence in ims:
-        im_pages = ImageSequence.Iterator(im_sequence) if save_all else [im_sequence]
-        for im in im_pages:
-            # FIXME: Should replace ASCIIHexDecode with RunLengthDecode
-            # (packbits) or LZWDecode (tiff/lzw compression).  Note that
-            # PDF 1.2 also supports Flatedecode (zip compression).
-
-            bits = 8
-            params = None
-            decode = None
-
-            #
-            # Get image characteristics
-
-            width, height = im.size
-
-            if im.mode == "1":
-                if features.check("libtiff"):
-                    filter = "CCITTFaxDecode"
-                    bits = 1
-                    params = PdfParser.PdfArray(
-                        [
-                            PdfParser.PdfDict(
-                                {
-                                    "K": -1,
-                                    "BlackIs1": True,
-                                    "Columns": width,
-                                    "Rows": height,
-                                }
-                            )
-                        ]
-                    )
-                else:
-                    filter = "DCTDecode"
-                colorspace = PdfParser.PdfName("DeviceGray")
-                procset = "ImageB"  # grayscale
-            elif im.mode == "L":
-                filter = "DCTDecode"
-                # params = f"<< /Predictor 15 /Columns {width-2} >>"
-                colorspace = PdfParser.PdfName("DeviceGray")
-                procset = "ImageB"  # grayscale
-            elif im.mode == "P":
-                filter = "ASCIIHexDecode"
-                palette = im.getpalette()
-                colorspace = [
-                    PdfParser.PdfName("Indexed"),
-                    PdfParser.PdfName("DeviceRGB"),
-                    255,
-                    PdfParser.PdfBinary(palette),
-                ]
-                procset = "ImageI"  # indexed color
-            elif im.mode == "RGB":
-                filter = "DCTDecode"
-                colorspace = PdfParser.PdfName("DeviceRGB")
-                procset = "ImageC"  # color images
-            elif im.mode == "RGBA":
-                filter = "JPXDecode"
-                colorspace = PdfParser.PdfName("DeviceRGB")
-                procset = "ImageC"  # color images
-            elif im.mode == "CMYK":
-                filter = "DCTDecode"
-                colorspace = PdfParser.PdfName("DeviceCMYK")
-                procset = "ImageC"  # color images
-                decode = [1, 0, 1, 0, 1, 0, 1, 0]
-            else:
-                msg = f"cannot save mode {im.mode}"
-                raise ValueError(msg)
-
-            #
-            # image
-
-            op = io.BytesIO()
-
-            if filter == "ASCIIHexDecode":
-                ImageFile._save(im, op, [("hex", (0, 0) + im.size, 0, im.mode)])
-            elif filter == "CCITTFaxDecode":
-                im.save(
-                    op,
-                    "TIFF",
-                    compression="group4",
-                    # use a single strip
-                    strip_size=math.ceil(im.width / 8) * im.height,
-                )
-            elif filter == "DCTDecode":
-                Image.SAVE["JPEG"](im, op, filename)
-            elif filter == "JPXDecode":
-                Image.SAVE["JPEG2000"](im, op, filename)
-            elif filter == "FlateDecode":
-                ImageFile._save(im, op, [("zip", (0, 0) + im.size, 0, im.mode)])
-            elif filter == "RunLengthDecode":
-                ImageFile._save(im, op, [("packbits", (0, 0) + im.size, 0, im.mode)])
-            else:
-                msg = f"unsupported PDF filter ({filter})"
-                raise ValueError(msg)
-
-            stream = op.getvalue()
-            if filter == "CCITTFaxDecode":
-                stream = stream[8:]
-                filter = PdfParser.PdfArray([PdfParser.PdfName(filter)])
-            else:
-                filter = PdfParser.PdfName(filter)
-
-            existing_pdf.write_obj(
-                image_refs[page_number],
-                stream=stream,
-                Type=PdfParser.PdfName("XObject"),
-                Subtype=PdfParser.PdfName("Image"),
-                Width=width,  # * 72.0 / x_resolution,
-                Height=height,  # * 72.0 / y_resolution,
-                Filter=filter,
-                BitsPerComponent=bits,
-                Decode=decode,
-                DecodeParms=params,
-                ColorSpace=colorspace,
-            )
-
-            #
-            # page
-
-            existing_pdf.write_page(
-                page_refs[page_number],
-                Resources=PdfParser.PdfDict(
-                    ProcSet=[PdfParser.PdfName("PDF"), PdfParser.PdfName(procset)],
-                    XObject=PdfParser.PdfDict(image=image_refs[page_number]),
-                ),
-                MediaBox=[
-                    0,
-                    0,
-                    width * 72.0 / x_resolution,
-                    height * 72.0 / y_resolution,
-                ],
-                Contents=contents_refs[page_number],
-            )
-
-            #
-            # page contents
-
-            page_contents = b"q %f 0 0 %f 0 0 cm /image Do Q\n" % (
-                width * 72.0 / x_resolution,
-                height * 72.0 / y_resolution,
-            )
-
-            existing_pdf.write_obj(contents_refs[page_number], stream=page_contents)
-
-            page_number += 1
-
-    #
-    # trailer
-    existing_pdf.write_xref_and_trailer()
-    if hasattr(fp, "flush"):
-        fp.flush()
-    existing_pdf.close()
-
-
-#
-# --------------------------------------------------------------------
-
-
-Image.register_save("PDF", _save)
-Image.register_save_all("PDF", _save_all)
-
-Image.register_extension("PDF", ".pdf")
-
-Image.register_mime("PDF", "application/pdf")
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/_core/_signals.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/_core/_signals.py
deleted file mode 100644
index 8ea54af86c4be12340de02dc2a6f7eba387e0d98..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/_core/_signals.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from __future__ import annotations
-
-from typing import AsyncIterator
-
-from ._compat import DeprecatedAsyncContextManager
-from ._eventloop import get_asynclib
-
-
-def open_signal_receiver(
-    *signals: int,
-) -> DeprecatedAsyncContextManager[AsyncIterator[int]]:
-    """
-    Start receiving operating system signals.
-
-    :param signals: signals to receive (e.g. ``signal.SIGINT``)
-    :return: an asynchronous context manager for an asynchronous iterator which yields signal
-        numbers
-
-    .. warning:: Windows does not support signals natively so it is best to avoid relying on this
-        in cross-platform applications.
-
-    .. warning:: On asyncio, this permanently replaces any previous signal handler for the given
-        signals, as set via :meth:`~asyncio.loop.add_signal_handler`.
-
-    """
-    return get_asynclib().open_signal_receiver(*signals)
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/store/abstract_doc_store.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/store/abstract_doc_store.py
deleted file mode 100644
index df7788f584a7c9f7170d70fbdd4d948de6a73c7f..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/store/abstract_doc_store.py
+++ /dev/null
@@ -1,104 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Dict, Iterator, List, Optional, Type
-
-from typing_extensions import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from docarray import BaseDoc, DocList
-
-
-class AbstractDocStore(ABC):
-    @staticmethod
-    @abstractmethod
-    def list(namespace: str, show_table: bool) -> List[str]:
-        """List all DocLists in the specified backend at the namespace.
-
-        :param namespace: The namespace to list
-        :param show_table: If true, a table is printed to the console
-        :return: A list of DocList names
-        """
-        ...
-
-    @staticmethod
-    @abstractmethod
-    def delete(name: str, missing_ok: bool) -> bool:
-        """Delete the DocList object at the specified name
-
-        :param name: The name of the DocList to delete
-        :param missing_ok: If true, no error will be raised if the DocList does not exist.
-        :return: True if the DocList was deleted, False if it did not exist.
-        """
-        ...
-
-    @staticmethod
-    @abstractmethod
-    def push(
-        docs: 'DocList',
-        name: str,
-        public: bool,
-        show_progress: bool,
-        branding: Optional[Dict],
-    ) -> Dict:
-        """Push this DocList to the specified name.
-
-        :param docs: The DocList to push
-        :param name: The name to push to
-        :param public: Whether the DocList should be publicly accessible
-        :param show_progress: If true, a progress bar will be displayed.
-        :param branding: Branding information to be stored with the DocList
-        """
-        ...
-
-    @staticmethod
-    @abstractmethod
-    def push_stream(
-        docs: Iterator['BaseDoc'],
-        url: str,
-        public: bool = True,
-        show_progress: bool = False,
-        branding: Optional[Dict] = None,
-    ) -> Dict:
-        """Push a stream of documents to the specified name.
-
-        :param docs: a stream of documents
-        :param url: The name to push to
-        :param public: Whether the DocList should be publicly accessible
-        :param show_progress: If true, a progress bar will be displayed.
-        :param branding: Branding information to be stored with the DocList
-        """
-        ...
-
-    @staticmethod
-    @abstractmethod
-    def pull(
-        docs_cls: Type['DocList'],
-        name: str,
-        show_progress: bool,
-        local_cache: bool,
-    ) -> 'DocList':
-        """Pull a DocList from the specified name.
-
-        :param docs_cls: The DocList class to instantiate
-        :param name: The name to pull from
-        :param show_progress: If true, a progress bar will be displayed.
-        :param local_cache: If true, the DocList will be cached locally
-        :return: A DocList
-        """
-        ...
-
-    @staticmethod
-    @abstractmethod
-    def pull_stream(
-        docs_cls: Type['DocList'],
-        name: str,
-        show_progress: bool,
-        local_cache: bool,
-    ) -> Iterator['BaseDoc']:
-        """Pull a stream of documents from the specified name.
-
-        :param docs_cls: The DocList class to instantiate
-        :param name: The name to pull from
-        :param show_progress: If true, a progress bar will be displayed.
-        :param local_cache: If true, the DocList will be cached locally
-        :return: An iterator of documents"""
-        ...
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/structures/instances.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/structures/instances.py
deleted file mode 100644
index c9579bce2730f42e256c6eed99d9014d09304c99..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/structures/instances.py
+++ /dev/null
@@ -1,194 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import itertools
-import warnings
-from typing import Any, Dict, List, Tuple, Union
-import torch
-
-
-class Instances:
-    """
-    This class represents a list of instances in an image.
-    It stores the attributes of instances (e.g., boxes, masks, labels, scores) as "fields".
-    All fields must have the same ``__len__`` which is the number of instances.
-
-    All other (non-field) attributes of this class are considered private:
-    they must start with '_' and are not modifiable by a user.
-
-    Some basic usage:
-
-    1. Set/get/check a field:
-
-       .. code-block:: python
-
-          instances.gt_boxes = Boxes(...)
-          print(instances.pred_masks)  # a tensor of shape (N, H, W)
-          print('gt_masks' in instances)
-
-    2. ``len(instances)`` returns the number of instances
-    3. Indexing: ``instances[indices]`` will apply the indexing on all the fields
-       and returns a new :class:`Instances`.
-       Typically, ``indices`` is a integer vector of indices,
-       or a binary mask of length ``num_instances``
-
-       .. code-block:: python
-
-          category_3_detections = instances[instances.pred_classes == 3]
-          confident_detections = instances[instances.scores > 0.9]
-    """
-
-    def __init__(self, image_size: Tuple[int, int], **kwargs: Any):
-        """
-        Args:
-            image_size (height, width): the spatial size of the image.
-            kwargs: fields to add to this `Instances`.
-        """
-        self._image_size = image_size
-        self._fields: Dict[str, Any] = {}
-        for k, v in kwargs.items():
-            self.set(k, v)
-
-    @property
-    def image_size(self) -> Tuple[int, int]:
-        """
-        Returns:
-            tuple: height, width
-        """
-        return self._image_size
-
-    def __setattr__(self, name: str, val: Any) -> None:
-        if name.startswith("_"):
-            super().__setattr__(name, val)
-        else:
-            self.set(name, val)
-
-    def __getattr__(self, name: str) -> Any:
-        if name == "_fields" or name not in self._fields:
-            raise AttributeError("Cannot find field '{}' in the given Instances!".format(name))
-        return self._fields[name]
-
-    def set(self, name: str, value: Any) -> None:
-        """
-        Set the field named `name` to `value`.
-        The length of `value` must be the number of instances,
-        and must agree with other existing fields in this object.
-        """
-        with warnings.catch_warnings(record=True):
-            data_len = len(value)
-        if len(self._fields):
-            assert (
-                len(self) == data_len
-            ), "Adding a field of length {} to a Instances of length {}".format(data_len, len(self))
-        self._fields[name] = value
-
-    def has(self, name: str) -> bool:
-        """
-        Returns:
-            bool: whether the field called `name` exists.
-        """
-        return name in self._fields
-
-    def remove(self, name: str) -> None:
-        """
-        Remove the field called `name`.
-        """
-        del self._fields[name]
-
-    def get(self, name: str) -> Any:
-        """
-        Returns the field called `name`.
-        """
-        return self._fields[name]
-
-    def get_fields(self) -> Dict[str, Any]:
-        """
-        Returns:
-            dict: a dict which maps names (str) to data of the fields
-
-        Modifying the returned dict will modify this instance.
-        """
-        return self._fields
-
-    # Tensor-like methods
-    def to(self, *args: Any, **kwargs: Any) -> "Instances":
-        """
-        Returns:
-            Instances: all fields are called with a `to(device)`, if the field has this method.
-        """
-        ret = Instances(self._image_size)
-        for k, v in self._fields.items():
-            if hasattr(v, "to"):
-                v = v.to(*args, **kwargs)
-            ret.set(k, v)
-        return ret
-
-    def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "Instances":
-        """
-        Args:
-            item: an index-like object and will be used to index all the fields.
-
-        Returns:
-            If `item` is a string, return the data in the corresponding field.
-            Otherwise, returns an `Instances` where all fields are indexed by `item`.
-        """
-        if type(item) == int:
-            if item >= len(self) or item < -len(self):
-                raise IndexError("Instances index out of range!")
-            else:
-                item = slice(item, None, len(self))
-
-        ret = Instances(self._image_size)
-        for k, v in self._fields.items():
-            ret.set(k, v[item])
-        return ret
-
-    def __len__(self) -> int:
-        for v in self._fields.values():
-            # use __len__ because len() has to be int and is not friendly to tracing
-            return v.__len__()
-        raise NotImplementedError("Empty Instances does not support __len__!")
-
-    def __iter__(self):
-        raise NotImplementedError("`Instances` object is not iterable!")
-
-    @staticmethod
-    def cat(instance_lists: List["Instances"]) -> "Instances":
-        """
-        Args:
-            instance_lists (list[Instances])
-
-        Returns:
-            Instances
-        """
-        assert all(isinstance(i, Instances) for i in instance_lists)
-        assert len(instance_lists) > 0
-        if len(instance_lists) == 1:
-            return instance_lists[0]
-
-        image_size = instance_lists[0].image_size
-        if not isinstance(image_size, torch.Tensor):  # could be a tensor in tracing
-            for i in instance_lists[1:]:
-                assert i.image_size == image_size
-        ret = Instances(image_size)
-        for k in instance_lists[0]._fields.keys():
-            values = [i.get(k) for i in instance_lists]
-            v0 = values[0]
-            if isinstance(v0, torch.Tensor):
-                values = torch.cat(values, dim=0)
-            elif isinstance(v0, list):
-                values = list(itertools.chain(*values))
-            elif hasattr(type(v0), "cat"):
-                values = type(v0).cat(values)
-            else:
-                raise ValueError("Unsupported type {} for concatenation".format(type(v0)))
-            ret.set(k, values)
-        return ret
-
-    def __str__(self) -> str:
-        s = self.__class__.__name__ + "("
-        s += "num_instances={}, ".format(len(self))
-        s += "image_height={}, ".format(self._image_size[0])
-        s += "image_width={}, ".format(self._image_size[1])
-        s += "fields=[{}])".format(", ".join((f"{k}: {v}" for k, v in self._fields.items())))
-        return s
-
-    __repr__ = __str__
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/configs/_base_/models/fcn_unet_s5-d16.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/configs/_base_/models/fcn_unet_s5-d16.py
deleted file mode 100644
index a33e7972877f902d0e7d18401ca675e3e4e60a18..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/configs/_base_/models/fcn_unet_s5-d16.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained=None,
-    backbone=dict(
-        type='UNet',
-        in_channels=3,
-        base_channels=64,
-        num_stages=5,
-        strides=(1, 1, 1, 1, 1),
-        enc_num_convs=(2, 2, 2, 2, 2),
-        dec_num_convs=(2, 2, 2, 2),
-        downsamples=(True, True, True, True),
-        enc_dilations=(1, 1, 1, 1, 1),
-        dec_dilations=(1, 1, 1, 1),
-        with_cp=False,
-        conv_cfg=None,
-        norm_cfg=norm_cfg,
-        act_cfg=dict(type='ReLU'),
-        upsample_cfg=dict(type='InterpConv'),
-        norm_eval=False),
-    decode_head=dict(
-        type='FCNHead',
-        in_channels=64,
-        in_index=4,
-        channels=64,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=2,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=128,
-        in_index=3,
-        channels=64,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=2,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='slide', crop_size=256, stride=170))
diff --git a/spaces/T2007/T/Dockerfile b/spaces/T2007/T/Dockerfile
deleted file mode 100644
index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000
--- a/spaces/T2007/T/Dockerfile
+++ /dev/null
@@ -1,21 +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" ]
\ No newline at end of file
diff --git a/spaces/TEnngal/bingo/README.md b/spaces/TEnngal/bingo/README.md
deleted file mode 100644
index d65eafbc8431818f738e8e086455fa6159f101bb..0000000000000000000000000000000000000000
--- a/spaces/TEnngal/bingo/README.md
+++ /dev/null
@@ -1,196 +0,0 @@
----
-title: bingo
-emoji: 📉
-colorFrom: red
-colorTo: red
-sdk: docker
-license: mit
-duplicated_from: hf4all/bingo
----
-
-<div align="center">
-
-# Bingo 
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-![Github stars](https://badgen.net/github/stars/weaigc/bingo?icon=github&label=stars)
-![Gthub issues](https://img.shields.io/github/issues/weaigc/bingo)
-[![docker build](https://github.com/weaigc/bingo/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![docker hub](https://badgen.net/docker/size/weaigc/bingo?icon=docker&label=image%20size)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![MIT License](https://img.shields.io/badge/license-MIT-97c50f)](https://github.com/weaigc/bingo/blob/main/license)
-
-</div>
-
-## 演示站点
-
-https://bing.github1s.tk
-
-
-
-[![img](./docs/images/demo.png)](https://bing.github1s.tk)
-
-## 功能和特点
-
-- 完全基于 Next.js 重写，高度还原 New Bing Web 版 UI，使用体验和 Bing AI 基本一致。
-- 支持 Docker 构建，方便快捷地部署和访问。
-- Cookie 可全局配置，全局共享。
-- 支持持续语音对话
-
-## RoadMap
-
- - [x] 支持 wss 转发
- - [x] 支持一键部署
- - [x] 优化移动端展示
- - [x] 支持画图
- - [x] 支持语音输入(支持语音指令，目前仅支持 PC 版 Edge 及 Chrome 浏览器)
- - [x] 支持语音输出(需要手动开启)
- - [x] 支持图片输入
- - [x] 支持自定义域名
- - [ ] 支持历史记录
- - [ ] 适配深色模式
- - [ ] 支持内置提示词
- - [ ] 支持离线访问
- - [ ] 国际化翻译
-
-## 一键部署
-你也可以一键部署自己的 New Bing AI 到 🤗 HuggingFace 。
-
-### 部署到 Huggingface
-1. 点击此图标
-[![Deploy to HuggingFace](https://img.shields.io/badge/%E7%82%B9%E5%87%BB%E9%83%A8%E7%BD%B2-%F0%9F%A4%97-fff)](https://huggingface.co/login?next=%2Fspaces%2Fhf4all%2Fbingo%3Fduplicate%3Dtrue%26visibility%3Dpublic)，配置可以不改。
-
-2. 部署署完成后，点击“设置” 》“站点域名”，点一下，复制一下 HF 域名信息，然后分享给别人即可。
-
-> Huggingface 不支持绑定自己的域名，不过我们可以使用曲线救国的方式来达到这个目的
-> 1. 方式二，借助 Cloudflare Workers [部署Cloudflare Workers](#使用Cloudflare-Workers自定义域名)
-> 2. 方式一，借助 Github Pages 及 iframe [如何绑定域名](https://github.com/weaigc/bingo/issues/4)
-
-### 使用Cloudflare Workers自定义域名
-
-> 核心代码 [worker.js](./cloudflare/worker.js)
-
-- [注册 Cloudflare 账号](https://dash.cloudflare.com/sign-up)
-
-- 添加一个新的网站，需要你有自己的域名并且将域名`Name Server`托管给 Cloudflare 才行（更多信息可自行 Google)
-
-- 通过左侧菜单进入「Workers」，并点击「Create a Worker」。
-
-- 创建 Worker 服务，复制 [worker.js](./cloudflare/worker.js) 全部代码，粘贴至创建的服务中，根据注释进行改动，保存并部署。
-
-- 触发器 中自定义访问域名。
-
-### 部署其它平台
-<details>
-<summary>
-由于其他平台目前遭到 New Bing 封杀，会遇到很多问题，不再做推荐，有需要的可以自行查看
-</summary>
-
-#### 部署到 Netlify
-[![Deploy to Netlify Button](https://www.netlify.com/img/deploy/button.svg)](https://app.netlify.com/start/deploy?repository=https://github.com/weaigc/bingo)
-
-#### 部署到 Vercel
-如果你是 Vercel 付费用户，可以点以下链接一键部署到 Vercel。免费版本有[接口超时限制](https://vercel.com/docs/concepts/limits/overview)，不推荐使用
-
-[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?demo-title=bingo&demo-description=bingo&demo-url=https%3A%2F%2Fbing.github1s.tk%2F&project-name=bingo&repository-name=bingo&repository-url=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo&from=templates&skippable-integrations=1&env=BING_HEADER&envDescription=%E5%A6%82%E6%9E%9C%E4%B8%8D%E7%9F%A5%E9%81%93%E6%80%8E%E4%B9%88%E9%85%8D%E7%BD%AE%E8%AF%B7%E7%82%B9%E5%8F%B3%E4%BE%A7Learn+More&envLink=https%3A%2F%2Fgithub.com%2Fweaigc%2Fbingo%2Fblob%2Fmain%2F.env.example)
-
-#### 部署到 Render
-
-[![Deploy to Render](https://render.com/images/deploy-to-render-button.svg)](https://render.com/deploy?repo=https://github.com/weaigc/bingo)
-</details>
-
-## 环境和依赖
-
-- Node.js >= 18
-- Bing AI 的[身份信息](#如何获取-BING_HEADER))
-
-## 安装和使用
-
-> 由于目前微软封杀比较严重，推荐优先使用 [部署 Huggingface](#部署到-huggingface) 。
-
-* 使用 Node 启动
-
-```bash
-git clone https://github.com/weaigc/bingo.git
-npm i # 推荐使用 pnpm i
-npm run build
-npm run start
-```
-
-* 使用 Docker 启动
-```bash
-docker pull weaigc/bingo
-docker run --rm -it -p 7860:7860 weaigc/bingo
-# 或者
-docker run --rm -it -e BING_HEADER=xxxx -p 7860:7860 weaigc/bingo
-```
-
-## 如何获取 BING_HEADER
-> 配置了 BING_HEADER 意味着你将自己的账号共享给所有使用此服务的人，如果不需要免登录画图的功能，不建议设置此变量
-
-打开 https://www.bing.com 并登录，然后访问 https://www.bing.com/turing/captcha/challenge ，通过人机校验，然后
-
-![BING HEADER](./docs/images/curl.png)
-
-> 复制出来的内容应该如下所示。确认格式无误后，打开 https://effulgent-bubblegum-e2f5df.netlify.app/#dialog=%22settings%22 ，粘贴进去，点击“转成 BING_HEADER 并复制”，然后从剪切板粘贴即可得到。（你也可以先在网页上进行验证）
-
-以下是格式参考，需要注意的是，网页端保存的格式是以`curl`开头, 而服务端配置的 `BING_HEADER` 是 `base64` 格式，两者不能互通。
-<details>
-<summary>正常格式/网页端保存的格式(格式仅供参考)</summary>
-
-```
-curl 'https://www.bing.com/turing/captcha/challenge' \
-  -H 'authority: www.bing.com' \
-  -H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7' \
-  -H 'accept-language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6' \
-  -H 'cache-control: max-age=0' \
-  -H 'cookie: MicrosoftApplicationsTelemetryDeviceId=3399c004-fd0e-48ec-bb92-d82a27b2bbd4; _EDGE_V=1; SRCHD=AF=NOFORM; SRCHUID=V=2&GUID=29EBDDA4E6674329ACCF1A0A423C3E98&dmnchg=1; _UR=QS=0&TQS=0; _HPVN=CS=eyJQbiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiUCJ9LCJTYyI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiSCJ9LCJReiI6eyJDbiI6MSwiU3QiOjAsIlFzIjowLCJQcm9kIjoiVCJ9LCJBcCI6dHJ1ZSwiTXV0ZSI6dHJ1ZSwiTGFkIjoiMjAyMy0wNy0yNVQwMDowMDowMFoiLCJJb3RkIjowLCJHd2IiOjAsIkRmdCI6bnVsbCwiTXZzIjowLCJGbHQiOjAsIkltcCI6Mn0=; _RwBf=ilt=1&ihpd=1&ispd=0&rc=0&rb=0&gb=0&rg=200&pc=0&mtu=0&rbb=0&g=0&cid=&clo=0&v=1&l=2023-07-25T07:00:00.0000000Z&lft=0001-01-01T00:00:00.0000000&aof=0&o=2&p=&c=&t=0&s=0001-01-01T00:00:00.0000000+00:00&ts=2023-07-25T11:00:31.7111548+00:00&rwred=0&wls=&lka=0&lkt=0&TH=&dci=0; ANON=A=0043C6590EA808ED6E395059FFFFFFFF&E=1c8b&W=1; NAP=V=1.9&E=1c31&C=DnaMSbDN_4efZ_xXqBF3Daorjr53kYqYoaP8YHsupjmiXnysX7a37A&W=1; PPLState=1; KievRPSSecAuth=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; WLS=C=9df3f9d8518fae19&N=wen; WLID=pGY8HgWCu4p5XYCOk2oa0+DBdftkMUfmNIn8XtSjSTKsgv/Il7GUlYs0Jpjf/E12jZMgV7x44Dy3fXOgjjUoJx7Y/ClLrLhsk20THksJJoI=; _EDGE_S=F=1&SID=17CF6EE006426448213C7DB907436588&mkt=zh-CN; MUID=225621093D8A6C27301632413C0E6D08; MUIDB=225621093D8A6C27301632413C0E6D08; SUID=A; SNRHOP=I=&TS=; _U=nGyzKQruEsDwLiu65fZFIG6e12hf2lwTJmroW__k8joUJIKmG3OIjayXKGW9dCVR3sNhF76mEVxyW6yjUGPodOfjtSa3s3J_DxMOrEK1BqXCOBI9bC66spAIASV7prsYFlVAJz73jVNENp_tBubLHJy6EbT0BKRe4AjrYkH-9uMnmCKB8Zmyg; _SS=SID=17CF6EE006426448213C7DB907436588&R=0&RB=0&GB=0&RG=200&RP=0&PC=U531; SRCHS=PC=U531; USRLOC=HS=1&ELOC=LAT=22.501529693603516|LON=113.9263687133789|N=%E5%8D%97%E5%B1%B1%E5%8C%BA%EF%BC%8C%E5%B9%BF%E4%B8%9C%E7%9C%81|ELT=2|&CLOC=LAT=22.50153029046461|LON=113.92637070632928|A=733.4464586120832|TS=230726151034|SRC=W; SRCHUSR=DOB=20230725&T=1690384908000&POEX=W; ipv6=hit=1690388509974&t=6; SRCHHPGUSR=HV=1690384945&SRCHLANG=zh-Hans&PV=15.0.0&BRW=MW&BRH=MT&CW=410&CH=794&SCW=410&SCH=794&DPR=1.5&UTC=480&DM=0&WTS=63825879627&PRVCW=410&PRVCH=794&PR=1.5; cct=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpny6Y_CVyi_MSyM94VyMWnjdYkkccVtm3czoIAtXUGQA; GC=AjWIBYOoVP-Afq6gWwtx80If6yHn6iBuEVHA1XHdAKpR3Y_D9Ytcks4Ht6XhadXk75dvhzP4YOUS0UmoEyqyxw' \
-  -H 'dnt: 1' \
-  -H 'sec-ch-ua: "Chromium";v="116", "Not)A;Brand";v="24", "Microsoft Edge";v="116"' \
-  -H 'sec-ch-ua-arch: "x86"' \
-  -H 'sec-ch-ua-bitness: "64"' \
-  -H 'sec-ch-ua-full-version: "116.0.1938.29"' \
-  -H 'sec-ch-ua-full-version-list: "Chromium";v="116.0.5845.42", "Not)A;Brand";v="24.0.0.0", "Microsoft Edge";v="116.0.1938.29"' \
-  -H 'sec-ch-ua-mobile: ?0' \
-  -H 'sec-ch-ua-model: ""' \
-  -H 'sec-ch-ua-platform: "Windows"' \
-  -H 'sec-ch-ua-platform-version: "15.0.0"' \
-  -H 'sec-fetch-dest: document' \
-  -H 'sec-fetch-mode: navigate' \
-  -H 'sec-fetch-site: none' \
-  -H 'sec-fetch-user: ?1' \
-  -H 'sec-ms-gec: B3F47AD4A283CAB374C0451C46AAFD147C6A4DACAFF6A1C13F34B2C72B024494' \
-  -H 'sec-ms-gec-version: 1-116.0.1938.29' \
-  -H 'upgrade-insecure-requests: 1' \
-  -H 'user-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36 Edg/116.0.0.0' \
-  -H 'x-client-data: eyIxIjoiMiIsIjEwIjoiXCJTMGg3R05HOTF2aDQ1TUZSUnZ5NHN2akRmMWdlaVJKenNxNlA3aU1WbnF3PVwiIiwiMiI6IjEiLCIzIjoiMSIsIjQiOiIyMTU4ODQ5NTM4MjY4OTM5NTA3IiwiNSI6IlwiSm9GUWpPTDk3OS9MbkRRZnlCd2N1M2FsOUN3eTZTQmdaMGNYMXBtOWVMZz1cIiIsIjYiOiJiZXRhIiwiNyI6IjE4MDM4ODYyNjQzNSIsIjkiOiJkZXNrdG9wIn0=' \
-  -H 'x-edge-shopping-flag: 1' \
-  --compressed
-```
-</details>
-
-<details>
-<summary>转成base64之后的格式(BING_HEADER只能使用 base64 之后的格式)</summary>
-
-```
-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
-```
-</details>
-
-
-## 鸣谢
- - 感谢 [EdgeGPT](https://github.com/acheong08/EdgeGPT) 提供的代理 API 的方法。
- - 感谢 [Vercel AI](https://github.com/vercel-labs/ai-chatbot) 提供的基础脚手架和 [ChatHub](https://github.com/chathub-dev/chathub) [go-proxy-bingai](https://github.com/adams549659584/go-proxy-bingai) 提供的部分代码。 
-
-
-## 答疑及交流
-
-<image src="./docs/images/wechat.png" width=240 />
-
-## License
-
-MIT © [LICENSE](https://github.com/weaigc/bingo/blob/main/LICENSE).
-
-
diff --git a/spaces/TRI-ML/risk_biased_prediction/tests/__init__.py b/spaces/TRI-ML/risk_biased_prediction/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/commands/check.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/commands/check.py
deleted file mode 100644
index 5efd0a3416041e3afdf32a2d346db01d99e8f7d9..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/commands/check.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import logging
-from optparse import Values
-from typing import List
-
-from pip._internal.cli.base_command import Command
-from pip._internal.cli.status_codes import ERROR, SUCCESS
-from pip._internal.operations.check import (
-    check_package_set,
-    create_package_set_from_installed,
-    warn_legacy_versions_and_specifiers,
-)
-from pip._internal.utils.misc import write_output
-
-logger = logging.getLogger(__name__)
-
-
-class CheckCommand(Command):
-    """Verify installed packages have compatible dependencies."""
-
-    usage = """
-      %prog [options]"""
-
-    def run(self, options: Values, args: List[str]) -> int:
-        package_set, parsing_probs = create_package_set_from_installed()
-        warn_legacy_versions_and_specifiers(package_set)
-        missing, conflicting = check_package_set(package_set)
-
-        for project_name in missing:
-            version = package_set[project_name].version
-            for dependency in missing[project_name]:
-                write_output(
-                    "%s %s requires %s, which is not installed.",
-                    project_name,
-                    version,
-                    dependency[0],
-                )
-
-        for project_name in conflicting:
-            version = package_set[project_name].version
-            for dep_name, dep_version, req in conflicting[project_name]:
-                write_output(
-                    "%s %s has requirement %s, but you have %s %s.",
-                    project_name,
-                    version,
-                    req,
-                    dep_name,
-                    dep_version,
-                )
-
-        if missing or conflicting or parsing_probs:
-            return ERROR
-        else:
-            write_output("No broken requirements found.")
-            return SUCCESS
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/importlib_resources/_legacy.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/importlib_resources/_legacy.py
deleted file mode 100644
index b1ea8105dad6e27eefd5a34f64dfee974a5c4f71..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/importlib_resources/_legacy.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import functools
-import os
-import pathlib
-import types
-import warnings
-
-from typing import Union, Iterable, ContextManager, BinaryIO, TextIO, Any
-
-from . import _common
-
-Package = Union[types.ModuleType, str]
-Resource = str
-
-
-def deprecated(func):
-    @functools.wraps(func)
-    def wrapper(*args, **kwargs):
-        warnings.warn(
-            f"{func.__name__} is deprecated. Use files() instead. "
-            "Refer to https://importlib-resources.readthedocs.io"
-            "/en/latest/using.html#migrating-from-legacy for migration advice.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        return func(*args, **kwargs)
-
-    return wrapper
-
-
-def normalize_path(path: Any) -> str:
-    """Normalize a path by ensuring it is a string.
-
-    If the resulting string contains path separators, an exception is raised.
-    """
-    str_path = str(path)
-    parent, file_name = os.path.split(str_path)
-    if parent:
-        raise ValueError(f'{path!r} must be only a file name')
-    return file_name
-
-
-@deprecated
-def open_binary(package: Package, resource: Resource) -> BinaryIO:
-    """Return a file-like object opened for binary reading of the resource."""
-    return (_common.files(package) / normalize_path(resource)).open('rb')
-
-
-@deprecated
-def read_binary(package: Package, resource: Resource) -> bytes:
-    """Return the binary contents of the resource."""
-    return (_common.files(package) / normalize_path(resource)).read_bytes()
-
-
-@deprecated
-def open_text(
-    package: Package,
-    resource: Resource,
-    encoding: str = 'utf-8',
-    errors: str = 'strict',
-) -> TextIO:
-    """Return a file-like object opened for text reading of the resource."""
-    return (_common.files(package) / normalize_path(resource)).open(
-        'r', encoding=encoding, errors=errors
-    )
-
-
-@deprecated
-def read_text(
-    package: Package,
-    resource: Resource,
-    encoding: str = 'utf-8',
-    errors: str = 'strict',
-) -> str:
-    """Return the decoded string of the resource.
-
-    The decoding-related arguments have the same semantics as those of
-    bytes.decode().
-    """
-    with open_text(package, resource, encoding, errors) as fp:
-        return fp.read()
-
-
-@deprecated
-def contents(package: Package) -> Iterable[str]:
-    """Return an iterable of entries in `package`.
-
-    Note that not all entries are resources.  Specifically, directories are
-    not considered resources.  Use `is_resource()` on each entry returned here
-    to check if it is a resource or not.
-    """
-    return [path.name for path in _common.files(package).iterdir()]
-
-
-@deprecated
-def is_resource(package: Package, name: str) -> bool:
-    """True if `name` is a resource inside `package`.
-
-    Directories are *not* resources.
-    """
-    resource = normalize_path(name)
-    return any(
-        traversable.name == resource and traversable.is_file()
-        for traversable in _common.files(package).iterdir()
-    )
-
-
-@deprecated
-def path(
-    package: Package,
-    resource: Resource,
-) -> ContextManager[pathlib.Path]:
-    """A context manager providing a file path object to the resource.
-
-    If the resource does not already exist on its own on the file system,
-    a temporary file will be created. If the file was created, the file
-    will be deleted upon exiting the context manager (no exception is
-    raised if the file was deleted prior to the context manager
-    exiting).
-    """
-    return _common.as_file(_common.files(package) / normalize_path(resource))
diff --git a/spaces/Truepic/ai-content-credentials/main.py b/spaces/Truepic/ai-content-credentials/main.py
deleted file mode 100644
index d659ec230f75b586ec3fcd6f3bd400ba3a6feab4..0000000000000000000000000000000000000000
--- a/spaces/Truepic/ai-content-credentials/main.py
+++ /dev/null
@@ -1,78 +0,0 @@
-from fastapi import FastAPI
-from fastapi.staticfiles import StaticFiles
-from fastapi.responses import FileResponse
-
-import subprocess
-import os
-import json
-import uuid
-import html
-
-import torch
-from diffusers import (
-    StableDiffusionPipeline,
-    DPMSolverMultistepScheduler,
-    EulerDiscreteScheduler,
-)
-
-app = FastAPI()
-
-
-@app.get("/generate")
-def generate_image(prompt, model):
-    torch.cuda.empty_cache()
-
-    prompt = html.escape(prompt)
-    model = html.escape(model)
-    
-    modelArray = model.split(",")
-    modelName = modelArray[0]
-    modelVersion = modelArray[1]
-
-    pipeline = StableDiffusionPipeline.from_pretrained(
-        str(modelName), torch_dtype=torch.float16
-    )
-    pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)
-    pipeline = pipeline.to("cuda")
-
-    image = pipeline(prompt, num_inference_steps=50, height=512, width=512).images[0]
-
-    filename = str(uuid.uuid4()) + ".jpg"
-    image.save(filename)
-
-    assertion = {
-        "assertions": [
-            {
-                "label": "com.truepic.custom.ai",
-                "data": {
-                    "model_name": modelName,
-                    "model_version": modelVersion,
-                    "prompt": prompt,
-                },
-            }
-        ]
-    }
-
-    json_object = json.dumps(assertion)
-
-    subprocess.check_output(
-        [
-            "./truepic",
-            "sign",
-            filename,
-            "--assertions-inline",
-            json_object,
-            "--output",
-            (os.getcwd() + "/static/" + filename),
-        ]
-    )
-
-    return {"response": filename}
-
-
-app.mount("/", StaticFiles(directory="static", html=True), name="static")
-
-
-@app.get("/")
-def index() -> FileResponse:
-    return FileResponse(path="/app/static/index.html", media_type="text/html")
diff --git a/spaces/UserXTheUnknown/stablediffusion-infinity/convert_checkpoint.py b/spaces/UserXTheUnknown/stablediffusion-infinity/convert_checkpoint.py
deleted file mode 100644
index 34efcf1ab17190b8b140f02e9ff3451daf2c6f9e..0000000000000000000000000000000000000000
--- a/spaces/UserXTheUnknown/stablediffusion-infinity/convert_checkpoint.py
+++ /dev/null
@@ -1,706 +0,0 @@
-# coding=utf-8
-# Copyright 2022 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.
-# https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py
-""" Conversion script for the LDM checkpoints. """
-
-import argparse
-import os
-
-import torch
-
-
-try:
-    from omegaconf import OmegaConf
-except ImportError:
-    raise ImportError(
-        "OmegaConf is required to convert the LDM checkpoints. Please install it with `pip install OmegaConf`."
-    )
-
-from diffusers import (
-    AutoencoderKL,
-    DDIMScheduler,
-    LDMTextToImagePipeline,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-    StableDiffusionPipeline,
-    UNet2DConditionModel,
-)
-from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
-from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
-from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer
-
-
-def shave_segments(path, n_shave_prefix_segments=1):
-    """
-    Removes segments. Positive values shave the first segments, negative shave the last segments.
-    """
-    if n_shave_prefix_segments >= 0:
-        return ".".join(path.split(".")[n_shave_prefix_segments:])
-    else:
-        return ".".join(path.split(".")[:n_shave_prefix_segments])
-
-
-def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
-    """
-    Updates paths inside resnets to the new naming scheme (local renaming)
-    """
-    mapping = []
-    for old_item in old_list:
-        new_item = old_item.replace("in_layers.0", "norm1")
-        new_item = new_item.replace("in_layers.2", "conv1")
-
-        new_item = new_item.replace("out_layers.0", "norm2")
-        new_item = new_item.replace("out_layers.3", "conv2")
-
-        new_item = new_item.replace("emb_layers.1", "time_emb_proj")
-        new_item = new_item.replace("skip_connection", "conv_shortcut")
-
-        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
-        mapping.append({"old": old_item, "new": new_item})
-
-    return mapping
-
-
-def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
-    """
-    Updates paths inside resnets to the new naming scheme (local renaming)
-    """
-    mapping = []
-    for old_item in old_list:
-        new_item = old_item
-
-        new_item = new_item.replace("nin_shortcut", "conv_shortcut")
-        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
-        mapping.append({"old": old_item, "new": new_item})
-
-    return mapping
-
-
-def renew_attention_paths(old_list, n_shave_prefix_segments=0):
-    """
-    Updates paths inside attentions to the new naming scheme (local renaming)
-    """
-    mapping = []
-    for old_item in old_list:
-        new_item = old_item
-
-        #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
-        #         new_item = new_item.replace('norm.bias', 'group_norm.bias')
-
-        #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
-        #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
-
-        #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
-        mapping.append({"old": old_item, "new": new_item})
-
-    return mapping
-
-
-def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
-    """
-    Updates paths inside attentions to the new naming scheme (local renaming)
-    """
-    mapping = []
-    for old_item in old_list:
-        new_item = old_item
-
-        new_item = new_item.replace("norm.weight", "group_norm.weight")
-        new_item = new_item.replace("norm.bias", "group_norm.bias")
-
-        new_item = new_item.replace("q.weight", "query.weight")
-        new_item = new_item.replace("q.bias", "query.bias")
-
-        new_item = new_item.replace("k.weight", "key.weight")
-        new_item = new_item.replace("k.bias", "key.bias")
-
-        new_item = new_item.replace("v.weight", "value.weight")
-        new_item = new_item.replace("v.bias", "value.bias")
-
-        new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
-        new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
-
-        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
-
-        mapping.append({"old": old_item, "new": new_item})
-
-    return mapping
-
-
-def assign_to_checkpoint(
-    paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
-):
-    """
-    This does the final conversion step: take locally converted weights and apply a global renaming
-    to them. It splits attention layers, and takes into account additional replacements
-    that may arise.
-
-    Assigns the weights to the new checkpoint.
-    """
-    assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
-
-    # Splits the attention layers into three variables.
-    if attention_paths_to_split is not None:
-        for path, path_map in attention_paths_to_split.items():
-            old_tensor = old_checkpoint[path]
-            channels = old_tensor.shape[0] // 3
-
-            target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
-
-            num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
-
-            old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
-            query, key, value = old_tensor.split(channels // num_heads, dim=1)
-
-            checkpoint[path_map["query"]] = query.reshape(target_shape)
-            checkpoint[path_map["key"]] = key.reshape(target_shape)
-            checkpoint[path_map["value"]] = value.reshape(target_shape)
-
-    for path in paths:
-        new_path = path["new"]
-
-        # These have already been assigned
-        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
-            continue
-
-        # Global renaming happens here
-        new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
-        new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
-        new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
-
-        if additional_replacements is not None:
-            for replacement in additional_replacements:
-                new_path = new_path.replace(replacement["old"], replacement["new"])
-
-        # proj_attn.weight has to be converted from conv 1D to linear
-        if "proj_attn.weight" in new_path:
-            checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
-        else:
-            checkpoint[new_path] = old_checkpoint[path["old"]]
-
-
-def conv_attn_to_linear(checkpoint):
-    keys = list(checkpoint.keys())
-    attn_keys = ["query.weight", "key.weight", "value.weight"]
-    for key in keys:
-        if ".".join(key.split(".")[-2:]) in attn_keys:
-            if checkpoint[key].ndim > 2:
-                checkpoint[key] = checkpoint[key][:, :, 0, 0]
-        elif "proj_attn.weight" in key:
-            if checkpoint[key].ndim > 2:
-                checkpoint[key] = checkpoint[key][:, :, 0]
-
-
-def create_unet_diffusers_config(original_config):
-    """
-    Creates a config for the diffusers based on the config of the LDM model.
-    """
-    unet_params = original_config.model.params.unet_config.params
-
-    block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
-
-    down_block_types = []
-    resolution = 1
-    for i in range(len(block_out_channels)):
-        block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D"
-        down_block_types.append(block_type)
-        if i != len(block_out_channels) - 1:
-            resolution *= 2
-
-    up_block_types = []
-    for i in range(len(block_out_channels)):
-        block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
-        up_block_types.append(block_type)
-        resolution //= 2
-
-    config = dict(
-        sample_size=unet_params.image_size,
-        in_channels=unet_params.in_channels,
-        out_channels=unet_params.out_channels,
-        down_block_types=tuple(down_block_types),
-        up_block_types=tuple(up_block_types),
-        block_out_channels=tuple(block_out_channels),
-        layers_per_block=unet_params.num_res_blocks,
-        cross_attention_dim=unet_params.context_dim,
-        attention_head_dim=unet_params.num_heads,
-    )
-
-    return config
-
-
-def create_vae_diffusers_config(original_config):
-    """
-    Creates a config for the diffusers based on the config of the LDM model.
-    """
-    vae_params = original_config.model.params.first_stage_config.params.ddconfig
-    _ = original_config.model.params.first_stage_config.params.embed_dim
-
-    block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
-    down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
-    up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
-
-    config = dict(
-        sample_size=vae_params.resolution,
-        in_channels=vae_params.in_channels,
-        out_channels=vae_params.out_ch,
-        down_block_types=tuple(down_block_types),
-        up_block_types=tuple(up_block_types),
-        block_out_channels=tuple(block_out_channels),
-        latent_channels=vae_params.z_channels,
-        layers_per_block=vae_params.num_res_blocks,
-    )
-    return config
-
-
-def create_diffusers_schedular(original_config):
-    schedular = DDIMScheduler(
-        num_train_timesteps=original_config.model.params.timesteps,
-        beta_start=original_config.model.params.linear_start,
-        beta_end=original_config.model.params.linear_end,
-        beta_schedule="scaled_linear",
-    )
-    return schedular
-
-
-def create_ldm_bert_config(original_config):
-    bert_params = original_config.model.parms.cond_stage_config.params
-    config = LDMBertConfig(
-        d_model=bert_params.n_embed,
-        encoder_layers=bert_params.n_layer,
-        encoder_ffn_dim=bert_params.n_embed * 4,
-    )
-    return config
-
-
-def convert_ldm_unet_checkpoint(checkpoint, config):
-    """
-    Takes a state dict and a config, and returns a converted checkpoint.
-    """
-
-    # extract state_dict for UNet
-    unet_state_dict = {}
-    unet_key = "model.diffusion_model."
-    keys = list(checkpoint.keys())
-    for key in keys:
-        if key.startswith(unet_key):
-            unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
-
-    new_checkpoint = {}
-
-    new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
-    new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
-    new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
-    new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
-
-    new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
-    new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
-
-    new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
-    new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
-    new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
-    new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
-
-    # Retrieves the keys for the input blocks only
-    num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
-    input_blocks = {
-        layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
-        for layer_id in range(num_input_blocks)
-    }
-
-    # Retrieves the keys for the middle blocks only
-    num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
-    middle_blocks = {
-        layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
-        for layer_id in range(num_middle_blocks)
-    }
-
-    # Retrieves the keys for the output blocks only
-    num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
-    output_blocks = {
-        layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
-        for layer_id in range(num_output_blocks)
-    }
-
-    for i in range(1, num_input_blocks):
-        block_id = (i - 1) // (config["layers_per_block"] + 1)
-        layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
-
-        resnets = [
-            key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
-        ]
-        attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
-
-        if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
-            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
-                f"input_blocks.{i}.0.op.weight"
-            )
-            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
-                f"input_blocks.{i}.0.op.bias"
-            )
-
-        paths = renew_resnet_paths(resnets)
-        meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
-        assign_to_checkpoint(
-            paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
-        )
-
-        if len(attentions):
-            paths = renew_attention_paths(attentions)
-            meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
-            assign_to_checkpoint(
-                paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
-            )
-
-    resnet_0 = middle_blocks[0]
-    attentions = middle_blocks[1]
-    resnet_1 = middle_blocks[2]
-
-    resnet_0_paths = renew_resnet_paths(resnet_0)
-    assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
-
-    resnet_1_paths = renew_resnet_paths(resnet_1)
-    assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
-
-    attentions_paths = renew_attention_paths(attentions)
-    meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
-    assign_to_checkpoint(
-        attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
-    )
-
-    for i in range(num_output_blocks):
-        block_id = i // (config["layers_per_block"] + 1)
-        layer_in_block_id = i % (config["layers_per_block"] + 1)
-        output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
-        output_block_list = {}
-
-        for layer in output_block_layers:
-            layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
-            if layer_id in output_block_list:
-                output_block_list[layer_id].append(layer_name)
-            else:
-                output_block_list[layer_id] = [layer_name]
-
-        if len(output_block_list) > 1:
-            resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
-            attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
-
-            resnet_0_paths = renew_resnet_paths(resnets)
-            paths = renew_resnet_paths(resnets)
-
-            meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
-            assign_to_checkpoint(
-                paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
-            )
-
-            if ["conv.weight", "conv.bias"] in output_block_list.values():
-                index = list(output_block_list.values()).index(["conv.weight", "conv.bias"])
-                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
-                    f"output_blocks.{i}.{index}.conv.weight"
-                ]
-                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
-                    f"output_blocks.{i}.{index}.conv.bias"
-                ]
-
-                # Clear attentions as they have been attributed above.
-                if len(attentions) == 2:
-                    attentions = []
-
-            if len(attentions):
-                paths = renew_attention_paths(attentions)
-                meta_path = {
-                    "old": f"output_blocks.{i}.1",
-                    "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
-                }
-                assign_to_checkpoint(
-                    paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
-                )
-        else:
-            resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
-            for path in resnet_0_paths:
-                old_path = ".".join(["output_blocks", str(i), path["old"]])
-                new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
-
-                new_checkpoint[new_path] = unet_state_dict[old_path]
-
-    return new_checkpoint
-
-
-def convert_ldm_vae_checkpoint(checkpoint, config):
-    # extract state dict for VAE
-    vae_state_dict = {}
-    vae_key = "first_stage_model."
-    keys = list(checkpoint.keys())
-    for key in keys:
-        if key.startswith(vae_key):
-            vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
-
-    new_checkpoint = {}
-
-    new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
-    new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
-    new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
-    new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
-    new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
-    new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
-
-    new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
-    new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
-    new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
-    new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
-    new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
-    new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
-
-    new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
-    new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
-    new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
-    new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
-
-    # Retrieves the keys for the encoder down blocks only
-    num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
-    down_blocks = {
-        layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
-    }
-
-    # Retrieves the keys for the decoder up blocks only
-    num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
-    up_blocks = {
-        layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
-    }
-
-    for i in range(num_down_blocks):
-        resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
-
-        if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
-            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
-                f"encoder.down.{i}.downsample.conv.weight"
-            )
-            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
-                f"encoder.down.{i}.downsample.conv.bias"
-            )
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
-        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
-    mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
-    num_mid_res_blocks = 2
-    for i in range(1, num_mid_res_blocks + 1):
-        resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
-        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
-    mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
-    paths = renew_vae_attention_paths(mid_attentions)
-    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
-    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-    conv_attn_to_linear(new_checkpoint)
-
-    for i in range(num_up_blocks):
-        block_id = num_up_blocks - 1 - i
-        resnets = [
-            key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
-        ]
-
-        if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
-            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
-                f"decoder.up.{block_id}.upsample.conv.weight"
-            ]
-            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
-                f"decoder.up.{block_id}.upsample.conv.bias"
-            ]
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
-        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
-    mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
-    num_mid_res_blocks = 2
-    for i in range(1, num_mid_res_blocks + 1):
-        resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
-
-        paths = renew_vae_resnet_paths(resnets)
-        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
-        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-
-    mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
-    paths = renew_vae_attention_paths(mid_attentions)
-    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
-    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
-    conv_attn_to_linear(new_checkpoint)
-    return new_checkpoint
-
-
-def convert_ldm_bert_checkpoint(checkpoint, config):
-    def _copy_attn_layer(hf_attn_layer, pt_attn_layer):
-        hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight
-        hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight
-        hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight
-
-        hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight
-        hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias
-
-    def _copy_linear(hf_linear, pt_linear):
-        hf_linear.weight = pt_linear.weight
-        hf_linear.bias = pt_linear.bias
-
-    def _copy_layer(hf_layer, pt_layer):
-        # copy layer norms
-        _copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0])
-        _copy_linear(hf_layer.final_layer_norm, pt_layer[1][0])
-
-        # copy attn
-        _copy_attn_layer(hf_layer.self_attn, pt_layer[0][1])
-
-        # copy MLP
-        pt_mlp = pt_layer[1][1]
-        _copy_linear(hf_layer.fc1, pt_mlp.net[0][0])
-        _copy_linear(hf_layer.fc2, pt_mlp.net[2])
-
-    def _copy_layers(hf_layers, pt_layers):
-        for i, hf_layer in enumerate(hf_layers):
-            if i != 0:
-                i += i
-            pt_layer = pt_layers[i : i + 2]
-            _copy_layer(hf_layer, pt_layer)
-
-    hf_model = LDMBertModel(config).eval()
-
-    # copy  embeds
-    hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight
-    hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight
-
-    # copy layer norm
-    _copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)
-
-    # copy hidden layers
-    _copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers)
-
-    _copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits)
-
-    return hf_model
-
-
-def convert_ldm_clip_checkpoint(checkpoint):
-    text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
-
-    keys = list(checkpoint.keys())
-
-    text_model_dict = {}
-
-    for key in keys:
-        if key.startswith("cond_stage_model.transformer"):
-            text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
-
-    text_model.load_state_dict(text_model_dict)
-
-    return text_model
-
-import os
-def convert_checkpoint(checkpoint_path, inpainting=False):
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--checkpoint_path", default=checkpoint_path, type=str, help="Path to the checkpoint to convert."
-    )
-    # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
-    parser.add_argument(
-        "--original_config_file",
-        default=None,
-        type=str,
-        help="The YAML config file corresponding to the original architecture.",
-    )
-    parser.add_argument(
-        "--scheduler_type",
-        default="pndm",
-        type=str,
-        help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim']",
-    )
-    parser.add_argument("--dump_path", default=None, type=str, help="Path to the output model.")
-
-    args = parser.parse_args([])
-    if args.original_config_file is None:
-        if inpainting:
-            args.original_config_file = "./models/v1-inpainting-inference.yaml"
-        else:
-            args.original_config_file = "./models/v1-inference.yaml"
-
-    original_config = OmegaConf.load(args.original_config_file)
-    checkpoint = torch.load(args.checkpoint_path)["state_dict"]
-
-    num_train_timesteps = original_config.model.params.timesteps
-    beta_start = original_config.model.params.linear_start
-    beta_end = original_config.model.params.linear_end
-    if args.scheduler_type == "pndm":
-        scheduler = PNDMScheduler(
-            beta_end=beta_end,
-            beta_schedule="scaled_linear",
-            beta_start=beta_start,
-            num_train_timesteps=num_train_timesteps,
-            skip_prk_steps=True,
-        )
-    elif args.scheduler_type == "lms":
-        scheduler = LMSDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")
-    elif args.scheduler_type == "ddim":
-        scheduler = DDIMScheduler(
-            beta_start=beta_start,
-            beta_end=beta_end,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False,
-        )
-    else:
-        raise ValueError(f"Scheduler of type {args.scheduler_type} doesn't exist!")
-
-    # Convert the UNet2DConditionModel model.
-    unet_config = create_unet_diffusers_config(original_config)
-    converted_unet_checkpoint = convert_ldm_unet_checkpoint(checkpoint, unet_config)
-
-    unet = UNet2DConditionModel(**unet_config)
-    unet.load_state_dict(converted_unet_checkpoint)
-
-    # Convert the VAE model.
-    vae_config = create_vae_diffusers_config(original_config)
-    converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
-
-    vae = AutoencoderKL(**vae_config)
-    vae.load_state_dict(converted_vae_checkpoint)
-
-    # Convert the text model.
-    text_model_type = original_config.model.params.cond_stage_config.target.split(".")[-1]
-    if text_model_type == "FrozenCLIPEmbedder":
-        text_model = convert_ldm_clip_checkpoint(checkpoint)
-        tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
-        safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
-        feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
-        pipe = StableDiffusionPipeline(
-            vae=vae,
-            text_encoder=text_model,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-    else:
-        text_config = create_ldm_bert_config(original_config)
-        text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
-        tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
-        pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
-
-    return pipe
diff --git a/spaces/XAI/Cleaning-ImageNet-Hard/README.md b/spaces/XAI/Cleaning-ImageNet-Hard/README.md
deleted file mode 100644
index 9c38be04e0505b8e9f037838d27da1a0407c821b..0000000000000000000000000000000000000000
--- a/spaces/XAI/Cleaning-ImageNet-Hard/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Cleaning ImageNet Hard
-emoji: 📊
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.28.0
-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/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/vision/models/xresnet.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/vision/models/xresnet.py
deleted file mode 100644
index accc600a15efb63f3d84d4ee68867d73e9f4a9f8..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/vision/models/xresnet.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import torch.nn as nn
-import torch,math,sys
-import torch.utils.model_zoo as model_zoo
-from functools import partial
-from ...torch_core import Module
-
-__all__ = ['XResNet', 'xresnet18', 'xresnet34', 'xresnet50', 'xresnet101', 'xresnet152']
-
-# or: ELU+init (a=0.54; gain=1.55)
-act_fn = nn.ReLU(inplace=True)
-
-class Flatten(Module):
-    def forward(self, x): return x.view(x.size(0), -1)
-
-def init_cnn(m):
-    if getattr(m, 'bias', None) is not None: nn.init.constant_(m.bias, 0)
-    if isinstance(m, (nn.Conv2d,nn.Linear)): nn.init.kaiming_normal_(m.weight)
-    for l in m.children(): init_cnn(l)
-
-def conv(ni, nf, ks=3, stride=1, bias=False):
-    return nn.Conv2d(ni, nf, kernel_size=ks, stride=stride, padding=ks//2, bias=bias)
-
-def noop(x): return x
-
-def conv_layer(ni, nf, ks=3, stride=1, zero_bn=False, act=True):
-    bn = nn.BatchNorm2d(nf)
-    nn.init.constant_(bn.weight, 0. if zero_bn else 1.)
-    layers = [conv(ni, nf, ks, stride=stride), bn]
-    if act: layers.append(act_fn)
-    return nn.Sequential(*layers)
-
-class ResBlock(Module):
-    def __init__(self, expansion, ni, nh, stride=1):
-        nf,ni = nh*expansion,ni*expansion
-        layers  = [conv_layer(ni, nh, 3, stride=stride),
-                   conv_layer(nh, nf, 3, zero_bn=True, act=False)
-        ] if expansion == 1 else [
-                   conv_layer(ni, nh, 1),
-                   conv_layer(nh, nh, 3, stride=stride),
-                   conv_layer(nh, nf, 1, zero_bn=True, act=False)
-        ]
-        self.convs = nn.Sequential(*layers)
-        # TODO: check whether act=True works better
-        self.idconv = noop if ni==nf else conv_layer(ni, nf, 1, act=False)
-        self.pool = noop if stride==1 else nn.AvgPool2d(2, ceil_mode=True)
-
-    def forward(self, x): return act_fn(self.convs(x) + self.idconv(self.pool(x)))
-
-def filt_sz(recep): return min(64, 2**math.floor(math.log2(recep*0.75)))
-
-class XResNet(nn.Sequential):
-    def __init__(self, expansion, layers, c_in=3, c_out=1000):
-        stem = []
-        sizes = [c_in,32,32,64]
-        for i in range(3):
-            stem.append(conv_layer(sizes[i], sizes[i+1], stride=2 if i==0 else 1))
-            #nf = filt_sz(c_in*9)
-            #stem.append(conv_layer(c_in, nf, stride=2 if i==1 else 1))
-            #c_in = nf
-
-        block_szs = [64//expansion,64,128,256,512]
-        blocks = [self._make_layer(expansion, block_szs[i], block_szs[i+1], l, 1 if i==0 else 2)
-                  for i,l in enumerate(layers)]
-        super().__init__(
-            *stem,
-            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
-            *blocks,
-            nn.AdaptiveAvgPool2d(1), Flatten(),
-            nn.Linear(block_szs[-1]*expansion, c_out),
-        )
-        init_cnn(self)
-
-    def _make_layer(self, expansion, ni, nf, blocks, stride):
-        return nn.Sequential(
-            *[ResBlock(expansion, ni if i==0 else nf, nf, stride if i==0 else 1)
-              for i in range(blocks)])
-
-def xresnet(expansion, n_layers, name, pretrained=False, **kwargs):
-    model = XResNet(expansion, n_layers, **kwargs)
-    if pretrained: model.load_state_dict(model_zoo.load_url(model_urls[name]))
-    return model
-
-me = sys.modules[__name__]
-for n,e,l in [
-    [ 18 , 1, [2,2,2 ,2] ],
-    [ 34 , 1, [3,4,6 ,3] ],
-    [ 50 , 4, [3,4,6 ,3] ],
-    [ 101, 4, [3,4,23,3] ],
-    [ 152, 4, [3,8,36,3] ],
-]:
-    name = f'xresnet{n}'
-    setattr(me, name, partial(xresnet, expansion=e, n_layers=l, name=name))
-
diff --git a/spaces/XzJosh/Bella-Bert-VITS2/text/english_bert_mock.py b/spaces/XzJosh/Bella-Bert-VITS2/text/english_bert_mock.py
deleted file mode 100644
index 3b894ced5b6d619a18d6bdd7d7606ba9e6532050..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Bella-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/YUANAI/DiffspeechResearch/utils/commons/multiprocess_utils.py b/spaces/YUANAI/DiffspeechResearch/utils/commons/multiprocess_utils.py
deleted file mode 100644
index 00789dab1247c8c58e134ac3b71956dce71cf03a..0000000000000000000000000000000000000000
--- a/spaces/YUANAI/DiffspeechResearch/utils/commons/multiprocess_utils.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import os
-import traceback
-from functools import partial
-from tqdm import tqdm
-
-
-def chunked_worker(worker_id, args_queue=None, results_queue=None, init_ctx_func=None):
-    ctx = init_ctx_func(worker_id) if init_ctx_func is not None else None
-    while True:
-        args = args_queue.get()
-        if args == '<KILL>':
-            return
-        job_idx, map_func, arg = args
-        try:
-            map_func_ = partial(map_func, ctx=ctx) if ctx is not None else map_func
-            if isinstance(arg, dict):
-                res = map_func_(**arg)
-            elif isinstance(arg, (list, tuple)):
-                res = map_func_(*arg)
-            else:
-                res = map_func_(arg)
-            results_queue.put((job_idx, res))
-        except:
-            traceback.print_exc()
-            results_queue.put((job_idx, None))
-
-
-class MultiprocessManager:
-    def __init__(self, num_workers=None, init_ctx_func=None, multithread=False):
-        if multithread:
-            from multiprocessing.dummy import Queue, Process
-        else:
-            from multiprocessing import Queue, Process
-        if num_workers is None:
-            num_workers = int(os.getenv('N_PROC', os.cpu_count()))
-        self.num_workers = num_workers
-        self.results_queue = Queue(maxsize=-1)
-        self.args_queue = Queue(maxsize=-1)
-        self.workers = []
-        self.total_jobs = 0
-        for i in range(num_workers):
-            p = Process(target=chunked_worker,
-                        args=(i, self.args_queue, self.results_queue, init_ctx_func),
-                        daemon=True)
-            self.workers.append(p)
-            p.start()
-
-    def add_job(self, func, args):
-        self.args_queue.put((self.total_jobs, func, args))
-        self.total_jobs += 1
-
-    def get_results(self):
-        for w in range(self.num_workers):
-            self.args_queue.put("<KILL>")
-        self.n_finished = 0
-        while self.n_finished < self.total_jobs:
-            job_id, res = self.results_queue.get()
-            yield job_id, res
-            self.n_finished += 1
-        for w in self.workers:
-            w.join()
-
-    def __len__(self):
-        return self.total_jobs
-
-
-def multiprocess_run_tqdm(map_func, args, num_workers=None, ordered=True, init_ctx_func=None,
-                          multithread=False, desc=None):
-    for i, res in tqdm(enumerate(
-            multiprocess_run(map_func, args, num_workers, ordered, init_ctx_func, multithread)),
-            total=len(args), desc=desc):
-        yield i, res
-
-
-def multiprocess_run(map_func, args, num_workers=None, ordered=True, init_ctx_func=None, multithread=False):
-    """
-    Multiprocessing running chunked jobs.
-
-    Examples:
-    >>> for res in tqdm(multiprocess_run(job_func, args):
-    >>>     print(res)
-
-    :param map_func:
-    :param args:
-    :param num_workers:
-    :param ordered:
-    :param init_ctx_func:
-    :param q_max_size:
-    :param multithread:
-    :return:
-    """
-    if num_workers is None:
-        num_workers = int(os.getenv('N_PROC', os.cpu_count()))
-    manager = MultiprocessManager(num_workers, init_ctx_func, multithread)
-    for arg in args:
-        manager.add_job(map_func, arg)
-    if ordered:
-        n_jobs = len(args)
-        results = ['<WAIT>' for _ in range(n_jobs)]
-        i_now = 0
-        for job_i, res in manager.get_results():
-            results[job_i] = res
-            while i_now < n_jobs and (not isinstance(results[i_now], str) or results[i_now] != '<WAIT>'):
-                yield results[i_now]
-                i_now += 1
-    else:
-        for res in manager.get_results():
-            yield res
diff --git a/spaces/Yabo/ControlVideo/models/RIFE/warplayer.py b/spaces/Yabo/ControlVideo/models/RIFE/warplayer.py
deleted file mode 100644
index e7b2617fd3aa36e116b34b60f3ab4071d8cfd355..0000000000000000000000000000000000000000
--- a/spaces/Yabo/ControlVideo/models/RIFE/warplayer.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import torch
-import torch.nn as nn
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-backwarp_tenGrid = {}
-
-
-def warp(tenInput, tenFlow):
-    k = (str(tenFlow.device), str(tenFlow.size()))
-    if k not in backwarp_tenGrid:
-        tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view(
-            1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1)
-        tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view(
-            1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3])
-        backwarp_tenGrid[k] = torch.cat(
-            [tenHorizontal, tenVertical], 1).to(device)
-
-    tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0),
-                         tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1)
-
-    g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1).to(dtype=tenInput.dtype)
-    return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True)
diff --git a/spaces/Yaroslav1234/PublicComment.AI/app.py b/spaces/Yaroslav1234/PublicComment.AI/app.py
deleted file mode 100644
index 07e3ad26882fa9512c856c78bf6dab8f91f1b8c4..0000000000000000000000000000000000000000
--- a/spaces/Yaroslav1234/PublicComment.AI/app.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import random
-import gradio as gr
-import os
-import sys
-import json 
-import requests
-
-import openai
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-openai.api_key = OPENAI_API_KEY
-
-MODEL = "gpt-3.5-turbo"
-DISABLED = False
-NUM_THREADS = 5
-
-def exception_handler(exception_type, exception, traceback):
-    print("%s: %s" % (exception_type.__name__, exception))
-sys.excepthook = exception_handler
-sys.tracebacklimit = 0
-
-title = """<h1 align="center"><a href="https://www.federalregister.gov/documents/2023/03/09/2023-03681/safeguarding-advisory-client-assets">Safeguarding Advisory Client Assets</a></h1><h4 algin="center" style='text-align: center;'>A Proposed Rule by the Securities and Exchange Commission on 03/09/2023</h4>"""
-if DISABLED:
-    title = """<h1 align="center" style="color:red">This app has reached OpenAI's usage limit. We are currently requesting an increase in our quota. Please check back in a few days.</h1>"""
-
-# gr.themes.builder()
-# theme = gr.themes.Default(primary_hue="green")                
-
-concerns = [
-    {"title": "Digital assets or crypto.", "content": "Digital assets like cryptocurrency, built on blockchain tech, transforming finance. Yet, regulatory uncertainties pose challenges. "},
-    {"title": "Impact on small businesses.", "content": "these reporting requirements will require small businesses and start up protocols who would not otherwise be required to track personal identifiable information to implement such tracking, which will cause expense to such businesses.These additional costs will put these projects at a disadvantage and could stifle innovation. "},
-    {"title": "Privacy.", "content": "Privacy and safety associated with allowing so many third parties to know my sensitive financial data and social security number."},
-    {"title": "Identify Theft.", "content": "The proposed regs will force many participants in DeFi to collect user information. This will cause sensitive taxpayer information to be stored without proper safeguards, and will encourage the creation of honey pots for identity theft under the guise of tax reporting."},
-    {"title": "Confusing.", "content": "The proposed regs create reporting requirements for too many different participants in DeFi. This will cause multiple, inconsistent, reports to be generated for the same transaction."},
-    {"title": "Too Sudden.", "content": "These proposed regs will come into effect before companies and protocols are able to accommodate them.  Impact Abroad."},
-    {"title": "Impact Abroad.", "content": "The proposed regs do not sufficiently limit reporting requirements for protocols run outside the US, and for users outside the US."},
-    {"title": "Poorly defined terms.", "content": "The proposed regs use undefined terms, like platform, software and ledger, that are susceptible to many interpretations, and its definition of other terms, like wallet and validator, are defined in ways that do not describe their technical meaning. This will lead to confusion."}
-]
-topIssue = [
-"Lack of Clarity on the Definition of Digital Assets (The proposal does not provide clear guidance on what constitutes a digital asset, leading to confusion and potential misinterpretation.)",
-"Inadequate Consideration of the Unique Properties of Cryptocurrency (The SEC does not take into account the decentralized nature and technological complexities of cryptocurrency, leading to impractical regulatory requirements.)",
-"Potential Overreach of Regulatory Authority (The SEC's proposed rule may exceed its regulatory authority, encroaching on areas that should be regulated by other agencies.)",
-"Insufficient Consideration of Global Regulatory Standards (The proposal does not align with international regulatory standards for digital assets, leading to fragmentation and hindering cross-border transactions.)",
-"Lack of Industry Expertise in Drafting the Proposal (The SEC does not have sufficient expertise in digital assets and cryptocurrency, leading to a lack of understanding of the industry's unique characteristics.)",
-"Burden on Small Businesses and Startups (The proposed rules place a disproportionate burden on small businesses and startups, hindering innovation and growth in the digital asset industry.)",
-"Potential Negative Impact on Investor Access (The proposed rules may restrict investor access to digital assets, limiting their ability to participate in this emerging asset class.)",
-"Lack of Clarity on Custody Requirements for Digital Assets (The proposal does not provide clear guidelines on custody requirements for digital assets, creating uncertainty for market participants.)",
-"Unequal Treatment of Different Types of Digital Assets (The SEC's proposed rules treat different types of digital assets inconsistently, leading to confusion and potential regulatory arbitrage.)",
-"Lack of Consideration for Privacy and Security Concerns (The proposal does not adequately address the privacy and security concerns associated with the custody of digital assets, putting investors' assets at risk.)",
-"Potential Negative Impact on Market Liquidity (The proposed rules may hinder market liquidity for digital assets, making it more difficult for investors to buy and sell these assets.)",
-"Lack of Flexibility for Innovative Custodial Solutions (The SEC's proposed rules do not provide room for innovative custodial solutions in the digital asset industry, stifling competition and hindering progress.)",
-"Potential Negative Impact on Competitiveness of US Companies (The proposed rules may put US companies at a competitive disadvantage compared to their international counterparts, leading to capital flight and loss of market share.)",
-"Absence of Regulatory Clarity for Security Tokens (The proposal does not provide clear regulatory guidelines for security tokens, creating uncertainty and hindering investor protection.)",
-"Potential Negative Impact on Decentralized Finance (DeFi) (The proposed rules may hinder the growth and development of decentralized finance projects, limiting innovation and potential financial inclusion.)",
-"Inconsistent Regulatory Treatment of Stablecoins (The SEC's proposed rules do not provide consistent regulatory treatment for stablecoins, creating uncertainty and potentially disrupting stablecoin markets.)",
-"Potential Negative Impact on Tokenization of Assets (The proposed rules may hinder the tokenization of traditional assets, limiting the potential benefits of blockchain technology.)",
-"Inadequate Consideration of Smart Contracts (The proposal does not adequately address the unique characteristics of smart contracts, leading to regulatory challenges and potential legal uncertainties.)",
-"Insufficient Cybersecurity Requirements for Custodians (The proposal does not impose stringent cybersecurity requirements on custodians of digital assets, increasing the risk of theft and fraud.)",
-"Potential Negative Impact on Financial Inclusion (The proposed rules may limit the ability of underserved populations to access digital assets and participate in the financial system.)",
-"Lack of Regulatory Coordination with Other Agencies (The SEC's proposed rules do not demonstrate sufficient coordination with other regulatory agencies, leading to fragmented and conflicting regulations.)",
-"Potential Negative Impact on Blockchain Innovation (The proposed rules may stifle blockchain innovation by imposing excessive regulatory burdens and impediments.)",
-"Inconsistent Regulatory Treatment of Utility Tokens (The proposal does not provide consistent regulatory treatment for utility tokens, leading to confusion and potential regulatory arbitrage.)",
-"Burden on Exchanges to Demonstrate Exclusive Control (The proposed rules place a burden on exchanges to demonstrate exclusive control over client assets, which may be impractical for certain types of digital assets.)",
-"Potential Negative Impact on Token Offerings (The proposed rules may hinder token offerings, limiting the ability of companies to raise capital through innovative means.)",
-"Lack of Clarity on Compliance Requirements for Non-Custodial Services (The proposal does not provide clear guidance on compliance requirements for non-custodial services, creating uncertainty for market participants.)",
-"Inadequate Consideration of Self-Custody Solutions (The SEC's proposed rules do not adequately consider self-custody solutions, hindering the development of user-controlled asset management.)",
-"Potential Negative Impact on International Cooperation (The proposed rules may hinder international cooperation and coordination on digital asset regulation, leading to regulatory fragmentation.)",
-"Lack of Clarity on Recordkeeping Requirements for Digital Assets (The proposal does not provide clear guidance on recordkeeping requirements for digital assets, creating challenges for market participants.)",
-"Unequal Treatment of Different Market Participants (The SEC's proposed rules treat different market participants inconsistently, leading to unfair competition and potential market distortions.)",
-"Potential Negative Impact on Tokenized Securities (The proposed rules may hinder the issuance and trading of tokenized securities, limiting the benefits of digitization for traditional financial assets.)",
-"Lack of Regulatory Clarity for Initial Coin Offerings (ICOs) (The proposal does not provide clear regulatory guidelines for ICOs, creating uncertainty and potential investor protection issues.)",
-"Insufficient Consideration of Blockchain Interoperability (The proposal does not adequately consider the interoperability of different blockchain networks, hindering the potential benefits of cross-chain asset management.)",
-"Potential Negative Impact on Token Liquidity (The proposed rules may hinder token liquidity, making it more difficult for investors to buy and sell tokens.)",
-"Lack of Clarity on Reporting Requirements for Digital Assets (The proposal does not provide clear guidance on reporting requirements for digital assets, creating challenges for market participants.)",
-"Inconsistent Regulatory Treatment of Decentralized Exchanges (The SEC's proposed rules do not provide consistent regulatory treatment for decentralized exchanges, creating uncertainty and potential regulatory arbitrage.)",
-"Burden on Exchanges to Segregate Client Assets (The proposed rules place a burden on exchanges to segregate client assets, which may be challenging for certain types of digital assets.)",
-"Potential Negative Impact on Token Utility (The proposed rules may limit the ability of tokens to function as utility tokens, hindering their intended use cases.)",
-"Lack of Consideration for Cross-Border Transactions (The proposal does not adequately consider the unique challenges of cross-border transactions involving digital assets, leading to compliance difficulties.)",
-"Insufficient Consideration of Privacy Coins (The SEC's proposed rules do not adequately consider the unique characteristics of privacy coins, potentially hindering their development and adoption.)",
-"Potential Negative Impact on Peer-to-Peer Exchanges (The proposed rules may hinder the growth and development of peer-to-peer exchanges, limiting user autonomy and financial sovereignty.)",
-"Lack of Regulatory Clarity for Non-Fungible Tokens (NFTs) (The proposal does not provide clear regulatory guidelines for NFTs, creating uncertainty and potential legal challenges.)",
-"Inadequate Consideration of Oracles (The proposal does not adequately consider the role of oracles in the custody and verification of digital assets, leading to regulatory challenges.)",
-"Burden on Exchanges to Verify Ownership of Digital Assets (The proposed rules place a burden on exchanges to verify ownership of digital assets, which may be difficult for certain types of assets.)",
-"Potential Negative Impact on Digital Identity Solutions (The proposed rules may hinder the development and adoption of digital identity solutions for the management of digital assets.)",
-"Lack of Clarity on Anti-Money Laundering (AML) and Know Your Customer (KYC) Requirements (The proposal does not provide clear guidance on AML and KYC requirements for digital assets, creating uncertainty for market participants.)",
-"Inconsistent Regulatory Treatment of Tokenized Real Estate (The SEC's proposed rules do not provide consistent regulatory treatment for tokenized real estate, leading to confusion and potential regulatory arbitrage.)",
-"Burden on Exchanges to Conduct Due Diligence on Digital Asset Issuers (The proposed rules place a burden on exchanges to conduct due diligence on digital asset issuers, which may be challenging for certain types of assets.)",
-"Potential Negative Impact on Open Finance (The proposed rules may hinder the development and adoption of open finance platforms, limiting the benefits of decentralized financial systems.)",
-"Lack of Clarity on Tax Implications of Digital Assets (The proposal does not provide clear guidance on the tax implications of digital assets, creating uncertainty for market participants.)",
-"Insufficient Consideration of Token Standards (The SEC's proposed rules do not adequately consider token standards, potentially hindering interoperability and asset portability.)",
-"Lack of Regulatory Clarity for Blockchain Governance (The proposal does not provide clear regulatory guidelines for blockchain governance, potentially hindering the development of decentralized decision-making processes.)",
-"Potential Negative Impact on Tokenized Commodities (The proposed rules may hinder the issuance and trading of tokenized commodities, limiting the benefits of digitization for the commodities market.)",
-"Burden on Exchanges to Monitor and Report Suspicious Activities (The proposed rules place a burden on exchanges to monitor and report suspicious activities, which may be challenging given the pseudonymous nature of blockchain transactions.)",
-"Lack of Clarity on Token Custody by Investment Advisers (The proposal does not provide clear guidance on token custody by investment advisers, creating uncertainty and potential compliance difficulties.)",
-"Inadequate Consideration of Decentralized Autonomous Organizations (The proposal does not adequately consider the unique characteristics and challenges of decentralized autonomous organizations, potentially hindering their development.)",
-"Potential Negative Impact on Tokenized Art (The proposed rules may hinder the tokenization of art assets, limiting the democratization of art ownership and investment.)",
-"Lack of Consideration for Environmental Impact (The proposal does not adequately consider the environmental impact of digital asset mining and transaction validation processes.)",
-"Insufficient Consideration of Blockchain Scalability Solutions (The SEC's proposed rules do not adequately consider blockchain scalability solutions, potentially hindering the adoption and scalability of digital assets.)",
-"Lack of Regulatory Clarity for Tokenized Derivatives (The proposal does not provide clear regulatory guidelines for tokenized derivatives, creating uncertainty and potential regulatory arbitrage.)",
-"Burden on Exchanges to Comply with Market Abuse and Manipulation Detection (The proposed rules place a burden on exchanges to comply with market abuse and manipulation detection, which may be challenging given the complexity of blockchain-based markets.)",
-"Potential Negative Impact on Tokenized Intellectual Property (The proposed rules may hinder the tokenization of intellectual property assets, limiting the potential for innovation and monetization.)",
-"Lack of Clarity on Cross-Chain Transactions (The proposal does not provide clear guidance on the regulatory treatment of cross-chain transactions, creating uncertainty for market participants.)",
-"Inadequate Consideration of Tokenized Debt Instruments (The proposal does not adequately consider the unique characteristics of tokenized debt instruments, potentially hindering their development and adoption.)",
-"Potential Negative Impact on Tokenized Supply Chain Solutions (The proposed rules may hinder the adoption of tokenized supply chain solutions, limiting the transparency and efficiency of supply chain management.)",
-"Lack of Regulatory Clarity for Tokenized Gaming Assets (The proposal does not provide clear regulatory guidelines for tokenized gaming assets, creating uncertainty and potential legal challenges.)",
-"Insufficient Consideration of Multi-Signature Wallets (The SEC's proposed rules do not adequately consider the use of multi-signature wallets for custody of digital assets, potentially hindering their adoption.)",
-"Burden on Exchanges to Implement Market Surveillance Systems (The proposed rules place a burden on exchanges to implement market surveillance systems, which may be costly and complex to develop.)",
-"Potential Negative Impact on Tokenized Agriculture Assets (The proposed rules may hinder the tokenization of agriculture assets, limiting the potential for investment and financing in the agricultural sector.)",
-"Lack of Clarity on Anti-Fraud Measures for Digital Assets (The proposal does not provide clear guidance on anti-fraud measures for digital assets, creating potential loopholes for fraudulent activities.)",
-"Inconsistent Regulatory Treatment of Tokenized Energy Assets (The SEC's proposed rules do not provide consistent regulatory treatment for tokenized energy assets, creating uncertainty and potential regulatory arbitrage.)",
-"Burden on Exchanges to Comply with Market Integrity Standards (The proposed rules place a burden on exchanges to comply with market integrity standards, which may be challenging given the decentralized nature of blockchain transactions.)"
-]
-prompt = {
-    "issue": '', 
-    "concern": '', 
-    "system": 'Do not include a heading and do not mention about the PDF summary in the letter you return.5. Once you are done, you ask the user if there are any other areas of concern he or she has for you to craft a public comment. As well as offer it up to the user to ask any general questions that are part of the proposal.', 
-    "content": """Here are some key points summarized for each subjects. I. Introduction:  The SEC is proposing a new rule to address the safeguarding of client assets by investment advisers. The rule aims to enhance investor protections and address gaps in the custody rule. It includes amendments to the current rule, recordkeeping, and registration requirements. II. Discussion:  A. Scope of Rule: The proposed rule expands the coverage to include a broader range of investments held in a client's account. It defines assets, includes discretionary authority in custody, and provides exceptions for specific situations. B. Qualified Custodian Protections: The rule enhances the protection of client assets by addressing how investment advisers safeguard these assets. It discusses the application of the rule to crypto assets and challenges in demonstrating exclusive control. C. Certain Assets Unable to be Maintained with a Qualified Custodian: The rule addresses how advisers can safeguard assets that cannot be maintained with a qualified custodian. Enhanced recordkeeping, separation of duties, and regular reviews are required. D. Segregation of Client Assets: The rule aims to ensure client assets are segregated from the adviser's assets. Exceptions are provided, but the rule prioritizes the protection of client assets. E. Investment Adviser Delivery of Notice to Clients: Advisers must notify clients in writing when opening an account with a custodian. The notice includes custodian information and custodial account number. F. Amendments to the Surprise Examination Requirement: The proposed changes require advisers to implement a written agreement with an independent public accountant for surprise examinations. This helps safeguard client assets and reduce the risk of loss. G. Exceptions from the Surprise Examination: Exceptions are provided for advisers with discretionary authority over client assets and those with custody solely due to a standing letter of authorization (SLOA). H. Amendments to the Investment Adviser Recordkeeping Rule: The amendments require advisers to maintain records related to client notifications, custodian information, transactions, and more. This helps improve oversight and investor protection. I. Changes to Form ADV: The changes require advisers to report custody of client assets and information about custodians and accountants involved in safeguarding assets. This enhances transparency and regulatory oversight. J. Staff No-Action Letters and Statements: Certain letters and statements regarding the custody rule are being reviewed, and interested parties can suggest their withdrawal. K. Transition Period and Compliance Date: The SEC proposes a one-year transition period for advisers to comply with the new rule. Compliance dates differ based on assets under management. III. Economic Analysis: The SEC considers the costs and benefits of proposed rule amendments, acknowledging the challenge of estimating economic effects due to varying practices among investment advisers. The analysis includes both qualitative and quantified assessments of the economic effects. B. Broad Economic Considerations: The revised SEC rules aim to mitigate principal-agent problems and market failures by addressing conflicts of interest between investment advisers and clients. Regulatory requirements can increase investor protection by incentivizing compliance and facilitating oversight and verification of adviser conduct. C. Baseline: The proposed rule aims to enhance investor protections by amending provisions of the current custody rule. Investment advisers reported custody of client assets worth $45.56 trillion, highlighting the importance of safeguarding these assets. D. Benefits and Costs of Proposed Rule: The proposed rule enhances investor protections by reducing asset loss risk. While providing benefits to investors, advisers will incur compliance costs. The magnitude will depend on current custodial practices and existing controls. E. Efficiency, Competition, and Capital Formation: The proposed rule aims to enhance client and investor protections, potentially increasing the efficiency of investments and promoting capital formation. However, it may also impact advisory services, competition, and compliance costs for qualified custodians. F. Reasonable Alternatives: The proposed safeguards strike a balance between investor protections and compliance costs. The SEC welcomes comments on reasonable alternatives to the proposed rule. G. Request for Comment: The SEC seeks public input on the economic analysis, benefits, costs, efficiency, competition, and capital formation effects of the proposed rule. Comments are also encouraged on reasonable alternatives and potential overlooked benefits and costs. IV. Paperwork Reduction Act Analysis: The proposed rule and amendments result in new collection of information requirements. The SEC is submitting these collections for review and approval, ensuring confidentiality for certain responses. B. Rule 223-1: The proposed rule includes requirements for maintaining client assets, obtaining reasonable assurances, providing account details, and exceptions. It impacts around 13,944 advisers, resulting in an estimated burden of 30,974 hours and a cost of $12,203,756. C. Exceptions: The proposed rule includes exceptions for certain assets, requiring written agreements and verification. The estimated cost burden for these exceptions is $322,956,000. D. Total Hour Burden: The SEC estimates a total annual hour burden of approximately 398,152 hours and external cost burden of $378,598,500 for advisers subject to the proposed rule. E. Rule 204-2: The proposed amendments to Rule 204-2 require maintaining specific records, resulting in an estimated total burden of 3,347,352 hours and a cost of $217,333,279. F. Form ADV: The proposed changes to Form ADV increase information required, estimating an annual compliance burden of 433,004 hours and external cost of $14,125,083. G. Request for Comments: The SEC seeks comments on the proposed collections of information, including their necessity, accuracy, quality, and burden. V. Initial Regulatory Flexibility Analysis: The SEC proposes rule amendments to enhance the safeguarding of client assets. The changes aim to improve investor protections, oversight, and risk assessment abilities. B. Legal Basis: The proposed rules are based on the legal authority provided by various sections of the Advisers Act. C. Small Entities Subject to the Rule and Rule Amendments: Most small advisers registered with state authorities won't be affected. Around 522 SEC-registered advisers, out of which 321 have custody of client assets, would be subject to the proposed rule. D. Projected Compliance Requirements: The proposed rule and related amendments impose reporting, compliance, and recordkeeping requirements on investment advisers, estimated at 13,944 advisers and a cost of $232,394.40 for small advisers. E. Duplicative, Overlapping, or Conflicting Federal Rules: The SEC believes there are no duplicative, overlapping, or conflicting rules regarding the proposed amendments. F. Significant Alternatives: The SEC considers the proposed rule appropriate for small advisers, providing benefits to clients. Further clarification or simplification is not deemed necessary. G. Solicitation of Comments: The SEC seeks comments on the potential impact of the proposed rule on small entities. VI. Consideration of Impact on the Economy: The proposed rule and amendments aim to enhance investor protections and SEC oversight of client assets in the advisory industry. VII. Statutory Authority: The proposed rule, amendments, and Form ADV changes are introduced under the authority granted by sections of the Advisers Act."""
-}
-def predict(inputs, outputLength, concernDropdown, issueDropdown, name):
-    userPrompt='Create a draft public comment on regulations.gov with no heading in response to the proposal "Safeguarding Advisory Client Assets" proposal from the agency - Securities and Exchange Commission. Please mix and match concerns and issues, as well as quote any prior law as long as it is relevant. Make strong as well as nuanced points against the SEC rule proposals.'
-    userPrompt += prompt['content']
-
-    if issueDropdown:
-        userPrompt += " Focus my issues around the following:" + issueDropdown
-
-    if concernDropdown:
-        selected_concern = [item for item in concerns if item['title'] == concernDropdown]
-        if len(selected_concern) > 0:
-            userPrompt += " Focus my concerns around the following topics:" + selected_concern[0]['title'] + ' ' + selected_concern[0]['content']
-
-    if inputs:
-        userPrompt += "Here is my own concern:" + inputs
-        
-    userPrompt += " The public comment should be about " + outputLength + " words."
-    userPrompt += " Please use name " + name + " in the letter."
-
-    messages=[
-        {"role": "system", "content": prompt['system']},
-        {"role": "user", "content": userPrompt}
-    ]
-    m = ""
-    completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages,temperature=1.15, stream=True)
-    for chunk in completion:
-        if 'content' in chunk.choices[0].delta:
-            t = chunk.choices[0].delta.content
-            m += t
-            if t == '[' or t == 'Your' or t == ' Name' or t == ']':
-                print(t)
-                if '[Your Name]' in m:
-                    m = m.replace('[Your Name]', name)
-                    yield[['', m]]
-            else:
-                yield [['', m]]
-
-def reset_textbox():
-    return gr.update(value='', interactive=False), gr.update(interactive=False)
-
-def toneChange(tone):
-    if tone < 50:
-        return f"<h4 style='color: #1e6f1e'>Value:{tone} (Low Variance)</h4>"
-    elif tone < 80:
-        return f"<h4 style='color: #bf8d33'>Value:{tone} (Medium Variance)</h4>"
-    elif tone < 100:
-        return f"<h4 style='color: #bf3030'>Value:{tone} (~Spicy!)</h4>"
-    else:
-        return f"<h4 style='color: #bf3030'>Value:{tone} (Over-the-top [Not Recommended])</h4>"
-
-def issueTopicChange(issue):
-    with gr.Group() as issueGroup:
-        for concern in topIssue:
-            gr.Checkbox(label=concern['title'], elem_classes="issue")
-    # return issueGroup
-
-ic = []
-cc = []
-def updateComment(text, chatbot, name):
-    if len(text.split(' ')) < 5:
-        gr.Warning('Kindly provide a message that consists of a minimum of 10 words.')
-        return text, chatbot
-    if not chatbot or not chatbot[0][1]:
-        gr.Warning('First generate the letter and then type your message to update it')
-        return text, chatbot
-    userPrompt = prompt['content'] + prompt['issue'] + prompt['concern']
-    userPrompt += " And here is the draft letter. \n '''" + chatbot[0][1] + "'''"
-    userPrompt += " please modify the draft letter regarding bellow content. "
-    userPrompt += text
-    messages=[
-        {"role": "system", "content": prompt['system']},
-        {"role": "user", "content": userPrompt}
-    ]
-    m = ""
-    completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages,temperature=1.15, stream=True)
-    for chunk in completion:
-        if 'content' in chunk.choices[0].delta:
-            t = chunk.choices[0].delta.content
-            m += t
-            if t == '[' or t == 'Your' or t == ' Name' or t == ']':
-                print(t)
-                if '[Your Name]' in m:
-                    m = m.replace('[Your Name]', name)
-                    yield '', [[text, m]]
-            else:
-                yield '', [[text, m]]
-
-with gr.Blocks(theme=gr.themes.Default(spacing_size=gr.themes.sizes.spacing_lg, text_size=gr.themes.sizes.text_lg), css = """.gradio-container { max-width: 100% !important; width: 100%; margin: 0; } .issue { display: flex; flex-direction: column; } .issue>div {order: 2}
-                 #chatbot {height: 520px; overflow: auto;}""") as demo:
-    
-    concernInputs = []
-    
-    with gr.Blocks() as concernBlock:
-        issueDropdown = gr.Dropdown(topIssue, label="Select an issue you want to address. (AI-generated)", info="")
-        for concern in concerns:
-            cc.append(concern['title'])
-        concernDropdown = gr.Dropdown(cc, label="Areas of concern (Human-generated)", info="")
-    txt = gr.Textbox(placeholder="Type your concern", label="Enter my own concern (optional) - Copy from the above lists")
-
-    outputLength = gr.Dropdown(
-        ["300", "800", "1600"],
-        value="800",
-        label="Output letter length"
-    )
-    name = gr.Textbox(placeholder="Type your name", label="")
-    btn = gr.Button(value="Generate")
-    chatbot = gr.Chatbot(elem_id='chatbot',show_copy_button=True)
-    btn.click(predict, [txt, outputLength, concernDropdown, issueDropdown, name], [chatbot])
-    # with gr.Group(visible=True) as updateGroup:
-    u_txt = gr.Textbox(placeholder="", label='Further, customize your Public Comment (Optional). For instance, "Rewrite in a passive-aggressive tone" or "Infuse with some 4chan humor."')
-    u_btn = gr.Button(value="Generate")
-    
-    u_btn.click(updateComment, [u_txt, chatbot, name], [u_txt, chatbot])
-    demo.load(
-            None,
-            None,
-            _js="""
-            () => {
-            document.body.classList.remove('dark');
-            document.querySelector('gradio-app').style.backgroundColor = 'var(--color-background-primary)'
-            }
-            """,
-        )
-    demo.queue(max_size=20, concurrency_count=NUM_THREADS, api_open=False)
-    # demo.queue()
-
-if __name__ == "__main__":
-    demo.launch(share=False)
-
diff --git a/spaces/Yntec/fast_diffusion/README.md b/spaces/Yntec/fast_diffusion/README.md
deleted file mode 100644
index ed5774e19b4f96759ccdfb06a7665a78ff08baa3..0000000000000000000000000000000000000000
--- a/spaces/Yntec/fast_diffusion/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 399 Models Fast Diffusion 
-emoji: 👩‍🎨👨‍🎨
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: true
-duplicated_from: classic_maximum_multiplier_places
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/YouLiXiya/Mobile-SAM/sam_extension/pipeline/__init__.py b/spaces/YouLiXiya/Mobile-SAM/sam_extension/pipeline/__init__.py
deleted file mode 100644
index 99348e740d57119c25212e4af83d041f2da541c1..0000000000000000000000000000000000000000
--- a/spaces/YouLiXiya/Mobile-SAM/sam_extension/pipeline/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .base import Pipeline
-from .sam import SAMEncoderPipeline, SAMDecoderPipeline
-from .owlvit import OwlViTVisionEncoderPipeline, OwlViTDecoderPipeline
-from .groundingdino import GroundingDinoPipeline
\ No newline at end of file
diff --git a/spaces/abdvl/datahub_qa_bot/docs/managed-datahub/datahub-api/graphql-api/incidents-api-beta.md b/spaces/abdvl/datahub_qa_bot/docs/managed-datahub/datahub-api/graphql-api/incidents-api-beta.md
deleted file mode 100644
index 89bacb2009e4945f99914b0fee5b4e8e2ca037dc..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/managed-datahub/datahub-api/graphql-api/incidents-api-beta.md
+++ /dev/null
@@ -1,410 +0,0 @@
----
-description: This page provides an overview of working with the DataHub Incidents API.
----
-import FeatureAvailability from '@site/src/components/FeatureAvailability';
-
-
-# Incidents API (Beta)
-<FeatureAvailability saasOnly />
-
-## Introduction
-
-**Incidents** are a concept used to flag particular Data Assets as being in an unhealthy state. Each incident has an independent lifecycle and details including a state (active, resolved), a title, a description, & more.
-
-A couple scenarios in which incidents can be useful are
-
-1. **Pipeline Circuit Breaking:** You can use Incidents as the basis for intelligent data pipelines that verify upstream inputs (e.g. datasets) are free of any active incidents before executing.
-2. \[Coming Soon] **Announcing Known-Bad Assets**: You can mark a known-bad data asset as under an ongoing incident so consumers and stakeholders can be informed about the health status of a data asset via the DataHub UI. Moreover, they can follow the incident as it progresses toward resolution.
-
-In the next section, we'll show you how to
-
-1. Create a new incident
-2. Fetch all incidents for a data asset
-3. Resolve an incident
-
-for **Datasets** using the Acryl [GraphQL API](docs/api/graphql/overview.md).
-
-Let's get started!
-
-## Creating an Incident
-
-:::info
-Creating incidents is currently only supported against **Dataset** assets.
-:::
-
-To create (i.e. raise) a new incident for a data asset, simply create a GraphQL request using the `raiseIncident` mutation.
-
-```
-type Mutation {
-    """
-    Raise a new incident for a data asset 
-    """
-    raiseIncident(input: RaiseIncidentInput!): String! # Returns new Incident URN.
-}
-
-input RaiseIncidentInput {
-  """
-  The type of incident, e.g. OPERATIONAL
-  """
-  type: IncidentType!
-
-  """
-  A custom type of incident. Present only if type is 'CUSTOM'
-  """
-  customType: String
-
-  """
-  An optional title associated with the incident
-  """
-  title: String
-
-  """
-  An optional description associated with the incident
-  """
-  description: String
-
-  """
-  The resource (dataset, dashboard, chart, dataFlow, etc) that the incident is associated with.
-  """
-  resourceUrn: String!
-
-  """
-  The source of the incident, i.e. how it was generated
-  """
-  source: IncidentSourceInput
-}
-```
-
-### Examples
-
-First, we'll create a demo GraphQL query, then show how to represent it via CURL & Python.
-
-Imagine we want to raise a new incident on a Dataset with URN `urn:li:dataset:(abc)` because it's failed automated quality checks. To do so, we could make the following GraphQL query:
-
-_Request_
-
-```
-mutation raiseIncident {
-  raiseIncident(input: {
-    type: OPERATIONAL
-    title: "Dataset Failed Quality Checks"
-    description: "Dataset failed 2/6 Quality Checks for suite run id xy123mksj812pk23."
-    resourceUrn: "urn:li:dataset:(urn:li:dataPlatform:kafka,SampleKafkaDataset,PROD)"
-  })
-}
-```
-
-After we make this query, we will get back a unique URN for the incident.
-
-_Response_
-
-```
-{
-  "data": {
-    "raiseIncident": "urn:li:incident:bfecab62-dc10-49a6-a305-78ce0cc6e5b1"
-  }
-}
-```
-
-Now we'll see how to issue this query using a CURL or Python.
-
-#### CURL
-
-To issue the above GraphQL as a CURL:
-
-```
-curl --location --request POST 'https://your-account.acryl.io/api/graphql' \
---header 'Authorization: Bearer your-access-token' \
---header 'Content-Type: application/json' \
---data-raw '{"query":"mutation raiseIncident {\n  raiseIncident(input: {\n    type: OPERATIONAL\n    title: \"Dataset Failed Quality Checks\"\n    description: \"Dataset failed 2/6 Quality Checks for suite run id xy123mksj812pk23.\"\n    resourceUrn: \"urn:li:dataset:(urn:li:dataPlatform:kafka,SampleKafkaDataset,PROD)\"\n  })\n}","variables":{}}'
-```
-
-#### Python
-
-To issue the above GraphQL query in Python (requests):
-
-```
-import requests
-
-datahub_session = requests.Session()
-
-headers = {
-    "Content-Type": "application/json",
-    "Authorization": "Bearer your-personal-access-token",
-}
-
-json = {
-    "query": """mutation raiseIncident {\n
-      raiseIncident(input: {\n
-        type: OPERATIONAL\n
-        resourceUrn: \"urn:li:dataset:(urn:li:dataPlatform:kafka,SampleKafkaDataset,PROD)\"\n
-      })}""",
-    "variables": {},
-}
-
-response = datahub_session.post(f"https://your-account.acryl.io/api/graphql", headers=headers, json=json)
-response.raise_for_status()
-res_data = response.json() # Get result as JSON
-```
-
-## Retrieving Active Incidents
-
-To fetch the the ongoing incidents for a data asset, we can use the `incidents` GraphQL field on the entity of interest.
-
-### Datasets
-
-To retrieve all incidents for a Dataset with a particular [URN](docs/what/urn.md), you can reference the 'incidents' field of the Dataset type:
-
-```
-type Dataset {
-  ....
-  """
-  Incidents associated with the Dataset
-  """
-  incidents(
-    """
-    Optional incident state to filter by, defaults to any state.
-    """
-    state: IncidentState,
-    """
-    Optional start offset, defaults to 0.
-    """
-    start: Int,
-    """
-    Optional start offset, defaults to 20.
-    """
-    count: Int): EntityIncidentsResult # Returns a list of incidents. 
-}
-```
-
-### Examples
-
-Now that we've raised an incident on it, imagine we want to fetch the first 10 "active" incidents for the Dataset with URN `urn:li:dataset:(abc`). To do so, we could issue the following request:
-
-_Request_
-
-```
-query dataset {
-  dataset(urn: "urn:li:dataset:(abc)") {
-    incidents(state: ACTIVE, start: 0, count: 10) {
-      total
-      incidents {
-        urn
-        title
-        description
-        status {
-          state
-        }
-      }
-    }
-  }
-}
-```
-
-After we make this query, we will get back a unique URN for the incident.
-
-_Response_
-
-```
-{
-  "data": {
-    "dataset": {
-      "incidents": {
-        "total": 1,
-        "incidents": [
-          {
-            "urn": "urn:li:incident:bfecab62-dc10-49a6-a305-78ce0cc6e5b1",
-            "title": "Dataset Failed Quality Check",
-            "description": "Dataset failed 2/6 Quality Checks for suite run id xy123mksj812pk23.",
-            "status": {
-              "state": "ACTIVE"
-            }
-          }
-        ]
-      }
-    }
-  }
-}
-```
-
-Now we'll see how to issue this query using a CURL or Python.
-
-#### CURL
-
-To issue the above GraphQL as a CURL:
-
-```
-curl --location --request POST 'https://your-account.acryl.io/api/graphql' \
---header 'Authorization: Bearer your-access-token' \
---header 'Content-Type: application/json' \
---data-raw '{"query":"query dataset {\n dataset(urn: "urn:li:dataset:(abc)") {\n incidents(state: ACTIVE, start: 0, count: 10) {\n total\n incidents {\n urn\n title\n description\n status {\n state\n }\n }\n }\n }\n}","variables":{}}'Python
-```
-
-To issue the above GraphQL query in Python (requests):
-
-```
-import requests
-
-datahub_session = requests.Session()
-
-headers = {
-    "Content-Type": "application/json",
-    "Authorization": "Bearer your-personal-access-token",
-}
-
-json = {
-    "query": """query dataset {\n
-                  dataset(urn: "urn:li:dataset:(abc)") {\n
-                    incidents(state: ACTIVE, start: 0, count: 10) {\n
-                      total\n
-                      incidents {\n
-                        urn\n
-                        title\n
-                        description\n
-                        status {\n
-                          state\n
-                        }\n
-                      }\n
-                    }\n
-                  }\n
-                }""",
-    "variables": {},
-}
-
-response = datahub_session.post(f"https://your-account.acryl.io/api/graphql", headers=headers, json=json)
-response.raise_for_status()
-res_data = response.json() # Get result as JSON
-```
-
-## Resolving an Incident
-
-To resolve an incident for a data asset, simply create a GraphQL request using the `updateIncidentStatus` mutation. To mark an incident as resolved, simply update its state to `RESOLVED`.
-
-```
-type Mutation {
-    """
-    Update an existing incident for a resource (asset)
-    """
-    updateIncidentStatus(
-      """
-      The urn for an existing incident
-      """
-      urn: String!
-
-      """
-      Input required to update the state of an existing incident
-      """
-      input: UpdateIncidentStatusInput!): String
-}
-
-"""
-Input required to update status of an existing incident
-"""
-input UpdateIncidentStatusInput {
-  """
-  The new state of the incident
-  """
-  state: IncidentState!
-
-  """
-  An optional message associated with the new state
-  """
-  message: String
-}
-```
-
-### Examples
-
-Imagine that we've fixed our Dataset with urn `urn:li:dataset:(abc)` so that it's passing validation. Now we want to mark the Dataset as healthy, so stakeholders and downstream consumers know it's ready to use.
-
-To do so, we need the URN of the Incident that we raised previously.
-
-_Request_
-
-```
-mutation updateIncidentStatus {
-  updateIncidentStatus(urn: "urn:li:incident:bfecab62-dc10-49a6-a305-78ce0cc6e5b1", 
-  input: {
-    state: RESOLVED
-    message: "Dataset is now passing validations. Verified by John Joyce on Data Platform eng."
-  })
-}
-```
-
-_Response_
-
-```
-{
-  "data": {
-    "updateIncidentStatus": "true"
-  }
-}
-```
-
-True is returned if the incident's was successfully marked as resolved.
-
-#### CURL
-
-To issue the above GraphQL as a CURL:
-
-```
-curl --location --request POST 'https://your-account.acryl.io/api/graphql' \
---header 'Authorization: Bearer your-access-token' \
---header 'Content-Type: application/json' \
---data-raw '{"query":"mutation updateIncidentStatus {\n updateIncidentStatus(urn: "urn:li:incident:bfecab62-dc10-49a6-a305-78ce0cc6e5b1", \n input: {\n state: RESOLVED\n message: "Dataset is now passing validations. Verified by John Joyce on Data Platform eng."\n })\n}","variables":{}}'Python
-```
-
-To issue the above GraphQL query in Python (requests):
-
-```
-import requests
-
-datahub_session = requests.Session()
-
-headers = {
-    "Content-Type": "application/json",
-    "Authorization": "Bearer your-personal-access-token",
-}
-
-json = {
-    "query": """mutation updateIncidentStatus {\n
-                  updateIncidentStatus(urn: \"urn:li:incident:bfecab62-dc10-49a6-a305-78ce0cc6e5b1\",\n
-                  input: {\n
-                    state: RESOLVED\n
-                    message: \"Dataset is now passing validations. Verified by John Joyce on Data Platform eng.\"\n
-                  })\n
-                }""",
-    "variables": {},
-}
-
-response = datahub_session.post(f"https://your-account.acryl.io/api/graphql", headers=headers, json=json)
-response.raise_for_status()
-res_data = response.json() # Get result as JSON  
-```
-
-## Tips
-
-:::info
-**Authorization**
-
-Remember to always provide a DataHub Personal Access Token when calling the GraphQL API. To do so, just add the 'Authorization' header as follows:
-
-```
-Authorization: Bearer <personal-access-token>
-```
-
-**Exploring GraphQL API**
-
-Also, remember that you can play with an interactive version of the Acryl GraphQL API at `https://your-account-id.acryl.io/api/graphiql`
-:::
-
-## Enabling Slack Notifications
-
-You can configure Acryl to send slack notifications to a specific channel when incidents are raised or their status is changed.
-
-These notifications are also able to tag the immediate asset's owners, along with the owners of downstream assets consuming it.
-
-![](../../imgs/saas/Screen-Shot-2022-03-22-at-6.46.41-PM.png)
-
-To do so, simply follow the [Slack Integration Guide](docs/managed-datahub/saas-slack-setup.md) and contact your Acryl customer success team to enable the feature! 
-
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/core/evaluation/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/core/evaluation/__init__.py
deleted file mode 100644
index f7cc4b23413a0639e9de00eeb0bf600632d2c6cd..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/core/evaluation/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from .class_names import get_classes, get_palette
-from .eval_hooks import DistEvalHook, EvalHook
-from .metrics import eval_metrics, mean_dice, mean_fscore, mean_iou
-
-__all__ = [
-    'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'mean_fscore',
-    'eval_metrics', 'get_classes', 'get_palette'
-]
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/utils/config.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/utils/config.py
deleted file mode 100644
index 17149353aefac6d737c67bb2f35a3a6cd2147b0a..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/utils/config.py
+++ /dev/null
@@ -1,688 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import ast
-import copy
-import os
-import os.path as osp
-import platform
-import shutil
-import sys
-import tempfile
-import uuid
-import warnings
-from argparse import Action, ArgumentParser
-from collections import abc
-from importlib import import_module
-
-from addict import Dict
-from yapf.yapflib.yapf_api import FormatCode
-
-from .misc import import_modules_from_strings
-from .path import check_file_exist
-
-if platform.system() == 'Windows':
-    import regex as re
-else:
-    import re
-
-BASE_KEY = '_base_'
-DELETE_KEY = '_delete_'
-DEPRECATION_KEY = '_deprecation_'
-RESERVED_KEYS = ['filename', 'text', 'pretty_text']
-
-
-class ConfigDict(Dict):
-
-    def __missing__(self, name):
-        raise KeyError(name)
-
-    def __getattr__(self, name):
-        try:
-            value = super(ConfigDict, self).__getattr__(name)
-        except KeyError:
-            ex = AttributeError(f"'{self.__class__.__name__}' object has no "
-                                f"attribute '{name}'")
-        except Exception as e:
-            ex = e
-        else:
-            return value
-        raise ex
-
-
-def add_args(parser, cfg, prefix=''):
-    for k, v in cfg.items():
-        if isinstance(v, str):
-            parser.add_argument('--' + prefix + k)
-        elif isinstance(v, int):
-            parser.add_argument('--' + prefix + k, type=int)
-        elif isinstance(v, float):
-            parser.add_argument('--' + prefix + k, type=float)
-        elif isinstance(v, bool):
-            parser.add_argument('--' + prefix + k, action='store_true')
-        elif isinstance(v, dict):
-            add_args(parser, v, prefix + k + '.')
-        elif isinstance(v, abc.Iterable):
-            parser.add_argument('--' + prefix + k, type=type(v[0]), nargs='+')
-        else:
-            print(f'cannot parse key {prefix + k} of type {type(v)}')
-    return parser
-
-
-class Config:
-    """A facility for config and config files.
-
-    It supports common file formats as configs: python/json/yaml. The interface
-    is the same as a dict object and also allows access config values as
-    attributes.
-
-    Example:
-        >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
-        >>> cfg.a
-        1
-        >>> cfg.b
-        {'b1': [0, 1]}
-        >>> cfg.b.b1
-        [0, 1]
-        >>> cfg = Config.fromfile('tests/data/config/a.py')
-        >>> cfg.filename
-        "/home/kchen/projects/mmcv/tests/data/config/a.py"
-        >>> cfg.item4
-        'test'
-        >>> cfg
-        "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: "
-        "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}"
-    """
-
-    @staticmethod
-    def _validate_py_syntax(filename):
-        with open(filename, 'r', encoding='utf-8') as f:
-            # Setting encoding explicitly to resolve coding issue on windows
-            content = f.read()
-        try:
-            ast.parse(content)
-        except SyntaxError as e:
-            raise SyntaxError('There are syntax errors in config '
-                              f'file {filename}: {e}')
-
-    @staticmethod
-    def _substitute_predefined_vars(filename, temp_config_name):
-        file_dirname = osp.dirname(filename)
-        file_basename = osp.basename(filename)
-        file_basename_no_extension = osp.splitext(file_basename)[0]
-        file_extname = osp.splitext(filename)[1]
-        support_templates = dict(
-            fileDirname=file_dirname,
-            fileBasename=file_basename,
-            fileBasenameNoExtension=file_basename_no_extension,
-            fileExtname=file_extname)
-        with open(filename, 'r', encoding='utf-8') as f:
-            # Setting encoding explicitly to resolve coding issue on windows
-            config_file = f.read()
-        for key, value in support_templates.items():
-            regexp = r'\{\{\s*' + str(key) + r'\s*\}\}'
-            value = value.replace('\\', '/')
-            config_file = re.sub(regexp, value, config_file)
-        with open(temp_config_name, 'w', encoding='utf-8') as tmp_config_file:
-            tmp_config_file.write(config_file)
-
-    @staticmethod
-    def _pre_substitute_base_vars(filename, temp_config_name):
-        """Substitute base variable placehoders to string, so that parsing
-        would work."""
-        with open(filename, 'r', encoding='utf-8') as f:
-            # Setting encoding explicitly to resolve coding issue on windows
-            config_file = f.read()
-        base_var_dict = {}
-        regexp = r'\{\{\s*' + BASE_KEY + r'\.([\w\.]+)\s*\}\}'
-        base_vars = set(re.findall(regexp, config_file))
-        for base_var in base_vars:
-            randstr = f'_{base_var}_{uuid.uuid4().hex.lower()[:6]}'
-            base_var_dict[randstr] = base_var
-            regexp = r'\{\{\s*' + BASE_KEY + r'\.' + base_var + r'\s*\}\}'
-            config_file = re.sub(regexp, f'"{randstr}"', config_file)
-        with open(temp_config_name, 'w', encoding='utf-8') as tmp_config_file:
-            tmp_config_file.write(config_file)
-        return base_var_dict
-
-    @staticmethod
-    def _substitute_base_vars(cfg, base_var_dict, base_cfg):
-        """Substitute variable strings to their actual values."""
-        cfg = copy.deepcopy(cfg)
-
-        if isinstance(cfg, dict):
-            for k, v in cfg.items():
-                if isinstance(v, str) and v in base_var_dict:
-                    new_v = base_cfg
-                    for new_k in base_var_dict[v].split('.'):
-                        new_v = new_v[new_k]
-                    cfg[k] = new_v
-                elif isinstance(v, (list, tuple, dict)):
-                    cfg[k] = Config._substitute_base_vars(
-                        v, base_var_dict, base_cfg)
-        elif isinstance(cfg, tuple):
-            cfg = tuple(
-                Config._substitute_base_vars(c, base_var_dict, base_cfg)
-                for c in cfg)
-        elif isinstance(cfg, list):
-            cfg = [
-                Config._substitute_base_vars(c, base_var_dict, base_cfg)
-                for c in cfg
-            ]
-        elif isinstance(cfg, str) and cfg in base_var_dict:
-            new_v = base_cfg
-            for new_k in base_var_dict[cfg].split('.'):
-                new_v = new_v[new_k]
-            cfg = new_v
-
-        return cfg
-
-    @staticmethod
-    def _file2dict(filename, use_predefined_variables=True):
-        filename = osp.abspath(osp.expanduser(filename))
-        check_file_exist(filename)
-        fileExtname = osp.splitext(filename)[1]
-        if fileExtname not in ['.py', '.json', '.yaml', '.yml']:
-            raise IOError('Only py/yml/yaml/json type are supported now!')
-
-        with tempfile.TemporaryDirectory() as temp_config_dir:
-            temp_config_file = tempfile.NamedTemporaryFile(
-                dir=temp_config_dir, suffix=fileExtname)
-            if platform.system() == 'Windows':
-                temp_config_file.close()
-            temp_config_name = osp.basename(temp_config_file.name)
-            # Substitute predefined variables
-            if use_predefined_variables:
-                Config._substitute_predefined_vars(filename,
-                                                   temp_config_file.name)
-            else:
-                shutil.copyfile(filename, temp_config_file.name)
-            # Substitute base variables from placeholders to strings
-            base_var_dict = Config._pre_substitute_base_vars(
-                temp_config_file.name, temp_config_file.name)
-
-            if filename.endswith('.py'):
-                temp_module_name = osp.splitext(temp_config_name)[0]
-                sys.path.insert(0, temp_config_dir)
-                Config._validate_py_syntax(filename)
-                mod = import_module(temp_module_name)
-                sys.path.pop(0)
-                cfg_dict = {
-                    name: value
-                    for name, value in mod.__dict__.items()
-                    if not name.startswith('__')
-                }
-                # delete imported module
-                del sys.modules[temp_module_name]
-            elif filename.endswith(('.yml', '.yaml', '.json')):
-                import annotator.uniformer.mmcv as mmcv
-                cfg_dict = mmcv.load(temp_config_file.name)
-            # close temp file
-            temp_config_file.close()
-
-        # check deprecation information
-        if DEPRECATION_KEY in cfg_dict:
-            deprecation_info = cfg_dict.pop(DEPRECATION_KEY)
-            warning_msg = f'The config file {filename} will be deprecated ' \
-                'in the future.'
-            if 'expected' in deprecation_info:
-                warning_msg += f' Please use {deprecation_info["expected"]} ' \
-                    'instead.'
-            if 'reference' in deprecation_info:
-                warning_msg += ' More information can be found at ' \
-                    f'{deprecation_info["reference"]}'
-            warnings.warn(warning_msg)
-
-        cfg_text = filename + '\n'
-        with open(filename, 'r', encoding='utf-8') as f:
-            # Setting encoding explicitly to resolve coding issue on windows
-            cfg_text += f.read()
-
-        if BASE_KEY in cfg_dict:
-            cfg_dir = osp.dirname(filename)
-            base_filename = cfg_dict.pop(BASE_KEY)
-            base_filename = base_filename if isinstance(
-                base_filename, list) else [base_filename]
-
-            cfg_dict_list = list()
-            cfg_text_list = list()
-            for f in base_filename:
-                _cfg_dict, _cfg_text = Config._file2dict(osp.join(cfg_dir, f))
-                cfg_dict_list.append(_cfg_dict)
-                cfg_text_list.append(_cfg_text)
-
-            base_cfg_dict = dict()
-            for c in cfg_dict_list:
-                duplicate_keys = base_cfg_dict.keys() & c.keys()
-                if len(duplicate_keys) > 0:
-                    raise KeyError('Duplicate key is not allowed among bases. '
-                                   f'Duplicate keys: {duplicate_keys}')
-                base_cfg_dict.update(c)
-
-            # Substitute base variables from strings to their actual values
-            cfg_dict = Config._substitute_base_vars(cfg_dict, base_var_dict,
-                                                    base_cfg_dict)
-
-            base_cfg_dict = Config._merge_a_into_b(cfg_dict, base_cfg_dict)
-            cfg_dict = base_cfg_dict
-
-            # merge cfg_text
-            cfg_text_list.append(cfg_text)
-            cfg_text = '\n'.join(cfg_text_list)
-
-        return cfg_dict, cfg_text
-
-    @staticmethod
-    def _merge_a_into_b(a, b, allow_list_keys=False):
-        """merge dict ``a`` into dict ``b`` (non-inplace).
-
-        Values in ``a`` will overwrite ``b``. ``b`` is copied first to avoid
-        in-place modifications.
-
-        Args:
-            a (dict): The source dict to be merged into ``b``.
-            b (dict): The origin dict to be fetch keys from ``a``.
-            allow_list_keys (bool): If True, int string keys (e.g. '0', '1')
-              are allowed in source ``a`` and will replace the element of the
-              corresponding index in b if b is a list. Default: False.
-
-        Returns:
-            dict: The modified dict of ``b`` using ``a``.
-
-        Examples:
-            # Normally merge a into b.
-            >>> Config._merge_a_into_b(
-            ...     dict(obj=dict(a=2)), dict(obj=dict(a=1)))
-            {'obj': {'a': 2}}
-
-            # Delete b first and merge a into b.
-            >>> Config._merge_a_into_b(
-            ...     dict(obj=dict(_delete_=True, a=2)), dict(obj=dict(a=1)))
-            {'obj': {'a': 2}}
-
-            # b is a list
-            >>> Config._merge_a_into_b(
-            ...     {'0': dict(a=2)}, [dict(a=1), dict(b=2)], True)
-            [{'a': 2}, {'b': 2}]
-        """
-        b = b.copy()
-        for k, v in a.items():
-            if allow_list_keys and k.isdigit() and isinstance(b, list):
-                k = int(k)
-                if len(b) <= k:
-                    raise KeyError(f'Index {k} exceeds the length of list {b}')
-                b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys)
-            elif isinstance(v,
-                            dict) and k in b and not v.pop(DELETE_KEY, False):
-                allowed_types = (dict, list) if allow_list_keys else dict
-                if not isinstance(b[k], allowed_types):
-                    raise TypeError(
-                        f'{k}={v} in child config cannot inherit from base '
-                        f'because {k} is a dict in the child config but is of '
-                        f'type {type(b[k])} in base config. You may set '
-                        f'`{DELETE_KEY}=True` to ignore the base config')
-                b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys)
-            else:
-                b[k] = v
-        return b
-
-    @staticmethod
-    def fromfile(filename,
-                 use_predefined_variables=True,
-                 import_custom_modules=True):
-        cfg_dict, cfg_text = Config._file2dict(filename,
-                                               use_predefined_variables)
-        if import_custom_modules and cfg_dict.get('custom_imports', None):
-            import_modules_from_strings(**cfg_dict['custom_imports'])
-        return Config(cfg_dict, cfg_text=cfg_text, filename=filename)
-
-    @staticmethod
-    def fromstring(cfg_str, file_format):
-        """Generate config from config str.
-
-        Args:
-            cfg_str (str): Config str.
-            file_format (str): Config file format corresponding to the
-               config str. Only py/yml/yaml/json type are supported now!
-
-        Returns:
-            obj:`Config`: Config obj.
-        """
-        if file_format not in ['.py', '.json', '.yaml', '.yml']:
-            raise IOError('Only py/yml/yaml/json type are supported now!')
-        if file_format != '.py' and 'dict(' in cfg_str:
-            # check if users specify a wrong suffix for python
-            warnings.warn(
-                'Please check "file_format", the file format may be .py')
-        with tempfile.NamedTemporaryFile(
-                'w', encoding='utf-8', suffix=file_format,
-                delete=False) as temp_file:
-            temp_file.write(cfg_str)
-            # on windows, previous implementation cause error
-            # see PR 1077 for details
-        cfg = Config.fromfile(temp_file.name)
-        os.remove(temp_file.name)
-        return cfg
-
-    @staticmethod
-    def auto_argparser(description=None):
-        """Generate argparser from config file automatically (experimental)"""
-        partial_parser = ArgumentParser(description=description)
-        partial_parser.add_argument('config', help='config file path')
-        cfg_file = partial_parser.parse_known_args()[0].config
-        cfg = Config.fromfile(cfg_file)
-        parser = ArgumentParser(description=description)
-        parser.add_argument('config', help='config file path')
-        add_args(parser, cfg)
-        return parser, cfg
-
-    def __init__(self, cfg_dict=None, cfg_text=None, filename=None):
-        if cfg_dict is None:
-            cfg_dict = dict()
-        elif not isinstance(cfg_dict, dict):
-            raise TypeError('cfg_dict must be a dict, but '
-                            f'got {type(cfg_dict)}')
-        for key in cfg_dict:
-            if key in RESERVED_KEYS:
-                raise KeyError(f'{key} is reserved for config file')
-
-        super(Config, self).__setattr__('_cfg_dict', ConfigDict(cfg_dict))
-        super(Config, self).__setattr__('_filename', filename)
-        if cfg_text:
-            text = cfg_text
-        elif filename:
-            with open(filename, 'r') as f:
-                text = f.read()
-        else:
-            text = ''
-        super(Config, self).__setattr__('_text', text)
-
-    @property
-    def filename(self):
-        return self._filename
-
-    @property
-    def text(self):
-        return self._text
-
-    @property
-    def pretty_text(self):
-
-        indent = 4
-
-        def _indent(s_, num_spaces):
-            s = s_.split('\n')
-            if len(s) == 1:
-                return s_
-            first = s.pop(0)
-            s = [(num_spaces * ' ') + line for line in s]
-            s = '\n'.join(s)
-            s = first + '\n' + s
-            return s
-
-        def _format_basic_types(k, v, use_mapping=False):
-            if isinstance(v, str):
-                v_str = f"'{v}'"
-            else:
-                v_str = str(v)
-
-            if use_mapping:
-                k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                attr_str = f'{k_str}: {v_str}'
-            else:
-                attr_str = f'{str(k)}={v_str}'
-            attr_str = _indent(attr_str, indent)
-
-            return attr_str
-
-        def _format_list(k, v, use_mapping=False):
-            # check if all items in the list are dict
-            if all(isinstance(_, dict) for _ in v):
-                v_str = '[\n'
-                v_str += '\n'.join(
-                    f'dict({_indent(_format_dict(v_), indent)}),'
-                    for v_ in v).rstrip(',')
-                if use_mapping:
-                    k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                    attr_str = f'{k_str}: {v_str}'
-                else:
-                    attr_str = f'{str(k)}={v_str}'
-                attr_str = _indent(attr_str, indent) + ']'
-            else:
-                attr_str = _format_basic_types(k, v, use_mapping)
-            return attr_str
-
-        def _contain_invalid_identifier(dict_str):
-            contain_invalid_identifier = False
-            for key_name in dict_str:
-                contain_invalid_identifier |= \
-                    (not str(key_name).isidentifier())
-            return contain_invalid_identifier
-
-        def _format_dict(input_dict, outest_level=False):
-            r = ''
-            s = []
-
-            use_mapping = _contain_invalid_identifier(input_dict)
-            if use_mapping:
-                r += '{'
-            for idx, (k, v) in enumerate(input_dict.items()):
-                is_last = idx >= len(input_dict) - 1
-                end = '' if outest_level or is_last else ','
-                if isinstance(v, dict):
-                    v_str = '\n' + _format_dict(v)
-                    if use_mapping:
-                        k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                        attr_str = f'{k_str}: dict({v_str}'
-                    else:
-                        attr_str = f'{str(k)}=dict({v_str}'
-                    attr_str = _indent(attr_str, indent) + ')' + end
-                elif isinstance(v, list):
-                    attr_str = _format_list(k, v, use_mapping) + end
-                else:
-                    attr_str = _format_basic_types(k, v, use_mapping) + end
-
-                s.append(attr_str)
-            r += '\n'.join(s)
-            if use_mapping:
-                r += '}'
-            return r
-
-        cfg_dict = self._cfg_dict.to_dict()
-        text = _format_dict(cfg_dict, outest_level=True)
-        # copied from setup.cfg
-        yapf_style = dict(
-            based_on_style='pep8',
-            blank_line_before_nested_class_or_def=True,
-            split_before_expression_after_opening_paren=True)
-        text, _ = FormatCode(text, style_config=yapf_style, verify=True)
-
-        return text
-
-    def __repr__(self):
-        return f'Config (path: {self.filename}): {self._cfg_dict.__repr__()}'
-
-    def __len__(self):
-        return len(self._cfg_dict)
-
-    def __getattr__(self, name):
-        return getattr(self._cfg_dict, name)
-
-    def __getitem__(self, name):
-        return self._cfg_dict.__getitem__(name)
-
-    def __setattr__(self, name, value):
-        if isinstance(value, dict):
-            value = ConfigDict(value)
-        self._cfg_dict.__setattr__(name, value)
-
-    def __setitem__(self, name, value):
-        if isinstance(value, dict):
-            value = ConfigDict(value)
-        self._cfg_dict.__setitem__(name, value)
-
-    def __iter__(self):
-        return iter(self._cfg_dict)
-
-    def __getstate__(self):
-        return (self._cfg_dict, self._filename, self._text)
-
-    def __setstate__(self, state):
-        _cfg_dict, _filename, _text = state
-        super(Config, self).__setattr__('_cfg_dict', _cfg_dict)
-        super(Config, self).__setattr__('_filename', _filename)
-        super(Config, self).__setattr__('_text', _text)
-
-    def dump(self, file=None):
-        cfg_dict = super(Config, self).__getattribute__('_cfg_dict').to_dict()
-        if self.filename.endswith('.py'):
-            if file is None:
-                return self.pretty_text
-            else:
-                with open(file, 'w', encoding='utf-8') as f:
-                    f.write(self.pretty_text)
-        else:
-            import annotator.uniformer.mmcv as mmcv
-            if file is None:
-                file_format = self.filename.split('.')[-1]
-                return mmcv.dump(cfg_dict, file_format=file_format)
-            else:
-                mmcv.dump(cfg_dict, file)
-
-    def merge_from_dict(self, options, allow_list_keys=True):
-        """Merge list into cfg_dict.
-
-        Merge the dict parsed by MultipleKVAction into this cfg.
-
-        Examples:
-            >>> options = {'model.backbone.depth': 50,
-            ...            'model.backbone.with_cp':True}
-            >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet'))))
-            >>> cfg.merge_from_dict(options)
-            >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
-            >>> assert cfg_dict == dict(
-            ...     model=dict(backbone=dict(depth=50, with_cp=True)))
-
-            # Merge list element
-            >>> cfg = Config(dict(pipeline=[
-            ...     dict(type='LoadImage'), dict(type='LoadAnnotations')]))
-            >>> options = dict(pipeline={'0': dict(type='SelfLoadImage')})
-            >>> cfg.merge_from_dict(options, allow_list_keys=True)
-            >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
-            >>> assert cfg_dict == dict(pipeline=[
-            ...     dict(type='SelfLoadImage'), dict(type='LoadAnnotations')])
-
-        Args:
-            options (dict): dict of configs to merge from.
-            allow_list_keys (bool): If True, int string keys (e.g. '0', '1')
-              are allowed in ``options`` and will replace the element of the
-              corresponding index in the config if the config is a list.
-              Default: True.
-        """
-        option_cfg_dict = {}
-        for full_key, v in options.items():
-            d = option_cfg_dict
-            key_list = full_key.split('.')
-            for subkey in key_list[:-1]:
-                d.setdefault(subkey, ConfigDict())
-                d = d[subkey]
-            subkey = key_list[-1]
-            d[subkey] = v
-
-        cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
-        super(Config, self).__setattr__(
-            '_cfg_dict',
-            Config._merge_a_into_b(
-                option_cfg_dict, cfg_dict, allow_list_keys=allow_list_keys))
-
-
-class DictAction(Action):
-    """
-    argparse action to split an argument into KEY=VALUE form
-    on the first = and append to a dictionary. List options can
-    be passed as comma separated values, i.e 'KEY=V1,V2,V3', or with explicit
-    brackets, i.e. 'KEY=[V1,V2,V3]'. It also support nested brackets to build
-    list/tuple values. e.g. 'KEY=[(V1,V2),(V3,V4)]'
-    """
-
-    @staticmethod
-    def _parse_int_float_bool(val):
-        try:
-            return int(val)
-        except ValueError:
-            pass
-        try:
-            return float(val)
-        except ValueError:
-            pass
-        if val.lower() in ['true', 'false']:
-            return True if val.lower() == 'true' else False
-        return val
-
-    @staticmethod
-    def _parse_iterable(val):
-        """Parse iterable values in the string.
-
-        All elements inside '()' or '[]' are treated as iterable values.
-
-        Args:
-            val (str): Value string.
-
-        Returns:
-            list | tuple: The expanded list or tuple from the string.
-
-        Examples:
-            >>> DictAction._parse_iterable('1,2,3')
-            [1, 2, 3]
-            >>> DictAction._parse_iterable('[a, b, c]')
-            ['a', 'b', 'c']
-            >>> DictAction._parse_iterable('[(1, 2, 3), [a, b], c]')
-            [(1, 2, 3), ['a', 'b'], 'c']
-        """
-
-        def find_next_comma(string):
-            """Find the position of next comma in the string.
-
-            If no ',' is found in the string, return the string length. All
-            chars inside '()' and '[]' are treated as one element and thus ','
-            inside these brackets are ignored.
-            """
-            assert (string.count('(') == string.count(')')) and (
-                    string.count('[') == string.count(']')), \
-                f'Imbalanced brackets exist in {string}'
-            end = len(string)
-            for idx, char in enumerate(string):
-                pre = string[:idx]
-                # The string before this ',' is balanced
-                if ((char == ',') and (pre.count('(') == pre.count(')'))
-                        and (pre.count('[') == pre.count(']'))):
-                    end = idx
-                    break
-            return end
-
-        # Strip ' and " characters and replace whitespace.
-        val = val.strip('\'\"').replace(' ', '')
-        is_tuple = False
-        if val.startswith('(') and val.endswith(')'):
-            is_tuple = True
-            val = val[1:-1]
-        elif val.startswith('[') and val.endswith(']'):
-            val = val[1:-1]
-        elif ',' not in val:
-            # val is a single value
-            return DictAction._parse_int_float_bool(val)
-
-        values = []
-        while len(val) > 0:
-            comma_idx = find_next_comma(val)
-            element = DictAction._parse_iterable(val[:comma_idx])
-            values.append(element)
-            val = val[comma_idx + 1:]
-        if is_tuple:
-            values = tuple(values)
-        return values
-
-    def __call__(self, parser, namespace, values, option_string=None):
-        options = {}
-        for kv in values:
-            key, val = kv.split('=', maxsplit=1)
-            options[key] = self._parse_iterable(val)
-        setattr(namespace, self.dest, options)
diff --git a/spaces/abtExp/source_separation/README.md b/spaces/abtExp/source_separation/README.md
deleted file mode 100644
index 5ff26f95805332dc5007a4471da0c84f5885459b..0000000000000000000000000000000000000000
--- a/spaces/abtExp/source_separation/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Source Separation
-emoji: 📊
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.27.0
-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/aijack/jojo/e4e/options/__init__.py b/spaces/aijack/jojo/e4e/options/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/akhaliq/Detic/detic/data/datasets/lvis_v1.py b/spaces/akhaliq/Detic/detic/data/datasets/lvis_v1.py
deleted file mode 100644
index 4b9b279f17663def1c4913321efbb7490d591e90..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Detic/detic/data/datasets/lvis_v1.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import os
-
-from fvcore.common.timer import Timer
-from detectron2.structures import BoxMode
-from fvcore.common.file_io import PathManager
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.data.datasets.lvis import get_lvis_instances_meta
-
-logger = logging.getLogger(__name__)
-
-__all__ = ["custom_load_lvis_json", "custom_register_lvis_instances"]
-
-
-def custom_register_lvis_instances(name, metadata, json_file, image_root):
-    """
-    """
-    DatasetCatalog.register(name, lambda: custom_load_lvis_json(
-        json_file, image_root, name))
-    MetadataCatalog.get(name).set(
-        json_file=json_file, image_root=image_root, 
-        evaluator_type="lvis", **metadata
-    )
-
-
-def custom_load_lvis_json(json_file, image_root, dataset_name=None):
-    '''
-    Modifications:
-      use `file_name`
-      convert neg_category_ids
-      add pos_category_ids
-    '''
-    from lvis import LVIS
-
-    json_file = PathManager.get_local_path(json_file)
-
-    timer = Timer()
-    lvis_api = LVIS(json_file)
-    if timer.seconds() > 1:
-        logger.info("Loading {} takes {:.2f} seconds.".format(
-            json_file, timer.seconds()))
-
-    catid2contid = {x['id']: i for i, x in enumerate(
-        sorted(lvis_api.dataset['categories'], key=lambda x: x['id']))}
-    if len(lvis_api.dataset['categories']) == 1203:
-        for x in lvis_api.dataset['categories']:
-            assert catid2contid[x['id']] == x['id'] - 1
-    img_ids = sorted(lvis_api.imgs.keys())
-    imgs = lvis_api.load_imgs(img_ids)
-    anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids]
-
-    ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
-    assert len(set(ann_ids)) == len(ann_ids), \
-        "Annotation ids in '{}' are not unique".format(json_file)
-
-    imgs_anns = list(zip(imgs, anns))
-    logger.info("Loaded {} images in the LVIS v1 format from {}".format(
-        len(imgs_anns), json_file))
-
-    dataset_dicts = []
-
-    for (img_dict, anno_dict_list) in imgs_anns:
-        record = {}
-        if "file_name" in img_dict:
-            file_name = img_dict["file_name"]
-            if img_dict["file_name"].startswith("COCO"):
-                file_name = file_name[-16:]
-            record["file_name"] = os.path.join(image_root, file_name)
-        elif 'coco_url' in img_dict:
-            # e.g., http://images.cocodataset.org/train2017/000000391895.jpg
-            file_name = img_dict["coco_url"][30:]
-            record["file_name"] = os.path.join(image_root, file_name)
-        elif 'tar_index' in img_dict:
-            record['tar_index'] = img_dict['tar_index']
-        
-        record["height"] = img_dict["height"]
-        record["width"] = img_dict["width"]
-        record["not_exhaustive_category_ids"] = img_dict.get(
-            "not_exhaustive_category_ids", [])
-        record["neg_category_ids"] = img_dict.get("neg_category_ids", [])
-        # NOTE: modified by Xingyi: convert to 0-based
-        record["neg_category_ids"] = [
-            catid2contid[x] for x in record["neg_category_ids"]]
-        if 'pos_category_ids' in img_dict:
-            record['pos_category_ids'] = [
-                catid2contid[x] for x in img_dict.get("pos_category_ids", [])]
-        if 'captions' in img_dict:
-            record['captions'] = img_dict['captions']
-        if 'caption_features' in img_dict:
-            record['caption_features'] = img_dict['caption_features']
-        image_id = record["image_id"] = img_dict["id"]
-
-        objs = []
-        for anno in anno_dict_list:
-            assert anno["image_id"] == image_id
-            if anno.get('iscrowd', 0) > 0:
-                continue
-            obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS}
-            obj["category_id"] = catid2contid[anno['category_id']] 
-            if 'segmentation' in anno:
-                segm = anno["segmentation"]
-                valid_segm = [poly for poly in segm \
-                    if len(poly) % 2 == 0 and len(poly) >= 6]
-                # assert len(segm) == len(
-                #     valid_segm
-                # ), "Annotation contains an invalid polygon with < 3 points"
-                if not len(segm) == len(valid_segm):
-                    print('Annotation contains an invalid polygon with < 3 points')
-                assert len(segm) > 0
-                obj["segmentation"] = segm
-            objs.append(obj)
-        record["annotations"] = objs
-        dataset_dicts.append(record)
-
-    return dataset_dicts
-
-_CUSTOM_SPLITS_LVIS = {
-    "lvis_v1_train+coco": ("coco/", "lvis/lvis_v1_train+coco_mask.json"),
-    "lvis_v1_train_norare": ("coco/", "lvis/lvis_v1_train_norare.json"),
-}
-
-
-for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items():
-    custom_register_lvis_instances(
-        key,
-        get_lvis_instances_meta(key),
-        os.path.join("datasets", json_file) if "://" not in json_file else json_file,
-        os.path.join("datasets", image_root),
-    )
-
-
-def get_lvis_22k_meta():
-    from .lvis_22k_categories import CATEGORIES
-    cat_ids = [k["id"] for k in CATEGORIES]
-    assert min(cat_ids) == 1 and max(cat_ids) == len(
-        cat_ids
-    ), "Category ids are not in [1, #categories], as expected"
-    # Ensure that the category list is sorted by id
-    lvis_categories = sorted(CATEGORIES, key=lambda x: x["id"])
-    thing_classes = [k["name"] for k in lvis_categories]
-    meta = {"thing_classes": thing_classes}
-    return meta
-
-_CUSTOM_SPLITS_LVIS_22K = {
-    "lvis_v1_train_22k": ("coco/", "lvis/lvis_v1_train_lvis-22k.json"),
-}
-
-for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS_22K.items():
-    custom_register_lvis_instances(
-        key,
-        get_lvis_22k_meta(),
-        os.path.join("datasets", json_file) if "://" not in json_file else json_file,
-        os.path.join("datasets", image_root),
-    )
\ No newline at end of file
diff --git a/spaces/akhaliq/Kapao/utils/activations.py b/spaces/akhaliq/Kapao/utils/activations.py
deleted file mode 100644
index 62eb532b3f95e753732d1ff8daf40871af44ddf6..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Kapao/utils/activations.py
+++ /dev/null
@@ -1,101 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Activation functions
-"""
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-# SiLU https://arxiv.org/pdf/1606.08415.pdf ----------------------------------------------------------------------------
-class SiLU(nn.Module):  # export-friendly version of nn.SiLU()
-    @staticmethod
-    def forward(x):
-        return x * torch.sigmoid(x)
-
-
-class Hardswish(nn.Module):  # export-friendly version of nn.Hardswish()
-    @staticmethod
-    def forward(x):
-        # return x * F.hardsigmoid(x)  # for torchscript and CoreML
-        return x * F.hardtanh(x + 3, 0., 6.) / 6.  # for torchscript, CoreML and ONNX
-
-
-# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
-class Mish(nn.Module):
-    @staticmethod
-    def forward(x):
-        return x * F.softplus(x).tanh()
-
-
-class MemoryEfficientMish(nn.Module):
-    class F(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, x):
-            ctx.save_for_backward(x)
-            return x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
-
-        @staticmethod
-        def backward(ctx, grad_output):
-            x = ctx.saved_tensors[0]
-            sx = torch.sigmoid(x)
-            fx = F.softplus(x).tanh()
-            return grad_output * (fx + x * sx * (1 - fx * fx))
-
-    def forward(self, x):
-        return self.F.apply(x)
-
-
-# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
-class FReLU(nn.Module):
-    def __init__(self, c1, k=3):  # ch_in, kernel
-        super().__init__()
-        self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
-        self.bn = nn.BatchNorm2d(c1)
-
-    def forward(self, x):
-        return torch.max(x, self.bn(self.conv(x)))
-
-
-# ACON https://arxiv.org/pdf/2009.04759.pdf ----------------------------------------------------------------------------
-class AconC(nn.Module):
-    r""" ACON activation (activate or not).
-    AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
-    according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
-    """
-
-    def __init__(self, c1):
-        super().__init__()
-        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
-        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
-        self.beta = nn.Parameter(torch.ones(1, c1, 1, 1))
-
-    def forward(self, x):
-        dpx = (self.p1 - self.p2) * x
-        return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x
-
-
-class MetaAconC(nn.Module):
-    r""" ACON activation (activate or not).
-    MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
-    according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
-    """
-
-    def __init__(self, c1, k=1, s=1, r=16):  # ch_in, kernel, stride, r
-        super().__init__()
-        c2 = max(r, c1 // r)
-        self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
-        self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
-        self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True)
-        self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True)
-        # self.bn1 = nn.BatchNorm2d(c2)
-        # self.bn2 = nn.BatchNorm2d(c1)
-
-    def forward(self, x):
-        y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
-        # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891
-        # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y)))))  # bug/unstable
-        beta = torch.sigmoid(self.fc2(self.fc1(y)))  # bug patch BN layers removed
-        dpx = (self.p1 - self.p2) * x
-        return dpx * torch.sigmoid(beta * dpx) + self.p2 * x
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/packaging/requirements.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/packaging/requirements.py
deleted file mode 100644
index 1eab7dd66d9bfdefea1a0e159303f1c09fa16d67..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/packaging/requirements.py
+++ /dev/null
@@ -1,146 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import re
-import string
-import urllib.parse
-from typing import List, Optional as TOptional, Set
-
-from pip._vendor.pyparsing import (  # noqa
-    Combine,
-    Literal as L,
-    Optional,
-    ParseException,
-    Regex,
-    Word,
-    ZeroOrMore,
-    originalTextFor,
-    stringEnd,
-    stringStart,
-)
-
-from .markers import MARKER_EXPR, Marker
-from .specifiers import LegacySpecifier, Specifier, SpecifierSet
-
-
-class InvalidRequirement(ValueError):
-    """
-    An invalid requirement was found, users should refer to PEP 508.
-    """
-
-
-ALPHANUM = Word(string.ascii_letters + string.digits)
-
-LBRACKET = L("[").suppress()
-RBRACKET = L("]").suppress()
-LPAREN = L("(").suppress()
-RPAREN = L(")").suppress()
-COMMA = L(",").suppress()
-SEMICOLON = L(";").suppress()
-AT = L("@").suppress()
-
-PUNCTUATION = Word("-_.")
-IDENTIFIER_END = ALPHANUM | (ZeroOrMore(PUNCTUATION) + ALPHANUM)
-IDENTIFIER = Combine(ALPHANUM + ZeroOrMore(IDENTIFIER_END))
-
-NAME = IDENTIFIER("name")
-EXTRA = IDENTIFIER
-
-URI = Regex(r"[^ ]+")("url")
-URL = AT + URI
-
-EXTRAS_LIST = EXTRA + ZeroOrMore(COMMA + EXTRA)
-EXTRAS = (LBRACKET + Optional(EXTRAS_LIST) + RBRACKET)("extras")
-
-VERSION_PEP440 = Regex(Specifier._regex_str, re.VERBOSE | re.IGNORECASE)
-VERSION_LEGACY = Regex(LegacySpecifier._regex_str, re.VERBOSE | re.IGNORECASE)
-
-VERSION_ONE = VERSION_PEP440 ^ VERSION_LEGACY
-VERSION_MANY = Combine(
-    VERSION_ONE + ZeroOrMore(COMMA + VERSION_ONE), joinString=",", adjacent=False
-)("_raw_spec")
-_VERSION_SPEC = Optional((LPAREN + VERSION_MANY + RPAREN) | VERSION_MANY)
-_VERSION_SPEC.setParseAction(lambda s, l, t: t._raw_spec or "")
-
-VERSION_SPEC = originalTextFor(_VERSION_SPEC)("specifier")
-VERSION_SPEC.setParseAction(lambda s, l, t: t[1])
-
-MARKER_EXPR = originalTextFor(MARKER_EXPR())("marker")
-MARKER_EXPR.setParseAction(
-    lambda s, l, t: Marker(s[t._original_start : t._original_end])
-)
-MARKER_SEPARATOR = SEMICOLON
-MARKER = MARKER_SEPARATOR + MARKER_EXPR
-
-VERSION_AND_MARKER = VERSION_SPEC + Optional(MARKER)
-URL_AND_MARKER = URL + Optional(MARKER)
-
-NAMED_REQUIREMENT = NAME + Optional(EXTRAS) + (URL_AND_MARKER | VERSION_AND_MARKER)
-
-REQUIREMENT = stringStart + NAMED_REQUIREMENT + stringEnd
-# pyparsing isn't thread safe during initialization, so we do it eagerly, see
-# issue #104
-REQUIREMENT.parseString("x[]")
-
-
-class Requirement:
-    """Parse a requirement.
-
-    Parse a given requirement string into its parts, such as name, specifier,
-    URL, and extras. Raises InvalidRequirement on a badly-formed requirement
-    string.
-    """
-
-    # TODO: Can we test whether something is contained within a requirement?
-    #       If so how do we do that? Do we need to test against the _name_ of
-    #       the thing as well as the version? What about the markers?
-    # TODO: Can we normalize the name and extra name?
-
-    def __init__(self, requirement_string: str) -> None:
-        try:
-            req = REQUIREMENT.parseString(requirement_string)
-        except ParseException as e:
-            raise InvalidRequirement(
-                f'Parse error at "{ requirement_string[e.loc : e.loc + 8]!r}": {e.msg}'
-            )
-
-        self.name: str = req.name
-        if req.url:
-            parsed_url = urllib.parse.urlparse(req.url)
-            if parsed_url.scheme == "file":
-                if urllib.parse.urlunparse(parsed_url) != req.url:
-                    raise InvalidRequirement("Invalid URL given")
-            elif not (parsed_url.scheme and parsed_url.netloc) or (
-                not parsed_url.scheme and not parsed_url.netloc
-            ):
-                raise InvalidRequirement(f"Invalid URL: {req.url}")
-            self.url: TOptional[str] = req.url
-        else:
-            self.url = None
-        self.extras: Set[str] = set(req.extras.asList() if req.extras else [])
-        self.specifier: SpecifierSet = SpecifierSet(req.specifier)
-        self.marker: TOptional[Marker] = req.marker if req.marker else None
-
-    def __str__(self) -> str:
-        parts: List[str] = [self.name]
-
-        if self.extras:
-            formatted_extras = ",".join(sorted(self.extras))
-            parts.append(f"[{formatted_extras}]")
-
-        if self.specifier:
-            parts.append(str(self.specifier))
-
-        if self.url:
-            parts.append(f"@ {self.url}")
-            if self.marker:
-                parts.append(" ")
-
-        if self.marker:
-            parts.append(f"; {self.marker}")
-
-        return "".join(parts)
-
-    def __repr__(self) -> str:
-        return f"<Requirement('{self}')>"
diff --git a/spaces/algomuffin/jojo_fork/util.py b/spaces/algomuffin/jojo_fork/util.py
deleted file mode 100644
index ed7f1124405c955bdb46b0bd2cec68d47653d100..0000000000000000000000000000000000000000
--- a/spaces/algomuffin/jojo_fork/util.py
+++ /dev/null
@@ -1,220 +0,0 @@
-from matplotlib import pyplot as plt
-import torch
-import torch.nn.functional as F
-import os
-import cv2
-import dlib
-from PIL import Image
-import numpy as np
-import math
-import torchvision
-import scipy
-import scipy.ndimage
-import torchvision.transforms as transforms
-
-from huggingface_hub import hf_hub_download
-
-
-shape_predictor_path = hf_hub_download(repo_id="akhaliq/jojogan_dlib", filename="shape_predictor_68_face_landmarks.dat")
-
-
-google_drive_paths = {
-    "models/stylegan2-ffhq-config-f.pt": "https://drive.google.com/uc?id=1Yr7KuD959btpmcKGAUsbAk5rPjX2MytK",
-    "models/dlibshape_predictor_68_face_landmarks.dat": "https://drive.google.com/uc?id=11BDmNKS1zxSZxkgsEvQoKgFd8J264jKp",
-    "models/e4e_ffhq_encode.pt": "https://drive.google.com/uc?id=1o6ijA3PkcewZvwJJ73dJ0fxhndn0nnh7",
-    "models/restyle_psp_ffhq_encode.pt": "https://drive.google.com/uc?id=1nbxCIVw9H3YnQsoIPykNEFwWJnHVHlVd",
-    "models/arcane_caitlyn.pt": "https://drive.google.com/uc?id=1gOsDTiTPcENiFOrhmkkxJcTURykW1dRc",
-    "models/arcane_caitlyn_preserve_color.pt": "https://drive.google.com/uc?id=1cUTyjU-q98P75a8THCaO545RTwpVV-aH",
-    "models/arcane_jinx_preserve_color.pt": "https://drive.google.com/uc?id=1jElwHxaYPod5Itdy18izJk49K1nl4ney",
-    "models/arcane_jinx.pt": "https://drive.google.com/uc?id=1quQ8vPjYpUiXM4k1_KIwP4EccOefPpG_",
-    "models/disney.pt": "https://drive.google.com/uc?id=1zbE2upakFUAx8ximYnLofFwfT8MilqJA",
-    "models/disney_preserve_color.pt": "https://drive.google.com/uc?id=1Bnh02DjfvN_Wm8c4JdOiNV4q9J7Z_tsi",
-    "models/jojo.pt": "https://drive.google.com/uc?id=13cR2xjIBj8Ga5jMO7gtxzIJj2PDsBYK4",
-    "models/jojo_preserve_color.pt": "https://drive.google.com/uc?id=1ZRwYLRytCEKi__eT2Zxv1IlV6BGVQ_K2",
-    "models/jojo_yasuho.pt": "https://drive.google.com/uc?id=1grZT3Gz1DLzFoJchAmoj3LoM9ew9ROX_",
-    "models/jojo_yasuho_preserve_color.pt": "https://drive.google.com/uc?id=1SKBu1h0iRNyeKBnya_3BBmLr4pkPeg_L",
-    "models/supergirl.pt": "https://drive.google.com/uc?id=1L0y9IYgzLNzB-33xTpXpecsKU-t9DpVC",
-    "models/supergirl_preserve_color.pt": "https://drive.google.com/uc?id=1VmKGuvThWHym7YuayXxjv0fSn32lfDpE",
-}
-
-@torch.no_grad()
-def load_model(generator, model_file_path):
-    ensure_checkpoint_exists(model_file_path)
-    ckpt = torch.load(model_file_path, map_location=lambda storage, loc: storage)
-    generator.load_state_dict(ckpt["g_ema"], strict=False)
-    return generator.mean_latent(50000)
-
-def ensure_checkpoint_exists(model_weights_filename):
-    if not os.path.isfile(model_weights_filename) and (
-        model_weights_filename in google_drive_paths
-    ):
-        gdrive_url = google_drive_paths[model_weights_filename]
-        try:
-            from gdown import download as drive_download
-
-            drive_download(gdrive_url, model_weights_filename, quiet=False)
-        except ModuleNotFoundError:
-            print(
-                "gdown module not found.",
-                "pip3 install gdown or, manually download the checkpoint file:",
-                gdrive_url
-            )
-
-    if not os.path.isfile(model_weights_filename) and (
-        model_weights_filename not in google_drive_paths
-    ):
-        print(
-            model_weights_filename,
-            " not found, you may need to manually download the model weights."
-        )
-
-# given a list of filenames, load the inverted style code
-@torch.no_grad()
-def load_source(files, generator, device='cuda'):
-    sources = []
-    
-    for file in files:
-        source = torch.load(f'./inversion_codes/{file}.pt')['latent'].to(device)
-
-        if source.size(0) != 1:
-            source = source.unsqueeze(0)
-
-        if source.ndim == 3:
-            source = generator.get_latent(source, truncation=1, is_latent=True)
-            source = list2style(source)
-            
-        sources.append(source)
-        
-    sources = torch.cat(sources, 0)
-    if type(sources) is not list:
-        sources = style2list(sources)
-        
-    return sources
-
-def display_image(image, size=None, mode='nearest', unnorm=False, title=''):
-    # image is [3,h,w] or [1,3,h,w] tensor [0,1]
-    if not isinstance(image, torch.Tensor):
-        image = transforms.ToTensor()(image).unsqueeze(0)
-    if image.is_cuda:
-        image = image.cpu()
-    if size is not None and image.size(-1) != size:
-        image = F.interpolate(image, size=(size,size), mode=mode)
-    if image.dim() == 4:
-        image = image[0]
-    image = image.permute(1, 2, 0).detach().numpy()
-    plt.figure()
-    plt.title(title)
-    plt.axis('off')
-    plt.imshow(image)
-
-def get_landmark(filepath, predictor):
-    """get landmark with dlib
-    :return: np.array shape=(68, 2)
-    """
-    detector = dlib.get_frontal_face_detector()
-
-    img = dlib.load_rgb_image(filepath)
-    dets = detector(img, 1)
-    assert len(dets) > 0, "Face not detected, try another face image"
-
-    for k, d in enumerate(dets):
-        shape = predictor(img, d)
-
-    t = list(shape.parts())
-    a = []
-    for tt in t:
-        a.append([tt.x, tt.y])
-    lm = np.array(a)
-    return lm
-
-
-def align_face(filepath, output_size=256, transform_size=1024, enable_padding=True):
-
-    """
-    :param filepath: str
-    :return: PIL Image
-    """
-    predictor = dlib.shape_predictor(shape_predictor_path)
-    lm = get_landmark(filepath, predictor)
-
-    lm_chin = lm[0: 17]  # left-right
-    lm_eyebrow_left = lm[17: 22]  # left-right
-    lm_eyebrow_right = lm[22: 27]  # left-right
-    lm_nose = lm[27: 31]  # top-down
-    lm_nostrils = lm[31: 36]  # top-down
-    lm_eye_left = lm[36: 42]  # left-clockwise
-    lm_eye_right = lm[42: 48]  # left-clockwise
-    lm_mouth_outer = lm[48: 60]  # left-clockwise
-    lm_mouth_inner = lm[60: 68]  # left-clockwise
-
-    # Calculate auxiliary vectors.
-    eye_left = np.mean(lm_eye_left, axis=0)
-    eye_right = np.mean(lm_eye_right, axis=0)
-    eye_avg = (eye_left + eye_right) * 0.5
-    eye_to_eye = eye_right - eye_left
-    mouth_left = lm_mouth_outer[0]
-    mouth_right = lm_mouth_outer[6]
-    mouth_avg = (mouth_left + mouth_right) * 0.5
-    eye_to_mouth = mouth_avg - eye_avg
-
-    # Choose oriented crop rectangle.
-    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
-    x /= np.hypot(*x)
-    x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
-    y = np.flipud(x) * [-1, 1]
-    c = eye_avg + eye_to_mouth * 0.1
-    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
-    qsize = np.hypot(*x) * 2
-
-    # read image
-    img = Image.open(filepath)
-
-    transform_size = output_size
-    enable_padding = True
-
-    # Shrink.
-    shrink = int(np.floor(qsize / output_size * 0.5))
-    if shrink > 1:
-        rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
-        img = img.resize(rsize, Image.ANTIALIAS)
-        quad /= shrink
-        qsize /= shrink
-
-    # Crop.
-    border = max(int(np.rint(qsize * 0.1)), 3)
-    crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-            int(np.ceil(max(quad[:, 1]))))
-    crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
-            min(crop[3] + border, img.size[1]))
-    if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
-        img = img.crop(crop)
-        quad -= crop[0:2]
-
-    # Pad.
-    pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-           int(np.ceil(max(quad[:, 1]))))
-    pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
-           max(pad[3] - img.size[1] + border, 0))
-    if enable_padding and max(pad) > border - 4:
-        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
-        img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
-        h, w, _ = img.shape
-        y, x, _ = np.ogrid[:h, :w, :1]
-        mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
-                          1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
-        blur = qsize * 0.02
-        img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
-        img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
-        img = Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
-        quad += pad[:2]
-
-    # Transform.
-    img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(), Image.BILINEAR)
-    if output_size < transform_size:
-        img = img.resize((output_size, output_size), Image.ANTIALIAS)
-
-    # Return aligned image.
-    return img
-
-def strip_path_extension(path):
-   return  os.path.splitext(path)[0]
diff --git a/spaces/alistairmcleay/cambridge-masters-project/src/crazyneuraluser/UBAR_code/config.py b/spaces/alistairmcleay/cambridge-masters-project/src/crazyneuraluser/UBAR_code/config.py
deleted file mode 100644
index 2568c1f065edee986e9f38e2cb6386c67242a8a8..0000000000000000000000000000000000000000
--- a/spaces/alistairmcleay/cambridge-masters-project/src/crazyneuraluser/UBAR_code/config.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import logging
-import os
-import time
-
-
-class _Config:
-    def __init__(self):
-        self._multiwoz_ubar_init()
-
-    def _multiwoz_ubar_init(self):
-        self.gpt_path = "distilgpt2"
-
-        self.vocab_path_train = "data/preprocessed/UBAR/multi-woz-processed/vocab"
-        self.vocab_path_eval = None
-        self.data_path = "data/preprocessed/UBAR/multi-woz-processed/"
-        self.data_file = "data_for_ubar.json"
-        self.dev_list = "data/raw/UBAR/multi-woz/valListFile.json"
-        self.test_list = "data/raw/UBAR/multi-woz/testListFile.json"
-        self.dbs = {
-            "attraction": "data/preprocessed/UBAR/db_processed/attraction_db_processed.json",
-            "hospital": "data/preprocessed/UBAR/db_processed/hospital_db_processed.json",
-            "hotel": "data/preprocessed/UBAR/db_processed/hotel_db_processed.json",
-            "police": "data/preprocessed/UBAR/db_processed/police_db_processed.json",
-            "restaurant": "data/preprocessed/UBAR/db_processed/restaurant_db_processed.json",
-            "taxi": "data/preprocessed/UBAR/db_processed/taxi_db_processed.json",
-            "train": "data/preprocessed/UBAR/db_processed/train_db_processed.json",
-        }
-        self.glove_path = "data/glove/glove.6B.50d.txt"
-        self.domain_file_path = "data/preprocessed/UBAR/multi-woz-processed/domain_files.json"
-        self.slot_value_set_path = "data/preprocessed/UBAR/db_processed/value_set_processed.json"
-        self.multi_acts_path = "data/preprocessed/UBAR/multi-woz-processed/multi_act_mapping_train.json"
-        self.exp_path = "to be generated"
-        self.log_time = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
-
-        # experiment settings
-        self.mode = "unknown"
-        self.cuda = False
-        self.cuda_device = [0]
-        self.exp_no = ""
-        self.seed = 11
-        self.save_log = True  # tensorboard
-        self.evaluate_during_training = False  # evaluate during training
-        self.report_interval = 200  # 485 for bs 128
-        self.max_nl_length = 60
-        self.max_span_length = 30
-        self.truncated = False
-
-        # training settings
-        self.lr = 1e-4
-        self.warmup_steps = -1
-        self.weight_decay = 0.0
-        self.gradient_accumulation_steps = 16
-        self.batch_size = 2
-
-        self.label_smoothing = 0.0
-        self.lr_decay = 0.5
-        self.epoch_num = 40
-        self.early_stop_count = 5
-        self.weight_decay_count = 3
-        self.teacher_force = 100
-        self.multi_acts_training = False
-        self.multi_act_sampling_num = 1
-        self.valid_loss = "score"
-
-        self.wandb_train_run_name = "Train with generated usr utterances"
-
-        # evaluation settings
-        self.eval_load_path = "models/UBAR/experiments/distilgpt-2_sd11_lr0.0001_bs16_ga2/epoch53_trloss0.59_gpt2"
-        self.model_output = "model_output_e2e_FFFT_fix_bs.json"
-        self.eval_per_domain = False
-        self.eval_set = "test"  # test, dev
-
-        self.wandb_eval_run_name = "US Generated usr utterances evaluation"
-
-        # my setting
-        self.use_true_prev_bspn = False
-        self.use_true_prev_aspn = False
-        self.use_true_db_pointer = False
-        self.use_true_prev_resp = False
-
-        self.use_true_curr_bspn = False
-        self.use_true_curr_aspn = False
-        self.use_all_previous_context = True
-
-        self.exp_domains = ["all"]  # hotel,train, attraction, restaurant, taxi
-        self.log_path = "logs_test"
-        self.low_resource = False
-        ###
-
-        # dst setting
-        self.fix_bs = True
-        self.use_nodelex_resp = True
-        self.max_context_length = 900
-        ##
-
-        # model settings
-        self.vocab_size = 3000
-        self.embed_size = 50
-        self.hidden_size = 100
-        self.pointer_dim = 6  # fixed
-        self.enc_layer_num = 1
-        self.dec_layer_num = 1
-        self.dropout = 0
-        self.layer_norm = False
-        self.skip_connect = False
-        self.encoder_share = False
-        self.attn_param_share = False
-        self.copy_param_share = False
-        self.enable_aspn = True
-        self.use_pvaspn = False
-        self.enable_bspn = True
-        self.bspn_mode = "bspn"  # 'bspn' or 'bsdx'
-        self.enable_dspn = False  # removed
-        self.enable_dst = False
-
-        self.use_true_bspn_for_ctr_eval = True
-        self.use_true_domain_for_ctr_eval = True
-        self.limit_bspn_vocab = False
-        self.limit_aspn_vocab = False
-        self.same_eval_as_cambridge = True
-        self.same_eval_act_f1_as_hdsa = False
-        self.aspn_decode_mode = "greedy"  # beam, greedy, nucleur_sampling, topk_sampling
-        self.beam_width = 5
-        self.nbest = 5
-        self.beam_diverse_param = 0.2
-        self.act_selection_scheme = "high_test_act_f1"
-        self.topk_num = 1
-        self.nucleur_p = 0.0
-        self.record_mode = False
-
-    def __str__(self):
-        s = ""
-        for k, v in self.__dict__.items():
-            s += "{} : {}\n".format(k, v)
-        return s
-
-    def _init_logging_handler(self, mode):
-        stderr_handler = logging.StreamHandler()
-        if not os.path.exists("log"):
-            os.mkdir("log")
-        if self.save_log and self.mode == "train":
-            file_handler = logging.FileHandler(
-                "log/log_{}_{}_{}_{}_sd{}.txt".format(
-                    self.log_time,
-                    mode,
-                    "-".join(self.exp_domains),
-                    self.exp_no,
-                    self.seed,
-                )
-            )
-            logging.basicConfig(handlers=[stderr_handler, file_handler])
-        elif self.mode == "test":
-            eval_log_path = os.path.join(self.eval_load_path, "eval_log.json")
-            # if os.path.exists(eval_log_path):
-            #     os.remove(eval_log_path)
-            file_handler = logging.FileHandler(eval_log_path)
-            logging.basicConfig(handlers=[stderr_handler, file_handler])
-        else:
-            logging.basicConfig(handlers=[stderr_handler])
-        logger = logging.getLogger()
-        logger.setLevel(logging.INFO)
-
-
-global_config = _Config()
diff --git a/spaces/aodianyun/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer.py b/spaces/aodianyun/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer.py
deleted file mode 100644
index fcb8742dbdde6e80fd38b11d064211f6935aae76..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer.py
+++ /dev/null
@@ -1,959 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# DINO
-# Copyright (c) 2022 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR Transformer class.
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Modified from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-from typing import Optional
-
-import torch
-import torch.utils.checkpoint as checkpoint
-from torch import Tensor, nn
-
-from groundingdino.util.misc import inverse_sigmoid
-
-from .fuse_modules import BiAttentionBlock
-from .ms_deform_attn import MultiScaleDeformableAttention as MSDeformAttn
-from .transformer_vanilla import TransformerEncoderLayer
-from .utils import (
-    MLP,
-    _get_activation_fn,
-    _get_clones,
-    gen_encoder_output_proposals,
-    gen_sineembed_for_position,
-    get_sine_pos_embed,
-)
-
-
-class Transformer(nn.Module):
-    def __init__(
-        self,
-        d_model=256,
-        nhead=8,
-        num_queries=300,
-        num_encoder_layers=6,
-        num_unicoder_layers=0,
-        num_decoder_layers=6,
-        dim_feedforward=2048,
-        dropout=0.0,
-        activation="relu",
-        normalize_before=False,
-        return_intermediate_dec=False,
-        query_dim=4,
-        num_patterns=0,
-        # for deformable encoder
-        num_feature_levels=1,
-        enc_n_points=4,
-        dec_n_points=4,
-        # init query
-        learnable_tgt_init=False,
-        # two stage
-        two_stage_type="no",  # ['no', 'standard', 'early', 'combine', 'enceachlayer', 'enclayer1']
-        embed_init_tgt=False,
-        # for text
-        use_text_enhancer=False,
-        use_fusion_layer=False,
-        use_checkpoint=False,
-        use_transformer_ckpt=False,
-        use_text_cross_attention=False,
-        text_dropout=0.1,
-        fusion_dropout=0.1,
-        fusion_droppath=0.0,
-    ):
-        super().__init__()
-        self.num_feature_levels = num_feature_levels
-        self.num_encoder_layers = num_encoder_layers
-        self.num_unicoder_layers = num_unicoder_layers
-        self.num_decoder_layers = num_decoder_layers
-        self.num_queries = num_queries
-        assert query_dim == 4
-
-        # choose encoder layer type
-        encoder_layer = DeformableTransformerEncoderLayer(
-            d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points
-        )
-
-        if use_text_enhancer:
-            text_enhance_layer = TransformerEncoderLayer(
-                d_model=d_model,
-                nhead=nhead // 2,
-                dim_feedforward=dim_feedforward // 2,
-                dropout=text_dropout,
-            )
-        else:
-            text_enhance_layer = None
-
-        if use_fusion_layer:
-            feature_fusion_layer = BiAttentionBlock(
-                v_dim=d_model,
-                l_dim=d_model,
-                embed_dim=dim_feedforward // 2,
-                num_heads=nhead // 2,
-                dropout=fusion_dropout,
-                drop_path=fusion_droppath,
-            )
-        else:
-            feature_fusion_layer = None
-
-        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
-        assert encoder_norm is None
-        self.encoder = TransformerEncoder(
-            encoder_layer,
-            num_encoder_layers,
-            d_model=d_model,
-            num_queries=num_queries,
-            text_enhance_layer=text_enhance_layer,
-            feature_fusion_layer=feature_fusion_layer,
-            use_checkpoint=use_checkpoint,
-            use_transformer_ckpt=use_transformer_ckpt,
-        )
-
-        # choose decoder layer type
-        decoder_layer = DeformableTransformerDecoderLayer(
-            d_model,
-            dim_feedforward,
-            dropout,
-            activation,
-            num_feature_levels,
-            nhead,
-            dec_n_points,
-            use_text_cross_attention=use_text_cross_attention,
-        )
-
-        decoder_norm = nn.LayerNorm(d_model)
-        self.decoder = TransformerDecoder(
-            decoder_layer,
-            num_decoder_layers,
-            decoder_norm,
-            return_intermediate=return_intermediate_dec,
-            d_model=d_model,
-            query_dim=query_dim,
-            num_feature_levels=num_feature_levels,
-        )
-
-        self.d_model = d_model
-        self.nhead = nhead
-        self.dec_layers = num_decoder_layers
-        self.num_queries = num_queries  # useful for single stage model only
-        self.num_patterns = num_patterns
-        if not isinstance(num_patterns, int):
-            Warning("num_patterns should be int but {}".format(type(num_patterns)))
-            self.num_patterns = 0
-
-        if num_feature_levels > 1:
-            if self.num_encoder_layers > 0:
-                self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
-            else:
-                self.level_embed = None
-
-        self.learnable_tgt_init = learnable_tgt_init
-        assert learnable_tgt_init, "why not learnable_tgt_init"
-        self.embed_init_tgt = embed_init_tgt
-        if (two_stage_type != "no" and embed_init_tgt) or (two_stage_type == "no"):
-            self.tgt_embed = nn.Embedding(self.num_queries, d_model)
-            nn.init.normal_(self.tgt_embed.weight.data)
-        else:
-            self.tgt_embed = None
-
-        # for two stage
-        self.two_stage_type = two_stage_type
-        assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
-            two_stage_type
-        )
-        if two_stage_type == "standard":
-            # anchor selection at the output of encoder
-            self.enc_output = nn.Linear(d_model, d_model)
-            self.enc_output_norm = nn.LayerNorm(d_model)
-            self.two_stage_wh_embedding = None
-
-        if two_stage_type == "no":
-            self.init_ref_points(num_queries)  # init self.refpoint_embed
-
-        self.enc_out_class_embed = None
-        self.enc_out_bbox_embed = None
-
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_uniform_(p)
-        for m in self.modules():
-            if isinstance(m, MSDeformAttn):
-                m._reset_parameters()
-        if self.num_feature_levels > 1 and self.level_embed is not None:
-            nn.init.normal_(self.level_embed)
-
-    def get_valid_ratio(self, mask):
-        _, H, W = mask.shape
-        valid_H = torch.sum(~mask[:, :, 0], 1)
-        valid_W = torch.sum(~mask[:, 0, :], 1)
-        valid_ratio_h = valid_H.float() / H
-        valid_ratio_w = valid_W.float() / W
-        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
-        return valid_ratio
-
-    def init_ref_points(self, use_num_queries):
-        self.refpoint_embed = nn.Embedding(use_num_queries, 4)
-
-    def forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_mask=None, text_dict=None):
-        """
-        Input:
-            - srcs: List of multi features [bs, ci, hi, wi]
-            - masks: List of multi masks [bs, hi, wi]
-            - refpoint_embed: [bs, num_dn, 4]. None in infer
-            - pos_embeds: List of multi pos embeds [bs, ci, hi, wi]
-            - tgt: [bs, num_dn, d_model]. None in infer
-
-        """
-        # prepare input for encoder
-        src_flatten = []
-        mask_flatten = []
-        lvl_pos_embed_flatten = []
-        spatial_shapes = []
-        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
-            bs, c, h, w = src.shape
-            spatial_shape = (h, w)
-            spatial_shapes.append(spatial_shape)
-
-            src = src.flatten(2).transpose(1, 2)  # bs, hw, c
-            mask = mask.flatten(1)  # bs, hw
-            pos_embed = pos_embed.flatten(2).transpose(1, 2)  # bs, hw, c
-            if self.num_feature_levels > 1 and self.level_embed is not None:
-                lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
-            else:
-                lvl_pos_embed = pos_embed
-            lvl_pos_embed_flatten.append(lvl_pos_embed)
-            src_flatten.append(src)
-            mask_flatten.append(mask)
-        src_flatten = torch.cat(src_flatten, 1)  # bs, \sum{hxw}, c
-        mask_flatten = torch.cat(mask_flatten, 1)  # bs, \sum{hxw}
-        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)  # bs, \sum{hxw}, c
-        spatial_shapes = torch.as_tensor(
-            spatial_shapes, dtype=torch.long, device=src_flatten.device
-        )
-        level_start_index = torch.cat(
-            (spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])
-        )
-        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
-
-        # two stage
-        enc_topk_proposals = enc_refpoint_embed = None
-
-        #########################################################
-        # Begin Encoder
-        #########################################################
-        memory, memory_text = self.encoder(
-            src_flatten,
-            pos=lvl_pos_embed_flatten,
-            level_start_index=level_start_index,
-            spatial_shapes=spatial_shapes,
-            valid_ratios=valid_ratios,
-            key_padding_mask=mask_flatten,
-            memory_text=text_dict["encoded_text"],
-            text_attention_mask=~text_dict["text_token_mask"],
-            # we ~ the mask . False means use the token; True means pad the token
-            position_ids=text_dict["position_ids"],
-            text_self_attention_masks=text_dict["text_self_attention_masks"],
-        )
-        #########################################################
-        # End Encoder
-        # - memory: bs, \sum{hw}, c
-        # - mask_flatten: bs, \sum{hw}
-        # - lvl_pos_embed_flatten: bs, \sum{hw}, c
-        # - enc_intermediate_output: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
-        # - enc_intermediate_refpoints: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
-        #########################################################
-        text_dict["encoded_text"] = memory_text
-        # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
-        #     if memory.isnan().any() | memory.isinf().any():
-        #         import ipdb; ipdb.set_trace()
-
-        if self.two_stage_type == "standard":
-            output_memory, output_proposals = gen_encoder_output_proposals(
-                memory, mask_flatten, spatial_shapes
-            )
-            output_memory = self.enc_output_norm(self.enc_output(output_memory))
-
-            if text_dict is not None:
-                enc_outputs_class_unselected = self.enc_out_class_embed(output_memory, text_dict)
-            else:
-                enc_outputs_class_unselected = self.enc_out_class_embed(output_memory)
-
-            topk_logits = enc_outputs_class_unselected.max(-1)[0]
-            enc_outputs_coord_unselected = (
-                self.enc_out_bbox_embed(output_memory) + output_proposals
-            )  # (bs, \sum{hw}, 4) unsigmoid
-            topk = self.num_queries
-
-            topk_proposals = torch.topk(topk_logits, topk, dim=1)[1]  # bs, nq
-
-            # gather boxes
-            refpoint_embed_undetach = torch.gather(
-                enc_outputs_coord_unselected, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
-            )  # unsigmoid
-            refpoint_embed_ = refpoint_embed_undetach.detach()
-            init_box_proposal = torch.gather(
-                output_proposals, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
-            ).sigmoid()  # sigmoid
-
-            # gather tgt
-            tgt_undetach = torch.gather(
-                output_memory, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)
-            )
-            if self.embed_init_tgt:
-                tgt_ = (
-                    self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
-                )  # nq, bs, d_model
-            else:
-                tgt_ = tgt_undetach.detach()
-
-            if refpoint_embed is not None:
-                refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
-                tgt = torch.cat([tgt, tgt_], dim=1)
-            else:
-                refpoint_embed, tgt = refpoint_embed_, tgt_
-
-        elif self.two_stage_type == "no":
-            tgt_ = (
-                self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
-            )  # nq, bs, d_model
-            refpoint_embed_ = (
-                self.refpoint_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
-            )  # nq, bs, 4
-
-            if refpoint_embed is not None:
-                refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
-                tgt = torch.cat([tgt, tgt_], dim=1)
-            else:
-                refpoint_embed, tgt = refpoint_embed_, tgt_
-
-            if self.num_patterns > 0:
-                tgt_embed = tgt.repeat(1, self.num_patterns, 1)
-                refpoint_embed = refpoint_embed.repeat(1, self.num_patterns, 1)
-                tgt_pat = self.patterns.weight[None, :, :].repeat_interleave(
-                    self.num_queries, 1
-                )  # 1, n_q*n_pat, d_model
-                tgt = tgt_embed + tgt_pat
-
-            init_box_proposal = refpoint_embed_.sigmoid()
-
-        else:
-            raise NotImplementedError("unknown two_stage_type {}".format(self.two_stage_type))
-        #########################################################
-        # End preparing tgt
-        # - tgt: bs, NQ, d_model
-        # - refpoint_embed(unsigmoid): bs, NQ, d_model
-        #########################################################
-
-        #########################################################
-        # Begin Decoder
-        #########################################################
-        hs, references = self.decoder(
-            tgt=tgt.transpose(0, 1),
-            memory=memory.transpose(0, 1),
-            memory_key_padding_mask=mask_flatten,
-            pos=lvl_pos_embed_flatten.transpose(0, 1),
-            refpoints_unsigmoid=refpoint_embed.transpose(0, 1),
-            level_start_index=level_start_index,
-            spatial_shapes=spatial_shapes,
-            valid_ratios=valid_ratios,
-            tgt_mask=attn_mask,
-            memory_text=text_dict["encoded_text"],
-            text_attention_mask=~text_dict["text_token_mask"],
-            # we ~ the mask . False means use the token; True means pad the token
-        )
-        #########################################################
-        # End Decoder
-        # hs: n_dec, bs, nq, d_model
-        # references: n_dec+1, bs, nq, query_dim
-        #########################################################
-
-        #########################################################
-        # Begin postprocess
-        #########################################################
-        if self.two_stage_type == "standard":
-            hs_enc = tgt_undetach.unsqueeze(0)
-            ref_enc = refpoint_embed_undetach.sigmoid().unsqueeze(0)
-        else:
-            hs_enc = ref_enc = None
-        #########################################################
-        # End postprocess
-        # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or (n_enc, bs, nq, d_model) or None
-        # ref_enc: (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or (n_enc, bs, nq, d_model) or None
-        #########################################################
-
-        return hs, references, hs_enc, ref_enc, init_box_proposal
-        # hs: (n_dec, bs, nq, d_model)
-        # references: sigmoid coordinates. (n_dec+1, bs, bq, 4)
-        # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or None
-        # ref_enc: sigmoid coordinates. \
-        #           (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or None
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-        self,
-        encoder_layer,
-        num_layers,
-        d_model=256,
-        num_queries=300,
-        enc_layer_share=False,
-        text_enhance_layer=None,
-        feature_fusion_layer=None,
-        use_checkpoint=False,
-        use_transformer_ckpt=False,
-    ):
-        """_summary_
-
-        Args:
-            encoder_layer (_type_): _description_
-            num_layers (_type_): _description_
-            norm (_type_, optional): _description_. Defaults to None.
-            d_model (int, optional): _description_. Defaults to 256.
-            num_queries (int, optional): _description_. Defaults to 300.
-            enc_layer_share (bool, optional): _description_. Defaults to False.
-
-        """
-        super().__init__()
-        # prepare layers
-        self.layers = []
-        self.text_layers = []
-        self.fusion_layers = []
-        if num_layers > 0:
-            self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share)
-
-            if text_enhance_layer is not None:
-                self.text_layers = _get_clones(
-                    text_enhance_layer, num_layers, layer_share=enc_layer_share
-                )
-            if feature_fusion_layer is not None:
-                self.fusion_layers = _get_clones(
-                    feature_fusion_layer, num_layers, layer_share=enc_layer_share
-                )
-        else:
-            self.layers = []
-            del encoder_layer
-
-            if text_enhance_layer is not None:
-                self.text_layers = []
-                del text_enhance_layer
-            if feature_fusion_layer is not None:
-                self.fusion_layers = []
-                del feature_fusion_layer
-
-        self.query_scale = None
-        self.num_queries = num_queries
-        self.num_layers = num_layers
-        self.d_model = d_model
-
-        self.use_checkpoint = use_checkpoint
-        self.use_transformer_ckpt = use_transformer_ckpt
-
-    @staticmethod
-    def get_reference_points(spatial_shapes, valid_ratios, device):
-        reference_points_list = []
-        for lvl, (H_, W_) in enumerate(spatial_shapes):
-
-            ref_y, ref_x = torch.meshgrid(
-                torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
-                torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),
-            )
-            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
-            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
-            ref = torch.stack((ref_x, ref_y), -1)
-            reference_points_list.append(ref)
-        reference_points = torch.cat(reference_points_list, 1)
-        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
-        return reference_points
-
-    def forward(
-        self,
-        # for images
-        src: Tensor,
-        pos: Tensor,
-        spatial_shapes: Tensor,
-        level_start_index: Tensor,
-        valid_ratios: Tensor,
-        key_padding_mask: Tensor,
-        # for texts
-        memory_text: Tensor = None,
-        text_attention_mask: Tensor = None,
-        pos_text: Tensor = None,
-        text_self_attention_masks: Tensor = None,
-        position_ids: Tensor = None,
-    ):
-        """
-        Input:
-            - src: [bs, sum(hi*wi), 256]
-            - pos: pos embed for src. [bs, sum(hi*wi), 256]
-            - spatial_shapes: h,w of each level [num_level, 2]
-            - level_start_index: [num_level] start point of level in sum(hi*wi).
-            - valid_ratios: [bs, num_level, 2]
-            - key_padding_mask: [bs, sum(hi*wi)]
-
-            - memory_text: bs, n_text, 256
-            - text_attention_mask: bs, n_text
-                False for no padding; True for padding
-            - pos_text: bs, n_text, 256
-
-            - position_ids: bs, n_text
-        Intermedia:
-            - reference_points: [bs, sum(hi*wi), num_level, 2]
-        Outpus:
-            - output: [bs, sum(hi*wi), 256]
-        """
-
-        output = src
-
-        # preparation and reshape
-        if self.num_layers > 0:
-            reference_points = self.get_reference_points(
-                spatial_shapes, valid_ratios, device=src.device
-            )
-
-        if self.text_layers:
-            # generate pos_text
-            bs, n_text, text_dim = memory_text.shape
-            if pos_text is None and position_ids is None:
-                pos_text = (
-                    torch.arange(n_text, device=memory_text.device)
-                    .float()
-                    .unsqueeze(0)
-                    .unsqueeze(-1)
-                    .repeat(bs, 1, 1)
-                )
-                pos_text = get_sine_pos_embed(pos_text, num_pos_feats=256, exchange_xy=False)
-            if position_ids is not None:
-                pos_text = get_sine_pos_embed(
-                    position_ids[..., None], num_pos_feats=256, exchange_xy=False
-                )
-
-        # main process
-        for layer_id, layer in enumerate(self.layers):
-            # if output.isnan().any() or memory_text.isnan().any():
-            #     if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
-            #         import ipdb; ipdb.set_trace()
-            if self.fusion_layers:
-                if self.use_checkpoint:
-                    output, memory_text = checkpoint.checkpoint(
-                        self.fusion_layers[layer_id],
-                        output,
-                        memory_text,
-                        key_padding_mask,
-                        text_attention_mask,
-                    )
-                else:
-                    output, memory_text = self.fusion_layers[layer_id](
-                        v=output,
-                        l=memory_text,
-                        attention_mask_v=key_padding_mask,
-                        attention_mask_l=text_attention_mask,
-                    )
-
-            if self.text_layers:
-                memory_text = self.text_layers[layer_id](
-                    src=memory_text.transpose(0, 1),
-                    src_mask=~text_self_attention_masks,  # note we use ~ for mask here
-                    src_key_padding_mask=text_attention_mask,
-                    pos=(pos_text.transpose(0, 1) if pos_text is not None else None),
-                ).transpose(0, 1)
-
-            # main process
-            if self.use_transformer_ckpt:
-                output = checkpoint.checkpoint(
-                    layer,
-                    output,
-                    pos,
-                    reference_points,
-                    spatial_shapes,
-                    level_start_index,
-                    key_padding_mask,
-                )
-            else:
-                output = layer(
-                    src=output,
-                    pos=pos,
-                    reference_points=reference_points,
-                    spatial_shapes=spatial_shapes,
-                    level_start_index=level_start_index,
-                    key_padding_mask=key_padding_mask,
-                )
-
-        return output, memory_text
-
-
-class TransformerDecoder(nn.Module):
-    def __init__(
-        self,
-        decoder_layer,
-        num_layers,
-        norm=None,
-        return_intermediate=False,
-        d_model=256,
-        query_dim=4,
-        num_feature_levels=1,
-    ):
-        super().__init__()
-        if num_layers > 0:
-            self.layers = _get_clones(decoder_layer, num_layers)
-        else:
-            self.layers = []
-        self.num_layers = num_layers
-        self.norm = norm
-        self.return_intermediate = return_intermediate
-        assert return_intermediate, "support return_intermediate only"
-        self.query_dim = query_dim
-        assert query_dim in [2, 4], "query_dim should be 2/4 but {}".format(query_dim)
-        self.num_feature_levels = num_feature_levels
-
-        self.ref_point_head = MLP(query_dim // 2 * d_model, d_model, d_model, 2)
-        self.query_pos_sine_scale = None
-
-        self.query_scale = None
-        self.bbox_embed = None
-        self.class_embed = None
-
-        self.d_model = d_model
-
-        self.ref_anchor_head = None
-
-    def forward(
-        self,
-        tgt,
-        memory,
-        tgt_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        memory_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-        refpoints_unsigmoid: Optional[Tensor] = None,  # num_queries, bs, 2
-        # for memory
-        level_start_index: Optional[Tensor] = None,  # num_levels
-        spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
-        valid_ratios: Optional[Tensor] = None,
-        # for text
-        memory_text: Optional[Tensor] = None,
-        text_attention_mask: Optional[Tensor] = None,
-    ):
-        """
-        Input:
-            - tgt: nq, bs, d_model
-            - memory: hw, bs, d_model
-            - pos: hw, bs, d_model
-            - refpoints_unsigmoid: nq, bs, 2/4
-            - valid_ratios/spatial_shapes: bs, nlevel, 2
-        """
-        output = tgt
-
-        intermediate = []
-        reference_points = refpoints_unsigmoid.sigmoid()
-        ref_points = [reference_points]
-
-        for layer_id, layer in enumerate(self.layers):
-
-            if reference_points.shape[-1] == 4:
-                reference_points_input = (
-                    reference_points[:, :, None]
-                    * torch.cat([valid_ratios, valid_ratios], -1)[None, :]
-                )  # nq, bs, nlevel, 4
-            else:
-                assert reference_points.shape[-1] == 2
-                reference_points_input = reference_points[:, :, None] * valid_ratios[None, :]
-            query_sine_embed = gen_sineembed_for_position(
-                reference_points_input[:, :, 0, :]
-            )  # nq, bs, 256*2
-
-            # conditional query
-            raw_query_pos = self.ref_point_head(query_sine_embed)  # nq, bs, 256
-            pos_scale = self.query_scale(output) if self.query_scale is not None else 1
-            query_pos = pos_scale * raw_query_pos
-            # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
-            #     if query_pos.isnan().any() | query_pos.isinf().any():
-            #         import ipdb; ipdb.set_trace()
-
-            # main process
-            output = layer(
-                tgt=output,
-                tgt_query_pos=query_pos,
-                tgt_query_sine_embed=query_sine_embed,
-                tgt_key_padding_mask=tgt_key_padding_mask,
-                tgt_reference_points=reference_points_input,
-                memory_text=memory_text,
-                text_attention_mask=text_attention_mask,
-                memory=memory,
-                memory_key_padding_mask=memory_key_padding_mask,
-                memory_level_start_index=level_start_index,
-                memory_spatial_shapes=spatial_shapes,
-                memory_pos=pos,
-                self_attn_mask=tgt_mask,
-                cross_attn_mask=memory_mask,
-            )
-            if output.isnan().any() | output.isinf().any():
-                print(f"output layer_id {layer_id} is nan")
-                try:
-                    num_nan = output.isnan().sum().item()
-                    num_inf = output.isinf().sum().item()
-                    print(f"num_nan {num_nan}, num_inf {num_inf}")
-                except Exception as e:
-                    print(e)
-                    # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
-                    #     import ipdb; ipdb.set_trace()
-
-            # iter update
-            if self.bbox_embed is not None:
-                # box_holder = self.bbox_embed(output)
-                # box_holder[..., :self.query_dim] += inverse_sigmoid(reference_points)
-                # new_reference_points = box_holder[..., :self.query_dim].sigmoid()
-
-                reference_before_sigmoid = inverse_sigmoid(reference_points)
-                delta_unsig = self.bbox_embed[layer_id](output)
-                outputs_unsig = delta_unsig + reference_before_sigmoid
-                new_reference_points = outputs_unsig.sigmoid()
-
-                reference_points = new_reference_points.detach()
-                # if layer_id != self.num_layers - 1:
-                ref_points.append(new_reference_points)
-
-            intermediate.append(self.norm(output))
-
-        return [
-            [itm_out.transpose(0, 1) for itm_out in intermediate],
-            [itm_refpoint.transpose(0, 1) for itm_refpoint in ref_points],
-        ]
-
-
-class DeformableTransformerEncoderLayer(nn.Module):
-    def __init__(
-        self,
-        d_model=256,
-        d_ffn=1024,
-        dropout=0.1,
-        activation="relu",
-        n_levels=4,
-        n_heads=8,
-        n_points=4,
-    ):
-        super().__init__()
-
-        # self attention
-        self.self_attn = MSDeformAttn(
-            embed_dim=d_model,
-            num_levels=n_levels,
-            num_heads=n_heads,
-            num_points=n_points,
-            batch_first=True,
-        )
-        self.dropout1 = nn.Dropout(dropout)
-        self.norm1 = nn.LayerNorm(d_model)
-
-        # ffn
-        self.linear1 = nn.Linear(d_model, d_ffn)
-        self.activation = _get_activation_fn(activation, d_model=d_ffn)
-        self.dropout2 = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(d_ffn, d_model)
-        self.dropout3 = nn.Dropout(dropout)
-        self.norm2 = nn.LayerNorm(d_model)
-
-    @staticmethod
-    def with_pos_embed(tensor, pos):
-        return tensor if pos is None else tensor + pos
-
-    def forward_ffn(self, src):
-        src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
-        src = src + self.dropout3(src2)
-        src = self.norm2(src)
-        return src
-
-    def forward(
-        self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None
-    ):
-        # self attention
-        # import ipdb; ipdb.set_trace()
-        src2 = self.self_attn(
-            query=self.with_pos_embed(src, pos),
-            reference_points=reference_points,
-            value=src,
-            spatial_shapes=spatial_shapes,
-            level_start_index=level_start_index,
-            key_padding_mask=key_padding_mask,
-        )
-        src = src + self.dropout1(src2)
-        src = self.norm1(src)
-
-        # ffn
-        src = self.forward_ffn(src)
-
-        return src
-
-
-class DeformableTransformerDecoderLayer(nn.Module):
-    def __init__(
-        self,
-        d_model=256,
-        d_ffn=1024,
-        dropout=0.1,
-        activation="relu",
-        n_levels=4,
-        n_heads=8,
-        n_points=4,
-        use_text_feat_guide=False,
-        use_text_cross_attention=False,
-    ):
-        super().__init__()
-
-        # cross attention
-        self.cross_attn = MSDeformAttn(
-            embed_dim=d_model,
-            num_levels=n_levels,
-            num_heads=n_heads,
-            num_points=n_points,
-            batch_first=True,
-        )
-        self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.norm1 = nn.LayerNorm(d_model)
-
-        # cross attention text
-        if use_text_cross_attention:
-            self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
-            self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-            self.catext_norm = nn.LayerNorm(d_model)
-
-        # self attention
-        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
-        self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.norm2 = nn.LayerNorm(d_model)
-
-        # ffn
-        self.linear1 = nn.Linear(d_model, d_ffn)
-        self.activation = _get_activation_fn(activation, d_model=d_ffn, batch_dim=1)
-        self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.linear2 = nn.Linear(d_ffn, d_model)
-        self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
-        self.norm3 = nn.LayerNorm(d_model)
-
-        self.key_aware_proj = None
-        self.use_text_feat_guide = use_text_feat_guide
-        assert not use_text_feat_guide
-        self.use_text_cross_attention = use_text_cross_attention
-
-    def rm_self_attn_modules(self):
-        self.self_attn = None
-        self.dropout2 = None
-        self.norm2 = None
-
-    @staticmethod
-    def with_pos_embed(tensor, pos):
-        return tensor if pos is None else tensor + pos
-
-    def forward_ffn(self, tgt):
-        with torch.cuda.amp.autocast(enabled=False):
-            tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
-        tgt = tgt + self.dropout4(tgt2)
-        tgt = self.norm3(tgt)
-        return tgt
-
-    def forward(
-        self,
-        # for tgt
-        tgt: Optional[Tensor],  # nq, bs, d_model
-        tgt_query_pos: Optional[Tensor] = None,  # pos for query. MLP(Sine(pos))
-        tgt_query_sine_embed: Optional[Tensor] = None,  # pos for query. Sine(pos)
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        tgt_reference_points: Optional[Tensor] = None,  # nq, bs, 4
-        memory_text: Optional[Tensor] = None,  # bs, num_token, d_model
-        text_attention_mask: Optional[Tensor] = None,  # bs, num_token
-        # for memory
-        memory: Optional[Tensor] = None,  # hw, bs, d_model
-        memory_key_padding_mask: Optional[Tensor] = None,
-        memory_level_start_index: Optional[Tensor] = None,  # num_levels
-        memory_spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
-        memory_pos: Optional[Tensor] = None,  # pos for memory
-        # sa
-        self_attn_mask: Optional[Tensor] = None,  # mask used for self-attention
-        cross_attn_mask: Optional[Tensor] = None,  # mask used for cross-attention
-    ):
-        """
-        Input:
-            - tgt/tgt_query_pos: nq, bs, d_model
-            -
-        """
-        assert cross_attn_mask is None
-
-        # self attention
-        if self.self_attn is not None:
-            # import ipdb; ipdb.set_trace()
-            q = k = self.with_pos_embed(tgt, tgt_query_pos)
-            tgt2 = self.self_attn(q, k, tgt, attn_mask=self_attn_mask)[0]
-            tgt = tgt + self.dropout2(tgt2)
-            tgt = self.norm2(tgt)
-
-        if self.use_text_cross_attention:
-            tgt2 = self.ca_text(
-                self.with_pos_embed(tgt, tgt_query_pos),
-                memory_text.transpose(0, 1),
-                memory_text.transpose(0, 1),
-                key_padding_mask=text_attention_mask,
-            )[0]
-            tgt = tgt + self.catext_dropout(tgt2)
-            tgt = self.catext_norm(tgt)
-
-        tgt2 = self.cross_attn(
-            query=self.with_pos_embed(tgt, tgt_query_pos).transpose(0, 1),
-            reference_points=tgt_reference_points.transpose(0, 1).contiguous(),
-            value=memory.transpose(0, 1),
-            spatial_shapes=memory_spatial_shapes,
-            level_start_index=memory_level_start_index,
-            key_padding_mask=memory_key_padding_mask,
-        ).transpose(0, 1)
-        tgt = tgt + self.dropout1(tgt2)
-        tgt = self.norm1(tgt)
-
-        # ffn
-        tgt = self.forward_ffn(tgt)
-
-        return tgt
-
-
-def build_transformer(args):
-    return Transformer(
-        d_model=args.hidden_dim,
-        dropout=args.dropout,
-        nhead=args.nheads,
-        num_queries=args.num_queries,
-        dim_feedforward=args.dim_feedforward,
-        num_encoder_layers=args.enc_layers,
-        num_decoder_layers=args.dec_layers,
-        normalize_before=args.pre_norm,
-        return_intermediate_dec=True,
-        query_dim=args.query_dim,
-        activation=args.transformer_activation,
-        num_patterns=args.num_patterns,
-        num_feature_levels=args.num_feature_levels,
-        enc_n_points=args.enc_n_points,
-        dec_n_points=args.dec_n_points,
-        learnable_tgt_init=True,
-        # two stage
-        two_stage_type=args.two_stage_type,  # ['no', 'standard', 'early']
-        embed_init_tgt=args.embed_init_tgt,
-        use_text_enhancer=args.use_text_enhancer,
-        use_fusion_layer=args.use_fusion_layer,
-        use_checkpoint=args.use_checkpoint,
-        use_transformer_ckpt=args.use_transformer_ckpt,
-        use_text_cross_attention=args.use_text_cross_attention,
-        text_dropout=args.text_dropout,
-        fusion_dropout=args.fusion_dropout,
-        fusion_droppath=args.fusion_droppath,
-    )
diff --git a/spaces/aodianyun/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py b/spaces/aodianyun/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py
deleted file mode 100644
index 10c0920c1a217af5bb3e1b13077568035ab3b7b5..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-DETR Transformer class.
-
-Copy-paste from torch.nn.Transformer with modifications:
-    * positional encodings are passed in MHattention
-    * extra LN at the end of encoder is removed
-    * decoder returns a stack of activations from all decoding layers
-"""
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-from torch import Tensor, nn
-
-from .utils import (
-    MLP,
-    _get_activation_fn,
-    _get_clones,
-    gen_encoder_output_proposals,
-    gen_sineembed_for_position,
-    sigmoid_focal_loss,
-)
-
-
-class TextTransformer(nn.Module):
-    def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1):
-        super().__init__()
-        self.num_layers = num_layers
-        self.d_model = d_model
-        self.nheads = nheads
-        self.dim_feedforward = dim_feedforward
-        self.norm = None
-
-        single_encoder_layer = TransformerEncoderLayer(
-            d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout
-        )
-        self.layers = _get_clones(single_encoder_layer, num_layers)
-
-    def forward(self, memory_text: torch.Tensor, text_attention_mask: torch.Tensor):
-        """
-
-        Args:
-            text_attention_mask: bs, num_token
-            memory_text: bs, num_token, d_model
-
-        Raises:
-            RuntimeError: _description_
-
-        Returns:
-            output: bs, num_token, d_model
-        """
-
-        output = memory_text.transpose(0, 1)
-
-        for layer in self.layers:
-            output = layer(output, src_key_padding_mask=text_attention_mask)
-
-        if self.norm is not None:
-            output = self.norm(output)
-
-        return output.transpose(0, 1)
-
-
-class TransformerEncoderLayer(nn.Module):
-    def __init__(
-        self,
-        d_model,
-        nhead,
-        dim_feedforward=2048,
-        dropout=0.1,
-        activation="relu",
-        normalize_before=False,
-    ):
-        super().__init__()
-        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
-        # Implementation of Feedforward model
-        self.linear1 = nn.Linear(d_model, dim_feedforward)
-        self.dropout = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(dim_feedforward, d_model)
-
-        self.norm1 = nn.LayerNorm(d_model)
-        self.norm2 = nn.LayerNorm(d_model)
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(dropout)
-
-        self.activation = _get_activation_fn(activation)
-        self.normalize_before = normalize_before
-        self.nhead = nhead
-
-    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
-        return tensor if pos is None else tensor + pos
-
-    def forward(
-        self,
-        src,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-    ):
-        # repeat attn mask
-        if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]:
-            # bs, num_q, num_k
-            src_mask = src_mask.repeat(self.nhead, 1, 1)
-
-        q = k = self.with_pos_embed(src, pos)
-
-        src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0]
-
-        # src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
-        src = src + self.dropout1(src2)
-        src = self.norm1(src)
-        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
-        src = src + self.dropout2(src2)
-        src = self.norm2(src)
-        return src
diff --git a/spaces/aodianyun/stable-diffusion-webui/extensions-builtin/Lora/preload.py b/spaces/aodianyun/stable-diffusion-webui/extensions-builtin/Lora/preload.py
deleted file mode 100644
index c47d7ef4e24893953e51b8dfb5a7ccf88c574546..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/extensions-builtin/Lora/preload.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import os
-from modules import paths
-
-
-def preload(parser):
-    parser.add_argument("--lora-dir", type=str, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))
diff --git a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_speedy_speech_train.py b/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_speedy_speech_train.py
deleted file mode 100644
index 530781ef887d17a290b65810d0f3c5760217c920..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_speedy_speech_train.py
+++ /dev/null
@@ -1,72 +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.tts.configs.speedy_speech_config import SpeedySpeechConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_speedy_speech_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-
-config = SpeedySpeechConfig(
-    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/",
-    run_eval=True,
-    test_delay_epochs=-1,
-    epochs=1,
-    print_step=1,
-    print_eval=True,
-    test_sentences=[
-        "Be a voice, not an echo.",
-    ],
-)
-config.audio.do_trim_silence = True
-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 for it.' --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/runtime/Lib/site-packages/adodbapi/examples/xls_read.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/examples/xls_read.py
deleted file mode 100644
index 525f200bc25f138c08e4c94c69b2a9f836a94acb..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/examples/xls_read.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import sys
-import adodbapi
-
-try:
-    import adodbapi.is64bit as is64bit
-
-    is64 = is64bit.Python()
-except ImportError:
-    is64 = False
-
-if is64:
-    driver = "Microsoft.ACE.OLEDB.12.0"
-else:
-    driver = "Microsoft.Jet.OLEDB.4.0"
-extended = 'Extended Properties="Excel 8.0;HDR=Yes;IMEX=1;"'
-
-try:  # first command line argument will be xls file name -- default to the one written by xls_write.py
-    filename = sys.argv[1]
-except IndexError:
-    filename = "xx.xls"
-
-constr = "Provider=%s;Data Source=%s;%s" % (driver, filename, extended)
-
-conn = adodbapi.connect(constr)
-
-try:  # second command line argument will be worksheet name -- default to first worksheet
-    sheet = sys.argv[2]
-except IndexError:
-    # use ADO feature to get the name of the first worksheet
-    sheet = conn.get_table_names()[0]
-
-print("Shreadsheet=%s  Worksheet=%s" % (filename, sheet))
-print("------------------------------------------------------------")
-crsr = conn.cursor()
-sql = "SELECT * from [%s]" % sheet
-crsr.execute(sql)
-for row in crsr.fetchmany(10):
-    print(repr(row))
-crsr.close()
-conn.close()
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/simple_heatmap.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/simple_heatmap.py
deleted file mode 100644
index 2242b2a5f2219651ef3131466ef934935eacbfb4..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/simple_heatmap.py
+++ /dev/null
@@ -1,24 +0,0 @@
-"""
-Simple Heatmap
---------------
-This example shows a simple heatmap for showing gridded data.
-"""
-# category: simple charts
-import altair as alt
-import numpy as np
-import pandas as pd
-
-# Compute x^2 + y^2 across a 2D grid
-x, y = np.meshgrid(range(-5, 5), range(-5, 5))
-z = x ** 2 + y ** 2
-
-# Convert this grid to columnar data expected by Altair
-source = pd.DataFrame({'x': x.ravel(),
-                     'y': y.ravel(),
-                     'z': z.ravel()})
-
-alt.Chart(source).mark_rect().encode(
-    x='x:O',
-    y='y:O',
-    color='z:Q'
-)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/simple_scatter_with_errorbars.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/simple_scatter_with_errorbars.py
deleted file mode 100644
index 4a232287cbf80188706afa6aedfe1fdfc6dccb61..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/simple_scatter_with_errorbars.py
+++ /dev/null
@@ -1,43 +0,0 @@
-"""
-Simple Scatter Plot with Errorbars
-----------------------------------
-A simple scatter plot of a data set with errorbars.
-"""
-# category: scatter plots
-import altair as alt
-import pandas as pd
-import numpy as np
-
-# generate some data points with uncertainties
-np.random.seed(0)
-x = [1, 2, 3, 4, 5]
-y = np.random.normal(10, 0.5, size=len(x))
-yerr = 0.2
-
-# set up data frame
-source = pd.DataFrame({"x": x, "y": y, "yerr": yerr})
-
-# the base chart
-base = alt.Chart(source).transform_calculate(
-    ymin="datum.y-datum.yerr",
-    ymax="datum.y+datum.yerr"
-)
-
-# generate the points
-points = base.mark_point(
-    filled=True,
-    size=50,
-    color='black'
-).encode(
-    x=alt.X('x', scale=alt.Scale(domain=(0, 6))),
-    y=alt.Y('y', scale=alt.Scale(zero=False))
-)
-
-# generate the error bars
-errorbars = base.mark_errorbar().encode(
-    x="x",
-    y="ymin:Q",
-    y2="ymax:Q"
-)
-
-points + errorbars
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/codedataset.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/codedataset.py
deleted file mode 100644
index a433091956ed2449f628028ea8da7be4c4895307..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/codedataset.py
+++ /dev/null
@@ -1,576 +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 json
-import logging
-import os
-import random
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.utils.data
-
-from . import data_utils
-from fairseq.data.fairseq_dataset import FairseqDataset
-
-F0_FRAME_SPACE = 0.005  # sec
-
-
-logger = logging.getLogger(__name__)
-
-
-class ExpressiveCodeDataConfig(object):
-    def __init__(self, json_path):
-        with open(json_path, "r") as f:
-            self.config = json.load(f)
-        self._manifests = self.config["manifests"]
-
-    @property
-    def manifests(self):
-        return self._manifests
-
-    @property
-    def n_units(self):
-        return self.config["n_units"]
-
-    @property
-    def sampling_rate(self):
-        return self.config["sampling_rate"]
-
-    @property
-    def code_hop_size(self):
-        return self.config["code_hop_size"]
-
-    @property
-    def f0_stats(self):
-        """pre-computed f0 statistics path"""
-        return self.config.get("f0_stats", None)
-
-    @property
-    def f0_vq_type(self):
-        """naive or precomp"""
-        return self.config["f0_vq_type"]
-
-    @property
-    def f0_vq_name(self):
-        return self.config["f0_vq_name"]
-
-    def get_f0_vq_naive_quantizer(self, log, norm_mean, norm_std):
-        key = "log" if log else "linear"
-        if norm_mean and norm_std:
-            key += "_mean_std_norm"
-        elif norm_mean:
-            key += "_mean_norm"
-        else:
-            key += "_none_norm"
-        return self.config["f0_vq_naive_quantizer"][key]
-
-    @property
-    def f0_vq_n_units(self):
-        return self.config["f0_vq_n_units"]
-
-    @property
-    def multispkr(self):
-        """how to parse speaker label from audio path"""
-        return self.config.get("multispkr", None)
-
-
-def get_f0(audio, rate=16000):
-    try:
-        import amfm_decompy.basic_tools as basic
-        import amfm_decompy.pYAAPT as pYAAPT
-        from librosa.util import normalize
-    except ImportError:
-        raise "Please install amfm_decompy (`pip install AMFM-decompy`) and librosa (`pip install librosa`)."
-
-    assert audio.ndim == 1
-    frame_length = 20.0  # ms
-    to_pad = int(frame_length / 1000 * rate) // 2
-
-    audio = normalize(audio) * 0.95
-    audio = np.pad(audio, (to_pad, to_pad), "constant", constant_values=0)
-    audio = basic.SignalObj(audio, rate)
-    pitch = pYAAPT.yaapt(
-        audio,
-        frame_length=frame_length,
-        frame_space=F0_FRAME_SPACE * 1000,
-        nccf_thresh1=0.25,
-        tda_frame_length=25.0,
-    )
-    f0 = pitch.samp_values
-    return f0
-
-
-def interpolate_f0(f0):
-    try:
-        from scipy.interpolate import interp1d
-    except ImportError:
-        raise "Please install scipy (`pip install scipy`)"
-
-    orig_t = np.arange(f0.shape[0])
-    f0_interp = f0[:]
-    ii = f0_interp != 0
-    if ii.sum() > 1:
-        f0_interp = interp1d(
-            orig_t[ii], f0_interp[ii], bounds_error=False, kind="linear", fill_value=0
-        )(orig_t)
-        f0_interp = torch.Tensor(f0_interp).type_as(f0).to(f0.device)
-    return f0_interp
-
-
-def naive_quantize(x, edges):
-    bin_idx = (x.view(-1, 1) > edges.view(1, -1)).long().sum(dim=1)
-    return bin_idx
-
-
-def load_wav(full_path):
-    try:
-        import soundfile as sf
-    except ImportError:
-        raise "Please install soundfile (`pip install SoundFile`)"
-    data, sampling_rate = sf.read(full_path)
-    return data, sampling_rate
-
-
-def parse_code(code_str, dictionary, append_eos):
-    code, duration = torch.unique_consecutive(
-        torch.ShortTensor(list(map(int, code_str.split()))), return_counts=True
-    )
-    code = " ".join(map(str, code.tolist()))
-    code = dictionary.encode_line(code, append_eos).short()
-
-    if append_eos:
-        duration = torch.cat((duration, duration.new_zeros((1,))), dim=0)  # eos
-    duration = duration.short()
-    return code, duration
-
-
-def parse_manifest(manifest, dictionary):
-    audio_files = []
-    codes = []
-    durations = []
-    speakers = []
-
-    with open(manifest) as info:
-        for line in info.readlines():
-            sample = eval(line.strip())
-            if "cpc_km100" in sample:
-                k = "cpc_km100"
-            elif "hubert_km100" in sample:
-                k = "hubert_km100"
-            elif "phone" in sample:
-                k = "phone"
-            else:
-                assert False, "unknown format"
-            code = sample[k]
-            code, duration = parse_code(code, dictionary, append_eos=True)
-
-            codes.append(code)
-            durations.append(duration)
-            audio_files.append(sample["audio"])
-            speakers.append(sample.get("speaker", None))
-
-    return audio_files, codes, durations, speakers
-
-
-def parse_speaker(path, method):
-    if type(path) == str:
-        path = Path(path)
-
-    if method == "parent_name":
-        return path.parent.name
-    elif method == "parent_parent_name":
-        return path.parent.parent.name
-    elif method == "_":
-        return path.name.split("_")[0]
-    elif method == "single":
-        return "A"
-    elif callable(method):
-        return method(path)
-    else:
-        raise NotImplementedError()
-
-
-def get_f0_by_filename(filename, tgt_sampling_rate):
-    audio, sampling_rate = load_wav(filename)
-    if sampling_rate != tgt_sampling_rate:
-        raise ValueError(
-            "{} SR doesn't match target {} SR".format(sampling_rate, tgt_sampling_rate)
-        )
-
-    # compute un-interpolated f0, and use Ann's interp in __getitem__ if set
-    f0 = get_f0(audio, rate=tgt_sampling_rate)
-    f0 = torch.from_numpy(f0.astype(np.float32))
-    return f0
-
-
-def align_f0_to_durations(f0, durations, f0_code_ratio, tol=1):
-    code_len = durations.sum()
-    targ_len = int(f0_code_ratio * code_len)
-    diff = f0.size(0) - targ_len
-    assert abs(diff) <= tol, (
-        f"Cannot subsample F0: |{f0.size(0)} - {f0_code_ratio}*{code_len}|"
-        f" > {tol} (dur=\n{durations})"
-    )
-    if diff > 0:
-        f0 = f0[:targ_len]
-    elif diff < 0:
-        f0 = torch.cat((f0, f0.new_full((-diff,), f0[-1])), 0)
-
-    f0_offset = 0.0
-    seg_f0s = []
-    for dur in durations:
-        f0_dur = dur.item() * f0_code_ratio
-        seg_f0 = f0[int(f0_offset) : int(f0_offset + f0_dur)]
-        seg_f0 = seg_f0[seg_f0 != 0]
-        if len(seg_f0) == 0:
-            seg_f0 = torch.tensor(0).type(seg_f0.type())
-        else:
-            seg_f0 = seg_f0.mean()
-        seg_f0s.append(seg_f0)
-        f0_offset += f0_dur
-
-    assert int(f0_offset) == f0.size(0), f"{f0_offset} {f0.size()} {durations.sum()}"
-    return torch.tensor(seg_f0s)
-
-
-class Paddings(object):
-    def __init__(self, code_val, dur_val=0, f0_val=-2.0):
-        self.code = code_val
-        self.dur = dur_val
-        self.f0 = f0_val
-
-
-class Shifts(object):
-    def __init__(self, shifts_str, pads):
-        self._shifts = list(map(int, shifts_str.split(",")))
-        assert len(self._shifts) == 2, self._shifts
-        assert all(s >= 0 for s in self._shifts)
-        self.extra_length = max(s for s in self._shifts)
-        self.pads = pads
-
-    @property
-    def dur(self):
-        return self._shifts[0]
-
-    @property
-    def f0(self):
-        return self._shifts[1]
-
-    @staticmethod
-    def shift_one(seq, left_pad_num, right_pad_num, pad):
-        assert seq.ndim == 1
-        bos = seq.new_full((left_pad_num,), pad)
-        eos = seq.new_full((right_pad_num,), pad)
-        seq = torch.cat([bos, seq, eos])
-        mask = torch.ones_like(seq).bool()
-        mask[left_pad_num : len(seq) - right_pad_num] = 0
-        return seq, mask
-
-    def __call__(self, code, dur, f0):
-        if self.extra_length == 0:
-            code_mask = torch.zeros_like(code).bool()
-            dur_mask = torch.zeros_like(dur).bool()
-            f0_mask = torch.zeros_like(f0).bool()
-            return code, code_mask, dur, dur_mask, f0, f0_mask
-
-        code, code_mask = self.shift_one(code, 0, self.extra_length, self.pads.code)
-        dur, dur_mask = self.shift_one(
-            dur, self.dur, self.extra_length - self.dur, self.pads.dur
-        )
-        f0, f0_mask = self.shift_one(
-            f0, self.f0, self.extra_length - self.f0, self.pads.f0
-        )
-        return code, code_mask, dur, dur_mask, f0, f0_mask
-
-
-class CodeDataset(FairseqDataset):
-    def __init__(
-        self,
-        manifest,
-        dictionary,
-        dur_dictionary,
-        f0_dictionary,
-        config,
-        discrete_dur,
-        discrete_f0,
-        log_f0,
-        normalize_f0_mean,
-        normalize_f0_std,
-        interpolate_f0,
-        return_filename=False,
-        strip_filename=True,
-        shifts="0,0",
-        return_continuous_f0=False,
-    ):
-        random.seed(1234)
-        self.dictionary = dictionary
-        self.dur_dictionary = dur_dictionary
-        self.f0_dictionary = f0_dictionary
-        self.config = config
-
-        # duration config
-        self.discrete_dur = discrete_dur
-
-        # pitch config
-        self.discrete_f0 = discrete_f0
-        self.log_f0 = log_f0
-        self.normalize_f0_mean = normalize_f0_mean
-        self.normalize_f0_std = normalize_f0_std
-        self.interpolate_f0 = interpolate_f0
-
-        self.return_filename = return_filename
-        self.strip_filename = strip_filename
-        self.f0_code_ratio = config.code_hop_size / (
-            config.sampling_rate * F0_FRAME_SPACE
-        )
-
-        # use lazy loading to avoid sharing file handlers across workers
-        self.manifest = manifest
-        self._codes = None
-        self._durs = None
-        self._f0s = None
-        with open(f"{manifest}.leng.txt", "r") as f:
-            lengs = [int(line.rstrip()) for line in f]
-            edges = np.cumsum([0] + lengs)
-            self.starts, self.ends = edges[:-1], edges[1:]
-        with open(f"{manifest}.path.txt", "r") as f:
-            self.file_names = [line.rstrip() for line in f]
-        logger.info(f"num entries: {len(self.starts)}")
-
-        if os.path.exists(f"{manifest}.f0_stat.pt"):
-            self.f0_stats = torch.load(f"{manifest}.f0_stat.pt")
-        elif config.f0_stats:
-            self.f0_stats = torch.load(config.f0_stats)
-
-        self.multispkr = config.multispkr
-        if config.multispkr:
-            with open(f"{manifest}.speaker.txt", "r") as f:
-                self.spkrs = [line.rstrip() for line in f]
-            self.id_to_spkr = sorted(self.spkrs)
-            self.spkr_to_id = {k: v for v, k in enumerate(self.id_to_spkr)}
-
-        self.pads = Paddings(
-            dictionary.pad(),
-            0,  # use 0 for duration padding
-            f0_dictionary.pad() if discrete_f0 else -5.0,
-        )
-        self.shifts = Shifts(shifts, pads=self.pads)
-        self.return_continuous_f0 = return_continuous_f0
-
-    def get_data_handlers(self):
-        logging.info(f"loading data for {self.manifest}")
-        self._codes = np.load(f"{self.manifest}.code.npy", mmap_mode="r")
-        self._durs = np.load(f"{self.manifest}.dur.npy", mmap_mode="r")
-
-        if self.discrete_f0:
-            if self.config.f0_vq_type == "precomp":
-                self._f0s = np.load(
-                    f"{self.manifest}.{self.config.f0_vq_name}.npy", mmap_mode="r"
-                )
-            elif self.config.f0_vq_type == "naive":
-                self._f0s = np.load(f"{self.manifest}.f0.npy", mmap_mode="r")
-                quantizers_path = self.config.get_f0_vq_naive_quantizer(
-                    self.log_f0, self.normalize_f0_mean, self.normalize_f0_std
-                )
-                quantizers = torch.load(quantizers_path)
-                n_units = self.config.f0_vq_n_units
-                self._f0_quantizer = torch.from_numpy(quantizers[n_units])
-            else:
-                raise ValueError(f"f0_vq_type {self.config.f0_vq_type} not supported")
-        else:
-            self._f0s = np.load(f"{self.manifest}.f0.npy", mmap_mode="r")
-
-    def preprocess_f0(self, f0, stats):
-        """
-        1. interpolate
-        2. log transform (keep unvoiced frame 0)
-        """
-        # TODO: change this to be dependent on config for naive quantizer
-        f0 = f0.clone()
-        if self.interpolate_f0:
-            f0 = interpolate_f0(f0)
-
-        mask = f0 != 0  # only process voiced frames
-        if self.log_f0:
-            f0[mask] = f0[mask].log()
-        if self.normalize_f0_mean:
-            mean = stats["logf0_mean"] if self.log_f0 else stats["f0_mean"]
-            f0[mask] = f0[mask] - mean
-        if self.normalize_f0_std:
-            std = stats["logf0_std"] if self.log_f0 else stats["f0_std"]
-            f0[mask] = f0[mask] / std
-        return f0
-
-    def _get_raw_item(self, index):
-        start, end = self.starts[index], self.ends[index]
-        if self._codes is None:
-            self.get_data_handlers()
-        code = torch.from_numpy(np.array(self._codes[start:end])).long()
-        dur = torch.from_numpy(np.array(self._durs[start:end]))
-        f0 = torch.from_numpy(np.array(self._f0s[start:end]))
-        return code, dur, f0
-
-    def __getitem__(self, index):
-        code, dur, f0 = self._get_raw_item(index)
-        code = torch.cat([code.new([self.dictionary.bos()]), code])
-
-        # use 0 for eos and bos
-        dur = torch.cat([dur.new([0]), dur])
-        if self.discrete_dur:
-            dur = self.dur_dictionary.encode_line(
-                " ".join(map(str, dur.tolist())), append_eos=False
-            ).long()
-        else:
-            dur = dur.float()
-
-        # TODO: find a more elegant approach
-        raw_f0 = None
-        if self.discrete_f0:
-            if self.config.f0_vq_type == "precomp":
-                f0 = self.f0_dictionary.encode_line(
-                    " ".join(map(str, f0.tolist())), append_eos=False
-                ).long()
-            else:
-                f0 = f0.float()
-                f0 = self.preprocess_f0(f0, self.f0_stats[self.spkrs[index]])
-                if self.return_continuous_f0:
-                    raw_f0 = f0
-                    raw_f0 = torch.cat([raw_f0.new([self.f0_dictionary.bos()]), raw_f0])
-                f0 = naive_quantize(f0, self._f0_quantizer)
-            f0 = torch.cat([f0.new([self.f0_dictionary.bos()]), f0])
-        else:
-            f0 = f0.float()
-            if self.multispkr:
-                f0 = self.preprocess_f0(f0, self.f0_stats[self.spkrs[index]])
-            else:
-                f0 = self.preprocess_f0(f0, self.f0_stats)
-            f0 = torch.cat([f0.new([0]), f0])
-
-        if raw_f0 is not None:
-            *_, raw_f0, raw_f0_mask = self.shifts(code, dur, raw_f0)
-        else:
-            raw_f0_mask = None
-
-        code, code_mask, dur, dur_mask, f0, f0_mask = self.shifts(code, dur, f0)
-        if raw_f0_mask is not None:
-            assert (raw_f0_mask == f0_mask).all()
-
-        # is a padded frame if either input or output is padded
-        feats = {
-            "source": code[:-1],
-            "target": code[1:],
-            "mask": code_mask[1:].logical_or(code_mask[:-1]),
-            "dur_source": dur[:-1],
-            "dur_target": dur[1:],
-            "dur_mask": dur_mask[1:].logical_or(dur_mask[:-1]),
-            "f0_source": f0[:-1],
-            "f0_target": f0[1:],
-            "f0_mask": f0_mask[1:].logical_or(f0_mask[:-1]),
-        }
-
-        if raw_f0 is not None:
-            feats["raw_f0"] = raw_f0[1:]
-
-        if self.return_filename:
-            fname = self.file_names[index]
-            feats["filename"] = (
-                fname if not self.strip_filename else Path(fname).with_suffix("").name
-            )
-        return feats
-
-    def __len__(self):
-        return len(self.starts)
-
-    def size(self, index):
-        return self.ends[index] - self.starts[index] + self.shifts.extra_length
-
-    def num_tokens(self, index):
-        return self.size(index)
-
-    def collater(self, samples):
-        pad_idx, eos_idx = self.dictionary.pad(), self.dictionary.eos()
-        if len(samples) == 0:
-            return {}
-
-        src_tokens = data_utils.collate_tokens(
-            [s["source"] for s in samples], pad_idx, eos_idx, left_pad=False
-        )
-
-        tgt_tokens = data_utils.collate_tokens(
-            [s["target"] for s in samples],
-            pad_idx=pad_idx,
-            eos_idx=pad_idx,  # appending padding, eos is there already
-            left_pad=False,
-        )
-
-        src_durs, tgt_durs = [
-            data_utils.collate_tokens(
-                [s[k] for s in samples],
-                pad_idx=self.pads.dur,
-                eos_idx=self.pads.dur,
-                left_pad=False,
-            )
-            for k in ["dur_source", "dur_target"]
-        ]
-
-        src_f0s, tgt_f0s = [
-            data_utils.collate_tokens(
-                [s[k] for s in samples],
-                pad_idx=self.pads.f0,
-                eos_idx=self.pads.f0,
-                left_pad=False,
-            )
-            for k in ["f0_source", "f0_target"]
-        ]
-
-        mask, dur_mask, f0_mask = [
-            data_utils.collate_tokens(
-                [s[k] for s in samples],
-                pad_idx=1,
-                eos_idx=1,
-                left_pad=False,
-            )
-            for k in ["mask", "dur_mask", "f0_mask"]
-        ]
-
-        src_lengths = torch.LongTensor([s["source"].numel() for s in samples])
-        n_tokens = sum(len(s["source"]) for s in samples)
-
-        result = {
-            "nsentences": len(samples),
-            "ntokens": n_tokens,
-            "net_input": {
-                "src_tokens": src_tokens,
-                "src_lengths": src_lengths,
-                "dur_src": src_durs,
-                "f0_src": src_f0s,
-            },
-            "target": tgt_tokens,
-            "dur_target": tgt_durs,
-            "f0_target": tgt_f0s,
-            "mask": mask,
-            "dur_mask": dur_mask,
-            "f0_mask": f0_mask,
-        }
-
-        if "filename" in samples[0]:
-            result["filename"] = [s["filename"] for s in samples]
-
-        # TODO: remove this hack into the inference dataset
-        if "prefix" in samples[0]:
-            result["prefix"] = [s["prefix"] for s in samples]
-
-        if "raw_f0" in samples[0]:
-            raw_f0s = data_utils.collate_tokens(
-                [s["raw_f0"] for s in samples],
-                pad_idx=self.pads.f0,
-                eos_idx=self.pads.f0,
-                left_pad=False,
-            )
-            result["raw_f0"] = raw_f0s
-        return result
diff --git a/spaces/autonomous019/Story_Generator_v2/README.md b/spaces/autonomous019/Story_Generator_v2/README.md
deleted file mode 100644
index 1a39568a9497d4f0aaccd932f56e4c858218094d..0000000000000000000000000000000000000000
--- a/spaces/autonomous019/Story_Generator_v2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Story Generator V2
-emoji: 🐠
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.2
-app_file: app.py
-pinned: false
-license: bsd
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/avichr/HebEMO_demo/spider_plot.py b/spaces/avichr/HebEMO_demo/spider_plot.py
deleted file mode 100644
index 7c1dfa6d113deae918f511b2081c0cdd273119e3..0000000000000000000000000000000000000000
--- a/spaces/avichr/HebEMO_demo/spider_plot.py
+++ /dev/null
@@ -1,39 +0,0 @@
-def spider_plot(df):
-  import matplotlib.pyplot as plt
-  import pandas as pd
-  from math import pi
-      
-  # number of variable
-  categories=list(df)
-  N = len(categories)
-   
-  # We are going to plot the first line of the data frame.
-  # But we need to repeat the first value to close the circular graph:
-  values=df.loc[0].values.flatten().tolist()
-  values += values[:1]
-  values
-   
-  # What will be the angle of each axis in the plot? (we divide the plot / number of variable)
-  angles = [n / float(N) * 2 * pi for n in range(N)]
-  angles += angles[:1]
-   
-  # Initialise the spider plot
-  fig, ax = plt.subplot(111, polar=True)
-   
-  # Draw one axe per variable + add labels
-  plt.xticks(angles[:-1], categories, color='grey', size=8)
-   
-  # Draw ylabels
-  ax.set_rlabel_position(0)
-  plt.yticks([10,20,30], ["10","20","30"], color="grey", size=7)
-  plt.ylim(0,1)
-   
-  # Plot data
-  ax.plot(angles, values, linewidth=1, linestyle='solid')
-   
-  # Fill area
-  ax.fill(angles, values, 'b', alpha=0.1)
-  
-  # Show the graph
-  return(fig, ax)
-  
\ No newline at end of file
diff --git a/spaces/awacke1/AW-04-GR-Seq-2-Seq-QA-Auto-Gen/README.md b/spaces/awacke1/AW-04-GR-Seq-2-Seq-QA-Auto-Gen/README.md
deleted file mode 100644
index 3fa7671e4ca62cf590e176442c66a946f631c381..0000000000000000000000000000000000000000
--- a/spaces/awacke1/AW-04-GR-Seq-2-Seq-QA-Auto-Gen/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: QA Gradio Seq 2 Seq QA Auto Gen
-emoji: 🙋QA❓
-colorFrom: purple
-colorTo: purple
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/PermutationsAndSequencesGPT/app.py b/spaces/awacke1/PermutationsAndSequencesGPT/app.py
deleted file mode 100644
index 3bb180f1647cc7a9e0a6f88577fed4e87246142f..0000000000000000000000000000000000000000
--- a/spaces/awacke1/PermutationsAndSequencesGPT/app.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import streamlit as st 
-
-
-# Import necessary libraries
-import xml.etree.ElementTree as ET
-
-# Mathematical principles and corresponding emojis
-principles = {
-    "Permutations_and_Combinations": {"emoji": "🔢", "name": "Permutations and Combinations"},
-    "Pigeonhole_Principle": {"emoji": "🕊️", "name": "Pigeonhole Principle"},
-    "Graph_Theory": {"emoji": "📊", "name": "Graph Theory"},
-    "Inclusion_Exclusion_Principle": {"emoji": "➕➖", "name": "Inclusion-Exclusion Principle"},
-    "Binomial_Coefficients": {"emoji": "🔣", "name": "Binomial Coefficients"},
-    "Partition_Theory": {"emoji": "🔀", "name": "Partition Theory"},
-    "Generating_Functions": {"emoji": "🔄", "name": "Generating Functions"},
-    "Recurrence_Relations": {"emoji": "🔄➡️", "name": "Recurrence Relations"},
-    "Combinatorial_Proofs": {"emoji": "✅", "name": "Combinatorial Proofs"},
-    "Catalan_Numbers": {"emoji": "🔀🔢", "name": "Catalan Numbers"},
-}
-
-# Parsing CCDA XML
-def parse_ccda(xml_file):
-    # Parse XML using ElementTree
-    tree = ET.parse(xml_file)
-    root = tree.getroot()
-
-    # Process each element in XML
-    for elem in root:
-        # Processing code here...
-        pass
-
-# Creating hyperlinks to Wikipedia and NLM
-def create_hyperlinks(principle_key):
-    base_wiki_url = "https://en.wikipedia.org/wiki/"
-    base_nlm_url = "https://www.nlm.nih.gov/mesh/"
-
-    # Concatenate base URL with principle key for the hyperlink
-    wiki_url = base_wiki_url + principle_key.replace("_", " ")
-    nlm_url = base_nlm_url + principle_key.replace("_", " ")
-
-    return wiki_url, nlm_url
-
-# Displaying each mathematical principle with unique keys, emojis, and names
-for key in principles.keys():
-    # Get the name and emoji
-    name = principles[key]["name"]
-    emoji = principles[key]["emoji"]
-
-    # Get the hyperlinks
-    wiki_url, nlm_url = create_hyperlinks(key)
-
-    # Display the principle
-    print(f"{emoji} {name}")
-    print(f"Wikipedia link: {wiki_url}")
-    print(f"NLM link: {nlm_url}\n")
-
-
-
-
-
-
-st.markdown("""
-
-
-Modify this program to add a user interface with unique keys and names that are similar and contain emojis to express the mathematical principles.  Add to the information here as a markdown code block.  Include a CCDA xml parser and references to wikipedia articles on HL7 format v3 the CCDA standard.  Include code that creates hyperlinks to the object ids that NLM has:  ermutations and Combinations (🔢): Just like there are numerous combinations and permutations in a set, there are countless ways to approach a problem. Persistence encourages us to keep trying different solutions until we succeed, and optimism assures us that there is a solution waiting to be discovered.
-
-Pigeonhole Principle (🕊️): This principle states that if you have more pigeons than pigeonholes, at least one hole must contain more than one pigeon. Similarly, if you persist and keep trying (pigeons), you're bound to find a solution (pigeonhole) at some point.
-
-Graph Theory (📊): Nodes and edges can represent challenges and connections respectively. Optimism and persistence can be seen as paths through these challenges, where the most connected nodes represent the most promising opportunities.
-
-Inclusion-Exclusion Principle (➕➖): This principle in set theory can be metaphorically related to the balance between optimism and realism. Including all possibilities (optimism), but also excluding infeasible ones (realism), leads to a balanced outlook.
-
-Binomial Coefficients (🔣): These represent the number of ways to choose a smaller set from a larger one. This can parallel the idea that there are many different paths to success, and optimism and persistence help us keep trying different paths.
-
-Partition Theory (🔀): This refers to the number of ways a number can be expressed as a sum of positive integers. This reflects the idea that there are many different ways to reach a goal, and optimism and persistence help us keep trying until we find a way that works.
-
-Generating Functions (🔄): These are used to encode sequences in a way that makes them easier to manipulate. This can be likened to the idea that optimism and persistence can help us reframe challenges and find new ways to approach them.
-
-Recurrence Relations (🔄➡️): This can parallel the idea that past experiences (recurrences) influence future outcomes, and that optimism and persistence help us learn from the past and apply it to the future.
-
-Combinatorial Proofs (✅): These demonstrate truths by counting in two different ways and showing the counts are the same. This can be a metaphor for how optimism and persistence can help us see problems from new perspectives, often leading to solutions.
-
-Catalan Numbers (🔀🔢): These count certain types of lattice paths, permutations, binary trees, etc. This diversity reminds us that there are many paths to success, and optimism and persistence help us explore them all.Permutations and Combinations (🔢): Just like there are numerous combinations and permutations in a set, there are countless ways to approach a problem. Persistence encourages us to keep trying different solutions until we succeed, and optimism assures us that there is a solution waiting to be discovered.
-
-Pigeonhole Principle (🕊️): This principle states that if you have more pigeons than pigeonholes, at least one hole must contain more than one pigeon. Similarly, if you persist and keep trying (pigeons), you're bound to find a solution (pigeonhole) at some point.
-
-Graph Theory (📊): Nodes and edges can represent challenges and connections respectively. Optimism and persistence can be seen as paths through these challenges, where the most connected nodes represent the most promising opportunities.
-
-Inclusion-Exclusion Principle (➕➖): This principle in set theory can be metaphorically related to the balance between optimism and realism. Including all possibilities (optimism), but also excluding infeasible ones (realism), leads to a balanced outlook.
-
-Binomial Coefficients (🔣): These represent the number of ways to choose a smaller set from a larger one. This can parallel the idea that there are many different paths to success, and optimism and persistence help us keep trying different paths.
-
-Partition Theory (🔀): This refers to the number of ways a number can be expressed as a sum of positive integers. This reflects the idea that there are many different ways to reach a goal, and optimism and persistence help us keep trying until we find a way that works.
-
-Generating Functions (🔄): These are used to encode sequences in a way that makes them easier to manipulate. This can be likened to the idea that optimism and persistence can help us reframe challenges and find new ways to approach them.
-
-Recurrence Relations (🔄➡️): This can parallel the idea that past experiences (recurrences) influence future outcomes, and that optimism and persistence help us learn from the past and apply it to the future.
-
-Combinatorial Proofs (✅): These demonstrate truths by counting in two different ways and showing the counts are the same. This can be a metaphor for how optimism and persistence can help us see problems from new perspectives, often leading to solutions.
-
-Catalan Numbers (🔀🔢): These count certain types of lattice paths, permutations, binary trees, etc. This diversity reminds us that there are many paths to success, and optimism and persistence help us explore them all.
-
-""")
-
diff --git a/spaces/azapi/img-to-music/README.md b/spaces/azapi/img-to-music/README.md
deleted file mode 100644
index ff1948d1b95ee1f8d7a3396aefb285c729d18687..0000000000000000000000000000000000000000
--- a/spaces/azapi/img-to-music/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Img To Music
-emoji: 🌅🎶
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.16.0
-app_file: app.py
-pinned: true
-duplicated_from: fffiloni/img-to-music
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/baixing/hackathon_chatbot_baixing_api/app.py b/spaces/baixing/hackathon_chatbot_baixing_api/app.py
deleted file mode 100644
index 301546cbd1077531a5f4a7507e470b5d6e8d3ba0..0000000000000000000000000000000000000000
--- a/spaces/baixing/hackathon_chatbot_baixing_api/app.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import gradio as gr
-import os
-import requests
-
-# 请记得要把 api 的 key 放到 settings 下面的 Repository Secrets 里。
-# 目前有个特别奇怪的问题： duplicate 的 key 如果和原来的 key 重名，build 就会失败。不知是否是今天正在 migrating 的原因。
-# 作为 workaround，请对 key 使用一个不同的名字，并且记得修改下面这行代码中的 key 的名字。
-key = os.getenv("baixing_key")
-
-# 如果你只打算通过 prompt 来定制机器人的行为，只需要修改这段 prompt 就够了。
-prompt = """请将下述文字改成只用emoji来表达，不要输出任何其它字符
-然后空两行，给出emoji表达的回复
-########
-
-
-"""
-
-url = "https://gpt.baixing.com/"
-
-# 修改本函数，来实现你自己的 chatbot
-# p: 对机器人说话的内容  
-# qid: 当前消息的唯一标识。例如 `'bxqid-cManAtRMszw...'`。由平台生成并传递给机器人，以便机器人区分单个问题（写日志、追踪调试、异步回调等）。同步调用可忽略。  
-# uid: 用户的唯一标识。例如`'bxuid-Aj8Spso8Xsp...'`。由平台生成并传递给机器人，以便机器人区分用户。可被用于实现多轮对话的功能。  
-# 返回值：[type, content]
-# 详见 https://huggingface.co/spaces/baixing/hackathon_test/blob/main/bot-api.md
-def chat(p, qid, uid): 
-    result = requests.get(url, params={"p": prompt + p, "k": key}).json()['data']
-    return ["text", result]
-
-iface = gr.Interface(fn=chat, 
-                     inputs=["text", "text", "text"], 
-                     outputs=["text", "text"],
-                     description="""这是一个极其简单的示范程序，把你的问话翻译成 emoji 并只用 emoji 来回答。
-注意：duplicate 本项目后，需要将你自己的 baixingai 的 apikey 设置到 settings 的 Repository Secrets 里，否则运行会报错。[了解详情](https://huggingface.co/spaces/baixing/hackathon_chatbot_openai_api/blob/main/%E6%B7%BB%E5%8A%A0%20secret%20%E7%9A%84%E6%96%B9%E6%B3%95.jpg)    
-
-[对话测试](https://huggingface.co/spaces/BaixingAI/hackathon_test)   [参考文档](https://huggingface.co/spaces/baixing/hackathon_test/blob/main/bot-api.md)    [Q & A](https://huggingface.co/spaces/baixing/hackathon_test/blob/main/qna.md)
-                     """
-                    )
-iface.launch()
\ No newline at end of file
diff --git a/spaces/balacoon/revoice/service_request.py b/spaces/balacoon/revoice/service_request.py
deleted file mode 100644
index 5dbe11df706272372caaf6157cb33848a197511c..0000000000000000000000000000000000000000
--- a/spaces/balacoon/revoice/service_request.py
+++ /dev/null
@@ -1,130 +0,0 @@
-"""
-Copyright 2023 Balacoon
-
-contains implementation
-for Revoice request
-"""
-
-import os
-import asyncio
-import base64
-import hashlib
-import json
-import ssl
-import time
-from typing import Tuple, Union
-
-import numpy as np
-import resampy
-import websockets
-
-
-def prepare_audio(audio: Tuple[int, np.ndarray]) -> np.ndarray:
-    """
-    ensures that audio is in int16 format, 16khz mono
-    """
-    sr, wav = audio
-    # ensure proper type
-    if wav.dtype == np.int32:
-        max_val = np.max(np.abs(wav))
-        mult = (32767.0 / 2**31) if max_val > 32768 else 1.0
-        wav = (wav.astype(np.float32) * mult).astype(np.int16)
-    elif wav.dtype == np.float32 or wav.dtype == np.float64:
-        mult = 32767.0 if np.max(np.abs(wav)) <= 1.0 else 1.0
-        wav = (wav * mult).astype(np.int16)
-
-    if wav.ndim == 2:
-        # average channels
-        if wav.shape[0] == 2:
-            wav = np.mean(wav, axis=0, keepdims=False)
-        if wav.shape[1] == 2:
-            wav = np.mean(wav, axis=1, keepdims=False)
-
-    if wav.ndim != 1:
-        return None
- 
-    # ensure proper sampling rate
-    if sr != 16000:
-        wav = (wav / 32768.0).astype(np.float)
-        wav = resampy.resample(wav, sr, 16000)
-        wav = (wav * 32768.0).astype(np.int16)
-    return wav
-
-
-def create_signature(api_secret: str) -> str:
-    """
-    helper function that creates signature,
-    required to authentificate the request
-    """
-    int_time = int(time.time() / 1000)
-    signature_input = (api_secret + str(int_time)).encode()
-    signature = hashlib.sha256(signature_input).hexdigest()
-    return signature
-
-
-async def async_service_request(source_str: str, source: np.ndarray, target: np.ndarray, api_key: str, api_secret: str) -> np.ndarray:
-
-    if target is None or len(target) == 0:
-        return None
-    
-    ssl_context = ssl.create_default_context()
-
-    async with websockets.connect(
-        os.environ["endpoint"], close_timeout=1024, ssl=ssl_context
-    ) as websocket:
-        request_dict = {
-            "target": base64.b64encode(target.tobytes()).decode("utf-8"),
-            "api_key": api_key,
-            "signature": create_signature(api_secret),
-        }
-        if source_str and len(source_str) > 0:
-            request_dict["source_str"] = source_str
-        elif source is not None and len(source) > 0:
-            request_dict["source"] = base64.b64encode(source.tobytes()).decode("utf-8")
-        else:
-            return None
-        request = json.dumps(request_dict)
-        await websocket.send(request)
-
-        # read reply
-        result_lst = []
-        while True:
-            try:
-                data = await asyncio.wait_for(websocket.recv(), timeout=30)
-                result_lst.append(np.frombuffer(data, dtype="int16"))
-            except websockets.exceptions.ConnectionClosed:
-                break
-            except asyncio.TimeoutError:
-                break
-            if data is None:
-                break
-        result = np.concatenate(result_lst) if result_lst else None
-        return result
-
-
-def service_request(
-    source_str: str, source_audio: Tuple[int, np.ndarray], target_audio: Tuple[int, np.ndarray],
-    api_key: str, api_secret: str,
-) -> Tuple[int, np.ndarray]:
-    """
-    prepares audio (has to be 16khz mono)
-    and runs request to a voice conversion service
-    """
-    src = None
-    if source_audio is not None:
-        src = prepare_audio(source_audio)
-    tgt = prepare_audio(target_audio)
-    if tgt is None:
-        return
-    if source_str is None and src is None:
-        return
-    if len(tgt) >= 30 * 16000:
-        # too long
-        return
-    if src is not None and len(src) >= 60 * 16000:
-        return
-    if source_str is not None and len(source_str) > 256:
-        return
-
-    res = asyncio.run(async_service_request(source_str, src, tgt, api_key, api_secret))
-    return 16000, res
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/exporters/MMDExporter.js b/spaces/banana-projects/web3d/node_modules/three/examples/jsm/exporters/MMDExporter.js
deleted file mode 100644
index 70333af87fafd4bc276f28825528e688bc8443db..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/exporters/MMDExporter.js
+++ /dev/null
@@ -1,218 +0,0 @@
-/**
- * @author takahiro / http://github.com/takahirox
- *
- * Dependencies
- *  - mmd-parser https://github.com/takahirox/mmd-parser
- */
-
-import {
-	Matrix4,
-	Quaternion,
-	Vector3
-} from "../../../build/three.module.js";
-
-var MMDExporter = function () {
-
-	// Unicode to Shift_JIS table
-	var u2sTable;
-
-	function unicodeToShiftjis( str ) {
-
-		if ( u2sTable === undefined ) {
-
-			var encoder = new MMDParser.CharsetEncoder();
-			var table = encoder.s2uTable;
-			u2sTable = {};
-
-			var keys = Object.keys( table );
-
-			for ( var i = 0, il = keys.length; i < il; i ++ ) {
-
-				var key = keys[ i ];
-
-				var value = table[ key ];
-				key = parseInt( key );
-
-				u2sTable[ value ] = key;
-
-			}
-
-		}
-
-		var array = [];
-
-		for ( var i = 0, il = str.length; i < il; i ++ ) {
-
-			var code = str.charCodeAt( i );
-
-			var value = u2sTable[ code ];
-
-			if ( value === undefined ) {
-
-				throw 'cannot convert charcode 0x' + code.toString( 16 );
-
-			} else if ( value > 0xff ) {
-
-				array.push( ( value >> 8 ) & 0xff );
-				array.push( value & 0xff );
-
-			} else {
-
-				array.push( value & 0xff );
-
-			}
-
-		}
-
-		return new Uint8Array( array );
-
-	}
-
-	function getBindBones( skin ) {
-
-		// any more efficient ways?
-		var poseSkin = skin.clone();
-		poseSkin.pose();
-		return poseSkin.skeleton.bones;
-
-	}
-
-	/* TODO: implement
-	// mesh -> pmd
-	this.parsePmd = function ( object ) {
-
-	};
-	*/
-
-	/* TODO: implement
-	// mesh -> pmx
-	this.parsePmx = function ( object ) {
-
-	};
-	*/
-
-	/*
-	 * skeleton -> vpd
-	 * Returns Shift_JIS encoded Uint8Array. Otherwise return strings.
-	 */
-	this.parseVpd = function ( skin, outputShiftJis, useOriginalBones ) {
-
-		if ( skin.isSkinnedMesh !== true ) {
-
-			console.warn( 'THREE.MMDExporter: parseVpd() requires SkinnedMesh instance.' );
-			return null;
-
-		}
-
-		function toStringsFromNumber( num ) {
-
-			if ( Math.abs( num ) < 1e-6 ) num = 0;
-
-			var a = num.toString();
-
-			if ( a.indexOf( '.' ) === - 1 ) {
-
-				a += '.';
-
-			}
-
-			a += '000000';
-
-			var index = a.indexOf( '.' );
-
-			var d = a.slice( 0, index );
-			var p = a.slice( index + 1, index + 7 );
-
-			return d + '.' + p;
-
-		}
-
-		function toStringsFromArray( array ) {
-
-			var a = [];
-
-			for ( var i = 0, il = array.length; i < il; i ++ ) {
-
-				a.push( toStringsFromNumber( array[ i ] ) );
-
-			}
-
-			return a.join( ',' );
-
-		}
-
-		skin.updateMatrixWorld( true );
-
-		var bones = skin.skeleton.bones;
-		var bones2 = getBindBones( skin );
-
-		var position = new Vector3();
-		var quaternion = new Quaternion();
-		var quaternion2 = new Quaternion();
-		var matrix = new Matrix4();
-
-		var array = [];
-		array.push( 'Vocaloid Pose Data file' );
-		array.push( '' );
-		array.push( ( skin.name !== '' ? skin.name.replace( /\s/g, '_' ) : 'skin' ) + '.osm;' );
-		array.push( bones.length + ';' );
-		array.push( '' );
-
-		for ( var i = 0, il = bones.length; i < il; i ++ ) {
-
-			var bone = bones[ i ];
-			var bone2 = bones2[ i ];
-
-			/*
-			 * use the bone matrix saved before solving IK.
-			 * see CCDIKSolver for the detail.
-			 */
-			if ( useOriginalBones === true &&
-				bone.userData.ik !== undefined &&
-				bone.userData.ik.originalMatrix !== undefined ) {
-
-				matrix.fromArray( bone.userData.ik.originalMatrix );
-
-			} else {
-
-				matrix.copy( bone.matrix );
-
-			}
-
-			position.setFromMatrixPosition( matrix );
-			quaternion.setFromRotationMatrix( matrix );
-
-			var pArray = position.sub( bone2.position ).toArray();
-			var qArray = quaternion2.copy( bone2.quaternion ).conjugate().multiply( quaternion ).toArray();
-
-			// right to left
-			pArray[ 2 ] = - pArray[ 2 ];
-			qArray[ 0 ] = - qArray[ 0 ];
-			qArray[ 1 ] = - qArray[ 1 ];
-
-			array.push( 'Bone' + i + '{' + bone.name );
-			array.push( '  ' + toStringsFromArray( pArray ) + ';' );
-			array.push( '  ' + toStringsFromArray( qArray ) + ';' );
-			array.push( '}' );
-			array.push( '' );
-
-		}
-
-		array.push( '' );
-
-		var lines = array.join( '\n' );
-
-		return ( outputShiftJis === true ) ? unicodeToShiftjis( lines ) : lines;
-
-	};
-
-	/* TODO: implement
-	// animation + skeleton -> vmd
-	this.parseVmd = function ( object ) {
-
-	};
-	*/
-
-};
-
-export { MMDExporter };
diff --git a/spaces/bhavanaraj/myaivoice/app.py b/spaces/bhavanaraj/myaivoice/app.py
deleted file mode 100644
index ca8b6d40b4ab898c70da92f4a4298de2baf703dc..0000000000000000000000000000000000000000
--- a/spaces/bhavanaraj/myaivoice/app.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import os
-import re
-import requests
-import json
-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')
-PLAY_HT_API_KEY=os.getenv('PLAY_HT_API_KEY')
-PLAY_HT_USER_ID=os.getenv('PLAY_HT_USER_ID')
-
-PLAY_HT_VOICE_ID=os.getenv('PLAY_HT_VOICE_ID')
-play_ht_api_get_audio_url = "https://play.ht/api/v2/tts"
-
-
-template = """You are a helpful assistant to answer 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,
-)
-
-headers = {
-      "accept": "text/event-stream",
-      "content-type": "application/json",
-      "AUTHORIZATION": "Bearer "+ PLAY_HT_API_KEY,
-      "X-USER-ID": PLAY_HT_USER_ID
-}
-
-
-def get_payload(text):
-  return {
-    "text": text,
-    "voice": PLAY_HT_VOICE_ID,
-    "quality": "medium",
-    "output_format": "mp3",
-    "speed": 1,
-    "sample_rate": 24000,
-    "seed": None,
-    "temperature": None
-  }
-
-def get_generated_audio(text):
-  payload = get_payload(text)
-  generated_response = {}
-  try:
-      response = requests.post(play_ht_api_get_audio_url, json=payload, headers=headers)
-      response.raise_for_status()
-      generated_response["type"]= 'SUCCESS'
-      generated_response["response"] = response.text
-  except requests.exceptions.RequestException as e:
-      generated_response["type"]= 'ERROR'
-      try:
-        response_text = json.loads(response.text)
-        if response_text['error_message']:
-          generated_response["response"] = response_text['error_message']
-        else:
-          generated_response["response"] = response.text
-      except Exception as e:
-        generated_response["response"] = response.text
-  except Exception as e:
-    generated_response["type"]= 'ERROR'
-    generated_response["response"] = response.text
-  return generated_response
-
-def extract_urls(text):
-    # Define the regex pattern for URLs
-    url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+[/\w\.-]*'
-
-    # Find all occurrences of URLs in the text
-    urls = re.findall(url_pattern, text)
-
-    return urls
-
-def get_audio_reply_for_question(text):
-  generated_audio_event = get_generated_audio(text)
-  #From get_generated_audio, you will get events in a string format, from that we need to extract the url
-  final_response = {
-      "audio_url": '',
-      "message": ''
-  }
-  if generated_audio_event["type"] == 'SUCCESS':
-    audio_urls = extract_urls(generated_audio_event["response"])
-    if len(audio_urls) == 0:
-      final_response['message'] = "No audio file link found in generated event"
-    else:
-      final_response['audio_url'] = audio_urls[-1]
-  else:
-    final_response['message'] = generated_audio_event['response']
-  return final_response
-
-def download_url(url):
-    try:
-        # Send a GET request to the URL to fetch the content
-        final_response = {
-            'content':'',
-            'error':''
-        }
-        response = requests.get(url)
-        # Check if the request was successful (status code 200)
-        if response.status_code == 200:
-            final_response['content'] = response.content
-        else:
-            final_response['error'] = f"Failed to download the URL. Status code: {response.status_code}"
-    except Exception as e:
-        final_response['error'] = f"Failed to download the URL. Error: {e}"
-    return final_response
-
-def get_filename_from_url(url):
-    # Use os.path.basename() to extract the file name from the URL
-    file_name = os.path.basename(url)
-    return file_name
-
-def get_text_response(user_message):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-def get_text_response_and_audio_response(user_message):
-    response = get_text_response(user_message) # Getting the reply from Open AI
-    audio_reply_for_question_response = get_audio_reply_for_question(response)
-    final_response = {
-        'output_file_path': '',
-        'message':''
-    }
-    audio_url = audio_reply_for_question_response['audio_url']
-    if audio_url:
-      output_file_path=get_filename_from_url(audio_url)
-      download_url_response = download_url(audio_url)
-      audio_content = download_url_response['content']
-      if audio_content:
-        with open(output_file_path, "wb") as audio_file:
-          audio_file.write(audio_content)
-          final_response['output_file_path'] = output_file_path
-      else:
-          final_response['message'] = download_url_response['error']
-    else:
-      final_response['message'] = audio_reply_for_question_response['message']
-    return final_response
-
-def chat_bot_response(message, history):
-    text_and_audio_response = get_text_response_and_audio_response(message)
-    output_file_path = text_and_audio_response['output_file_path']
-    if output_file_path:
-      return (text_and_audio_response['output_file_path'],)
-    else:
-      return text_and_audio_response['message']
-
-demo = gr.ChatInterface(chat_bot_response,examples=["How are you doing?","What are your interests?","Which places do you like to visit?"])
-
-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/bigjoker/stable-diffusion-webui/scripts/prompt_matrix.py b/spaces/bigjoker/stable-diffusion-webui/scripts/prompt_matrix.py
deleted file mode 100644
index 51c70998866d4b0853a46e4de73d86c3d9ec9b93..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/scripts/prompt_matrix.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import math
-from collections import namedtuple
-from copy import copy
-import random
-
-import modules.scripts as scripts
-import gradio as gr
-
-from modules import images
-from modules.processing import process_images, Processed
-from modules.shared import opts, cmd_opts, state
-import modules.sd_samplers
-
-
-def draw_xy_grid(xs, ys, x_label, y_label, cell):
-    res = []
-
-    ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
-    hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
-
-    first_processed = None
-
-    state.job_count = len(xs) * len(ys)
-
-    for iy, y in enumerate(ys):
-        for ix, x in enumerate(xs):
-            state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
-
-            processed = cell(x, y)
-            if first_processed is None:
-                first_processed = processed
-
-            res.append(processed.images[0])
-
-    grid = images.image_grid(res, rows=len(ys))
-    grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
-
-    first_processed.images = [grid]
-
-    return first_processed
-
-
-class Script(scripts.Script):
-    def title(self):
-        return "Prompt matrix"
-
-    def ui(self, is_img2img):
-        gr.HTML('<br />')
-        with gr.Row():
-            with gr.Column():
-                put_at_start = gr.Checkbox(label='Put variable parts at start of prompt', value=False, elem_id=self.elem_id("put_at_start"))
-                different_seeds = gr.Checkbox(label='Use different seed for each picture', value=False, elem_id=self.elem_id("different_seeds"))
-            with gr.Column():
-                prompt_type = gr.Radio(["positive", "negative"], label="Select prompt", elem_id=self.elem_id("prompt_type"), value="positive")
-                variations_delimiter = gr.Radio(["comma", "space"], label="Select joining char", elem_id=self.elem_id("variations_delimiter"), value="comma")
-            with gr.Column():
-                margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
-
-        return [put_at_start, different_seeds, prompt_type, variations_delimiter, margin_size]
-
-    def run(self, p, put_at_start, different_seeds, prompt_type, variations_delimiter, margin_size):
-        modules.processing.fix_seed(p)
-        # Raise error if promp type is not positive or negative
-        if prompt_type not in ["positive", "negative"]:
-            raise ValueError(f"Unknown prompt type {prompt_type}")
-        # Raise error if variations delimiter is not comma or space
-        if variations_delimiter not in ["comma", "space"]:
-            raise ValueError(f"Unknown variations delimiter {variations_delimiter}")
-
-        prompt = p.prompt if prompt_type == "positive" else p.negative_prompt
-        original_prompt = prompt[0] if type(prompt) == list else prompt
-        positive_prompt = p.prompt[0] if type(p.prompt) == list else p.prompt
-
-        delimiter = ", " if variations_delimiter == "comma" else " "
-
-        all_prompts = []
-        prompt_matrix_parts = original_prompt.split("|")
-        combination_count = 2 ** (len(prompt_matrix_parts) - 1)
-        for combination_num in range(combination_count):
-            selected_prompts = [text.strip().strip(',') for n, text in enumerate(prompt_matrix_parts[1:]) if combination_num & (1 << n)]
-
-            if put_at_start:
-                selected_prompts = selected_prompts + [prompt_matrix_parts[0]]
-            else:
-                selected_prompts = [prompt_matrix_parts[0]] + selected_prompts
-
-            all_prompts.append(delimiter.join(selected_prompts))
-
-        p.n_iter = math.ceil(len(all_prompts) / p.batch_size)
-        p.do_not_save_grid = True
-
-        print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.")
-
-        if prompt_type == "positive":
-            p.prompt = all_prompts
-        else:
-            p.negative_prompt = all_prompts
-        p.seed = [p.seed + (i if different_seeds else 0) for i in range(len(all_prompts))]
-        p.prompt_for_display = positive_prompt
-        processed = process_images(p)
-
-        grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2)) 
-        grid = images.draw_prompt_matrix(grid, processed.images[0].width, processed.images[1].height, prompt_matrix_parts, margin_size)
-        processed.images.insert(0, grid)
-        processed.index_of_first_image = 1
-        processed.infotexts.insert(0, processed.infotexts[0])
-
-        if opts.grid_save:
-            images.save_image(processed.images[0], p.outpath_grids, "prompt_matrix", extension=opts.grid_format, prompt=original_prompt, seed=processed.seed, grid=True, p=p)
-
-        return processed
diff --git a/spaces/binxu/Ancient-Chinese-Add-Punctuation/README.md b/spaces/binxu/Ancient-Chinese-Add-Punctuation/README.md
deleted file mode 100644
index d908d8a675caa17d83739471bf4fb6e7c540e172..0000000000000000000000000000000000000000
--- a/spaces/binxu/Ancient-Chinese-Add-Punctuation/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Ancient Chinese Add Punctuation
-emoji: 🏢
-colorFrom: green
-colorTo: green
-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/bioriAsaeru/text-to-voice/?x????ong??v Vom??? ?k ?ny??ez.md b/spaces/bioriAsaeru/text-to-voice/?x????ong??v Vom??? ?k ?ny??ez.md
deleted file mode 100644
index e9a6cc7f2bcd97dca470166ae9c31abdc861072d..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/?x????ong??v Vom??? ?k ?ny??ez.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/bioriAsaeru/text-to-voice/Automata Theory by Aadesh Kumar Pandey A Comprehensive Introduction to Formal Languages and Computation.md b/spaces/bioriAsaeru/text-to-voice/Automata Theory by Aadesh Kumar Pandey A Comprehensive Introduction to Formal Languages and Computation.md
deleted file mode 100644
index 41540f5611d7cc19b1e8e885e94c26360f22184c..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Automata Theory by Aadesh Kumar Pandey A Comprehensive Introduction to Formal Languages and Computation.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-
-<p>Automata theory is the study of abstract machines and automata, as well as the computational problems that can be solved using them. It is a theory in theoretical computer science. The word <i>automata</i> (the plural of <i>automaton</i>) come from theGreek word αὐτόματα, which means "self-making".</p>
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-<p>Many other branches of science also involve unbelievable levelsof complexity, impossibly large degrees of variation, and apparently randomprocesses, so it makes sense that automata theory can contribute to a betterscientific understanding of these areas as well. The modern-day pioneer ofcellular automata applications is StephenWolfram, who argues that the entire universe might eventually be describable as a machinewith finite sets of states and rules and a single initial condition. He relatesautomata theory to a wide variety of scientific pursuits, including:</p>
-<p>Automata theory is the basis for the theory of <strong>formal languages</strong>. <b>Automata theory</b> is the study of abstract machines and <b>automata</b>, as well as the computational problems that can be solved using them.</p> aaccfb2cb3<br />
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\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Capture the Action with the N-Strike Elite Nerf Cam ECS-12 Blaster.md b/spaces/bioriAsaeru/text-to-voice/Capture the Action with the N-Strike Elite Nerf Cam ECS-12 Blaster.md
deleted file mode 100644
index 12191a463660fbc6809d9e686a2c081ead268650..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Capture the Action with the N-Strike Elite Nerf Cam ECS-12 Blaster.md	
+++ /dev/null
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diff --git a/spaces/bioriAsaeru/text-to-voice/HD Online Player (world4free 4u 300MB MOVIES) PORTABLE.md b/spaces/bioriAsaeru/text-to-voice/HD Online Player (world4free 4u 300MB MOVIES) PORTABLE.md
deleted file mode 100644
index 6afa059601255f06e0fa68eb5c245e991ed57f82..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/HD Online Player (world4free 4u 300MB MOVIES) PORTABLE.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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-
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-<br />
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diff --git a/spaces/birkancelik18/chatbot/app.py b/spaces/birkancelik18/chatbot/app.py
deleted file mode 100644
index f39c408b1b5b0a5eab4ca68c672aefc91ad07b28..0000000000000000000000000000000000000000
--- a/spaces/birkancelik18/chatbot/app.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-import openai
-import gradio
-import os
-from dotenv import load_dotenv
-
-# Load environment variables from the .env file
-#load_dotenv()
-openai.api_key = os.environ["OPENAI_API_KEY"]
-#openai.api_key = os.getenv("openai_api_key")
-#openai.organization = os.getenv("openai_organization_id")
-messages = [{"role": "system", "content": "You are an intelligent assistant from Iron Man - your name is J.A.R.V.İ.S-. "}]
-
-def CustomChatGPT(user_input):
-    messages.append({"role": "user", "content": "You are an intelligent assistant from Iron Man - your name is J.A.R.V.İ.S-. Act as you are the JARVIS and answer such " +  user_input})
-    response = openai.ChatCompletion.create(
-        model = "gpt-4",
-        messages = messages
-    )
-    ChatGPT_reply = response["choices"][0]["message"]["content"]
-    messages.append({"role": "assistant", "content":  ChatGPT_reply})
-    return ChatGPT_reply
-
-demo = gradio.Interface(fn=CustomChatGPT, inputs = "text", outputs = "text", title = "J.A.R.V.I.S.")
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/bradarrML/stablediffusion-infinity/PyPatchMatch/examples/cpp_example_run.sh b/spaces/bradarrML/stablediffusion-infinity/PyPatchMatch/examples/cpp_example_run.sh
deleted file mode 100644
index cfb62557fe4a5bb0ee1aa48d7aa8582c23f4e6fc..0000000000000000000000000000000000000000
--- a/spaces/bradarrML/stablediffusion-infinity/PyPatchMatch/examples/cpp_example_run.sh
+++ /dev/null
@@ -1,18 +0,0 @@
-#! /bin/bash
-#
-# cpp_example_run.sh
-# Copyright (C) 2020 Jiayuan Mao <maojiayuan@gmail.com>
-#
-# Distributed under terms of the MIT license.
-#
-
-set -x
-
-CFLAGS="-std=c++14 -O2 $(pkg-config --cflags opencv)"
-LDFLAGS="$(pkg-config --libs opencv)"
-g++ $CFLAGS cpp_example.cpp -I../csrc/ -L../ -lpatchmatch $LDFLAGS -o cpp_example.exe
-
-export DYLD_LIBRARY_PATH=../:$DYLD_LIBRARY_PATH  # For macOS
-export LD_LIBRARY_PATH=../:$LD_LIBRARY_PATH  # For Linux
-time ./cpp_example.exe
-
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/docs/notes/contributing.md b/spaces/brjathu/HMR2.0/vendor/detectron2/docs/notes/contributing.md
deleted file mode 100644
index 95181235eaff1cb5cbb2dc554e8d4991b603d0e5..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/docs/notes/contributing.md
+++ /dev/null
@@ -1 +0,0 @@
-../../.github/CONTRIBUTING.md
\ No newline at end of file
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointRend/point_rend/color_augmentation.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointRend/point_rend/color_augmentation.py
deleted file mode 100644
index cdcb051623d20e3bfad5167715e8082974d51ec2..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointRend/point_rend/color_augmentation.py
+++ /dev/null
@@ -1,98 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-import random
-import cv2
-from fvcore.transforms.transform import Transform
-
-
-class ColorAugSSDTransform(Transform):
-    """
-    A color related data augmentation used in Single Shot Multibox Detector (SSD).
-
-    Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy,
-       Scott Reed, Cheng-Yang Fu, Alexander C. Berg.
-       SSD: Single Shot MultiBox Detector. ECCV 2016.
-
-    Implementation based on:
-
-     https://github.com/weiliu89/caffe/blob
-       /4817bf8b4200b35ada8ed0dc378dceaf38c539e4
-       /src/caffe/util/im_transforms.cpp
-
-     https://github.com/chainer/chainercv/blob
-       /7159616642e0be7c5b3ef380b848e16b7e99355b/chainercv
-       /links/model/ssd/transforms.py
-    """
-
-    def __init__(
-        self,
-        img_format,
-        brightness_delta=32,
-        contrast_low=0.5,
-        contrast_high=1.5,
-        saturation_low=0.5,
-        saturation_high=1.5,
-        hue_delta=18,
-    ):
-        super().__init__()
-        assert img_format in ["BGR", "RGB"]
-        self.is_rgb = img_format == "RGB"
-        del img_format
-        self._set_attributes(locals())
-
-    def apply_coords(self, coords):
-        return coords
-
-    def apply_segmentation(self, segmentation):
-        return segmentation
-
-    def apply_image(self, img, interp=None):
-        if self.is_rgb:
-            img = img[:, :, [2, 1, 0]]
-        img = self.brightness(img)
-        if random.randrange(2):
-            img = self.contrast(img)
-            img = self.saturation(img)
-            img = self.hue(img)
-        else:
-            img = self.saturation(img)
-            img = self.hue(img)
-            img = self.contrast(img)
-        if self.is_rgb:
-            img = img[:, :, [2, 1, 0]]
-        return img
-
-    def convert(self, img, alpha=1, beta=0):
-        img = img.astype(np.float32) * alpha + beta
-        img = np.clip(img, 0, 255)
-        return img.astype(np.uint8)
-
-    def brightness(self, img):
-        if random.randrange(2):
-            return self.convert(
-                img, beta=random.uniform(-self.brightness_delta, self.brightness_delta)
-            )
-        return img
-
-    def contrast(self, img):
-        if random.randrange(2):
-            return self.convert(img, alpha=random.uniform(self.contrast_low, self.contrast_high))
-        return img
-
-    def saturation(self, img):
-        if random.randrange(2):
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
-            img[:, :, 1] = self.convert(
-                img[:, :, 1], alpha=random.uniform(self.saturation_low, self.saturation_high)
-            )
-            return cv2.cvtColor(img, cv2.COLOR_HSV2BGR)
-        return img
-
-    def hue(self, img):
-        if random.randrange(2):
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
-            img[:, :, 0] = (
-                img[:, :, 0].astype(int) + random.randint(-self.hue_delta, self.hue_delta)
-            ) % 180
-            return cv2.cvtColor(img, cv2.COLOR_HSV2BGR)
-        return img
diff --git a/spaces/caffeinum/VToonify/vtoonify/model/raft/train_mixed.sh b/spaces/caffeinum/VToonify/vtoonify/model/raft/train_mixed.sh
deleted file mode 100644
index d9b979f143902a17a0ba7b0a8f960598b7096e0b..0000000000000000000000000000000000000000
--- a/spaces/caffeinum/VToonify/vtoonify/model/raft/train_mixed.sh
+++ /dev/null
@@ -1,6 +0,0 @@
-#!/bin/bash
-mkdir -p checkpoints
-python -u train.py --name raft-chairs --stage chairs --validation chairs --gpus 0 --num_steps 120000 --batch_size 8 --lr 0.00025 --image_size 368 496 --wdecay 0.0001 --mixed_precision 
-python -u train.py --name raft-things --stage things --validation sintel --restore_ckpt checkpoints/raft-chairs.pth --gpus 0 --num_steps 120000 --batch_size 5 --lr 0.0001 --image_size 400 720 --wdecay 0.0001 --mixed_precision
-python -u train.py --name raft-sintel --stage sintel --validation sintel --restore_ckpt checkpoints/raft-things.pth --gpus 0 --num_steps 120000 --batch_size 5 --lr 0.0001 --image_size 368 768 --wdecay 0.00001 --gamma=0.85 --mixed_precision
-python -u train.py --name raft-kitti  --stage kitti --validation kitti --restore_ckpt checkpoints/raft-sintel.pth --gpus 0 --num_steps 50000 --batch_size 5 --lr 0.0001 --image_size 288 960 --wdecay 0.00001 --gamma=0.85 --mixed_precision
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/MViTv2/configs/cascade_mask_rcnn_mvitv2_b_3x.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/MViTv2/configs/cascade_mask_rcnn_mvitv2_b_3x.py
deleted file mode 100644
index 61366bf11477136e8950b81dd24a1a7af9b37f8b..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/MViTv2/configs/cascade_mask_rcnn_mvitv2_b_3x.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from .cascade_mask_rcnn_mvitv2_t_3x import model, dataloader, optimizer, lr_multiplier, train
-
-
-model.backbone.bottom_up.depth = 24
-model.backbone.bottom_up.last_block_indexes = (1, 4, 20, 23)
-model.backbone.bottom_up.drop_path_rate = 0.4
-
-train.init_checkpoint = "detectron2://ImageNetPretrained/mvitv2/MViTv2_B_in1k.pyth"
diff --git a/spaces/carolineec/informativedrawings/README.md b/spaces/carolineec/informativedrawings/README.md
deleted file mode 100644
index 2667d2c3d8bfedae5180a181fe843a7a4d52ac80..0000000000000000000000000000000000000000
--- a/spaces/carolineec/informativedrawings/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Informativedrawings
-emoji: 📉
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/aiohttp/web_ws.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/aiohttp/web_ws.py
deleted file mode 100644
index 0d32a218b52b87ec04f36a6f95bfb303984b2e43..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/aiohttp/web_ws.py
+++ /dev/null
@@ -1,487 +0,0 @@
-import asyncio
-import base64
-import binascii
-import hashlib
-import json
-from typing import Any, Iterable, Optional, Tuple, cast
-
-import async_timeout
-import attr
-from multidict import CIMultiDict
-
-from . import hdrs
-from .abc import AbstractStreamWriter
-from .helpers import call_later, set_result
-from .http import (
-    WS_CLOSED_MESSAGE,
-    WS_CLOSING_MESSAGE,
-    WS_KEY,
-    WebSocketError,
-    WebSocketReader,
-    WebSocketWriter,
-    WSCloseCode,
-    WSMessage,
-    WSMsgType as WSMsgType,
-    ws_ext_gen,
-    ws_ext_parse,
-)
-from .log import ws_logger
-from .streams import EofStream, FlowControlDataQueue
-from .typedefs import Final, JSONDecoder, JSONEncoder
-from .web_exceptions import HTTPBadRequest, HTTPException
-from .web_request import BaseRequest
-from .web_response import StreamResponse
-
-__all__ = (
-    "WebSocketResponse",
-    "WebSocketReady",
-    "WSMsgType",
-)
-
-THRESHOLD_CONNLOST_ACCESS: Final[int] = 5
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class WebSocketReady:
-    ok: bool
-    protocol: Optional[str]
-
-    def __bool__(self) -> bool:
-        return self.ok
-
-
-class WebSocketResponse(StreamResponse):
-
-    _length_check = False
-
-    def __init__(
-        self,
-        *,
-        timeout: float = 10.0,
-        receive_timeout: Optional[float] = None,
-        autoclose: bool = True,
-        autoping: bool = True,
-        heartbeat: Optional[float] = None,
-        protocols: Iterable[str] = (),
-        compress: bool = True,
-        max_msg_size: int = 4 * 1024 * 1024,
-    ) -> None:
-        super().__init__(status=101)
-        self._protocols = protocols
-        self._ws_protocol: Optional[str] = None
-        self._writer: Optional[WebSocketWriter] = None
-        self._reader: Optional[FlowControlDataQueue[WSMessage]] = None
-        self._closed = False
-        self._closing = False
-        self._conn_lost = 0
-        self._close_code: Optional[int] = None
-        self._loop: Optional[asyncio.AbstractEventLoop] = None
-        self._waiting: Optional[asyncio.Future[bool]] = None
-        self._exception: Optional[BaseException] = None
-        self._timeout = timeout
-        self._receive_timeout = receive_timeout
-        self._autoclose = autoclose
-        self._autoping = autoping
-        self._heartbeat = heartbeat
-        self._heartbeat_cb: Optional[asyncio.TimerHandle] = None
-        if heartbeat is not None:
-            self._pong_heartbeat = heartbeat / 2.0
-        self._pong_response_cb: Optional[asyncio.TimerHandle] = None
-        self._compress = compress
-        self._max_msg_size = max_msg_size
-
-    def _cancel_heartbeat(self) -> None:
-        if self._pong_response_cb is not None:
-            self._pong_response_cb.cancel()
-            self._pong_response_cb = None
-
-        if self._heartbeat_cb is not None:
-            self._heartbeat_cb.cancel()
-            self._heartbeat_cb = None
-
-    def _reset_heartbeat(self) -> None:
-        self._cancel_heartbeat()
-
-        if self._heartbeat is not None:
-            assert self._loop is not None
-            self._heartbeat_cb = call_later(
-                self._send_heartbeat, self._heartbeat, self._loop
-            )
-
-    def _send_heartbeat(self) -> None:
-        if self._heartbeat is not None and not self._closed:
-            assert self._loop is not None
-            # fire-and-forget a task is not perfect but maybe ok for
-            # sending ping. Otherwise we need a long-living heartbeat
-            # task in the class.
-            self._loop.create_task(self._writer.ping())  # type: ignore[union-attr]
-
-            if self._pong_response_cb is not None:
-                self._pong_response_cb.cancel()
-            self._pong_response_cb = call_later(
-                self._pong_not_received, self._pong_heartbeat, self._loop
-            )
-
-    def _pong_not_received(self) -> None:
-        if self._req is not None and self._req.transport is not None:
-            self._closed = True
-            self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-            self._exception = asyncio.TimeoutError()
-            self._req.transport.close()
-
-    async def prepare(self, request: BaseRequest) -> AbstractStreamWriter:
-        # make pre-check to don't hide it by do_handshake() exceptions
-        if self._payload_writer is not None:
-            return self._payload_writer
-
-        protocol, writer = self._pre_start(request)
-        payload_writer = await super().prepare(request)
-        assert payload_writer is not None
-        self._post_start(request, protocol, writer)
-        await payload_writer.drain()
-        return payload_writer
-
-    def _handshake(
-        self, request: BaseRequest
-    ) -> Tuple["CIMultiDict[str]", str, bool, bool]:
-        headers = request.headers
-        if "websocket" != headers.get(hdrs.UPGRADE, "").lower().strip():
-            raise HTTPBadRequest(
-                text=(
-                    "No WebSocket UPGRADE hdr: {}\n Can "
-                    '"Upgrade" only to "WebSocket".'
-                ).format(headers.get(hdrs.UPGRADE))
-            )
-
-        if "upgrade" not in headers.get(hdrs.CONNECTION, "").lower():
-            raise HTTPBadRequest(
-                text="No CONNECTION upgrade hdr: {}".format(
-                    headers.get(hdrs.CONNECTION)
-                )
-            )
-
-        # find common sub-protocol between client and server
-        protocol = None
-        if hdrs.SEC_WEBSOCKET_PROTOCOL in headers:
-            req_protocols = [
-                str(proto.strip())
-                for proto in headers[hdrs.SEC_WEBSOCKET_PROTOCOL].split(",")
-            ]
-
-            for proto in req_protocols:
-                if proto in self._protocols:
-                    protocol = proto
-                    break
-            else:
-                # No overlap found: Return no protocol as per spec
-                ws_logger.warning(
-                    "Client protocols %r don’t overlap server-known ones %r",
-                    req_protocols,
-                    self._protocols,
-                )
-
-        # check supported version
-        version = headers.get(hdrs.SEC_WEBSOCKET_VERSION, "")
-        if version not in ("13", "8", "7"):
-            raise HTTPBadRequest(text=f"Unsupported version: {version}")
-
-        # check client handshake for validity
-        key = headers.get(hdrs.SEC_WEBSOCKET_KEY)
-        try:
-            if not key or len(base64.b64decode(key)) != 16:
-                raise HTTPBadRequest(text=f"Handshake error: {key!r}")
-        except binascii.Error:
-            raise HTTPBadRequest(text=f"Handshake error: {key!r}") from None
-
-        accept_val = base64.b64encode(
-            hashlib.sha1(key.encode() + WS_KEY).digest()
-        ).decode()
-        response_headers = CIMultiDict(
-            {
-                hdrs.UPGRADE: "websocket",
-                hdrs.CONNECTION: "upgrade",
-                hdrs.SEC_WEBSOCKET_ACCEPT: accept_val,
-            }
-        )
-
-        notakeover = False
-        compress = 0
-        if self._compress:
-            extensions = headers.get(hdrs.SEC_WEBSOCKET_EXTENSIONS)
-            # Server side always get return with no exception.
-            # If something happened, just drop compress extension
-            compress, notakeover = ws_ext_parse(extensions, isserver=True)
-            if compress:
-                enabledext = ws_ext_gen(
-                    compress=compress, isserver=True, server_notakeover=notakeover
-                )
-                response_headers[hdrs.SEC_WEBSOCKET_EXTENSIONS] = enabledext
-
-        if protocol:
-            response_headers[hdrs.SEC_WEBSOCKET_PROTOCOL] = protocol
-        return (
-            response_headers,
-            protocol,
-            compress,
-            notakeover,
-        )  # type: ignore[return-value]
-
-    def _pre_start(self, request: BaseRequest) -> Tuple[str, WebSocketWriter]:
-        self._loop = request._loop
-
-        headers, protocol, compress, notakeover = self._handshake(request)
-
-        self.set_status(101)
-        self.headers.update(headers)
-        self.force_close()
-        self._compress = compress
-        transport = request._protocol.transport
-        assert transport is not None
-        writer = WebSocketWriter(
-            request._protocol, transport, compress=compress, notakeover=notakeover
-        )
-
-        return protocol, writer
-
-    def _post_start(
-        self, request: BaseRequest, protocol: str, writer: WebSocketWriter
-    ) -> None:
-        self._ws_protocol = protocol
-        self._writer = writer
-
-        self._reset_heartbeat()
-
-        loop = self._loop
-        assert loop is not None
-        self._reader = FlowControlDataQueue(request._protocol, 2**16, loop=loop)
-        request.protocol.set_parser(
-            WebSocketReader(self._reader, self._max_msg_size, compress=self._compress)
-        )
-        # disable HTTP keepalive for WebSocket
-        request.protocol.keep_alive(False)
-
-    def can_prepare(self, request: BaseRequest) -> WebSocketReady:
-        if self._writer is not None:
-            raise RuntimeError("Already started")
-        try:
-            _, protocol, _, _ = self._handshake(request)
-        except HTTPException:
-            return WebSocketReady(False, None)
-        else:
-            return WebSocketReady(True, protocol)
-
-    @property
-    def closed(self) -> bool:
-        return self._closed
-
-    @property
-    def close_code(self) -> Optional[int]:
-        return self._close_code
-
-    @property
-    def ws_protocol(self) -> Optional[str]:
-        return self._ws_protocol
-
-    @property
-    def compress(self) -> bool:
-        return self._compress
-
-    def exception(self) -> Optional[BaseException]:
-        return self._exception
-
-    async def ping(self, message: bytes = b"") -> None:
-        if self._writer is None:
-            raise RuntimeError("Call .prepare() first")
-        await self._writer.ping(message)
-
-    async def pong(self, message: bytes = b"") -> None:
-        # unsolicited pong
-        if self._writer is None:
-            raise RuntimeError("Call .prepare() first")
-        await self._writer.pong(message)
-
-    async def send_str(self, data: str, compress: Optional[bool] = None) -> None:
-        if self._writer is None:
-            raise RuntimeError("Call .prepare() first")
-        if not isinstance(data, str):
-            raise TypeError("data argument must be str (%r)" % type(data))
-        await self._writer.send(data, binary=False, compress=compress)
-
-    async def send_bytes(self, data: bytes, compress: Optional[bool] = None) -> None:
-        if self._writer is None:
-            raise RuntimeError("Call .prepare() first")
-        if not isinstance(data, (bytes, bytearray, memoryview)):
-            raise TypeError("data argument must be byte-ish (%r)" % type(data))
-        await self._writer.send(data, binary=True, compress=compress)
-
-    async def send_json(
-        self,
-        data: Any,
-        compress: Optional[bool] = None,
-        *,
-        dumps: JSONEncoder = json.dumps,
-    ) -> None:
-        await self.send_str(dumps(data), compress=compress)
-
-    async def write_eof(self) -> None:  # type: ignore[override]
-        if self._eof_sent:
-            return
-        if self._payload_writer is None:
-            raise RuntimeError("Response has not been started")
-
-        await self.close()
-        self._eof_sent = True
-
-    async def close(self, *, code: int = WSCloseCode.OK, message: bytes = b"") -> bool:
-        if self._writer is None:
-            raise RuntimeError("Call .prepare() first")
-
-        self._cancel_heartbeat()
-        reader = self._reader
-        assert reader is not None
-
-        # we need to break `receive()` cycle first,
-        # `close()` may be called from different task
-        if self._waiting is not None and not self._closed:
-            reader.feed_data(WS_CLOSING_MESSAGE, 0)
-            await self._waiting
-
-        if not self._closed:
-            self._closed = True
-            try:
-                await self._writer.close(code, message)
-                writer = self._payload_writer
-                assert writer is not None
-                await writer.drain()
-            except (asyncio.CancelledError, asyncio.TimeoutError):
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                raise
-            except Exception as exc:
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                self._exception = exc
-                return True
-
-            if self._closing:
-                return True
-
-            reader = self._reader
-            assert reader is not None
-            try:
-                async with async_timeout.timeout(self._timeout):
-                    msg = await reader.read()
-            except asyncio.CancelledError:
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                raise
-            except Exception as exc:
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                self._exception = exc
-                return True
-
-            if msg.type == WSMsgType.CLOSE:
-                self._close_code = msg.data
-                return True
-
-            self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-            self._exception = asyncio.TimeoutError()
-            return True
-        else:
-            return False
-
-    async def receive(self, timeout: Optional[float] = None) -> WSMessage:
-        if self._reader is None:
-            raise RuntimeError("Call .prepare() first")
-
-        loop = self._loop
-        assert loop is not None
-        while True:
-            if self._waiting is not None:
-                raise RuntimeError("Concurrent call to receive() is not allowed")
-
-            if self._closed:
-                self._conn_lost += 1
-                if self._conn_lost >= THRESHOLD_CONNLOST_ACCESS:
-                    raise RuntimeError("WebSocket connection is closed.")
-                return WS_CLOSED_MESSAGE
-            elif self._closing:
-                return WS_CLOSING_MESSAGE
-
-            try:
-                self._waiting = loop.create_future()
-                try:
-                    async with async_timeout.timeout(timeout or self._receive_timeout):
-                        msg = await self._reader.read()
-                    self._reset_heartbeat()
-                finally:
-                    waiter = self._waiting
-                    set_result(waiter, True)
-                    self._waiting = None
-            except (asyncio.CancelledError, asyncio.TimeoutError):
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                raise
-            except EofStream:
-                self._close_code = WSCloseCode.OK
-                await self.close()
-                return WSMessage(WSMsgType.CLOSED, None, None)
-            except WebSocketError as exc:
-                self._close_code = exc.code
-                await self.close(code=exc.code)
-                return WSMessage(WSMsgType.ERROR, exc, None)
-            except Exception as exc:
-                self._exception = exc
-                self._closing = True
-                self._close_code = WSCloseCode.ABNORMAL_CLOSURE
-                await self.close()
-                return WSMessage(WSMsgType.ERROR, exc, None)
-
-            if msg.type == WSMsgType.CLOSE:
-                self._closing = True
-                self._close_code = msg.data
-                if not self._closed and self._autoclose:
-                    await self.close()
-            elif msg.type == WSMsgType.CLOSING:
-                self._closing = True
-            elif msg.type == WSMsgType.PING and self._autoping:
-                await self.pong(msg.data)
-                continue
-            elif msg.type == WSMsgType.PONG and self._autoping:
-                continue
-
-            return msg
-
-    async def receive_str(self, *, timeout: Optional[float] = None) -> str:
-        msg = await self.receive(timeout)
-        if msg.type != WSMsgType.TEXT:
-            raise TypeError(
-                "Received message {}:{!r} is not WSMsgType.TEXT".format(
-                    msg.type, msg.data
-                )
-            )
-        return cast(str, msg.data)
-
-    async def receive_bytes(self, *, timeout: Optional[float] = None) -> bytes:
-        msg = await self.receive(timeout)
-        if msg.type != WSMsgType.BINARY:
-            raise TypeError(f"Received message {msg.type}:{msg.data!r} is not bytes")
-        return cast(bytes, msg.data)
-
-    async def receive_json(
-        self, *, loads: JSONDecoder = json.loads, timeout: Optional[float] = None
-    ) -> Any:
-        data = await self.receive_str(timeout=timeout)
-        return loads(data)
-
-    async def write(self, data: bytes) -> None:
-        raise RuntimeError("Cannot call .write() for websocket")
-
-    def __aiter__(self) -> "WebSocketResponse":
-        return self
-
-    async def __anext__(self) -> WSMessage:
-        msg = await self.receive()
-        if msg.type in (WSMsgType.CLOSE, WSMsgType.CLOSING, WSMsgType.CLOSED):
-            raise StopAsyncIteration
-        return msg
-
-    def _cancel(self, exc: BaseException) -> None:
-        if self._reader is not None:
-            self._reader.set_exception(exc)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/C_P_A_L_.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/C_P_A_L_.py
deleted file mode 100644
index 03eb851e8c02edc509e8f1f3681dca5b5b740145..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/tables/C_P_A_L_.py
+++ /dev/null
@@ -1,297 +0,0 @@
-# Copyright 2013 Google, Inc. All Rights Reserved.
-#
-# Google Author(s): Behdad Esfahbod
-
-from fontTools.misc.textTools import bytesjoin, safeEval
-from . import DefaultTable
-import array
-from collections import namedtuple
-import struct
-import sys
-
-
-class table_C_P_A_L_(DefaultTable.DefaultTable):
-
-    NO_NAME_ID = 0xFFFF
-    DEFAULT_PALETTE_TYPE = 0
-
-    def __init__(self, tag=None):
-        DefaultTable.DefaultTable.__init__(self, tag)
-        self.palettes = []
-        self.paletteTypes = []
-        self.paletteLabels = []
-        self.paletteEntryLabels = []
-
-    def decompile(self, data, ttFont):
-        (
-            self.version,
-            self.numPaletteEntries,
-            numPalettes,
-            numColorRecords,
-            goffsetFirstColorRecord,
-        ) = struct.unpack(">HHHHL", data[:12])
-        assert (
-            self.version <= 1
-        ), "Version of CPAL table is higher than I know how to handle"
-        self.palettes = []
-        pos = 12
-        for i in range(numPalettes):
-            startIndex = struct.unpack(">H", data[pos : pos + 2])[0]
-            assert startIndex + self.numPaletteEntries <= numColorRecords
-            pos += 2
-            palette = []
-            ppos = goffsetFirstColorRecord + startIndex * 4
-            for j in range(self.numPaletteEntries):
-                palette.append(Color(*struct.unpack(">BBBB", data[ppos : ppos + 4])))
-                ppos += 4
-            self.palettes.append(palette)
-        if self.version == 0:
-            offsetToPaletteTypeArray = 0
-            offsetToPaletteLabelArray = 0
-            offsetToPaletteEntryLabelArray = 0
-        else:
-            pos = 12 + numPalettes * 2
-            (
-                offsetToPaletteTypeArray,
-                offsetToPaletteLabelArray,
-                offsetToPaletteEntryLabelArray,
-            ) = struct.unpack(">LLL", data[pos : pos + 12])
-        self.paletteTypes = self._decompileUInt32Array(
-            data,
-            offsetToPaletteTypeArray,
-            numPalettes,
-            default=self.DEFAULT_PALETTE_TYPE,
-        )
-        self.paletteLabels = self._decompileUInt16Array(
-            data, offsetToPaletteLabelArray, numPalettes, default=self.NO_NAME_ID
-        )
-        self.paletteEntryLabels = self._decompileUInt16Array(
-            data,
-            offsetToPaletteEntryLabelArray,
-            self.numPaletteEntries,
-            default=self.NO_NAME_ID,
-        )
-
-    def _decompileUInt16Array(self, data, offset, numElements, default=0):
-        if offset == 0:
-            return [default] * numElements
-        result = array.array("H", data[offset : offset + 2 * numElements])
-        if sys.byteorder != "big":
-            result.byteswap()
-        assert len(result) == numElements, result
-        return result.tolist()
-
-    def _decompileUInt32Array(self, data, offset, numElements, default=0):
-        if offset == 0:
-            return [default] * numElements
-        result = array.array("I", data[offset : offset + 4 * numElements])
-        if sys.byteorder != "big":
-            result.byteswap()
-        assert len(result) == numElements, result
-        return result.tolist()
-
-    def compile(self, ttFont):
-        colorRecordIndices, colorRecords = self._compileColorRecords()
-        paletteTypes = self._compilePaletteTypes()
-        paletteLabels = self._compilePaletteLabels()
-        paletteEntryLabels = self._compilePaletteEntryLabels()
-        numColorRecords = len(colorRecords) // 4
-        offsetToFirstColorRecord = 12 + len(colorRecordIndices)
-        if self.version >= 1:
-            offsetToFirstColorRecord += 12
-        header = struct.pack(
-            ">HHHHL",
-            self.version,
-            self.numPaletteEntries,
-            len(self.palettes),
-            numColorRecords,
-            offsetToFirstColorRecord,
-        )
-        if self.version == 0:
-            dataList = [header, colorRecordIndices, colorRecords]
-        else:
-            pos = offsetToFirstColorRecord + len(colorRecords)
-            if len(paletteTypes) == 0:
-                offsetToPaletteTypeArray = 0
-            else:
-                offsetToPaletteTypeArray = pos
-                pos += len(paletteTypes)
-            if len(paletteLabels) == 0:
-                offsetToPaletteLabelArray = 0
-            else:
-                offsetToPaletteLabelArray = pos
-                pos += len(paletteLabels)
-            if len(paletteEntryLabels) == 0:
-                offsetToPaletteEntryLabelArray = 0
-            else:
-                offsetToPaletteEntryLabelArray = pos
-                pos += len(paletteLabels)
-            header1 = struct.pack(
-                ">LLL",
-                offsetToPaletteTypeArray,
-                offsetToPaletteLabelArray,
-                offsetToPaletteEntryLabelArray,
-            )
-            dataList = [
-                header,
-                colorRecordIndices,
-                header1,
-                colorRecords,
-                paletteTypes,
-                paletteLabels,
-                paletteEntryLabels,
-            ]
-        return bytesjoin(dataList)
-
-    def _compilePalette(self, palette):
-        assert len(palette) == self.numPaletteEntries
-        pack = lambda c: struct.pack(">BBBB", c.blue, c.green, c.red, c.alpha)
-        return bytesjoin([pack(color) for color in palette])
-
-    def _compileColorRecords(self):
-        colorRecords, colorRecordIndices, pool = [], [], {}
-        for palette in self.palettes:
-            packedPalette = self._compilePalette(palette)
-            if packedPalette in pool:
-                index = pool[packedPalette]
-            else:
-                index = len(colorRecords)
-                colorRecords.append(packedPalette)
-                pool[packedPalette] = index
-            colorRecordIndices.append(struct.pack(">H", index * self.numPaletteEntries))
-        return bytesjoin(colorRecordIndices), bytesjoin(colorRecords)
-
-    def _compilePaletteTypes(self):
-        if self.version == 0 or not any(self.paletteTypes):
-            return b""
-        assert len(self.paletteTypes) == len(self.palettes)
-        result = bytesjoin([struct.pack(">I", ptype) for ptype in self.paletteTypes])
-        assert len(result) == 4 * len(self.palettes)
-        return result
-
-    def _compilePaletteLabels(self):
-        if self.version == 0 or all(l == self.NO_NAME_ID for l in self.paletteLabels):
-            return b""
-        assert len(self.paletteLabels) == len(self.palettes)
-        result = bytesjoin([struct.pack(">H", label) for label in self.paletteLabels])
-        assert len(result) == 2 * len(self.palettes)
-        return result
-
-    def _compilePaletteEntryLabels(self):
-        if self.version == 0 or all(
-            l == self.NO_NAME_ID for l in self.paletteEntryLabels
-        ):
-            return b""
-        assert len(self.paletteEntryLabels) == self.numPaletteEntries
-        result = bytesjoin(
-            [struct.pack(">H", label) for label in self.paletteEntryLabels]
-        )
-        assert len(result) == 2 * self.numPaletteEntries
-        return result
-
-    def toXML(self, writer, ttFont):
-        numPalettes = len(self.palettes)
-        paletteLabels = {i: nameID for (i, nameID) in enumerate(self.paletteLabels)}
-        paletteTypes = {i: typ for (i, typ) in enumerate(self.paletteTypes)}
-        writer.simpletag("version", value=self.version)
-        writer.newline()
-        writer.simpletag("numPaletteEntries", value=self.numPaletteEntries)
-        writer.newline()
-        for index, palette in enumerate(self.palettes):
-            attrs = {"index": index}
-            paletteType = paletteTypes.get(index, self.DEFAULT_PALETTE_TYPE)
-            paletteLabel = paletteLabels.get(index, self.NO_NAME_ID)
-            if self.version > 0 and paletteLabel != self.NO_NAME_ID:
-                attrs["label"] = paletteLabel
-            if self.version > 0 and paletteType != self.DEFAULT_PALETTE_TYPE:
-                attrs["type"] = paletteType
-            writer.begintag("palette", **attrs)
-            writer.newline()
-            if (
-                self.version > 0
-                and paletteLabel != self.NO_NAME_ID
-                and ttFont
-                and "name" in ttFont
-            ):
-                name = ttFont["name"].getDebugName(paletteLabel)
-                if name is not None:
-                    writer.comment(name)
-                    writer.newline()
-            assert len(palette) == self.numPaletteEntries
-            for cindex, color in enumerate(palette):
-                color.toXML(writer, ttFont, cindex)
-            writer.endtag("palette")
-            writer.newline()
-        if self.version > 0 and not all(
-            l == self.NO_NAME_ID for l in self.paletteEntryLabels
-        ):
-            writer.begintag("paletteEntryLabels")
-            writer.newline()
-            for index, label in enumerate(self.paletteEntryLabels):
-                if label != self.NO_NAME_ID:
-                    writer.simpletag("label", index=index, value=label)
-                    if self.version > 0 and label and ttFont and "name" in ttFont:
-                        name = ttFont["name"].getDebugName(label)
-                        if name is not None:
-                            writer.comment(name)
-                    writer.newline()
-            writer.endtag("paletteEntryLabels")
-            writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if name == "palette":
-            self.paletteLabels.append(int(attrs.get("label", self.NO_NAME_ID)))
-            self.paletteTypes.append(int(attrs.get("type", self.DEFAULT_PALETTE_TYPE)))
-            palette = []
-            for element in content:
-                if isinstance(element, str):
-                    continue
-                attrs = element[1]
-                color = Color.fromHex(attrs["value"])
-                palette.append(color)
-            self.palettes.append(palette)
-        elif name == "paletteEntryLabels":
-            colorLabels = {}
-            for element in content:
-                if isinstance(element, str):
-                    continue
-                elementName, elementAttr, _ = element
-                if elementName == "label":
-                    labelIndex = safeEval(elementAttr["index"])
-                    nameID = safeEval(elementAttr["value"])
-                    colorLabels[labelIndex] = nameID
-            self.paletteEntryLabels = [
-                colorLabels.get(i, self.NO_NAME_ID)
-                for i in range(self.numPaletteEntries)
-            ]
-        elif "value" in attrs:
-            value = safeEval(attrs["value"])
-            setattr(self, name, value)
-            if name == "numPaletteEntries":
-                self.paletteEntryLabels = [self.NO_NAME_ID] * self.numPaletteEntries
-
-
-class Color(namedtuple("Color", "blue green red alpha")):
-    def hex(self):
-        return "#%02X%02X%02X%02X" % (self.red, self.green, self.blue, self.alpha)
-
-    def __repr__(self):
-        return self.hex()
-
-    def toXML(self, writer, ttFont, index=None):
-        writer.simpletag("color", value=self.hex(), index=index)
-        writer.newline()
-
-    @classmethod
-    def fromHex(cls, value):
-        if value[0] == "#":
-            value = value[1:]
-        red = int(value[0:2], 16)
-        green = int(value[2:4], 16)
-        blue = int(value[4:6], 16)
-        alpha = int(value[6:8], 16) if len(value) >= 8 else 0xFF
-        return cls(red=red, green=green, blue=blue, alpha=alpha)
-
-    @classmethod
-    def fromRGBA(cls, red, green, blue, alpha):
-        return cls(red=red, green=green, blue=blue, alpha=alpha)
diff --git a/spaces/cihyFjudo/fairness-paper-search/Luv Shv Pyar Vyar Full Movie Watch Online Free Hd - How Hijacker Bijgij Requ Changed Everything.md b/spaces/cihyFjudo/fairness-paper-search/Luv Shv Pyar Vyar Full Movie Watch Online Free Hd - How Hijacker Bijgij Requ Changed Everything.md
deleted file mode 100644
index e7f4ba4edfb3927dcf66a20b1b7f6a3449467057..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Luv Shv Pyar Vyar Full Movie Watch Online Free Hd - How Hijacker Bijgij Requ Changed Everything.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/cihyFjudo/fairness-paper-search/R... Rajkumar in hindi torrent download How to get the best quality of the hilarious Bollywood hit.md b/spaces/cihyFjudo/fairness-paper-search/R... Rajkumar in hindi torrent download How to get the best quality of the hilarious Bollywood hit.md
deleted file mode 100644
index 9329b1368d10528f9cb94252da3d7abcc60eadcd..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/R... Rajkumar in hindi torrent download How to get the best quality of the hilarious Bollywood hit.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>R... Rajkumar in hindi download torrent</h2><br /><p><b><b>DOWNLOAD</b> &rarr;&rarr;&rarr; <a href="https://tinurli.com/2uwkE8">https://tinurli.com/2uwkE8</a></b></p><br /><br />
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Skype Descargar Gratis Para Mac Os X 106 8 Videollamadas y Mensajera Gratuitas.md b/spaces/cihyFjudo/fairness-paper-search/Skype Descargar Gratis Para Mac Os X 106 8 Videollamadas y Mensajera Gratuitas.md
deleted file mode 100644
index 8399a4846b21d6dc664c6c406160b93b04aa218f..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Skype Descargar Gratis Para Mac Os X 106 8 Videollamadas y Mensajera Gratuitas.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/The Secrets of 13B 3 Full Hd 1080p Movie Behind the Scenes of the Scariest Film Ever.md b/spaces/cihyFjudo/fairness-paper-search/The Secrets of 13B 3 Full Hd 1080p Movie Behind the Scenes of the Scariest Film Ever.md
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+++ /dev/null
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Toilet Ek Prem Katha Mp4 Movies.md b/spaces/cihyFjudo/fairness-paper-search/Toilet Ek Prem Katha Mp4 Movies.md
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diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/expr/funcs.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/expr/funcs.py
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-from .core import FunctionExpression
-
-
-FUNCTION_LISTING = {
-    "isArray": r"Returns true if _value_ is an array, false otherwise.",
-    "isBoolean": r"Returns true if _value_ is a boolean (`true` or `false`), false otherwise.",
-    "isDate": r"Returns true if _value_ is a Date object, false otherwise. This method will return false for timestamp numbers or date-formatted strings; it recognizes Date objects only.",
-    "isDefined": r"Returns true if _value_ is a defined value, false if _value_ equals `undefined`. This method will return true for `null` and `NaN` values.",
-    "isNumber": r"Returns true if _value_ is a number, false otherwise. `NaN` and `Infinity` are considered numbers.",
-    "isObject": r"Returns true if _value_ is an object (including arrays and Dates), false otherwise.",
-    "isRegExp": r"Returns true if _value_ is a RegExp (regular expression) object, false otherwise.",
-    "isString": r"Returns true if _value_ is a string, false otherwise.",
-    "isValid": r"Returns true if _value_ is not `null`, `undefined`, or `NaN`, false otherwise.",
-    "toBoolean": r"Coerces the input _value_ to a string. Null values and empty strings are mapped to `null`.",
-    "toDate": r"Coerces the input _value_ to a Date instance. Null values and empty strings are mapped to `null`. If an optional _parser_ function is provided, it is used to perform date parsing, otherwise `Date.parse` is used. Be aware that `Date.parse` has different implementations across browsers!",
-    "toNumber": r"Coerces the input _value_ to a number. Null values and empty strings are mapped to `null`.",
-    "toString": r"Coerces the input _value_ to a string. Null values and empty strings are mapped to `null`.",
-    "if": r"If _test_ is truthy, returns _thenValue_. Otherwise, returns _elseValue_. The _if_ function is equivalent to the ternary operator `a ? b : c`.",
-    "isNaN": r"Returns true if _value_ is not a number. Same as JavaScript's `isNaN`.",
-    "isFinite": r"Returns true if _value_ is a finite number. Same as JavaScript's `isFinite`.",
-    "abs": r"Returns the absolute value of _value_. Same as JavaScript's `Math.abs`.",
-    "acos": r"Trigonometric arccosine. Same as JavaScript's `Math.acos`.",
-    "asin": r"Trigonometric arcsine. Same as JavaScript's `Math.asin`.",
-    "atan": r"Trigonometric arctangent. Same as JavaScript's `Math.atan`.",
-    "atan2": r"Returns the arctangent of _dy / dx_. Same as JavaScript's `Math.atan2`.",
-    "ceil": r"Rounds _value_ to the nearest integer of equal or greater value. Same as JavaScript's `Math.ceil`.",
-    "clamp": r"Restricts _value_ to be between the specified _min_ and _max_.",
-    "cos": r"Trigonometric cosine. Same as JavaScript's `Math.cos`.",
-    "exp": r"Returns the value of _e_ raised to the provided _exponent_. Same as JavaScript's `Math.exp`.",
-    "floor": r"Rounds _value_ to the nearest integer of equal or lower value. Same as JavaScript's `Math.floor`.",
-    "hypot": r"Returns the square root of the sum of squares of its arguments. Same as JavaScript's `Math.hypot`.",
-    "log": r"Returns the natural logarithm of _value_. Same as JavaScript's `Math.log`.",
-    "max": r"Returns the maximum argument value. Same as JavaScript's `Math.max`.",
-    "min": r"Returns the minimum argument value. Same as JavaScript's `Math.min`.",
-    "pow": r"Returns _value_ raised to the given _exponent_. Same as JavaScript's `Math.pow`.",
-    "random": r"Returns a pseudo-random number in the range [0,1). Same as JavaScript's `Math.random`.",
-    "round": r"Rounds _value_ to the nearest integer. Same as JavaScript's `Math.round`.",
-    "sin": r"Trigonometric sine. Same as JavaScript's `Math.sin`.",
-    "sqrt": r"Square root function. Same as JavaScript's `Math.sqrt`.",
-    "tan": r"Trigonometric tangent. Same as JavaScript's `Math.tan`.",
-    "sampleNormal": r"Returns a sample from a univariate [normal (Gaussian) probability distribution](https://en.wikipedia.org/wiki/Normal_distribution) with specified _mean_ and standard deviation _stdev_. If unspecified, the mean defaults to `0` and the standard deviation defaults to `1`.",
-    "cumulativeNormal": r"Returns the value of the [cumulative distribution function](https://en.wikipedia.org/wiki/Cumulative_distribution_function) at the given input domain _value_ for a normal distribution with specified _mean_ and standard deviation _stdev_. If unspecified, the mean defaults to `0` and the standard deviation defaults to `1`.",
-    "densityNormal": r"Returns the value of the [probability density function](https://en.wikipedia.org/wiki/Probability_density_function) at the given input domain _value_, for a normal distribution with specified _mean_ and standard deviation _stdev_. If unspecified, the mean defaults to `0` and the standard deviation defaults to `1`.",
-    "quantileNormal": r"Returns the quantile value (the inverse of the [cumulative distribution function](https://en.wikipedia.org/wiki/Cumulative_distribution_function)) for the given input _probability_, for a normal distribution with specified _mean_ and standard deviation _stdev_. If unspecified, the mean defaults to `0` and the standard deviation defaults to `1`.",
-    "sampleLogNormal": r"Returns a sample from a univariate [log-normal probability distribution](https://en.wikipedia.org/wiki/Log-normal_distribution) with specified log _mean_ and log standard deviation _stdev_. If unspecified, the log mean defaults to `0` and the log standard deviation defaults to `1`.",
-    "cumulativeLogNormal": r"Returns the value of the [cumulative distribution function](https://en.wikipedia.org/wiki/Cumulative_distribution_function) at the given input domain _value_ for a log-normal distribution with specified log _mean_ and log standard deviation _stdev_. If unspecified, the log mean defaults to `0` and the log standard deviation defaults to `1`.",
-    "densityLogNormal": r"Returns the value of the [probability density function](https://en.wikipedia.org/wiki/Probability_density_function) at the given input domain _value_, for a log-normal distribution with specified log _mean_ and log standard deviation _stdev_. If unspecified, the log mean defaults to `0` and the log standard deviation defaults to `1`.",
-    "quantileLogNormal": r"Returns the quantile value (the inverse of the [cumulative distribution function](https://en.wikipedia.org/wiki/Cumulative_distribution_function)) for the given input _probability_, for a log-normal distribution with specified log _mean_ and log standard deviation _stdev_. If unspecified, the log mean defaults to `0` and the log standard deviation defaults to `1`.",
-    "sampleUniform": r"Returns a sample from a univariate [continuous uniform probability distribution](https://en.wikipedia.org/wiki/Uniform_distribution_(continuous)) over the interval [_min_, _max_). If unspecified, _min_ defaults to `0` and _max_ defaults to `1`. If only one argument is provided, it is interpreted as the _max_ value.",
-    "cumulativeUniform": r"Returns the value of the [cumulative distribution function](https://en.wikipedia.org/wiki/Cumulative_distribution_function) at the given input domain _value_ for a uniform distribution over the interval [_min_, _max_). If unspecified, _min_ defaults to `0` and _max_ defaults to `1`. If only one argument is provided, it is interpreted as the _max_ value.",
-    "densityUniform": r"Returns the value of the [probability density function](https://en.wikipedia.org/wiki/Probability_density_function) at the given input domain _value_,  for a uniform distribution over the interval [_min_, _max_). If unspecified, _min_ defaults to `0` and _max_ defaults to `1`. If only one argument is provided, it is interpreted as the _max_ value.",
-    "quantileUniform": r"Returns the quantile value (the inverse of the [cumulative distribution function](https://en.wikipedia.org/wiki/Cumulative_distribution_function)) for the given input _probability_,  for a uniform distribution over the interval [_min_, _max_). If unspecified, _min_ defaults to `0` and _max_ defaults to `1`. If only one argument is provided, it is interpreted as the _max_ value.",
-    "now": r"Returns the timestamp for the current time.",
-    "datetime": r"Returns a new `Date` instance. The _month_ is 0-based, such that `1` represents February.",
-    "date": r"Returns the day of the month for the given _datetime_ value, in local time.",
-    "day": r"Returns the day of the week for the given _datetime_ value, in local time.",
-    "dayofyear": r"Returns the one-based day of the year for the given _datetime_ value, in local time.",
-    "year": r"Returns the year for the given _datetime_ value, in local time.",
-    "quarter": r"Returns the quarter of the year (0-3) for the given _datetime_ value, in local time.",
-    "month": r"Returns the (zero-based) month for the given _datetime_ value, in local time.",
-    "week": r"Returns the week number of the year for the given _datetime_, in local time. This function assumes Sunday-based weeks. Days before the first Sunday of the year are considered to be in week 0, the first Sunday of the year is the start of week 1, the second Sunday week 2, _etc._.",
-    "hours": r"Returns the hours component for the given _datetime_ value, in local time.",
-    "minutes": r"Returns the minutes component for the given _datetime_ value, in local time.",
-    "seconds": r"Returns the seconds component for the given _datetime_ value, in local time.",
-    "milliseconds": r"Returns the milliseconds component for the given _datetime_ value, in local time.",
-    "time": r"Returns the epoch-based timestamp for the given _datetime_ value.",
-    "timezoneoffset": r"Returns the timezone offset from the local timezone to UTC for the given _datetime_ value.",
-    "timeOffset": r"Returns a new `Date` instance that offsets the given _date_ by the specified time [_unit_](../api/time/#time-units) in the local timezone. The optional _step_ argument indicates the number of time unit steps to offset by (default 1).",
-    "timeSequence": r"Returns an array of `Date` instances from _start_ (inclusive) to _stop_ (exclusive), with each entry separated by the given time [_unit_](../api/time/#time-units) in the local timezone. The optional _step_ argument indicates the number of time unit steps to take between each sequence entry (default 1).",
-    "utc": r"Returns a timestamp for the given UTC date. The _month_ is 0-based, such that `1` represents February.",
-    "utcdate": r"Returns the day of the month for the given _datetime_ value, in UTC time.",
-    "utcday": r"Returns the day of the week for the given _datetime_ value, in UTC time.",
-    "utcdayofyear": r"Returns the one-based day of the year for the given _datetime_ value, in UTC time.",
-    "utcyear": r"Returns the year for the given _datetime_ value, in UTC time.",
-    "utcquarter": r"Returns the quarter of the year (0-3) for the given _datetime_ value, in UTC time.",
-    "utcmonth": r"Returns the (zero-based) month for the given _datetime_ value, in UTC time.",
-    "utcweek": r"Returns the week number of the year for the given _datetime_, in UTC time. This function assumes Sunday-based weeks. Days before the first Sunday of the year are considered to be in week 0, the first Sunday of the year is the start of week 1, the second Sunday week 2, _etc._.",
-    "utchours": r"Returns the hours component for the given _datetime_ value, in UTC time.",
-    "utcminutes": r"Returns the minutes component for the given _datetime_ value, in UTC time.",
-    "utcseconds": r"Returns the seconds component for the given _datetime_ value, in UTC time.",
-    "utcmilliseconds": r"Returns the milliseconds component for the given _datetime_ value, in UTC time.",
-    "utcOffset": r"Returns a new `Date` instance that offsets the given _date_ by the specified time [_unit_](../api/time/#time-units) in UTC time. The optional _step_ argument indicates the number of time unit steps to offset by (default 1).",
-    "utcSequence": r"Returns an array of `Date` instances from _start_ (inclusive) to _stop_ (exclusive), with each entry separated by the given time [_unit_](../api/time/#time-units) in UTC time. The optional _step_ argument indicates the number of time unit steps to take between each sequence entry (default 1).",
-    "extent": r"Returns a new _[min, max]_ array with the minimum and maximum values of the input array, ignoring `null`, `undefined`, and `NaN` values.",
-    "clampRange": r"Clamps a two-element _range_ array in a span-preserving manner. If the span of the input _range_ is less than _(max - min)_ and an endpoint exceeds either the _min_ or _max_ value, the range is translated such that the span is preserved and one endpoint touches the boundary of the _[min, max]_ range. If the span exceeds _(max - min)_, the range _[min, max]_ is returned.",
-    "indexof": r"Returns the first index of _value_ in the input _array_, or the first index of _substring_ in the input _string_..",
-    "inrange": r"Tests whether _value_ lies within (or is equal to either) the first and last values of the _range_ array.",
-    "join": r"Returns a new string by concatenating all of the elements of the input _array_, separated by commas or a specified _separator_ string.",
-    "lastindexof": r"Returns the last index of _value_ in the input _array_, or the last index of _substring_ in the input _string_..",
-    "length": r"Returns the length of the input _array_, or the length of the input _string_.",
-    "lerp": r"Returns the linearly interpolated value between the first and last entries in the _array_ for the provided interpolation _fraction_ (typically between 0 and 1). For example, `lerp([0, 50], 0.5)` returns 25.",
-    "peek": r"Returns the last element in the input _array_. Similar to the built-in `Array.pop` method, except that it does not remove the last element. This method is a convenient shorthand for `array[array.length - 1]`.",
-    "pluck": r"Retrieves the value for the specified *field* from a given *array* of objects. The input *field* string may include nested properties (e.g., `foo.bar.bz`).",
-    "reverse": r"Returns a new array with elements in a reverse order of the input _array_. The first array element becomes the last, and the last array element becomes the first.",
-    "sequence": r"Returns an array containing an arithmetic sequence of numbers. If _step_ is omitted, it defaults to 1. If _start_ is omitted, it defaults to 0. The _stop_ value is exclusive; it is not included in the result. If _step_ is positive, the last element is the largest _start + i * step_ less than _stop_; if _step_ is negative, the last element is the smallest _start + i * step_ greater than _stop_. If the returned array would contain an infinite number of values, an empty range is returned. The arguments are not required to be integers.",
-    "slice": r"Returns a section of _array_ between the _start_ and _end_ indices. If the _end_ argument is negative, it is treated as an offset from the end of the array (_length(array) + end_).",
-    "span": r"Returns the span of _array_: the difference between the last and first elements, or _array[array.length-1] - array[0]_. Or if input is a string: a section of _string_ between the _start_ and _end_ indices. If the _end_ argument is negative, it is treated as an offset from the end of the string (_length(string) + end_)..",
-    "lower": r"Transforms _string_ to lower-case letters.",
-    "pad": r"Pads a _string_ value with repeated instances of a _character_ up to a specified _length_. If _character_ is not specified, a space (' ') is used. By default, padding is added to the end of a string. An optional _align_ parameter specifies if padding should be added to the `'left'` (beginning), `'center'`, or `'right'` (end) of the input string.",
-    "parseFloat": r"Parses the input _string_ to a floating-point value. Same as JavaScript's `parseFloat`.",
-    "parseInt": r"Parses the input _string_ to an integer value. Same as JavaScript's `parseInt`.",
-    "replace": r"Returns a new string with some or all matches of _pattern_ replaced by a _replacement_ string. The _pattern_ can be a string or a regular expression. If _pattern_ is a string, only the first instance will be replaced. Same as [JavaScript's String.replace](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/String/replace).",
-    "split": r"Returns an array of tokens created by splitting the input _string_ according to a provided _separator_ pattern. The result can optionally be constrained to return at most _limit_ tokens.",
-    "substring": r"Returns a section of _string_ between the _start_ and _end_ indices.",
-    "trim": r"Returns a trimmed string with preceding and trailing whitespace removed.",
-    "truncate": r"Truncates an input _string_ to a target _length_. The optional _align_ argument indicates what part of the string should be truncated: `'left'` (the beginning), `'center'`, or `'right'` (the end). By default, the `'right'` end of the string is truncated. The optional _ellipsis_ argument indicates the string to use to indicate truncated content; by default the ellipsis character `...` (`\\u2026`) is used.",
-    "upper": r"Transforms _string_ to upper-case letters.",
-    "merge": r"Merges the input objects _object1_, _object2_, etc into a new output object. Inputs are visited in sequential order, such that key values from later arguments can overwrite those from earlier arguments. Example: `merge({a:1, b:2}, {a:3}) -> {a:3, b:2}`.",
-    "dayFormat": r"Formats a (0-6) _weekday_ number as a full week day name, according to the current locale. For example: `dayFormat(0) -> \"Sunday\"`.",
-    "dayAbbrevFormat": r"Formats a (0-6) _weekday_ number as an abbreviated week day name, according to the current locale. For example: `dayAbbrevFormat(0) -> \"Sun\"`.",
-    "format": r"Formats a numeric _value_ as a string. The _specifier_ must be a valid [d3-format specifier](https://github.com/d3/d3-format/) (e.g., `format(value, ',.2f')`.",
-    "monthFormat": r"Formats a (zero-based) _month_ number as a full month name, according to the current locale. For example: `monthFormat(0) -> \"January\"`.",
-    "monthAbbrevFormat": r"Formats a (zero-based) _month_ number as an abbreviated month name, according to the current locale. For example: `monthAbbrevFormat(0) -> \"Jan\"`.",
-    "timeUnitSpecifier": r"Returns a time format specifier string for the given time [_units_](../api/time/#time-units). The optional _specifiers_ object provides a set of specifier sub-strings for customizing the format; for more, see the [timeUnitSpecifier API documentation](../api/time/#timeUnitSpecifier). The resulting specifier string can then be used as input to the [timeFormat](#timeFormat) or [utcFormat](#utcFormat) functions, or as the _format_ parameter of an axis or legend. For example: `timeFormat(date, timeUnitSpecifier('year'))` or `timeFormat(date, timeUnitSpecifier(['hours', 'minutes']))`.",
-    "timeFormat": r"Formats a datetime _value_ (either a `Date` object or timestamp) as a string, according to the local time. The _specifier_ must be a valid [d3-time-format specifier](https://github.com/d3/d3-time-format/). For example: `timeFormat(timestamp, '%A')`.",
-    "timeParse": r"Parses a _string_ value to a Date object, according to the local time. The _specifier_ must be a valid [d3-time-format specifier](https://github.com/d3/d3-time-format/). For example: `timeParse('June 30, 2015', '%B %d, %Y')`.",
-    "utcFormat": r"Formats a datetime _value_ (either a `Date` object or timestamp) as a string, according to [UTC](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) time. The _specifier_ must be a valid [d3-time-format specifier](https://github.com/d3/d3-time-format/). For example: `utcFormat(timestamp, '%A')`.",
-    "utcParse": r"Parses a _string_ value to a Date object, according to [UTC](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) time. The _specifier_ must be a valid [d3-time-format specifier](https://github.com/d3/d3-time-format/). For example: `utcParse('June 30, 2015', '%B %d, %Y')`.",
-    "regexp": r"Creates a regular expression instance from an input _pattern_ string and optional _flags_. Same as [JavaScript's `RegExp`](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/RegExp).",
-    "test": r"Evaluates a regular expression _regexp_ against the input _string_, returning `true` if the string matches the pattern, `false` otherwise. For example: `test(/\\d{3}/, \"32-21-9483\") -> true`.",
-    "rgb": r"Constructs a new [RGB](https://en.wikipedia.org/wiki/RGB_color_model) color. If _r_, _g_ and _b_ are specified, these represent the channel values of the returned color; an _opacity_ may also be specified. If a CSS Color Module Level 3 _specifier_ string is specified, it is parsed and then converted to the RGB color space. Uses [d3-color's rgb function](https://github.com/d3/d3-color#rgb).",
-    "hsl": r"Constructs a new [HSL](https://en.wikipedia.org/wiki/HSL_and_HSV) color. If _h_, _s_ and _l_ are specified, these represent the channel values of the returned color; an _opacity_ may also be specified. If a CSS Color Module Level 3 _specifier_ string is specified, it is parsed and then converted to the HSL color space. Uses [d3-color's hsl function](https://github.com/d3/d3-color#hsl).",
-    "lab": r"Constructs a new [CIE LAB](https://en.wikipedia.org/wiki/Lab_color_space#CIELAB) color. If _l_, _a_ and _b_ are specified, these represent the channel values of the returned color; an _opacity_ may also be specified. If a CSS Color Module Level 3 _specifier_ string is specified, it is parsed and then converted to the LAB color space. Uses [d3-color's lab function](https://github.com/d3/d3-color#lab).",
-    "hcl": r"Constructs a new [HCL](https://en.wikipedia.org/wiki/Lab_color_space#CIELAB) (hue, chroma, luminance) color. If _h_, _c_ and _l_ are specified, these represent the channel values of the returned color; an _opacity_ may also be specified. If a CSS Color Module Level 3 _specifier_ string is specified, it is parsed and then converted to the HCL color space. Uses [d3-color's hcl function](https://github.com/d3/d3-color#hcl).",
-    "luminance": r"Returns the luminance for the given color _specifier_ (compatible with [d3-color's rgb function](https://github.com/d3/d3-color#rgb)). The luminance is calculated according to the [W3C Web Content Accessibility Guidelines](https://www.w3.org/TR/2008/REC-WCAG20-20081211/#relativeluminancedef).",
-    "contrast": r"Returns the contrast ratio between the input color specifiers as a float between 1 and 21. The contrast is calculated according to the [W3C Web Content Accessibility Guidelines](https://www.w3.org/TR/2008/REC-WCAG20-20081211/#contrast-ratiodef).",
-    "item": r"Returns the current scenegraph item that is the target of the event.",
-    "group": r"Returns the scenegraph group mark item in which the current event has occurred. If no arguments are provided, the immediate parent group is returned. If a group name is provided, the matching ancestor group item is returned.",
-    "xy": r"Returns the x- and y-coordinates for the current event as a two-element array. If no arguments are provided, the top-level coordinate space of the view is used. If a scenegraph _item_ (or string group name) is provided, the coordinate space of the group item is used.",
-    "x": r"Returns the x coordinate for the current event. If no arguments are provided, the top-level coordinate space of the view is used. If a scenegraph _item_ (or string group name) is provided, the coordinate space of the group item is used.",
-    "y": r"Returns the y coordinate for the current event. If no arguments are provided, the top-level coordinate space of the view is used. If a scenegraph _item_ (or string group name) is provided, the coordinate space of the group item is used.",
-    "pinchDistance": r"Returns the pixel distance between the first two touch points of a multi-touch event.",
-    "pinchAngle": r"Returns the angle of the line connecting the first two touch points of a multi-touch event.",
-    "inScope": r"Returns true if the given scenegraph _item_ is a descendant of the group mark in which the event handler was defined, false otherwise.",
-    "data": r"Returns the array of data objects for the Vega data set with the given _name_. If the data set is not found, returns an empty array.",
-    "indata": r"Tests if the data set with a given _name_ contains a datum with a _field_ value that matches the input _value_. For example: `indata('table', 'category', value)`.",
-    "scale": r"Applies the named scale transform (or projection) to the specified _value_. The optional _group_ argument takes a scenegraph group mark item to indicate the specific scope in which to look up the scale or projection.",
-    "invert": r"Inverts the named scale transform (or projection) for the specified _value_. The optional _group_ argument takes a scenegraph group mark item to indicate the specific scope in which to look up the scale or projection.",
-    "copy": r"Returns a copy (a new cloned instance) of the named scale transform of projection, or `undefined` if no scale or projection is found. The optional _group_ argument takes a scenegraph group mark item to indicate the specific scope in which to look up the scale or projection.",
-    "domain": r"Returns the scale domain array for the named scale transform, or an empty array if the scale is not found. The optional _group_ argument takes a scenegraph group mark item to indicate the specific scope in which to look up the scale.",
-    "range": r"Returns the scale range array for the named scale transform, or an empty array if the scale is not found. The optional _group_ argument takes a scenegraph group mark item to indicate the specific scope in which to look up the scale.",
-    "bandwidth": r"Returns the current band width for the named band scale transform, or zero if the scale is not found or is not a band scale. The optional _group_ argument takes a scenegraph group mark item to indicate the specific scope in which to look up the scale.",
-    "bandspace": r"Returns the number of steps needed within a band scale, based on the _count_ of domain elements and the inner and outer padding values. While normally calculated within the scale itself, this function can be helpful for determining the size of a chart's layout.",
-    "gradient": r"Returns a linear color gradient for the _scale_ (whose range must be a [continuous color scheme](../schemes)) and starting and ending points _p0_ and _p1_, each an _[x, y]_ array. The points _p0_ and _p1_ should be expressed in normalized coordinates in the domain [0, 1], relative to the bounds of the item being colored. If unspecified, _p0_ defaults to `[0, 0]` and _p1_ defaults to `[1, 0]`, for a horizontal gradient that spans the full bounds of an item. The optional _count_ argument indicates a desired target number of sample points to take from the color scale.",
-    "panLinear": r"Given a linear scale _domain_ array with numeric or datetime values, returns a new two-element domain array that is the result of panning the domain by a fractional _delta_. The _delta_ value represents fractional units of the scale range; for example, `0.5` indicates panning the scale domain to the right by half the scale range.",
-    "panLog": r"Given a log scale _domain_ array with numeric or datetime values, returns a new two-element domain array that is the result of panning the domain by a fractional _delta_. The _delta_ value represents fractional units of the scale range; for example, `0.5` indicates panning the scale domain to the right by half the scale range.",
-    "panPow": r"Given a power scale _domain_ array with numeric or datetime values and the given _exponent_, returns a new two-element domain array that is the result of panning the domain by a fractional _delta_. The _delta_ value represents fractional units of the scale range; for example, `0.5` indicates panning the scale domain to the right by half the scale range.",
-    "panSymlog": r"Given a symmetric log scale _domain_ array with numeric or datetime values parameterized by the given _constant_, returns a new two-element domain array that is the result of panning the domain by a fractional _delta_. The _delta_ value represents fractional units of the scale range; for example, `0.5` indicates panning the scale domain to the right by half the scale range.",
-    "zoomLinear": r"Given a linear scale _domain_ array with numeric or datetime values, returns a new two-element domain array that is the result of zooming the domain by a _scaleFactor_, centered at the provided fractional _anchor_. The _anchor_ value represents the zoom position in terms of fractional units of the scale range; for example, `0.5` indicates a zoom centered on the mid-point of the scale range.",
-    "zoomLog": r"Given a log scale _domain_ array with numeric or datetime values, returns a new two-element domain array that is the result of zooming the domain by a _scaleFactor_, centered at the provided fractional _anchor_. The _anchor_ value represents the zoom position in terms of fractional units of the scale range; for example, `0.5` indicates a zoom centered on the mid-point of the scale range.",
-    "zoomPow": r"Given a power scale _domain_ array with numeric or datetime values and the given _exponent_, returns a new two-element domain array that is the result of zooming the domain by a _scaleFactor_, centered at the provided fractional _anchor_. The _anchor_ value represents the zoom position in terms of fractional units of the scale range; for example, `0.5` indicates a zoom centered on the mid-point of the scale range.",
-    "zoomSymlog": r"Given a symmetric log scale _domain_ array with numeric or datetime values parameterized by the given _constant_, returns a new two-element domain array that is the result of zooming the domain by a _scaleFactor_, centered at the provided fractional _anchor_. The _anchor_ value represents the zoom position in terms of fractional units of the scale range; for example, `0.5` indicates a zoom centered on the mid-point of the scale range.",
-    "geoArea": r"Returns the projected planar area (typically in square pixels) of a GeoJSON _feature_ according to the named _projection_. If the _projection_ argument is `null`, computes the spherical area in steradians using unprojected longitude, latitude coordinates. The optional _group_ argument takes a scenegraph group mark item to indicate the specific scope in which to look up the projection. Uses d3-geo's [geoArea](https://github.com/d3/d3-geo#geoArea) and [path.area](https://github.com/d3/d3-geo#path_area) methods.",
-    "geoBounds": r"Returns the projected planar bounding box (typically in pixels) for the specified GeoJSON _feature_, according to the named _projection_. The bounding box is represented by a two-dimensional array: [[_x0_, _y0_], [_x1_, _y1_]], where _x0_ is the minimum x-coordinate, _y0_ is the minimum y-coordinate, _x1_ is the maximum x-coordinate, and _y1_ is the maximum y-coordinate. If the _projection_ argument is `null`, computes the spherical bounding box using unprojected longitude, latitude coordinates. The optional _group_ argument takes a scenegraph group mark item to indicate the specific scope in which to look up the projection. Uses d3-geo's [geoBounds](https://github.com/d3/d3-geo#geoBounds) and [path.bounds](https://github.com/d3/d3-geo#path_bounds) methods.",
-    "geoCentroid": r"Returns the projected planar centroid (typically in pixels) for the specified GeoJSON _feature_, according to the named _projection_. If the _projection_ argument is `null`, computes the spherical centroid using unprojected longitude, latitude coordinates. The optional _group_ argument takes a scenegraph group mark item to indicate the specific scope in which to look up the projection. Uses d3-geo's [geoCentroid](https://github.com/d3/d3-geo#geoCentroid) and [path.centroid](https://github.com/d3/d3-geo#path_centroid) methods.",
-    "treePath": r"For the hierarchy data set with the given _name_, returns the shortest path through from the _source_ node id to the _target_ node id. The path starts at the _source_ node, ascends to the least common ancestor of the _source_ node and the _target_ node, and then descends to the _target_ node.",
-    "treeAncestors": r"For the hierarchy data set with the given _name_, returns the array of ancestors nodes, starting with the input _node_, then followed by each parent up to the root.",
-    "containerSize": r"Returns the current CSS box size (`[el.clientWidth, el.clientHeight]`) of the parent DOM element that contains the Vega view. If there is no container element, returns `[undefined, undefined]`.",
-    "screen": r"Returns the [`window.screen`](https://developer.mozilla.org/en-US/docs/Web/API/Window/screen) object, or `{}` if Vega is not running in a browser environment.",
-    "windowSize": r"Returns the current window size (`[window.innerWidth, window.innerHeight]`) or `[undefined, undefined]` if Vega is not running in a browser environment.",
-    "warn": r"Logs a warning message and returns the last argument. For the message to appear in the console, the visualization view must have the appropriate logging level set.",
-    "info": r"Logs an informative message and returns the last argument. For the message to appear in the console, the visualization view must have the appropriate logging level set.",
-    "debug": r"Logs a debugging message and returns the last argument. For the message to appear in the console, the visualization view must have the appropriate logging level set.",
-}
-
-
-# This maps vega expression function names to the Python name
-NAME_MAP = {"if": "if_"}
-
-
-class ExprFunc:
-    def __init__(self, name, doc):
-        self.name = name
-        self.doc = doc
-        self.__doc__ = """{}(*args)\n    {}""".format(name, doc)
-
-    def __call__(self, *args):
-        return FunctionExpression(self.name, args)
-
-    def __repr__(self):
-        return "<function expr.{}(*args)>".format(self.name)
-
-
-def _populate_namespace():
-    globals_ = globals()
-    for name, doc in FUNCTION_LISTING.items():
-        py_name = NAME_MAP.get(name, name)
-        globals_[py_name] = ExprFunc(name, doc)
-        yield py_name
-
-
-__all__ = list(_populate_namespace())
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/pens/momentsPen.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/pens/momentsPen.py
deleted file mode 100644
index dab0d10e2c63b2552cf44005fdd5d2ecea3dfe12..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/pens/momentsPen.py
+++ /dev/null
@@ -1,882 +0,0 @@
-from fontTools.pens.basePen import BasePen, OpenContourError
-
-try:
-    import cython
-
-    COMPILED = cython.compiled
-except (AttributeError, ImportError):
-    # if cython not installed, use mock module with no-op decorators and types
-    from fontTools.misc import cython
-
-    COMPILED = False
-
-
-__all__ = ["MomentsPen"]
-
-
-class MomentsPen(BasePen):
-    def __init__(self, glyphset=None):
-        BasePen.__init__(self, glyphset)
-
-        self.area = 0
-        self.momentX = 0
-        self.momentY = 0
-        self.momentXX = 0
-        self.momentXY = 0
-        self.momentYY = 0
-
-    def _moveTo(self, p0):
-        self.__startPoint = p0
-
-    def _closePath(self):
-        p0 = self._getCurrentPoint()
-        if p0 != self.__startPoint:
-            self._lineTo(self.__startPoint)
-
-    def _endPath(self):
-        p0 = self._getCurrentPoint()
-        if p0 != self.__startPoint:
-            # Green theorem is not defined on open contours.
-            raise OpenContourError("Green theorem is not defined on open contours.")
-
-    @cython.locals(r0=cython.double)
-    @cython.locals(r1=cython.double)
-    @cython.locals(r2=cython.double)
-    @cython.locals(r3=cython.double)
-    @cython.locals(r4=cython.double)
-    @cython.locals(r5=cython.double)
-    @cython.locals(r6=cython.double)
-    @cython.locals(r7=cython.double)
-    @cython.locals(r8=cython.double)
-    @cython.locals(r9=cython.double)
-    @cython.locals(r10=cython.double)
-    @cython.locals(r11=cython.double)
-    @cython.locals(r12=cython.double)
-    @cython.locals(x0=cython.double, y0=cython.double)
-    @cython.locals(x1=cython.double, y1=cython.double)
-    def _lineTo(self, p1):
-        x0, y0 = self._getCurrentPoint()
-        x1, y1 = p1
-
-        r0 = x1 * y0
-        r1 = x1 * y1
-        r2 = x1**2
-        r3 = r2 * y1
-        r4 = y0 - y1
-        r5 = r4 * x0
-        r6 = x0**2
-        r7 = 2 * y0
-        r8 = y0**2
-        r9 = y1**2
-        r10 = x1**3
-        r11 = y0**3
-        r12 = y1**3
-
-        self.area += -r0 / 2 - r1 / 2 + x0 * (y0 + y1) / 2
-        self.momentX += -r2 * y0 / 6 - r3 / 3 - r5 * x1 / 6 + r6 * (r7 + y1) / 6
-        self.momentY += (
-            -r0 * y1 / 6 - r8 * x1 / 6 - r9 * x1 / 6 + x0 * (r8 + r9 + y0 * y1) / 6
-        )
-        self.momentXX += (
-            -r10 * y0 / 12
-            - r10 * y1 / 4
-            - r2 * r5 / 12
-            - r4 * r6 * x1 / 12
-            + x0**3 * (3 * y0 + y1) / 12
-        )
-        self.momentXY += (
-            -r2 * r8 / 24
-            - r2 * r9 / 8
-            - r3 * r7 / 24
-            + r6 * (r7 * y1 + 3 * r8 + r9) / 24
-            - x0 * x1 * (r8 - r9) / 12
-        )
-        self.momentYY += (
-            -r0 * r9 / 12
-            - r1 * r8 / 12
-            - r11 * x1 / 12
-            - r12 * x1 / 12
-            + x0 * (r11 + r12 + r8 * y1 + r9 * y0) / 12
-        )
-
-    @cython.locals(r0=cython.double)
-    @cython.locals(r1=cython.double)
-    @cython.locals(r2=cython.double)
-    @cython.locals(r3=cython.double)
-    @cython.locals(r4=cython.double)
-    @cython.locals(r5=cython.double)
-    @cython.locals(r6=cython.double)
-    @cython.locals(r7=cython.double)
-    @cython.locals(r8=cython.double)
-    @cython.locals(r9=cython.double)
-    @cython.locals(r10=cython.double)
-    @cython.locals(r11=cython.double)
-    @cython.locals(r12=cython.double)
-    @cython.locals(r13=cython.double)
-    @cython.locals(r14=cython.double)
-    @cython.locals(r15=cython.double)
-    @cython.locals(r16=cython.double)
-    @cython.locals(r17=cython.double)
-    @cython.locals(r18=cython.double)
-    @cython.locals(r19=cython.double)
-    @cython.locals(r20=cython.double)
-    @cython.locals(r21=cython.double)
-    @cython.locals(r22=cython.double)
-    @cython.locals(r23=cython.double)
-    @cython.locals(r24=cython.double)
-    @cython.locals(r25=cython.double)
-    @cython.locals(r26=cython.double)
-    @cython.locals(r27=cython.double)
-    @cython.locals(r28=cython.double)
-    @cython.locals(r29=cython.double)
-    @cython.locals(r30=cython.double)
-    @cython.locals(r31=cython.double)
-    @cython.locals(r32=cython.double)
-    @cython.locals(r33=cython.double)
-    @cython.locals(r34=cython.double)
-    @cython.locals(r35=cython.double)
-    @cython.locals(r36=cython.double)
-    @cython.locals(r37=cython.double)
-    @cython.locals(r38=cython.double)
-    @cython.locals(r39=cython.double)
-    @cython.locals(r40=cython.double)
-    @cython.locals(r41=cython.double)
-    @cython.locals(r42=cython.double)
-    @cython.locals(r43=cython.double)
-    @cython.locals(r44=cython.double)
-    @cython.locals(r45=cython.double)
-    @cython.locals(r46=cython.double)
-    @cython.locals(r47=cython.double)
-    @cython.locals(r48=cython.double)
-    @cython.locals(r49=cython.double)
-    @cython.locals(r50=cython.double)
-    @cython.locals(r51=cython.double)
-    @cython.locals(r52=cython.double)
-    @cython.locals(r53=cython.double)
-    @cython.locals(x0=cython.double, y0=cython.double)
-    @cython.locals(x1=cython.double, y1=cython.double)
-    @cython.locals(x2=cython.double, y2=cython.double)
-    def _qCurveToOne(self, p1, p2):
-        x0, y0 = self._getCurrentPoint()
-        x1, y1 = p1
-        x2, y2 = p2
-
-        r0 = 2 * y1
-        r1 = r0 * x2
-        r2 = x2 * y2
-        r3 = 3 * r2
-        r4 = 2 * x1
-        r5 = 3 * y0
-        r6 = x1**2
-        r7 = x2**2
-        r8 = 4 * y1
-        r9 = 10 * y2
-        r10 = 2 * y2
-        r11 = r4 * x2
-        r12 = x0**2
-        r13 = 10 * y0
-        r14 = r4 * y2
-        r15 = x2 * y0
-        r16 = 4 * x1
-        r17 = r0 * x1 + r2
-        r18 = r2 * r8
-        r19 = y1**2
-        r20 = 2 * r19
-        r21 = y2**2
-        r22 = r21 * x2
-        r23 = 5 * r22
-        r24 = y0**2
-        r25 = y0 * y2
-        r26 = 5 * r24
-        r27 = x1**3
-        r28 = x2**3
-        r29 = 30 * y1
-        r30 = 6 * y1
-        r31 = 10 * r7 * x1
-        r32 = 5 * y2
-        r33 = 12 * r6
-        r34 = 30 * x1
-        r35 = x1 * y1
-        r36 = r3 + 20 * r35
-        r37 = 12 * x1
-        r38 = 20 * r6
-        r39 = 8 * r6 * y1
-        r40 = r32 * r7
-        r41 = 60 * y1
-        r42 = 20 * r19
-        r43 = 4 * r19
-        r44 = 15 * r21
-        r45 = 12 * x2
-        r46 = 12 * y2
-        r47 = 6 * x1
-        r48 = 8 * r19 * x1 + r23
-        r49 = 8 * y1**3
-        r50 = y2**3
-        r51 = y0**3
-        r52 = 10 * y1
-        r53 = 12 * y1
-
-        self.area += (
-            -r1 / 6
-            - r3 / 6
-            + x0 * (r0 + r5 + y2) / 6
-            + x1 * y2 / 3
-            - y0 * (r4 + x2) / 6
-        )
-        self.momentX += (
-            -r11 * (-r10 + y1) / 30
-            + r12 * (r13 + r8 + y2) / 30
-            + r6 * y2 / 15
-            - r7 * r8 / 30
-            - r7 * r9 / 30
-            + x0 * (r14 - r15 - r16 * y0 + r17) / 30
-            - y0 * (r11 + 2 * r6 + r7) / 30
-        )
-        self.momentY += (
-            -r18 / 30
-            - r20 * x2 / 30
-            - r23 / 30
-            - r24 * (r16 + x2) / 30
-            + x0 * (r0 * y2 + r20 + r21 + r25 + r26 + r8 * y0) / 30
-            + x1 * y2 * (r10 + y1) / 15
-            - y0 * (r1 + r17) / 30
-        )
-        self.momentXX += (
-            r12 * (r1 - 5 * r15 - r34 * y0 + r36 + r9 * x1) / 420
-            + 2 * r27 * y2 / 105
-            - r28 * r29 / 420
-            - r28 * y2 / 4
-            - r31 * (r0 - 3 * y2) / 420
-            - r6 * x2 * (r0 - r32) / 105
-            + x0**3 * (r30 + 21 * y0 + y2) / 84
-            - x0
-            * (
-                r0 * r7
-                + r15 * r37
-                - r2 * r37
-                - r33 * y2
-                + r38 * y0
-                - r39
-                - r40
-                + r5 * r7
-            )
-            / 420
-            - y0 * (8 * r27 + 5 * r28 + r31 + r33 * x2) / 420
-        )
-        self.momentXY += (
-            r12 * (r13 * y2 + 3 * r21 + 105 * r24 + r41 * y0 + r42 + r46 * y1) / 840
-            - r16 * x2 * (r43 - r44) / 840
-            - r21 * r7 / 8
-            - r24 * (r38 + r45 * x1 + 3 * r7) / 840
-            - r41 * r7 * y2 / 840
-            - r42 * r7 / 840
-            + r6 * y2 * (r32 + r8) / 210
-            + x0
-            * (
-                -r15 * r8
-                + r16 * r25
-                + r18
-                + r21 * r47
-                - r24 * r34
-                - r26 * x2
-                + r35 * r46
-                + r48
-            )
-            / 420
-            - y0 * (r16 * r2 + r30 * r7 + r35 * r45 + r39 + r40) / 420
-        )
-        self.momentYY += (
-            -r2 * r42 / 420
-            - r22 * r29 / 420
-            - r24 * (r14 + r36 + r52 * x2) / 420
-            - r49 * x2 / 420
-            - r50 * x2 / 12
-            - r51 * (r47 + x2) / 84
-            + x0
-            * (
-                r19 * r46
-                + r21 * r5
-                + r21 * r52
-                + r24 * r29
-                + r25 * r53
-                + r26 * y2
-                + r42 * y0
-                + r49
-                + 5 * r50
-                + 35 * r51
-            )
-            / 420
-            + x1 * y2 * (r43 + r44 + r9 * y1) / 210
-            - y0 * (r19 * r45 + r2 * r53 - r21 * r4 + r48) / 420
-        )
-
-    @cython.locals(r0=cython.double)
-    @cython.locals(r1=cython.double)
-    @cython.locals(r2=cython.double)
-    @cython.locals(r3=cython.double)
-    @cython.locals(r4=cython.double)
-    @cython.locals(r5=cython.double)
-    @cython.locals(r6=cython.double)
-    @cython.locals(r7=cython.double)
-    @cython.locals(r8=cython.double)
-    @cython.locals(r9=cython.double)
-    @cython.locals(r10=cython.double)
-    @cython.locals(r11=cython.double)
-    @cython.locals(r12=cython.double)
-    @cython.locals(r13=cython.double)
-    @cython.locals(r14=cython.double)
-    @cython.locals(r15=cython.double)
-    @cython.locals(r16=cython.double)
-    @cython.locals(r17=cython.double)
-    @cython.locals(r18=cython.double)
-    @cython.locals(r19=cython.double)
-    @cython.locals(r20=cython.double)
-    @cython.locals(r21=cython.double)
-    @cython.locals(r22=cython.double)
-    @cython.locals(r23=cython.double)
-    @cython.locals(r24=cython.double)
-    @cython.locals(r25=cython.double)
-    @cython.locals(r26=cython.double)
-    @cython.locals(r27=cython.double)
-    @cython.locals(r28=cython.double)
-    @cython.locals(r29=cython.double)
-    @cython.locals(r30=cython.double)
-    @cython.locals(r31=cython.double)
-    @cython.locals(r32=cython.double)
-    @cython.locals(r33=cython.double)
-    @cython.locals(r34=cython.double)
-    @cython.locals(r35=cython.double)
-    @cython.locals(r36=cython.double)
-    @cython.locals(r37=cython.double)
-    @cython.locals(r38=cython.double)
-    @cython.locals(r39=cython.double)
-    @cython.locals(r40=cython.double)
-    @cython.locals(r41=cython.double)
-    @cython.locals(r42=cython.double)
-    @cython.locals(r43=cython.double)
-    @cython.locals(r44=cython.double)
-    @cython.locals(r45=cython.double)
-    @cython.locals(r46=cython.double)
-    @cython.locals(r47=cython.double)
-    @cython.locals(r48=cython.double)
-    @cython.locals(r49=cython.double)
-    @cython.locals(r50=cython.double)
-    @cython.locals(r51=cython.double)
-    @cython.locals(r52=cython.double)
-    @cython.locals(r53=cython.double)
-    @cython.locals(r54=cython.double)
-    @cython.locals(r55=cython.double)
-    @cython.locals(r56=cython.double)
-    @cython.locals(r57=cython.double)
-    @cython.locals(r58=cython.double)
-    @cython.locals(r59=cython.double)
-    @cython.locals(r60=cython.double)
-    @cython.locals(r61=cython.double)
-    @cython.locals(r62=cython.double)
-    @cython.locals(r63=cython.double)
-    @cython.locals(r64=cython.double)
-    @cython.locals(r65=cython.double)
-    @cython.locals(r66=cython.double)
-    @cython.locals(r67=cython.double)
-    @cython.locals(r68=cython.double)
-    @cython.locals(r69=cython.double)
-    @cython.locals(r70=cython.double)
-    @cython.locals(r71=cython.double)
-    @cython.locals(r72=cython.double)
-    @cython.locals(r73=cython.double)
-    @cython.locals(r74=cython.double)
-    @cython.locals(r75=cython.double)
-    @cython.locals(r76=cython.double)
-    @cython.locals(r77=cython.double)
-    @cython.locals(r78=cython.double)
-    @cython.locals(r79=cython.double)
-    @cython.locals(r80=cython.double)
-    @cython.locals(r81=cython.double)
-    @cython.locals(r82=cython.double)
-    @cython.locals(r83=cython.double)
-    @cython.locals(r84=cython.double)
-    @cython.locals(r85=cython.double)
-    @cython.locals(r86=cython.double)
-    @cython.locals(r87=cython.double)
-    @cython.locals(r88=cython.double)
-    @cython.locals(r89=cython.double)
-    @cython.locals(r90=cython.double)
-    @cython.locals(r91=cython.double)
-    @cython.locals(r92=cython.double)
-    @cython.locals(r93=cython.double)
-    @cython.locals(r94=cython.double)
-    @cython.locals(r95=cython.double)
-    @cython.locals(r96=cython.double)
-    @cython.locals(r97=cython.double)
-    @cython.locals(r98=cython.double)
-    @cython.locals(r99=cython.double)
-    @cython.locals(r100=cython.double)
-    @cython.locals(r101=cython.double)
-    @cython.locals(r102=cython.double)
-    @cython.locals(r103=cython.double)
-    @cython.locals(r104=cython.double)
-    @cython.locals(r105=cython.double)
-    @cython.locals(r106=cython.double)
-    @cython.locals(r107=cython.double)
-    @cython.locals(r108=cython.double)
-    @cython.locals(r109=cython.double)
-    @cython.locals(r110=cython.double)
-    @cython.locals(r111=cython.double)
-    @cython.locals(r112=cython.double)
-    @cython.locals(r113=cython.double)
-    @cython.locals(r114=cython.double)
-    @cython.locals(r115=cython.double)
-    @cython.locals(r116=cython.double)
-    @cython.locals(r117=cython.double)
-    @cython.locals(r118=cython.double)
-    @cython.locals(r119=cython.double)
-    @cython.locals(r120=cython.double)
-    @cython.locals(r121=cython.double)
-    @cython.locals(r122=cython.double)
-    @cython.locals(r123=cython.double)
-    @cython.locals(r124=cython.double)
-    @cython.locals(r125=cython.double)
-    @cython.locals(r126=cython.double)
-    @cython.locals(r127=cython.double)
-    @cython.locals(r128=cython.double)
-    @cython.locals(r129=cython.double)
-    @cython.locals(r130=cython.double)
-    @cython.locals(r131=cython.double)
-    @cython.locals(r132=cython.double)
-    @cython.locals(x0=cython.double, y0=cython.double)
-    @cython.locals(x1=cython.double, y1=cython.double)
-    @cython.locals(x2=cython.double, y2=cython.double)
-    @cython.locals(x3=cython.double, y3=cython.double)
-    def _curveToOne(self, p1, p2, p3):
-        x0, y0 = self._getCurrentPoint()
-        x1, y1 = p1
-        x2, y2 = p2
-        x3, y3 = p3
-
-        r0 = 6 * y2
-        r1 = r0 * x3
-        r2 = 10 * y3
-        r3 = r2 * x3
-        r4 = 3 * y1
-        r5 = 6 * x1
-        r6 = 3 * x2
-        r7 = 6 * y1
-        r8 = 3 * y2
-        r9 = x2**2
-        r10 = 45 * r9
-        r11 = r10 * y3
-        r12 = x3**2
-        r13 = r12 * y2
-        r14 = r12 * y3
-        r15 = 7 * y3
-        r16 = 15 * x3
-        r17 = r16 * x2
-        r18 = x1**2
-        r19 = 9 * r18
-        r20 = x0**2
-        r21 = 21 * y1
-        r22 = 9 * r9
-        r23 = r7 * x3
-        r24 = 9 * y2
-        r25 = r24 * x2 + r3
-        r26 = 9 * x2
-        r27 = x2 * y3
-        r28 = -r26 * y1 + 15 * r27
-        r29 = 3 * x1
-        r30 = 45 * x1
-        r31 = 12 * x3
-        r32 = 45 * r18
-        r33 = 5 * r12
-        r34 = r8 * x3
-        r35 = 105 * y0
-        r36 = 30 * y0
-        r37 = r36 * x2
-        r38 = 5 * x3
-        r39 = 15 * y3
-        r40 = 5 * y3
-        r41 = r40 * x3
-        r42 = x2 * y2
-        r43 = 18 * r42
-        r44 = 45 * y1
-        r45 = r41 + r43 + r44 * x1
-        r46 = y2 * y3
-        r47 = r46 * x3
-        r48 = y2**2
-        r49 = 45 * r48
-        r50 = r49 * x3
-        r51 = y3**2
-        r52 = r51 * x3
-        r53 = y1**2
-        r54 = 9 * r53
-        r55 = y0**2
-        r56 = 21 * x1
-        r57 = 6 * x2
-        r58 = r16 * y2
-        r59 = r39 * y2
-        r60 = 9 * r48
-        r61 = r6 * y3
-        r62 = 3 * y3
-        r63 = r36 * y2
-        r64 = y1 * y3
-        r65 = 45 * r53
-        r66 = 5 * r51
-        r67 = x2**3
-        r68 = x3**3
-        r69 = 630 * y2
-        r70 = 126 * x3
-        r71 = x1**3
-        r72 = 126 * x2
-        r73 = 63 * r9
-        r74 = r73 * x3
-        r75 = r15 * x3 + 15 * r42
-        r76 = 630 * x1
-        r77 = 14 * x3
-        r78 = 21 * r27
-        r79 = 42 * x1
-        r80 = 42 * x2
-        r81 = x1 * y2
-        r82 = 63 * r42
-        r83 = x1 * y1
-        r84 = r41 + r82 + 378 * r83
-        r85 = x2 * x3
-        r86 = r85 * y1
-        r87 = r27 * x3
-        r88 = 27 * r9
-        r89 = r88 * y2
-        r90 = 42 * r14
-        r91 = 90 * x1
-        r92 = 189 * r18
-        r93 = 378 * r18
-        r94 = r12 * y1
-        r95 = 252 * x1 * x2
-        r96 = r79 * x3
-        r97 = 30 * r85
-        r98 = r83 * x3
-        r99 = 30 * x3
-        r100 = 42 * x3
-        r101 = r42 * x1
-        r102 = r10 * y2 + 14 * r14 + 126 * r18 * y1 + r81 * r99
-        r103 = 378 * r48
-        r104 = 18 * y1
-        r105 = r104 * y2
-        r106 = y0 * y1
-        r107 = 252 * y2
-        r108 = r107 * y0
-        r109 = y0 * y3
-        r110 = 42 * r64
-        r111 = 378 * r53
-        r112 = 63 * r48
-        r113 = 27 * x2
-        r114 = r27 * y2
-        r115 = r113 * r48 + 42 * r52
-        r116 = x3 * y3
-        r117 = 54 * r42
-        r118 = r51 * x1
-        r119 = r51 * x2
-        r120 = r48 * x1
-        r121 = 21 * x3
-        r122 = r64 * x1
-        r123 = r81 * y3
-        r124 = 30 * r27 * y1 + r49 * x2 + 14 * r52 + 126 * r53 * x1
-        r125 = y2**3
-        r126 = y3**3
-        r127 = y1**3
-        r128 = y0**3
-        r129 = r51 * y2
-        r130 = r112 * y3 + r21 * r51
-        r131 = 189 * r53
-        r132 = 90 * y2
-
-        self.area += (
-            -r1 / 20
-            - r3 / 20
-            - r4 * (x2 + x3) / 20
-            + x0 * (r7 + r8 + 10 * y0 + y3) / 20
-            + 3 * x1 * (y2 + y3) / 20
-            + 3 * x2 * y3 / 10
-            - y0 * (r5 + r6 + x3) / 20
-        )
-        self.momentX += (
-            r11 / 840
-            - r13 / 8
-            - r14 / 3
-            - r17 * (-r15 + r8) / 840
-            + r19 * (r8 + 2 * y3) / 840
-            + r20 * (r0 + r21 + 56 * y0 + y3) / 168
-            + r29 * (-r23 + r25 + r28) / 840
-            - r4 * (10 * r12 + r17 + r22) / 840
-            + x0
-            * (
-                12 * r27
-                + r30 * y2
-                + r34
-                - r35 * x1
-                - r37
-                - r38 * y0
-                + r39 * x1
-                - r4 * x3
-                + r45
-            )
-            / 840
-            - y0 * (r17 + r30 * x2 + r31 * x1 + r32 + r33 + 18 * r9) / 840
-        )
-        self.momentY += (
-            -r4 * (r25 + r58) / 840
-            - r47 / 8
-            - r50 / 840
-            - r52 / 6
-            - r54 * (r6 + 2 * x3) / 840
-            - r55 * (r56 + r57 + x3) / 168
-            + x0
-            * (
-                r35 * y1
-                + r40 * y0
-                + r44 * y2
-                + 18 * r48
-                + 140 * r55
-                + r59
-                + r63
-                + 12 * r64
-                + r65
-                + r66
-            )
-            / 840
-            + x1 * (r24 * y1 + 10 * r51 + r59 + r60 + r7 * y3) / 280
-            + x2 * y3 * (r15 + r8) / 56
-            - y0 * (r16 * y1 + r31 * y2 + r44 * x2 + r45 + r61 - r62 * x1) / 840
-        )
-        self.momentXX += (
-            -r12 * r72 * (-r40 + r8) / 9240
-            + 3 * r18 * (r28 + r34 - r38 * y1 + r75) / 3080
-            + r20
-            * (
-                r24 * x3
-                - r72 * y0
-                - r76 * y0
-                - r77 * y0
-                + r78
-                + r79 * y3
-                + r80 * y1
-                + 210 * r81
-                + r84
-            )
-            / 9240
-            - r29
-            * (
-                r12 * r21
-                + 14 * r13
-                + r44 * r9
-                - r73 * y3
-                + 54 * r86
-                - 84 * r87
-                - r89
-                - r90
-            )
-            / 9240
-            - r4 * (70 * r12 * x2 + 27 * r67 + 42 * r68 + r74) / 9240
-            + 3 * r67 * y3 / 220
-            - r68 * r69 / 9240
-            - r68 * y3 / 4
-            - r70 * r9 * (-r62 + y2) / 9240
-            + 3 * r71 * (r24 + r40) / 3080
-            + x0**3 * (r24 + r44 + 165 * y0 + y3) / 660
-            + x0
-            * (
-                r100 * r27
-                + 162 * r101
-                + r102
-                + r11
-                + 63 * r18 * y3
-                + r27 * r91
-                - r33 * y0
-                - r37 * x3
-                + r43 * x3
-                - r73 * y0
-                - r88 * y1
-                + r92 * y2
-                - r93 * y0
-                - 9 * r94
-                - r95 * y0
-                - r96 * y0
-                - r97 * y1
-                - 18 * r98
-                + r99 * x1 * y3
-            )
-            / 9240
-            - y0
-            * (
-                r12 * r56
-                + r12 * r80
-                + r32 * x3
-                + 45 * r67
-                + 14 * r68
-                + 126 * r71
-                + r74
-                + r85 * r91
-                + 135 * r9 * x1
-                + r92 * x2
-            )
-            / 9240
-        )
-        self.momentXY += (
-            -r103 * r12 / 18480
-            - r12 * r51 / 8
-            - 3 * r14 * y2 / 44
-            + 3 * r18 * (r105 + r2 * y1 + 18 * r46 + 15 * r48 + 7 * r51) / 6160
-            + r20
-            * (
-                1260 * r106
-                + r107 * y1
-                + r108
-                + 28 * r109
-                + r110
-                + r111
-                + r112
-                + 30 * r46
-                + 2310 * r55
-                + r66
-            )
-            / 18480
-            - r54 * (7 * r12 + 18 * r85 + 15 * r9) / 18480
-            - r55 * (r33 + r73 + r93 + r95 + r96 + r97) / 18480
-            - r7 * (42 * r13 + r82 * x3 + 28 * r87 + r89 + r90) / 18480
-            - 3 * r85 * (r48 - r66) / 220
-            + 3 * r9 * y3 * (r62 + 2 * y2) / 440
-            + x0
-            * (
-                -r1 * y0
-                - 84 * r106 * x2
-                + r109 * r56
-                + 54 * r114
-                + r117 * y1
-                + 15 * r118
-                + 21 * r119
-                + 81 * r120
-                + r121 * r46
-                + 54 * r122
-                + 60 * r123
-                + r124
-                - r21 * x3 * y0
-                + r23 * y3
-                - r54 * x3
-                - r55 * r72
-                - r55 * r76
-                - r55 * r77
-                + r57 * y0 * y3
-                + r60 * x3
-                + 84 * r81 * y0
-                + 189 * r81 * y1
-            )
-            / 9240
-            + x1
-            * (
-                r104 * r27
-                - r105 * x3
-                - r113 * r53
-                + 63 * r114
-                + r115
-                - r16 * r53
-                + 28 * r47
-                + r51 * r80
-            )
-            / 3080
-            - y0
-            * (
-                54 * r101
-                + r102
-                + r116 * r5
-                + r117 * x3
-                + 21 * r13
-                - r19 * y3
-                + r22 * y3
-                + r78 * x3
-                + 189 * r83 * x2
-                + 60 * r86
-                + 81 * r9 * y1
-                + 15 * r94
-                + 54 * r98
-            )
-            / 9240
-        )
-        self.momentYY += (
-            -r103 * r116 / 9240
-            - r125 * r70 / 9240
-            - r126 * x3 / 12
-            - 3 * r127 * (r26 + r38) / 3080
-            - r128 * (r26 + r30 + x3) / 660
-            - r4 * (r112 * x3 + r115 - 14 * r119 + 84 * r47) / 9240
-            - r52 * r69 / 9240
-            - r54 * (r58 + r61 + r75) / 9240
-            - r55
-            * (r100 * y1 + r121 * y2 + r26 * y3 + r79 * y2 + r84 + 210 * x2 * y1)
-            / 9240
-            + x0
-            * (
-                r108 * y1
-                + r110 * y0
-                + r111 * y0
-                + r112 * y0
-                + 45 * r125
-                + 14 * r126
-                + 126 * r127
-                + 770 * r128
-                + 42 * r129
-                + r130
-                + r131 * y2
-                + r132 * r64
-                + 135 * r48 * y1
-                + 630 * r55 * y1
-                + 126 * r55 * y2
-                + 14 * r55 * y3
-                + r63 * y3
-                + r65 * y3
-                + r66 * y0
-            )
-            / 9240
-            + x1
-            * (
-                27 * r125
-                + 42 * r126
-                + 70 * r129
-                + r130
-                + r39 * r53
-                + r44 * r48
-                + 27 * r53 * y2
-                + 54 * r64 * y2
-            )
-            / 3080
-            + 3 * x2 * y3 * (r48 + r66 + r8 * y3) / 220
-            - y0
-            * (
-                r100 * r46
-                + 18 * r114
-                - 9 * r118
-                - 27 * r120
-                - 18 * r122
-                - 30 * r123
-                + r124
-                + r131 * x2
-                + r132 * x3 * y1
-                + 162 * r42 * y1
-                + r50
-                + 63 * r53 * x3
-                + r64 * r99
-            )
-            / 9240
-        )
-
-
-if __name__ == "__main__":
-    from fontTools.misc.symfont import x, y, printGreenPen
-
-    printGreenPen(
-        "MomentsPen",
-        [
-            ("area", 1),
-            ("momentX", x),
-            ("momentY", y),
-            ("momentXX", x**2),
-            ("momentXY", x * y),
-            ("momentYY", y**2),
-        ],
-    )
diff --git a/spaces/colakin/video-generater/public/ffmpeg/fftools/ffmpeg_hw.c b/spaces/colakin/video-generater/public/ffmpeg/fftools/ffmpeg_hw.c
deleted file mode 100644
index e67145211c8a48aa2ea373b3400857ba9f03c603..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/fftools/ffmpeg_hw.c
+++ /dev/null
@@ -1,572 +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
- */
-
-#include <string.h>
-
-#include "libavutil/avstring.h"
-#include "libavutil/pixdesc.h"
-#include "libavfilter/buffersink.h"
-
-#include "ffmpeg.h"
-
-static int nb_hw_devices;
-static HWDevice **hw_devices;
-
-static HWDevice *hw_device_get_by_type(enum AVHWDeviceType type)
-{
-    HWDevice *found = NULL;
-    int i;
-    for (i = 0; i < nb_hw_devices; i++) {
-        if (hw_devices[i]->type == type) {
-            if (found)
-                return NULL;
-            found = hw_devices[i];
-        }
-    }
-    return found;
-}
-
-HWDevice *hw_device_get_by_name(const char *name)
-{
-    int i;
-    for (i = 0; i < nb_hw_devices; i++) {
-        if (!strcmp(hw_devices[i]->name, name))
-            return hw_devices[i];
-    }
-    return NULL;
-}
-
-static HWDevice *hw_device_add(void)
-{
-    int err;
-    err = av_reallocp_array(&hw_devices, nb_hw_devices + 1,
-                            sizeof(*hw_devices));
-    if (err) {
-        nb_hw_devices = 0;
-        return NULL;
-    }
-    hw_devices[nb_hw_devices] = av_mallocz(sizeof(HWDevice));
-    if (!hw_devices[nb_hw_devices])
-        return NULL;
-    return hw_devices[nb_hw_devices++];
-}
-
-static char *hw_device_default_name(enum AVHWDeviceType type)
-{
-    // Make an automatic name of the form "type%d".  We arbitrarily
-    // limit at 1000 anonymous devices of the same type - there is
-    // probably something else very wrong if you get to this limit.
-    const char *type_name = av_hwdevice_get_type_name(type);
-    char *name;
-    size_t index_pos;
-    int index, index_limit = 1000;
-    index_pos = strlen(type_name);
-    name = av_malloc(index_pos + 4);
-    if (!name)
-        return NULL;
-    for (index = 0; index < index_limit; index++) {
-        snprintf(name, index_pos + 4, "%s%d", type_name, index);
-        if (!hw_device_get_by_name(name))
-            break;
-    }
-    if (index >= index_limit) {
-        av_freep(&name);
-        return NULL;
-    }
-    return name;
-}
-
-int hw_device_init_from_string(const char *arg, HWDevice **dev_out)
-{
-    // "type=name"
-    // "type=name,key=value,key2=value2"
-    // "type=name:device,key=value,key2=value2"
-    // "type:device,key=value,key2=value2"
-    // -> av_hwdevice_ctx_create()
-    // "type=name@name"
-    // "type@name"
-    // -> av_hwdevice_ctx_create_derived()
-
-    AVDictionary *options = NULL;
-    const char *type_name = NULL, *name = NULL, *device = NULL;
-    enum AVHWDeviceType type;
-    HWDevice *dev, *src;
-    AVBufferRef *device_ref = NULL;
-    int err;
-    const char *errmsg, *p, *q;
-    size_t k;
-
-    k = strcspn(arg, ":=@");
-    p = arg + k;
-
-    type_name = av_strndup(arg, k);
-    if (!type_name) {
-        err = AVERROR(ENOMEM);
-        goto fail;
-    }
-    type = av_hwdevice_find_type_by_name(type_name);
-    if (type == AV_HWDEVICE_TYPE_NONE) {
-        errmsg = "unknown device type";
-        goto invalid;
-    }
-
-    if (*p == '=') {
-        k = strcspn(p + 1, ":@,");
-
-        name = av_strndup(p + 1, k);
-        if (!name) {
-            err = AVERROR(ENOMEM);
-            goto fail;
-        }
-        if (hw_device_get_by_name(name)) {
-            errmsg = "named device already exists";
-            goto invalid;
-        }
-
-        p += 1 + k;
-    } else {
-        name = hw_device_default_name(type);
-        if (!name) {
-            err = AVERROR(ENOMEM);
-            goto fail;
-        }
-    }
-
-    if (!*p) {
-        // New device with no parameters.
-        err = av_hwdevice_ctx_create(&device_ref, type,
-                                     NULL, NULL, 0);
-        if (err < 0)
-            goto fail;
-
-    } else if (*p == ':') {
-        // New device with some parameters.
-        ++p;
-        q = strchr(p, ',');
-        if (q) {
-            if (q - p > 0) {
-                device = av_strndup(p, q - p);
-                if (!device) {
-                    err = AVERROR(ENOMEM);
-                    goto fail;
-                }
-            }
-            err = av_dict_parse_string(&options, q + 1, "=", ",", 0);
-            if (err < 0) {
-                errmsg = "failed to parse options";
-                goto invalid;
-            }
-        }
-
-        err = av_hwdevice_ctx_create(&device_ref, type,
-                                     q ? device : p[0] ? p : NULL,
-                                     options, 0);
-        if (err < 0)
-            goto fail;
-
-    } else if (*p == '@') {
-        // Derive from existing device.
-
-        src = hw_device_get_by_name(p + 1);
-        if (!src) {
-            errmsg = "invalid source device name";
-            goto invalid;
-        }
-
-        err = av_hwdevice_ctx_create_derived(&device_ref, type,
-                                             src->device_ref, 0);
-        if (err < 0)
-            goto fail;
-    } else if (*p == ',') {
-        err = av_dict_parse_string(&options, p + 1, "=", ",", 0);
-
-        if (err < 0) {
-            errmsg = "failed to parse options";
-            goto invalid;
-        }
-
-        err = av_hwdevice_ctx_create(&device_ref, type,
-                                     NULL, options, 0);
-        if (err < 0)
-            goto fail;
-    } else {
-        errmsg = "parse error";
-        goto invalid;
-    }
-
-    dev = hw_device_add();
-    if (!dev) {
-        err = AVERROR(ENOMEM);
-        goto fail;
-    }
-
-    dev->name = name;
-    dev->type = type;
-    dev->device_ref = device_ref;
-
-    if (dev_out)
-        *dev_out = dev;
-
-    name = NULL;
-    err = 0;
-done:
-    av_freep(&type_name);
-    av_freep(&name);
-    av_freep(&device);
-    av_dict_free(&options);
-    return err;
-invalid:
-    av_log(NULL, AV_LOG_ERROR,
-           "Invalid device specification \"%s\": %s\n", arg, errmsg);
-    err = AVERROR(EINVAL);
-    goto done;
-fail:
-    av_log(NULL, AV_LOG_ERROR,
-           "Device creation failed: %d.\n", err);
-    av_buffer_unref(&device_ref);
-    goto done;
-}
-
-static int hw_device_init_from_type(enum AVHWDeviceType type,
-                                    const char *device,
-                                    HWDevice **dev_out)
-{
-    AVBufferRef *device_ref = NULL;
-    HWDevice *dev;
-    char *name;
-    int err;
-
-    name = hw_device_default_name(type);
-    if (!name) {
-        err = AVERROR(ENOMEM);
-        goto fail;
-    }
-
-    err = av_hwdevice_ctx_create(&device_ref, type, device, NULL, 0);
-    if (err < 0) {
-        av_log(NULL, AV_LOG_ERROR,
-               "Device creation failed: %d.\n", err);
-        goto fail;
-    }
-
-    dev = hw_device_add();
-    if (!dev) {
-        err = AVERROR(ENOMEM);
-        goto fail;
-    }
-
-    dev->name = name;
-    dev->type = type;
-    dev->device_ref = device_ref;
-
-    if (dev_out)
-        *dev_out = dev;
-
-    return 0;
-
-fail:
-    av_freep(&name);
-    av_buffer_unref(&device_ref);
-    return err;
-}
-
-void hw_device_free_all(void)
-{
-    int i;
-    for (i = 0; i < nb_hw_devices; i++) {
-        av_freep(&hw_devices[i]->name);
-        av_buffer_unref(&hw_devices[i]->device_ref);
-        av_freep(&hw_devices[i]);
-    }
-    av_freep(&hw_devices);
-    nb_hw_devices = 0;
-}
-
-static HWDevice *hw_device_match_by_codec(const AVCodec *codec)
-{
-    const AVCodecHWConfig *config;
-    HWDevice *dev;
-    int i;
-    for (i = 0;; i++) {
-        config = avcodec_get_hw_config(codec, i);
-        if (!config)
-            return NULL;
-        if (!(config->methods & AV_CODEC_HW_CONFIG_METHOD_HW_DEVICE_CTX))
-            continue;
-        dev = hw_device_get_by_type(config->device_type);
-        if (dev)
-            return dev;
-    }
-}
-
-int hw_device_setup_for_decode(InputStream *ist)
-{
-    const AVCodecHWConfig *config;
-    enum AVHWDeviceType type;
-    HWDevice *dev = NULL;
-    int err, auto_device = 0;
-
-    if (ist->hwaccel_device) {
-        dev = hw_device_get_by_name(ist->hwaccel_device);
-        if (!dev) {
-            if (ist->hwaccel_id == HWACCEL_AUTO) {
-                auto_device = 1;
-            } else if (ist->hwaccel_id == HWACCEL_GENERIC) {
-                type = ist->hwaccel_device_type;
-                err = hw_device_init_from_type(type, ist->hwaccel_device,
-                                               &dev);
-            } else {
-                // This will be dealt with by API-specific initialisation
-                // (using hwaccel_device), so nothing further needed here.
-                return 0;
-            }
-        } else {
-            if (ist->hwaccel_id == HWACCEL_AUTO) {
-                ist->hwaccel_device_type = dev->type;
-            } else if (ist->hwaccel_device_type != dev->type) {
-                av_log(NULL, AV_LOG_ERROR, "Invalid hwaccel device "
-                       "specified for decoder: device %s of type %s is not "
-                       "usable with hwaccel %s.\n", dev->name,
-                       av_hwdevice_get_type_name(dev->type),
-                       av_hwdevice_get_type_name(ist->hwaccel_device_type));
-                return AVERROR(EINVAL);
-            }
-        }
-    } else {
-        if (ist->hwaccel_id == HWACCEL_AUTO) {
-            auto_device = 1;
-        } else if (ist->hwaccel_id == HWACCEL_GENERIC) {
-            type = ist->hwaccel_device_type;
-            dev = hw_device_get_by_type(type);
-
-            // When "-qsv_device device" is used, an internal QSV device named
-            // as "__qsv_device" is created. Another QSV device is created too
-            // if "-init_hw_device qsv=name:device" is used. There are 2 QSV devices
-            // if both "-qsv_device device" and "-init_hw_device qsv=name:device"
-            // are used, hw_device_get_by_type(AV_HWDEVICE_TYPE_QSV) returns NULL.
-            // To keep back-compatibility with the removed ad-hoc libmfx setup code,
-            // call hw_device_get_by_name("__qsv_device") to select the internal QSV
-            // device.
-            if (!dev && type == AV_HWDEVICE_TYPE_QSV)
-                dev = hw_device_get_by_name("__qsv_device");
-
-            if (!dev)
-                err = hw_device_init_from_type(type, NULL, &dev);
-        } else {
-            dev = hw_device_match_by_codec(ist->dec);
-            if (!dev) {
-                // No device for this codec, but not using generic hwaccel
-                // and therefore may well not need one - ignore.
-                return 0;
-            }
-        }
-    }
-
-    if (auto_device) {
-        int i;
-        if (!avcodec_get_hw_config(ist->dec, 0)) {
-            // Decoder does not support any hardware devices.
-            return 0;
-        }
-        for (i = 0; !dev; i++) {
-            config = avcodec_get_hw_config(ist->dec, i);
-            if (!config)
-                break;
-            type = config->device_type;
-            dev = hw_device_get_by_type(type);
-            if (dev) {
-                av_log(NULL, AV_LOG_INFO, "Using auto "
-                       "hwaccel type %s with existing device %s.\n",
-                       av_hwdevice_get_type_name(type), dev->name);
-            }
-        }
-        for (i = 0; !dev; i++) {
-            config = avcodec_get_hw_config(ist->dec, i);
-            if (!config)
-                break;
-            type = config->device_type;
-            // Try to make a new device of this type.
-            err = hw_device_init_from_type(type, ist->hwaccel_device,
-                                           &dev);
-            if (err < 0) {
-                // Can't make a device of this type.
-                continue;
-            }
-            if (ist->hwaccel_device) {
-                av_log(NULL, AV_LOG_INFO, "Using auto "
-                       "hwaccel type %s with new device created "
-                       "from %s.\n", av_hwdevice_get_type_name(type),
-                       ist->hwaccel_device);
-            } else {
-                av_log(NULL, AV_LOG_INFO, "Using auto "
-                       "hwaccel type %s with new default device.\n",
-                       av_hwdevice_get_type_name(type));
-            }
-        }
-        if (dev) {
-            ist->hwaccel_device_type = type;
-        } else {
-            av_log(NULL, AV_LOG_INFO, "Auto hwaccel "
-                   "disabled: no device found.\n");
-            ist->hwaccel_id = HWACCEL_NONE;
-            return 0;
-        }
-    }
-
-    if (!dev) {
-        av_log(NULL, AV_LOG_ERROR, "No device available "
-               "for decoder: device type %s needed for codec %s.\n",
-               av_hwdevice_get_type_name(type), ist->dec->name);
-        return err;
-    }
-
-    ist->dec_ctx->hw_device_ctx = av_buffer_ref(dev->device_ref);
-    if (!ist->dec_ctx->hw_device_ctx)
-        return AVERROR(ENOMEM);
-
-    return 0;
-}
-
-int hw_device_setup_for_encode(OutputStream *ost)
-{
-    const AVCodecHWConfig *config;
-    HWDevice *dev = NULL;
-    AVBufferRef *frames_ref = NULL;
-    int i;
-
-    if (ost->filter) {
-        frames_ref = av_buffersink_get_hw_frames_ctx(ost->filter->filter);
-        if (frames_ref &&
-            ((AVHWFramesContext*)frames_ref->data)->format ==
-            ost->enc_ctx->pix_fmt) {
-            // Matching format, will try to use hw_frames_ctx.
-        } else {
-            frames_ref = NULL;
-        }
-    }
-
-    for (i = 0;; i++) {
-        config = avcodec_get_hw_config(ost->enc_ctx->codec, i);
-        if (!config)
-            break;
-
-        if (frames_ref &&
-            config->methods & AV_CODEC_HW_CONFIG_METHOD_HW_FRAMES_CTX &&
-            (config->pix_fmt == AV_PIX_FMT_NONE ||
-             config->pix_fmt == ost->enc_ctx->pix_fmt)) {
-            av_log(ost->enc_ctx, AV_LOG_VERBOSE, "Using input "
-                   "frames context (format %s) with %s encoder.\n",
-                   av_get_pix_fmt_name(ost->enc_ctx->pix_fmt),
-                   ost->enc_ctx->codec->name);
-            ost->enc_ctx->hw_frames_ctx = av_buffer_ref(frames_ref);
-            if (!ost->enc_ctx->hw_frames_ctx)
-                return AVERROR(ENOMEM);
-            return 0;
-        }
-
-        if (!dev &&
-            config->methods & AV_CODEC_HW_CONFIG_METHOD_HW_DEVICE_CTX)
-            dev = hw_device_get_by_type(config->device_type);
-    }
-
-    if (dev) {
-        av_log(ost->enc_ctx, AV_LOG_VERBOSE, "Using device %s "
-               "(type %s) with %s encoder.\n", dev->name,
-               av_hwdevice_get_type_name(dev->type), ost->enc_ctx->codec->name);
-        ost->enc_ctx->hw_device_ctx = av_buffer_ref(dev->device_ref);
-        if (!ost->enc_ctx->hw_device_ctx)
-            return AVERROR(ENOMEM);
-    } else {
-        // No device required, or no device available.
-    }
-    return 0;
-}
-
-static int hwaccel_retrieve_data(AVCodecContext *avctx, AVFrame *input)
-{
-    InputStream *ist = avctx->opaque;
-    AVFrame *output = NULL;
-    enum AVPixelFormat output_format = ist->hwaccel_output_format;
-    int err;
-
-    if (input->format == output_format) {
-        // Nothing to do.
-        return 0;
-    }
-
-    output = av_frame_alloc();
-    if (!output)
-        return AVERROR(ENOMEM);
-
-    output->format = output_format;
-
-    err = av_hwframe_transfer_data(output, input, 0);
-    if (err < 0) {
-        av_log(avctx, AV_LOG_ERROR, "Failed to transfer data to "
-               "output frame: %d.\n", err);
-        goto fail;
-    }
-
-    err = av_frame_copy_props(output, input);
-    if (err < 0) {
-        av_frame_unref(output);
-        goto fail;
-    }
-
-    av_frame_unref(input);
-    av_frame_move_ref(input, output);
-    av_frame_free(&output);
-
-    return 0;
-
-fail:
-    av_frame_free(&output);
-    return err;
-}
-
-int hwaccel_decode_init(AVCodecContext *avctx)
-{
-    InputStream *ist = avctx->opaque;
-
-    ist->hwaccel_retrieve_data = &hwaccel_retrieve_data;
-
-    return 0;
-}
-
-AVBufferRef *hw_device_for_filter(void)
-{
-    // Pick the last hardware device if the user doesn't pick the device for
-    // filters explicitly with the filter_hw_device option.
-    if (filter_hw_device)
-        return filter_hw_device->device_ref;
-    else if (nb_hw_devices > 0) {
-        HWDevice *dev = hw_devices[nb_hw_devices - 1];
-
-        if (nb_hw_devices > 1)
-            av_log(NULL, AV_LOG_WARNING, "There are %d hardware devices. device "
-                   "%s of type %s is picked for filters by default. Set hardware "
-                   "device explicitly with the filter_hw_device option if device "
-                   "%s is not usable for filters.\n",
-                   nb_hw_devices, dev->name,
-                   av_hwdevice_get_type_name(dev->type), dev->name);
-
-        return dev->device_ref;
-    }
-
-    return NULL;
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/hevc_mc_uniw_lsx.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/hevc_mc_uniw_lsx.c
deleted file mode 100644
index c4e79225d38dcf626ec1fc1ef77adcd834dfeb8e..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/loongarch/hevc_mc_uniw_lsx.c
+++ /dev/null
@@ -1,298 +0,0 @@
-/*
- * Copyright (c) 2022 Loongson Technology Corporation Limited
- * Contributed by Lu Wang <wanglu@loongson.cn>
- *                Hao Chen <chenhao@loongson.cn>
- *
- * 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/loongarch/loongson_intrinsics.h"
-#include "hevcdsp_lsx.h"
-
-static const uint8_t ff_hevc_mask_arr[16 * 2] __attribute__((aligned(0x40))) = {
-    /* 8 width cases */
-    0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8,
-    /* 4 width cases */
-    0, 1, 1, 2, 2, 3, 3, 4, 16, 17, 17, 18, 18, 19, 19, 20
-};
-
-static av_always_inline
-void hevc_hv_8t_8x2_lsx(const uint8_t *src, int32_t src_stride, uint8_t *dst,
-                        int32_t dst_stride, const int8_t *filter_x,
-                        const int8_t *filter_y, int32_t height, int32_t weight,
-                        int32_t offset, int32_t rnd_val, int32_t width)
-{
-    uint32_t loop_cnt, cnt;
-    const uint8_t *src_tmp;
-    uint8_t *dst_tmp;
-    const int32_t src_stride_2x = (src_stride << 1);
-    const int32_t dst_stride_2x = (dst_stride << 1);
-    const int32_t src_stride_4x = (src_stride << 2);
-    const int32_t src_stride_3x = src_stride_2x + src_stride;
-
-    __m128i src0, src1, src2, src3, src4, src5, src6, src7, src8;
-    __m128i filt0, filt1, filt2, filt3;
-    __m128i filt_h0, filt_h1, filt_h2, filt_h3;
-    __m128i mask1, mask2, mask3;
-    __m128i filter_vec;
-    __m128i vec0, vec1, vec2, vec3, vec4, vec5, vec6, vec7;
-    __m128i vec8, vec9, vec10, vec11, vec12, vec13, vec14, vec15;
-    __m128i dst0, dst1, dst2, dst3, dst4, dst5, dst6, dst7, dst8;
-    __m128i dst0_r, dst0_l, dst1_r, dst1_l;
-    __m128i dst10_r, dst32_r, dst54_r, dst76_r;
-    __m128i dst10_l, dst32_l, dst54_l, dst76_l;
-    __m128i dst21_r, dst43_r, dst65_r, dst87_r;
-    __m128i dst21_l, dst43_l, dst65_l, dst87_l;
-    __m128i weight_vec, offset_vec, rnd_vec;
-    __m128i mask0 = __lsx_vld(ff_hevc_mask_arr, 0);
-
-    src -= (src_stride_3x + 3);
-    weight_vec = __lsx_vreplgr2vr_w(weight);
-    offset_vec = __lsx_vreplgr2vr_w(offset);
-    rnd_vec = __lsx_vreplgr2vr_w(rnd_val);
-
-    DUP4_ARG2(__lsx_vldrepl_h, filter_x, 0, filter_x, 2, filter_x, 4,
-              filter_x, 6, filt0, filt1, filt2, filt3);
-    filter_vec = __lsx_vld(filter_y, 0);
-    filter_vec = __lsx_vsllwil_h_b(filter_vec, 0);
-    DUP4_ARG2(__lsx_vreplvei_w, filter_vec, 0, filter_vec, 1, filter_vec, 2,
-              filter_vec, 3, filt_h0, filt_h1, filt_h2, filt_h3);
-    DUP2_ARG2(__lsx_vaddi_bu, mask0, 2, mask0, 4, mask1, mask2);
-    mask3 = __lsx_vaddi_bu(mask0, 6);
-
-    for (cnt = width >> 3; cnt--;) {
-        src_tmp = src;
-        dst_tmp = dst;
-
-        src0 = __lsx_vld(src_tmp, 0);
-        DUP2_ARG2(__lsx_vldx, src_tmp, src_stride, src_tmp, src_stride_2x,
-                  src1, src2);
-        src3 = __lsx_vldx(src_tmp, src_stride_3x);
-        src_tmp += src_stride_4x;
-        src4 = __lsx_vld(src_tmp, 0);
-        DUP2_ARG2(__lsx_vldx, src_tmp, src_stride, src_tmp, src_stride_2x,
-                  src5, src6);
-        src_tmp += src_stride_3x;
-
-        DUP4_ARG3(__lsx_vshuf_b, src0, src0, mask0, src0, src0, mask1, src0,
-                  src0, mask2, src0, src0, mask3, vec0, vec1, vec2, vec3);
-        DUP4_ARG3(__lsx_vshuf_b, src1, src1, mask0, src1, src1, mask1, src1,
-                  src1, mask2, src1, src1, mask3, vec4, vec5, vec6, vec7);
-        DUP4_ARG3(__lsx_vshuf_b, src2, src2, mask0, src2, src2, mask1, src2,
-                  src2, mask2, src2, src2, mask3, vec8, vec9, vec10, vec11);
-        DUP4_ARG3(__lsx_vshuf_b, src3, src3, mask0, src3, src3, mask1, src3,
-                  src3, mask2, src3, src3, mask3, vec12, vec13, vec14, vec15);
-        DUP4_ARG2(__lsx_vdp2_h_bu_b, vec0, filt0, vec4, filt0, vec8, filt0,
-                  vec12, filt0, dst0, dst1, dst2, dst3);
-        DUP4_ARG3(__lsx_vdp2add_h_bu_b, dst0, vec1, filt1, dst1, vec5, filt1,
-                  dst2, vec9, filt1, dst3, vec13, filt1, dst0, dst1, dst2, dst3);
-        DUP4_ARG3(__lsx_vdp2add_h_bu_b, dst0, vec2, filt2, dst1, vec6, filt2,
-                  dst2, vec10, filt2, dst3, vec14, filt2, dst0, dst1, dst2, dst3);
-        DUP4_ARG3(__lsx_vdp2add_h_bu_b, dst0, vec3, filt3, dst1, vec7, filt3,
-                  dst2, vec11, filt3, dst3, vec15, filt3, dst0, dst1, dst2, dst3);
-        DUP4_ARG3(__lsx_vshuf_b, src4, src4, mask0, src4, src4, mask1, src4,
-                  src4, mask2, src4, src4, mask3, vec0, vec1, vec2, vec3);
-        DUP4_ARG3(__lsx_vshuf_b, src5, src5, mask0, src5, src5, mask1, src5,
-                  src5, mask2, src5, src5, mask3, vec4, vec5, vec6, vec7);
-        DUP4_ARG3(__lsx_vshuf_b, src6, src6, mask0, src6, src6, mask1, src6,
-                  src6, mask2, src6, src6, mask3, vec8, vec9, vec10, vec11);
-        DUP2_ARG2(__lsx_vdp2_h_bu_b, vec0, filt0, vec4, filt0, dst4, dst5);
-        dst6 = __lsx_vdp2_h_bu_b(vec8, filt0);
-        DUP4_ARG3(__lsx_vdp2add_h_bu_b, dst4, vec1, filt1, dst5, vec5, filt1,
-                  dst6, vec9, filt1, dst4, vec2, filt2, dst4, dst5, dst6, dst4);
-        DUP4_ARG3(__lsx_vdp2add_h_bu_b, dst5, vec6, filt2, dst6, vec10, filt2,
-                  dst4, vec3, filt3, dst5, vec7, filt3, dst5, dst6, dst4, dst5);
-        dst6 = __lsx_vdp2add_h_bu_b(dst6, vec11, filt3);
-
-        DUP4_ARG2(__lsx_vilvl_h, dst1, dst0, dst3, dst2, dst5, dst4, dst2,
-                  dst1, dst10_r, dst32_r, dst54_r, dst21_r);
-        DUP2_ARG2(__lsx_vilvl_h, dst4, dst3, dst6, dst5, dst43_r, dst65_r);
-        DUP4_ARG2(__lsx_vilvh_h, dst1, dst0, dst3, dst2, dst5, dst4, dst2,
-                  dst1, dst10_l, dst32_l, dst54_l, dst21_l);
-        DUP2_ARG2(__lsx_vilvh_h, dst4, dst3, dst6, dst5, dst43_l, dst65_l);
-
-        for (loop_cnt = height >> 1; loop_cnt--;) {
-            src7 = __lsx_vld(src_tmp, 0);
-            src8 = __lsx_vldx(src_tmp, src_stride);
-            src_tmp += src_stride_2x;
-            DUP4_ARG3(__lsx_vshuf_b, src7, src7, mask0, src7, src7, mask1, src7,
-                      src7, mask2, src7, src7, mask3, vec0, vec1, vec2, vec3);
-            dst7 = __lsx_vdp2_h_bu_b(vec0, filt0);
-            DUP2_ARG3(__lsx_vdp2add_h_bu_b, dst7, vec1, filt1, dst7, vec2,
-                      filt2, dst7, dst7);
-            dst7 = __lsx_vdp2add_h_bu_b(dst7, vec3, filt3);
-            dst76_r = __lsx_vilvl_h(dst7, dst6);
-            dst76_l = __lsx_vilvh_h(dst7, dst6);
-            DUP2_ARG2(__lsx_vdp2_w_h, dst10_r, filt_h0, dst10_l, filt_h0,
-                      dst0_r, dst0_l);
-            DUP4_ARG3(__lsx_vdp2add_w_h, dst0_r, dst32_r, filt_h1, dst0_l,
-                      dst32_l, filt_h1, dst0_r, dst54_r, filt_h2, dst0_l,
-                      dst54_l, filt_h2, dst0_r, dst0_l, dst0_r, dst0_l);
-            DUP2_ARG3(__lsx_vdp2add_w_h, dst0_r, dst76_r, filt_h3, dst0_l,
-                      dst76_l, filt_h3, dst0_r, dst0_l);
-            DUP2_ARG2(__lsx_vsrai_w, dst0_r, 6, dst0_l, 6, dst0_r, dst0_l);
-
-            /* row 8 */
-            DUP4_ARG3(__lsx_vshuf_b, src8, src8, mask0, src8, src8, mask1, src8,
-                      src8, mask2, src8, src8, mask3, vec0, vec1, vec2, vec3);
-            dst8 = __lsx_vdp2_h_bu_b(vec0, filt0);
-            DUP2_ARG3(__lsx_vdp2add_h_bu_b, dst8, vec1, filt1, dst8, vec2,
-                      filt2, dst8, dst8);
-            dst8 = __lsx_vdp2add_h_bu_b(dst8, vec3, filt3);
-
-            dst87_r = __lsx_vilvl_h(dst8, dst7);
-            dst87_l = __lsx_vilvh_h(dst8, dst7);
-            DUP2_ARG2(__lsx_vdp2_w_h, dst21_r, filt_h0, dst21_l, filt_h0,
-                      dst1_r, dst1_l);
-            DUP4_ARG3(__lsx_vdp2add_w_h, dst1_r, dst43_r, filt_h1, dst1_l,
-                      dst43_l, filt_h1, dst1_r, dst65_r, filt_h2, dst1_l,
-                      dst65_l, filt_h2, dst1_r, dst1_l, dst1_r, dst1_l);
-            DUP2_ARG3(__lsx_vdp2add_w_h, dst1_r, dst87_r, filt_h3, dst1_l,
-                      dst87_l, filt_h3, dst1_r, dst1_l);
-            DUP2_ARG2(__lsx_vsrai_w, dst1_r, 6, dst1_l, 6, dst1_r, dst1_l);
-
-            DUP2_ARG2(__lsx_vmul_w, dst0_r, weight_vec, dst0_l, weight_vec,
-                      dst0_r, dst0_l);
-            DUP2_ARG2(__lsx_vmul_w, dst1_r, weight_vec, dst1_l, weight_vec,
-                      dst1_r, dst1_l);
-            DUP4_ARG2(__lsx_vsrar_w, dst0_r, rnd_vec, dst1_r, rnd_vec, dst0_l,
-                     rnd_vec, dst1_l, rnd_vec, dst0_r, dst1_r, dst0_l, dst1_l);
-
-            DUP2_ARG2(__lsx_vadd_w, dst0_r, offset_vec, dst0_l, offset_vec,
-                      dst0_r, dst0_l);
-            DUP2_ARG2(__lsx_vadd_w, dst1_r, offset_vec, dst1_l, offset_vec,
-                      dst1_r, dst1_l);
-            DUP4_ARG1(__lsx_vclip255_w, dst0_r, dst1_r, dst0_l, dst1_l, dst0_r,
-                      dst1_r, dst0_l, dst1_l);
-            DUP2_ARG2(__lsx_vpickev_h, dst0_l, dst0_r, dst1_l, dst1_r,
-                      dst0_r, dst1_r);
-            dst0_r = __lsx_vpickev_b(dst1_r, dst0_r);
-
-            __lsx_vstelm_d(dst0_r, dst_tmp, 0, 0);
-            __lsx_vstelm_d(dst0_r, dst_tmp + dst_stride, 0, 1);
-            dst_tmp += dst_stride_2x;
-
-            dst10_r = dst32_r;
-            dst32_r = dst54_r;
-            dst54_r = dst76_r;
-            dst10_l = dst32_l;
-            dst32_l = dst54_l;
-            dst54_l = dst76_l;
-            dst21_r = dst43_r;
-            dst43_r = dst65_r;
-            dst65_r = dst87_r;
-            dst21_l = dst43_l;
-            dst43_l = dst65_l;
-            dst65_l = dst87_l;
-            dst6 = dst8;
-        }
-
-        src += 8;
-        dst += 8;
-    }
-}
-
-static
-void hevc_hv_8t_8w_lsx(const uint8_t *src, int32_t src_stride, uint8_t *dst,
-                       int32_t dst_stride, const int8_t *filter_x,
-                       const int8_t *filter_y, int32_t height, int32_t weight,
-                       int32_t offset, int32_t rnd_val)
-{
-    hevc_hv_8t_8x2_lsx(src, src_stride, dst, dst_stride, filter_x,
-                       filter_y, height, weight, offset, rnd_val, 8);
-}
-
-static
-void hevc_hv_8t_16w_lsx(const uint8_t *src, int32_t src_stride, uint8_t *dst,
-                        int32_t dst_stride, const int8_t *filter_x,
-                        const int8_t *filter_y, int32_t height, int32_t weight,
-                        int32_t offset, int32_t rnd_val)
-{
-    hevc_hv_8t_8x2_lsx(src, src_stride, dst, dst_stride, filter_x,
-                       filter_y, height, weight, offset, rnd_val, 16);
-}
-
-static
-void hevc_hv_8t_24w_lsx(const uint8_t *src, int32_t src_stride, uint8_t *dst,
-                        int32_t dst_stride, const int8_t *filter_x,
-                        const int8_t *filter_y, int32_t height, int32_t weight,
-                        int32_t offset, int32_t rnd_val)
-{
-    hevc_hv_8t_8x2_lsx(src, src_stride, dst, dst_stride, filter_x,
-                       filter_y, height, weight, offset, rnd_val, 24);
-}
-
-static
-void hevc_hv_8t_32w_lsx(const uint8_t *src, int32_t src_stride, uint8_t *dst,
-                        int32_t dst_stride, const int8_t *filter_x,
-                        const int8_t *filter_y, int32_t height, int32_t weight,
-                        int32_t offset, int32_t rnd_val)
-{
-    hevc_hv_8t_8x2_lsx(src, src_stride, dst, dst_stride, filter_x,
-                       filter_y, height, weight, offset, rnd_val, 32);
-}
-
-static
-void hevc_hv_8t_48w_lsx(const uint8_t *src, int32_t src_stride, uint8_t *dst,
-                        int32_t dst_stride, const int8_t *filter_x,
-                        const int8_t *filter_y, int32_t height, int32_t weight,
-                        int32_t offset, int32_t rnd_val)
-{
-    hevc_hv_8t_8x2_lsx(src, src_stride, dst, dst_stride, filter_x,
-                       filter_y, height, weight, offset, rnd_val, 48);
-}
-
-static
-void hevc_hv_8t_64w_lsx(const uint8_t *src, int32_t src_stride, uint8_t *dst,
-                        int32_t dst_stride, const int8_t *filter_x,
-                        const int8_t *filter_y, int32_t height, int32_t weight,
-                        int32_t offset, int32_t rnd_val)
-{
-    hevc_hv_8t_8x2_lsx(src, src_stride, dst, dst_stride, filter_x,
-                       filter_y, height, weight, offset, rnd_val, 64);
-}
-
-
-#define UNI_W_MC_HV(PEL, WIDTH, TAP)                                           \
-void ff_hevc_put_hevc_uni_w_##PEL##_hv##WIDTH##_8_lsx(uint8_t *dst,            \
-                                                      ptrdiff_t dst_stride,    \
-                                                      const uint8_t *src,      \
-                                                      ptrdiff_t src_stride,    \
-                                                      int height,              \
-                                                      int denom,               \
-                                                      int weight,              \
-                                                      int offset,              \
-                                                      intptr_t mx,             \
-                                                      intptr_t my,             \
-                                                      int width)               \
-{                                                                              \
-    const int8_t *filter_x = ff_hevc_##PEL##_filters[mx - 1];                  \
-    const int8_t *filter_y = ff_hevc_##PEL##_filters[my - 1];                  \
-    int shift = denom + 14 - 8;                                                \
-                                                                               \
-    hevc_hv_##TAP##t_##WIDTH##w_lsx(src, src_stride, dst, dst_stride, filter_x,\
-                                    filter_y,  height, weight, offset, shift); \
-}
-
-UNI_W_MC_HV(qpel, 8, 8);
-UNI_W_MC_HV(qpel, 16, 8);
-UNI_W_MC_HV(qpel, 24, 8);
-UNI_W_MC_HV(qpel, 32, 8);
-UNI_W_MC_HV(qpel, 48, 8);
-UNI_W_MC_HV(qpel, 64, 8);
-
-#undef UNI_W_MC_HV
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download 15 xmu Your Source for New Releases Leaks and Emerging Artists.md b/spaces/congsaPfin/Manga-OCR/logs/Download 15 xmu Your Source for New Releases Leaks and Emerging Artists.md
deleted file mode 100644
index 815b18f34d2e474c183abfc1532ed8b45e68ccbc..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download 15 xmu Your Source for New Releases Leaks and Emerging Artists.md	
+++ /dev/null
@@ -1,92 +0,0 @@
-
-<h1>Download 15 XMU: What Is It and How to Listen to It</h1>
- <p>If you are a fan of indie music, you might have heard of Download 15 XMU, a weekly playlist of the most downloaded songs on Sirius XMU, the indie rock channel on SiriusXM satellite radio. Download 15 XMU is more than just a countdown, though. It is also a curated selection of new and emerging indie artists from different genres, styles, and influences. It features guest hosts, interviews, and exclusive sessions with some of the most exciting names in the indie scene. In this article, we will tell you everything you need to know about Download 15 XMU, including its history, benefits, challenges, and tips for enjoying it.</p>
-<h2>download 15 xmu</h2><br /><p><b><b>Download</b> &#11088; <a href="https://urlca.com/2uOccu">https://urlca.com/2uOccu</a></b></p><br /><br />
- <h2>The History of Download 15 XMU</h2>
- <p>Download 15 XMU started as a simple idea: to rank the most downloaded songs on Sirius XMU every week based on listener feedback. The first episode aired in 2006, and since then, it has become a staple of the channel's programming. Every Saturday at noon ET, a new episode premieres, featuring 15 songs that have been downloaded the most by Sirius XMU listeners in the past week.</p>
- <p>Over the years, Download 15 XMU has evolved into more than just a countdown. It has also become a source of discovery and inspiration for indie music fans who want to hear new and emerging artists that they might not find elsewhere. Download 15 XMU showcases a wide range of indie music, from rock to pop to electronic to folk and everything in between. It also features guest hosts from time to time, such as Phoebe Bridgers, Matt Berninger, Kevin Morby, and more, who share their personal picks and insights on the playlist.</p>
- <p>Another highlight of Download 15 XMU is the exclusive sessions that it hosts with some of the most promising indie artists in the scene. These sessions are recorded live at SiriusXM studios or other locations, and they offer listeners a chance to hear intimate and stripped-down versions of their favorite songs. Some of the artists who have performed for Download 15 XMU sessions include Boygenius, Sylvan Esso, Little Dragon, Palehound, Bully, Beach Fossils, The Drums, Daughter, Jungle, Blondshell, and many more.</p>
- <h2>The Benefits of Download 15 XMU</h2>
- <p>One of the main benefits of Download 15 XMU is that it helps listeners discover new music and stay updated on the indie scene. By listening to Download 15 XMU, you can hear what other indie music fans are downloading and enjoying every week. You can also hear new releases from established and upcoming indie artists that might not get enough exposure or attention elsewhere.</p>
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- <p>Another benefit of Download 15 XMU is that it showcases diverse genres, styles, and influences in indie music. By listening to Download 15 XMU, you can expand your musical horizons and appreciate the variety and creativity that indie music has to offer. You can also find songs that suit your mood and taste, whether you are looking for something upbeat or mellow, catchy or experimental, <p>funny or serious, indie or mainstream, or anything in between.</p>
- <p>A third benefit of Download 15 XMU is that it supports independent and underground artists who might not have the resources or opportunities to reach a wider audience. By listening to Download 15 XMU, you can show your support and appreciation for these artists who are making music with passion and originality. You can also help them gain more exposure and recognition by downloading their songs, following them on social media, and sharing their music with others.</p>
- <h2>The Challenges of Download 15 XMU</h2>
- <p>As much as Download 15 XMU is a great playlist for indie music fans, it also has some challenges that might make it hard to enjoy for some listeners. One of the challenges is that it can be hard to find the playlist online or on the app. Download 15 XMU is not available on the SiriusXM website or app, and the only way to access it is through the SiriusXM blog, where they post the playlist every week. However, the blog is not very user-friendly or easy to navigate, and it does not provide links to the songs or artists.</p>
- <p>Another challenge of Download 15 XMU is that it can be difficult to keep track of the songs and artists that are featured on the playlist. Download 15 XMU does not provide any information or details about the songs or artists, such as their names, genres, backgrounds, or influences. The only way to find out more about them is to search for them online or on other platforms, which can be time-consuming and tedious.</p>
- <p>A third challenge of Download 15 XMU is that it can be overwhelming to listen to all the songs in one sitting. Download 15 XMU features 15 songs every week, which means that it can take more than an hour to listen to the whole playlist. For some listeners, this might be too long or too much to handle, especially if they are not familiar with the songs or artists. They might lose interest or attention halfway through, or they might skip some songs that they don't like or don't know.</p>
- <h2>The Tips for Enjoying Download 15 XMU</h2>
- <p>Despite these challenges, there are some tips that can help you enjoy Download 15 XMU more and make the most out of it. Here are some of them:</p>
- <ul>
-<li>How to access the playlist on different platforms and devices: If you have a SiriusXM subscription, you can listen to Download 15 XMU on channel 35 every Saturday at noon ET. You can also listen to it on demand on your SiriusXM app or device. If you don't have a SiriusXM subscription, you can still listen to Download 15 XMU on the SiriusXM blog, where they post the playlist every week. You can also find some unofficial sources that upload the playlist on YouTube or Spotify.</li>
-<li>How to follow the playlist on social media and Spotify: If you want to stay updated on Download 15 XMU and get notified when a new episode is out, you can follow them on social media. They have accounts on Twitter (@Download15XMU), Instagram (@download15xmu), and Facebook (Download 15 XMU). You can also follow them on Spotify (Download 15 XMU), where they update their playlist every week.</li>
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-</ul>
- <h1>Conclusion</h1>
- <p>Download 15 XMU is a weekly playlist of the most downloaded songs on Sirius XMU, the indie rock channel on SiriusXM satellite radio. It is a curated selection of new and emerging indie artists from different genres, styles, and influences. It features guest hosts, interviews, and exclusive sessions with some of the most exciting names in the indie scene. It helps listeners discover new music and stay updated on the indie scene. It showcases diverse genres, styles, and influences in indie music. It supports independent and underground artists who might not have the resources or opportunities to reach a wider audience.</p>
- <p>However, Download 15 XMU also has some challenges that might make it hard to enjoy for some listeners. It can be hard to find the playlist online or on the app. It can be difficult to keep track of the songs and artists that are featured on the playlist. It can be overwhelming to listen to all the songs in one sitting. To overcome these challenges, there are some tips that can help you enjoy Download 15 XMU more and make the most out of it. You can access the playlist on different platforms and devices, follow the playlist on social media and Spotify, and create your own playlist based on your preferences and mood.</p>
- <p>If you are a fan of indie music, Download 15 XMU is a playlist that you should not miss. It is a great way to discover new music and stay updated on the indie scene. It showcases diverse genres, styles, and influences in indie music. It supports independent and underground artists who deserve more recognition and exposure. So, what are you waiting for? Tune in to Download 15 XMU every Saturday at noon ET on Sirius XMU, or listen to it online or on the app. You might just find your next favorite song or artist.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Download 15 XMU:</p>
- <ol>
-<li>What is Download 15 XMU?</li>
-<p>Download 15 XMU is a weekly playlist of the most downloaded songs on Sirius XMU, the indie rock channel on SiriusXM satellite radio. It is a curated selection of new and emerging indie artists from different genres, styles, and influences.</p>
-<li>How can I listen to Download 15 XMU?</li>
-<p>You can listen to Download 15 XMU on channel 35 every Saturday at noon ET on SiriusXM satellite radio. You can also listen to it on demand on your SiriusXM app or device. If you don't have a SiriusXM subscription, you can still listen to Download 15 XMU on the SiriusXM blog, where they post the playlist every week. You can also find some unofficial sources that upload the playlist on YouTube or Spotify.</p>
-<li>Who are some of the artists that have been featured on Download 15 XMU?</li>
-<p>Some of the artists that have been featured on Download 15 XMU include Boygenius, Sylvan Esso, Little Dragon, Palehound, Bully, Beach Fossils, The Drums, Daughter, Jungle, Blondshell, and many more.</p>
-<li>Who are some of the guest hosts that have hosted Download 15 XMU?</li>
-<p>Some of the guest hosts that have hosted Download 15 XMU include Phoebe Bridgers, Matt Berninger, Kevin Morby, and more.</p>
-<li>What are some of the exclusive sessions that have been recorded for Download 15 XMU?</li>
-<p>Some of the exclusive sessions that have been recorded for Download 15 XMU include Boygenius, Sylvan Esso, Little Dragon, Palehound, Bully, Beach Fossils, The Drums, Daughter, Jungle, Blondshell, and many more.</p>
-</ol></p> 401be4b1e0<br />
-<br />
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\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download and Install Taming io on Your Computer - A Guide.md b/spaces/congsaPfin/Manga-OCR/logs/Download and Install Taming io on Your Computer - A Guide.md
deleted file mode 100644
index 0f034359c6b1bbefd1945a42d620ad95571ebbfd..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download and Install Taming io on Your Computer - A Guide.md	
+++ /dev/null
@@ -1,157 +0,0 @@
-
-<table>
-<tr>
-<td>
-<h1>How to Download Taming.io</h1>
-<p>Have you ever dreamed of having a magical pet that fights by your side in a wild survival adventure? If so, you might want to check out <strong>Taming.io</strong>, a new online multiplayer game that lets you do just that. In this article, we will show you what Taming.io is, why you should play it, how to play it online or offline, and some tips and tricks to help you become the savviest survivor.</p>
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-<h2>What is Taming.io?</h2>
-<p>Taming.io is a survival .io game with magical pets that fight by your side. It was developed by LapaMauve, the same creator of popular games like Starve.io and Limax.io. The game was released in September 2022 and has been updated regularly since then. You can play it for free on your browser or download it on your device.</p>
-<p>The game's genre is action-survival with elements of crafting, building, and battling. The game's gameplay is simple but addictive. You start with a pet of your choice and explore a vast map full of resources, enemies, and secrets. You can gather wood, stone, food, and other materials to craft weapons, armor, tools, and buildings. You can also age up by collecting enough experience points to unlock new items and abilities. You can tame more pets by feeding them their favorite food and adding them to your team. You can also chat and team up with other players to form alliances or fight against them for loot and territory.</p>
-<h2>Why Play Taming.io?</h2>
-<p>Taming.io is a fun and exciting game that offers many benefits and advantages over other similar games. Here are some of the reasons why you should play Taming.io:</p>
-<ul>
-<li><strong>It's free and accessible.</strong> You don't need to pay anything or download anything to play Taming.io online. You just need a browser and an internet connection. You can also download it on your device if you prefer to play offline or on the go.</li>
-<li><strong>It's easy and intuitive.</strong> You don't need to learn complicated controls or rules to play Taming.io. You just need to use your mouse and keyboard or your touch screen to move, attack, craft, build, and interact. The game also has a tutorial mode that guides you through the basics.</li>
-<li><strong>It's diverse and customizable.</strong> You can choose from a variety of pets, each with their own strengths, weaknesses, and abilities. You can also customize your character's appearance, name, and color. You can also craft and upgrade your items and buildings to suit your needs and preferences.</li>
-<li><strong>It's dynamic and challenging.</strong> You can experience different seasons, weather, day and night cycles, and events that affect the game world. You can also encounter different enemies, such as zombies, skeletons, dragons, and bosses that require strategy and teamwork to defeat. You can also compete with other players for resources, territory, and leaderboard rankings.</li>
-<li><strong>It's social and cooperative.</strong> You can chat and team up with other players to form alliances or friendships. You can also trade items, share resources, help each other out, or have fun together. You can also join clans or create your own clan to have a sense of belonging and community.</li>
-</ul>
-<h2>How to Play Taming.io Online</h2>
-<p>If you want to play Taming.io online, you just need to follow these simple steps:</p>
-<ol>
-<li>Go to the official website of Taming.io or search for it on your browser.</li>
-<li>Click on the "Play" button on the homepage.</li>
-<li>Select a server from the list or click on the "Auto" button to join the best server for you.</li>
-<li>Select a pet from the list or click on the "Random" button to get a random pet.</li>
-<li>Enter a name for your character or click on the "Random" button to get a random name.</li>
-<li>Click on the "Play" button again to start the game.</li>
-</ol>
-<h3>How to Choose a Server</h3>
-<p>When you play Taming.io online, you need to choose a server that matches your location and preferences. Here are some tips on how to choose a server:</p>
-<ul>
-<li><strong>Look at the ping.</strong> The ping is the number that shows how fast your connection is to the server. The lower the ping, the better. A high ping can cause lag or delay in the game. Try to choose a server with a ping below 100 ms.</li>
-<li><strong>Look at the players.</strong> The players are the number that shows how many people are playing on the server. The more players, the more fun and competitive the game can be. However, too many players can also cause overcrowding or lag in the game. Try to choose a server with a moderate number of players, such as 50-100.</li>
-<li><strong>Look at the mode.</strong> The mode is the type of game that is played on the server. There are two modes in Taming.io: normal mode and hardcore mode. Normal mode is the default mode where you can respawn after dying. Hardcore mode is a more challenging mode where you lose everything after dying. Try to choose a mode that suits your skill level and preference.</li>
-</ul> <h3>How to Choose a Pet</h3>
-<p>When you play Taming.io online, you need to choose a pet that matches your playstyle and personality. Here are some tips on how to choose a pet:</p>
-<ul>
-<li><strong>Look at the stats.</strong> The stats are the numbers that show how strong your pet is in different aspects. There are four stats in Taming.io: health, damage, speed, and range. Health is how much damage your pet can take before dying. Damage is how much damage your pet can deal to enemies. Speed is how fast your pet can move. Range is how far your pet can attack from. Try to choose a pet with balanced stats or stats that complement your strategy.</li>
-<li><strong>Look at the abilities.</strong> The abilities are the special skills that your pet can use in the game. There are two types of abilities in Taming.io: passive and active. Passive abilities are always active and give your pet a permanent boost or effect. Active abilities are activated by pressing a key and give your pet a temporary boost or effect. Try to choose a pet with abilities that suit your playstyle and preference.</li>
-<li><strong>Look at the appearance.</strong> The appearance is how your pet looks like in the game. There are many different pets in Taming.io, each with their own unique design and color. Some pets are cute, some pets are cool, some pets are scary, and some pets are funny. Try to choose a pet that reflects your personality and taste.</li>
-</ul>
-<h3>How to Craft and Upgrade</h3>
-<p>When you play Taming.io online, you need to craft and upgrade your items and buildings to survive and thrive in the game. Here are some tips on how to craft and upgrade:</p>
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-<ul>
-<li><strong>Gather resources.</strong> You need resources to craft and upgrade your items and buildings. There are many types of resources in Taming.io, such as wood, stone, food, gold, iron, diamond, and ruby. You can gather resources by hitting trees, rocks, bushes, animals, chests, and other objects with your weapon or tool. You can also get resources by killing enemies or looting other players.</li>
-<li><strong>Craft items.</strong> You need items to fight, defend, heal, and improve yourself in the game. There are many types of items in Taming.io, such as weapons, armor, tools, potions, and accessories. You can craft items by opening the crafting menu (C key) and selecting the item you want to craft. You need to have enough resources and meet the age requirement to craft an item.</li>
-<li><strong>Upgrade items.</strong> You need to upgrade your items to make them stronger and more effective in the game. There are two ways to upgrade your items in Taming.io: leveling up and enchanting. You can level up your items by using them more often and gaining experience points for them. You can enchant your items by using gems that you can find or buy in the game. You can see the level and enchantment of your items by hovering over them in the inventory menu (I key).</li>
-<li><strong>Build buildings.</strong> You need buildings to protect yourself, store your resources, and enhance your abilities in the game. There are many types of buildings in Taming.io, such as walls, doors, chests, furnaces, anvils, altars, towers, traps, and farms. You can build buildings by opening the building menu (B key) and selecting the building you want to build. You need to have enough resources and meet the age requirement to build a building.</li>
-<li><strong>Upgrade buildings.</strong> You need to upgrade your buildings to make them more durable and more functional in the game. There are two ways to upgrade your buildings in Taming.io: repairing and reinforcing. You can repair your buildings by hitting them with a hammer or a tool when they are damaged by enemies or players. You can reinforce your buildings by adding more resources or gems to them when they are fully repaired.</li>
-</ul>
-<h3>How to Build a Base</h3>
-<p>When you play Taming.io online, you need to build a base to secure your position and resources in the game. Here are some tips on how to build a base:</p>
-<ul>
-<li><strong>Choose a location.</strong> You need to choose a location that is suitable for your base. A good location should have enough space for your buildings, enough resources for your needs, enough protection from enemies and players, and enough access to other areas of the map.</li>
-<li><strong>Lay out a plan.</strong> You need to lay out a plan for your base before you start building it. A good plan should have a clear structure, a logical layout, an efficient design, and an aesthetic appeal.</li <li><strong>Build your base.</strong> You need to build your base according to your plan and your resources. A good base should have a strong defense, a secure storage, a comfortable living, and a useful utility. You can use walls, doors, chests, furnaces, anvils, altars, towers, traps, and farms to build your base.</li>
-<li><strong>Maintain your base.</strong> You need to maintain your base to keep it in good condition and prevent it from being destroyed or raided by enemies or players. You can repair, reinforce, upgrade, and decorate your base as needed. You can also expand or modify your base as you progress in the game.</li>
-</ul>
-<h3>How to Tame More Pets</h3>
-<p>When you play Taming.io online, you need to tame more pets to increase your power and diversity in the game. Here are some tips on how to tame more pets:</p>
-<ul>
-<li><strong>Find a pet.</strong> You need to find a pet that you want to tame in the game. There are many different pets in Taming.io, each with their own location, rarity, and behavior. Some pets are easy to find and tame, while some pets are hard to find and tame. You can use the map (M key) or the pet menu (P key) to see the available pets and their locations.</li>
-<li><strong>Feed a pet.</strong> You need to feed a pet that you want to tame in the game. Each pet has its own favorite food that you need to give them to make them happy and loyal. You can craft or buy food in the game or find it in the map. You can see the pet's food preference by hovering over them in the pet menu (P key).</li>
-<li><strong>Tame a pet.</strong> You need to tame a pet that you want to keep in the game. Each pet has its own taming bar that shows how close they are to being tamed. You need to fill up the taming bar by feeding them more food or by staying near them for a while. You can see the pet's taming bar by hovering over them in the map or in the pet menu (P key).</li>
-<li><strong>Add a pet.</strong> You need to add a pet that you have tamed in the game. Each player can have up to four pets at a time in Taming.io. You can switch between your pets by pressing the number keys (1-4) or by clicking on them in the pet menu (P key). You can also remove a pet by pressing the delete key or by clicking on the trash icon in the pet menu (P key).</li>
-</ul>
-<h3>How to Make Friends</h3>
-<p>When you play Taming.io online, you need to make friends to have more fun and cooperation in the game. Here are some tips on how to make friends:</p>
-<ul>
-<li><strong>Chat with other players.</strong> You can chat with other players in Taming.io by typing messages in the chat box (Enter key) or by using emojis (E key). You can also use voice chat (V key) if you have a microphone and headphones. You can chat with everyone on the server or with your clan members only.</li>
-<li><strong>Team up with other players.</strong> You can team up with other players in Taming.io by inviting them or accepting their invitations. You can invite a player by clicking on their name in the leaderboard (L key) or by typing /invite followed by their name in the chat box. You can accept an invitation by clicking on the green check mark that appears on your screen or by typing /accept in the chat box.</li>
-<li><strong>Join a clan or create your own clan.</strong> You can join a clan or create your own clan in Taming.io by opening the clan menu (K key). A clan is a group of players that share a name, a color, and a chat channel. A clan can have up to 10 members at a time. You can join an existing clan by clicking on their name in the clan menu or by typing /join followed by their name in the chat box. You can create your own clan by clicking on the "Create Clan" button in the clan menu or by typing /create followed by your clan name in the chat box.</li>
-</ul>
-<h2>How to Download Taming.io Offline</h2>
-<p>If you want to play Taming.io offline, you need to download and install it on your device. Here are some steps on how to download Taming.io offline:</p>
-<h3>How to Download Taming.io for Windows</h3>
-<p>If you want to play Taming.io offline on your Windows device, you need to follow these steps:</p>
-<ol>
-<li>Go to the official download page of Taming.io for Windows or search for it on your browser.</li>
-<li>Click on the "Download" button on the page.</li>
-<li>Save the file to your device and wait for it to finish downloading.</li>
-<li>Open the file and follow the instructions to install Taming.io on your device.</li>
-<li>Launch Taming.io from your desktop or start menu and enjoy playing offline.</li>
-</ol>
-<h3>How to Download Taming.io for Mac</h3>
-<p>If you want to play Taming.io offline on your Mac device, you need to follow these steps:</p>
-<ol>
-<li>Go to the official download page of Taming.io for Mac or search for it on your browser.</li>
-<li>Click on the "Download" button on the page.</li>
-<li>Save the file to your device and wait for it to finish downloading.</li>
-<li>Open the file and drag the Taming.io icon to your applications folder.</li>
-<li>Launch Taming.io from your applications folder and enjoy playing offline.</li>
-</ol>
-<h3>How to Download Taming.io for Android</h3>
-<p>If you want to play Taming.io offline on your Android device, you need to follow these steps:</p>
-<ol>
-<li>Go to the official download page of Taming.io for Android or search for it on your browser.</li>
-<li>Click on the "Download" button on the page.</li>
-<li>Save the file to your device and wait for it to finish downloading.</li>
-<li>Open the file and tap on "Install" to install Taming.io on your device.</li>
-<li>Launch Taming.io from your home screen or app drawer and enjoy playing offline.</li>
-</ol>
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-<p>If you want to play Taming.io offline on your iOS device, you need to follow these steps:</p>
-<ol>
-<li>Go to the official download page of Taming.io for iOS or search for it on your browser.</li>
-<li>Click on the "Download" button on the page.</li>
-<li>Save the file to your device and wait for it to finish downloading.</li>
-<li>Open the file and tap on "Install" to install Taming.io on your device.</li>
-<li>Launch Taming.io from your home screen and enjoy playing offline.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Taming.io is a survival .io game with magical pets that fight by your side. You can play it online or offline, depending on your preference. You can also choose from a variety of pets, craft and upgrade your items and buildings, build a base, tame more pets, and make friends in the game. Taming.io is a fun and exciting game that will keep you entertained for hours. If you are looking for a new game to try, why not give Taming.io a shot? You might find yourself hooked in no time!</p>
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- <p>Here are some of the most common questions that people ask about Taming.io:</p>
- <h4>Q: How do I save my progress in Taming.io?</h4>
- <p>A: You can save your progress in Taming.io by creating an account or logging in with Google or Facebook. You can also use a code that is generated when you play as a guest. You can see your code by clicking on the "Code" button in the settings menu (S key). You can enter your code by clicking on the "Enter Code" button in the homepage. Your progress will be saved across different devices and servers as long as you use the same account or code.</p>
- <h4>Q: How do I change my pet's name in Taming.io?</h4>
- <p>A: You can change your pet's name in Taming.io by clicking on their name in the pet menu (P key) or by typing /name followed by their new name in the chat box. You can only change your pet's name once per game session, so choose wisely!</p>
- <h4>Q: How do I get gems in Taming.io?</h4>
- <p>A: You can get gems in Taming.io by finding them in chests, killing bosses, completing quests, or buying them with real money. Gems are used to enchant your items and buildings, making them more powerful and effective. Gems come in different colors and qualities, depending on their rarity and value.</p>
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- <p>A: You can report a bug or a hacker in Taming.io by clicking on the "Report" button in the settings menu (S key) or by typing /report followed by a description of the issue in the chat box. You can also attach a screenshot or a video of the bug or the hacker to provide more evidence. The developers of Taming.io will review your report and take appropriate actions.</p>
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- <p>A: You can contact the developers of Taming.io by visiting their official website or social media pages. You can also send them an email or a message with your feedback, suggestions, questions, or concerns. The developers of Taming.io are very responsive and friendly, and they appreciate your support and input.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Download M Nike and Unlock Exclusive Sneaker Deals.md b/spaces/congsaPfin/Manga-OCR/logs/How to Download M Nike and Unlock Exclusive Sneaker Deals.md
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+++ /dev/null
@@ -1,83 +0,0 @@
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/ICC Cricket Mobile Download the Latest Version of the Official ICC Game for Android.md b/spaces/congsaPfin/Manga-OCR/logs/ICC Cricket Mobile Download the Latest Version of the Official ICC Game for Android.md
deleted file mode 100644
index 4bc37714f408c8fd9c355e9255ce79ae32442976..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/ICC Cricket Mobile Download the Latest Version of the Official ICC Game for Android.md	
+++ /dev/null
@@ -1,100 +0,0 @@
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-<h3>Step 3: Tap on Install and wait for the download to finish</h3>
-<p>Tap on the green Install button and accept the permissions required by the game. Wait for the download to finish, which may take a few minutes depending on your internet speed. Once the download is complete, you can open the game and start playing.</p>
-<h your PC or Mac device. There are many emulators available online, but one of the most popular and reliable ones is BlueStacks. Here are the steps you need to follow to download ICC Cricket Mobile on your PC or Mac device using BlueStacks:</p>
-<h3>Step 1: Download and install BlueStacks</h3>
-<p>Go to the official website of BlueStacks and download the latest version of the emulator for your PC or Mac device. Follow the instructions on the screen to install BlueStacks on your device. You may need to grant some permissions and restart your device to complete the installation.</p>
-<h3>Step 2: Launch the emulator and sign in with your Google account</h3>
-<p>Open BlueStacks and sign in with your Google account. If you don't have one, you can create one for free. Signing in with your Google account will allow you to access the Google Play Store and sync your game data across devices.</p>
-<h3>Step 3: Search for ICC Cricket Mobile in the emulator's app store</h3>
-<p>Click on the Google Play Store icon on the home screen of BlueStacks. Type "ICC Cricket Mobile" in the search bar and tap on the first result that appears. You can also use this link to go directly to the game's page.</p>
-<h3>Step 4: Click on Install and wait for the game to load</h3>
-<p>Click on the green Install button and accept the permissions required by the game. Wait for the game to download and install, which may take a few minutes depending on your internet speed and device performance. Once the game is installed, you can click on Open to launch it.</p>
-<h2>How to play ICC Cricket Mobile</h2>
-<p>Now that you have downloaded ICC Cricket Mobile on your device, you are ready to play it. Here are some tips and tricks to help you enjoy the game:</p>
-<h3>Choose your game mode and difficulty level</h3>
-<p>When you start the game, you will see a menu with different game modes. You can choose from Quick Match, Tournament, ICC Vault, Turning Point, Match of the Day, World Rivals, Super Over, and Multiplayer. Each game mode has its own rules and objectives, so make sure you read them carefully before playing. You can also choose your difficulty level from Easy, Medium, Hard, or Expert, depending on your skill and preference.</p>
-<h3>Select your team and customize your players</h3>
-<p>After choosing your game mode and difficulty level, you can select your team from among 16 international cricket teams. You can also customize your players' names, appearances, skills, and equipment. You can unlock more options by earning coins and gems in the game or by making in-app purchases.</p>
-<h3>Enjoy the realistic cricket action and commentary</h3>
-<p>Once you have selected your team and customized your players, you can start playing the game. You can control your players using simple taps and swipes on the screen. You can also use various gestures to perform different shots and deliveries. The game features realistic cricket physics and animations that make it feel like you are playing a real match. You can also enjoy live commentary from famous commentators like Ian Bishop and Jatin Sapru in English or Hindi languages.</p>
-<h2>Conclusion</h2>
-<p>In conclusion, ICC Cricket Mobile is a fun and exciting cricket game that lets you play international cricket on your mobile device. You can download it for free from the Google Play Store or use an emulator like BlueStacks to play it on your PC or Mac device. You can choose from various game modes, teams, players, and tournaments, and enjoy realistic cricket action and commentary. If you are a cricket fan, you should definitely give this game a try.</p>
-<h3>FAQs</h3>
-<ul>
-<li><strong>Q: How much space does ICC Cricket Mobile require on my device?</strong></li>
-<li>A: ICC Cricket Mobile requires about 500 MB of free space on your device.</li>
-<li><strong>Q: Can I play ICC Cricket Mobile offline?</strong></li>
-<li>A: Yes, you can play ICC Cricket Mobile offline in some game modes like Quick Match, Tournament, ICC Vault, Turning Point, Match of the Day, World Rivals, Super Over. However, you will need an internet connection to play Multiplayer mode or to access some features like leaderboards, achievements, events, etc.</li>
-<li><strong>Q: How can I save my progress in ICC Cricket Mobile?</strong></li>
-<li>A: You can save your progress in ICC Cricket Mobile by signing in with your Google account or Facebook account. This will also allow you to sync your game data across devices.</li>
-<li><strong>Q: How can I contact the developers of ICC Cricket Mobile?</strong></li>
-<li>A: You can contact the developers of ICC Cricket Mobile by sending an email to support@ncoregames.com or by visiting their website at https://www.ncoregames.com/.</li>
-<li><strong>Q: How can I get more coins and gems in ICC Cricket Mobile?</strong></li>
-<li>A: You can get more coins and gems in ICC Cricket Mobile by playing the game regularly, completing achievements, participating in events, watching ads, or making in-app purchases.</li>
-</ul>
-<p>I hope you enjoyed reading this article and learned something new. If you have any questions or feedback, please feel free to leave a comment below. And don't forget to share this article with your friends and family who love cricket. Thank you for your time and attention.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/eFootball PES 2023 Download APK for Android and Play with Friends.md b/spaces/congsaPfin/Manga-OCR/logs/eFootball PES 2023 Download APK for Android and Play with Friends.md
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-
-<h1>eFootball™ 2023 APKPure: How to Download and Play the Latest Soccer Game</h1>
- <p>If you are a fan of soccer games, you might have heard of <strong>eFootball™ 2023</strong>, the latest installment in the popular <strong>PES</strong> series. But what is eFootball™ 2023, and how can you download and play it on your device? In this article, we will answer these questions and more, using <strong>APKPure</strong> as an example of a third-party app store that offers eFootball™ 2023 APK for free. Let's get started!</p>
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- <h2>What is eFootball™ 2023?</h2>
- <p>eFootball™ 2023 is a soccer game developed and published by <strong>Konami</strong>, the Japanese company behind the PES franchise. It is the successor of PES 2022, which was released in September 2022. However, eFootball™ 2023 is not just a simple update or rebranding of PES 2022. It is a whole new era of digital soccer, with many changes and improvements that make it stand out from its predecessors.</p>
- <h3>The evolution of PES</h3>
- <p>PES, which stands for <em>Pro Evolution Soccer</em>, is one of the most popular and long-running soccer game series in the world. It started in 1995 as <em>Goal Storm</em>, and then changed its name to <em>Winning Eleven</em> in Japan and <em>ISS Pro</em> in Europe. In 2001, it adopted the name <em>PES</em> for all regions, except for North America, where it was called <em>World Soccer: Winning Eleven</em>. Since then, PES has released more than 20 titles, each with new features, modes, teams, players, graphics, and gameplay.</p>
- <p>However, in July 2021, Konami announced that PES would be evolving into <em>eFootball™</em>, a new brand name that reflects its vision of creating a next-generation soccer gaming experience. According to Konami, eFootball™ is not just a game, but a platform that connects soccer fans around the world through online matches and tournaments. Moreover, eFootball™ is designed to be more realistic, immersive, and accessible than ever before, thanks to its new engine, Unreal Engine 4.</p>
- <h3>The features of eFootball™ 2023</h3>
- <p>eFootball™ 2023 has many features that make it different from PES 2022 and other soccer games. Some of these features are:</p>
- <ul>
-<li><strong>New gameplay mechanics:</strong> eFootball™ 2023 introduces new ways to control the ball, pass, shoot, dribble, tackle, and defend. For example, you can use the right stick to perform precise ball touches and feints, or use the left trigger to activate a special skill move. You can also use the new motion matching system to execute realistic animations based on your input and situation.</li>
-<li><strong>New game modes:</strong> eFootball™ 2023 offers various game modes for different preferences and skill levels. For example, you can play <em>Creative Teams</em>, where you can create your own team from scratch and compete with other players online. You can also play <em>Cross-Platform Matchday</em>, where you can join forces with other players from different platforms (PC, console, mobile ) and participate in weekly events. You can also play <em>Master League</em>, where you can manage your own club and lead it to glory.</li>
-<li><strong>New graphics and sound:</strong> eFootball™ 2023 uses Unreal Engine 4, which allows it to create stunning visuals and realistic physics. You can see the details of the players, stadiums, crowds, weather, and lighting. You can also hear the authentic sounds of the ball, the fans, the commentators, and the players.</li>
-<li><strong>New licenses and partnerships:</strong> eFootball™ 2023 has secured the official licenses and partnerships of many soccer clubs, leagues, and associations. For example, you can play with teams like <em>Barcelona</em>, <em>Juventus</em>, <em>Manchester United</em>, <em>Boca Juniors</em>, <em>Bayern Munich</em>, and more. You can also play in competitions like <em>UEFA Champions League</em>, <em>Copa Libertadores</em>, <em>AFC Champions League</em>, and more.</li>
-</ul>
- <h3>The platforms and devices supported by eFootball™ 2023</h3>
- <p>eFootball™ 2023 is available for various platforms and devices, including:</p>
- <ul>
-<li><strong>PC:</strong> You can play eFootball™ 2023 on your Windows PC via Steam. You need to have a minimum of 8 GB of RAM, an Intel Core i5-3470 or AMD FX-4350 processor, an NVIDIA GeForce GTX 670 or AMD Radeon HD 7870 graphics card, and 40 GB of free disk space.</li>
-<li><strong>Console:</strong> You can play eFootball™ 2023 on your PlayStation 4, PlayStation 5, Xbox One, or Xbox Series X/S. You need to have a minimum of 50 GB of free disk space.</li>
-<li><strong>Mobile:</strong> You can play eFootball™ 2023 on your Android or iOS device. You need to have a minimum of 2 GB of RAM, an Android 6.0 or iOS 10.0 operating system, and 1.9 GB of free storage space.</li>
-</ul>
- <h2>What is APKPure?</h2>
- <p>APKPure is a third-party app store that offers free APK files for Android users. APK stands for <em>Android Package Kit</em>, which is a file format that contains all the components of an Android app. APKPure allows you to download and install apps that are not available on the official Google Play Store, such as eFootball™ 2023.</p>
- <h3>The benefits of using APKPure</h3>
- <p>Some of the benefits of using APKPure are:</p>
- <ul>
-<li><strong>No region restrictions:</strong> APKPure lets you access apps that are not available in your country or region. For example, if eFootball™ 2023 is not released in your area yet, you can still download it from APKPure.</li>
-<li><strong>No device limitations:</strong> APKPure lets you run apps that are not compatible with your device or operating system. For example, if eFootball™ 2023 requires a higher Android version than your device has, you can still install it from APKPure.</li>
-<li><strong>No update delays:</strong> APKPure lets you get the latest updates of apps as soon as they are released by the developers. For example, if eFootball™ 2023 has a new patch or feature, you can download it from APKPure without waiting for the Google Play Store to approve it.</li>
-<li><strong>No extra costs:</strong> APKPure lets you download apps for free without any hidden fees or subscriptions. For example, if eFootball™ 2023 has a premium version or in-app purchases, you can get them from APKPure without paying anything.</li>
-</ul>
- <h3>The risks of using APKPure</h3>
- <p>However, using APKPure also comes with some risks that you should be aware of. Some of these risks are:</p>
- <ul>
-<li><strong>Potential malware:</strong> APKPure does not guarantee that all the apps on its platform are safe and virus-free. Some apps may contain malicious code that can harm your device or steal your personal information. For example, if eFootball™ 2023 has a fake or modified version on APKPure, it may infect your device with malware.</li>
-<li><strong>Possible legal issues:</strong> APKPure does not have the official permission or license to distribute some of the apps on its platform. Some apps may violate the intellectual property rights or terms of service of the original developers or publishers. For example, if eFootball™ 2023 has an exclusive contract with Google Play Store, downloading it from APKPure may be illegal or banned or blocked by the developers or publishers.</li>
-<li><strong>Poor performance:</strong> APKPure does not optimize the apps on its platform for different devices or operating systems. Some apps may not run smoothly or properly on your device. For example, if eFootball™ 2023 has high graphics or system requirements, it may lag or crash on your device.</li>
-<li><strong>Limited support:</strong> APKPure does not provide any customer service or technical support for the apps on its platform. If you encounter any problems or issues with the apps, you have to rely on yourself or the app developers to solve them. For example, if eFootball™ 2023 has a bug or a glitch, you may not get any help from APKPure or Konami.</li>
-</ul>
- <h3>The alternatives to APKPure</h3>
- <p>If you are not comfortable with using APKPure, you can try some of the alternatives that also offer eFootball™ 2023 APK for free. Some of these alternatives are:</p>
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- <ul>
-<li><strong>APKMirror:</strong> APKMirror is a website that hosts APK files of various apps, including eFootball™ 2023. It claims to be safe and secure, as it verifies the signatures and certificates of the apps before uploading them. It also updates the apps regularly and provides multiple versions for different devices and operating systems.</li>
-<li><strong>Aptoide:</strong> Aptoide is an app store that allows users to create and manage their own app stores. It has millions of apps, including eFootball™ 2023, that are uploaded by users or developers. It also has a reputation system that rates the apps based on their quality and reliability. It also has a malware detection system that scans the apps for viruses and threats.</li>
-<li><strong>Uptodown:</strong> Uptodown is an app store that offers APK files of various apps, including eFootball™ 2023. It has a simple and user-friendly interface that makes it easy to find and download the apps you want. It also has a review system that gives feedback and ratings on the apps based on their functionality and performance.</li>
-</ul>
- <h2>How to download and play eFootball™ 2023 using APKPure?</h2>
- <p>If you want to download and play eFootball™ 2023 using APKPure, you need to follow these steps:</p>
- <h3>The steps to download and install eFootball™ 2023 APK from APKPure</h3>
- <ol>
-<li><strong>Download and install APKPure app:</strong> You need to have the APKPure app on your device to access its app store. You can download it from its official website (https://apkpure.com/) or from other sources. You need to enable the <em>Unknown Sources</em> option in your device settings to allow the installation of apps from outside the Google Play Store.</li>
-<li><strong>Search and download eFootball™ 2023 APK:</strong> You need to open the APKPure app and search for eFootball™ 2023 in its search bar. You will see the app icon and some information about it. You need to tap on the <em>Download</em> button to start downloading the eFootball™ 2023 APK file to your device.</li>
-<li><strong>Install eFootball™ 2023 APK:</strong> You need to wait for the download to finish and then tap on the <em>Install</em> button to start installing the eFootball™ 2023 APK file on your device. You may need to grant some permissions and accept some terms and conditions before proceeding with the installation.</li>
-</ol>
- <h3>The steps to launch and play eFootball™ 2023 on your device</h3>
- <ol>
-<li><strong>Launch eFootball™ 2023 app:</strong> You need to find and tap on the eFootball™ 2023 app icon on your device home screen or app drawer to launch it. You may need to wait for some loading screens and updates before accessing the main menu.</li>
-<li><strong>Create or log in to your Konami ID account:</strong> You need to have a Konami ID account to play eFootball™ 2023 online. You can create one for free by entering your email address and password, or log in with your existing account if you have one. You can also link your Konami ID account with your social media accounts, such as Facebook, Twitter, or Google.</li>
-<li><strong>Select your game mode and start playing:</strong> You need to select the game mode you want to play from the main menu, such as <em>Creative Teams</em>, <em>Cross-Platform Matchday</em>, <em>Master League</em>, or others. You can also customize your settings, such as language, controls, camera, sound, and graphics. You can also access the <em>Options</em> menu to view your profile, achievements, rewards, and more. Once you are ready, you can start playing eFootball™ 2023 on your device and enjoy the realistic and immersive soccer action.</li>
-</ol>
- <h3>The tips and tricks to enjoy eFootball™ 2023 better</h3>
- <p>Here are some tips and tricks that can help you enjoy eFootball™ 2023 better:</p>
- <ul>
-<li><strong>Practice your skills:</strong> eFootball™ 2023 has a <em>Training Mode</em> where you can practice your skills and learn new moves. You can also access the <em>Tutorial Mode</em> where you can get tips and instructions on how to play the game. You can also watch the <em>Replay Mode</em> where you can review your matches and analyze your performance.</li>
-<li><strong>Build your team:</strong> eFootball™ 2023 allows you to create and customize your own team from scratch. You can choose your team name, logo, kit, stadium, manager, and players. You can also scout, sign, trade, and train your players to improve their abilities and chemistry. You can also join or create a <em>Clan</em> where you can cooperate with other players and compete in clan matches.</li>
-<li><strong>Explore the modes:</strong> eFootball™ 2023 has various modes that cater to different preferences and skill levels. You can play <em>Creative Teams</em>, where you can create your own team and compete with other players online. You can play <em>Cross-Platform Matchday</em>, where you can join forces with other players from different platforms and participate in weekly events. You can play <em>Master League</em>, where you can manage your own club and lead it to glory. You can also play other modes, such as <em>eFootball™ League</em>, <em>eFootball™ Cup</em>, <em>Friendly Match</em>, <em>Co-op Match</em>, and more.</li>
-<li><strong>Earn rewards:</strong> eFootball™ 2023 rewards you for playing the game and completing various tasks and challenges. You can earn coins, GP, eFootball™ points, tickets, scouts, players, items, and more. You can use these rewards to enhance your team, unlock new features, or purchase premium content.</li>
-<li><strong>Have fun:</strong> eFootball™ 2023 is a game that is meant to be fun and enjoyable for everyone. You can play it solo or with friends, casually or competitively, offline or online. You can also interact with other players through chat, voice, or gestures. You can also share your screenshots, videos, or live streams of your matches on social media or other platforms.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>eFootball™ 2023 is a soccer game that is the latest installment in the PES series. It is a new era of digital soccer that is more realistic, immersive, and accessible than ever before. It has many features that make it different from PES 2022 and other soccer games. It is available for various platforms and devices, including PC, console, and mobile.</p>
- <p>If you want to download and play eFootball™ 2023 on your Android device for free, you can use APKPure as an example of a third-party app store that offers eFootball™ 2023 APK. However, you should be aware of the benefits and risks of using APKPure before doing so. You should also follow the steps to download and install eFootball™ 2023 APK from APKPure correctly. You should also follow the tips and tricks to enjoy eFootball™ 2023 better.</p>
- <p>We hope this article has helped you learn more about eFootball™ 2023 APKPure and how to download and play it on your device. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about eFootball™ 2023 APKPure:</p>
- <ol>
-<li><strong>Is eFootball™ 2023 free to play?</strong></li>
-<p>Yes, eFootball™ 2023 is free to play on all platforms and devices. However, it may have some optional in-app purchases or premium content that require real money.</p>
-<li><strong>Is eFootball™ 2023 online or offline?</strong></li>
-<p>eFootball™ 2023 is mainly an online game that requires an internet connection to access most of its features and modes. However, it may have some offline modes that you can play without an internet connection.</p>
-<li><strong>Is eFootball™ 2023 cross-platform?</strong></li>
-<p>Yes, eFootball™ 2023 is cross-platform, which means you can play with or against other players from different platforms and devices. For example, you can play with your friends who are using PC, console, or mobile.</p>
-<li><strong>Is eFootball™ 2023 compatible with controllers?</strong></li>
-<p>Yes, eFootball™ 2023 is compatible with controllers, such as gamepads, joysticks, or keyboards. You can connect your controller to your device via Bluetooth, USB, or wireless adapter. You can also customize your controller settings in the game options.</p>
-<li><strong>Is eFootball™ 2023 safe to download and play?</strong></li>
-<p>eFootball™ 2023 is safe to download and play from the official sources, such as Steam, PlayStation Store, Xbox Store, Google Play Store, or App Store. However, if you download and play eFootball™ 2023 from third-party sources, such as APKPure, you may encounter some risks, such as malware, legal issues, poor performance, or limited support. You should be careful and cautious when using third-party sources and follow the steps to download and install eFootball™ 2023 APK correctly.</p>
-<li><strong>How can I contact Konami or APKPure for support or feedback?</strong></li>
-<p>If you have any problems or issues with eFootball™ 2023 or APKPure, you can contact Konami or APKPure for support or feedback. You can find their contact information on their official websites (https://www.konami.com/wepes/2023/ or https://apkpure.com/) or on their social media accounts (Facebook, Twitter, Instagram, YouTube, etc.). You can also check their FAQs or forums for more information.</p>
-</ol></p> 197e85843d<br />
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--- a/spaces/contluForse/HuggingGPT/assets/Days Of Tafree Movie Download 720p In Hindi REPACK.md	
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diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/masked_conv.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/masked_conv.py
deleted file mode 100644
index cd514cc204c1d571ea5dc7e74b038c0f477a008b..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/masked_conv.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
-    '_ext', ['masked_im2col_forward', 'masked_col2im_forward'])
-
-
-class MaskedConv2dFunction(Function):
-
-    @staticmethod
-    def symbolic(g, features, mask, weight, bias, padding, stride):
-        return g.op(
-            'mmcv::MMCVMaskedConv2d',
-            features,
-            mask,
-            weight,
-            bias,
-            padding_i=padding,
-            stride_i=stride)
-
-    @staticmethod
-    def forward(ctx, features, mask, weight, bias, padding=0, stride=1):
-        assert mask.dim() == 3 and mask.size(0) == 1
-        assert features.dim() == 4 and features.size(0) == 1
-        assert features.size()[2:] == mask.size()[1:]
-        pad_h, pad_w = _pair(padding)
-        stride_h, stride_w = _pair(stride)
-        if stride_h != 1 or stride_w != 1:
-            raise ValueError(
-                'Stride could not only be 1 in masked_conv2d currently.')
-        out_channel, in_channel, kernel_h, kernel_w = weight.size()
-
-        batch_size = features.size(0)
-        out_h = int(
-            math.floor((features.size(2) + 2 * pad_h -
-                        (kernel_h - 1) - 1) / stride_h + 1))
-        out_w = int(
-            math.floor((features.size(3) + 2 * pad_w -
-                        (kernel_h - 1) - 1) / stride_w + 1))
-        mask_inds = torch.nonzero(mask[0] > 0, as_tuple=False)
-        output = features.new_zeros(batch_size, out_channel, out_h, out_w)
-        if mask_inds.numel() > 0:
-            mask_h_idx = mask_inds[:, 0].contiguous()
-            mask_w_idx = mask_inds[:, 1].contiguous()
-            data_col = features.new_zeros(in_channel * kernel_h * kernel_w,
-                                          mask_inds.size(0))
-            ext_module.masked_im2col_forward(
-                features,
-                mask_h_idx,
-                mask_w_idx,
-                data_col,
-                kernel_h=kernel_h,
-                kernel_w=kernel_w,
-                pad_h=pad_h,
-                pad_w=pad_w)
-
-            masked_output = torch.addmm(1, bias[:, None], 1,
-                                        weight.view(out_channel, -1), data_col)
-            ext_module.masked_col2im_forward(
-                masked_output,
-                mask_h_idx,
-                mask_w_idx,
-                output,
-                height=out_h,
-                width=out_w,
-                channels=out_channel)
-        return output
-
-    @staticmethod
-    @once_differentiable
-    def backward(ctx, grad_output):
-        return (None, ) * 5
-
-
-masked_conv2d = MaskedConv2dFunction.apply
-
-
-class MaskedConv2d(nn.Conv2d):
-    """A MaskedConv2d which inherits the official Conv2d.
-
-    The masked forward doesn't implement the backward function and only
-    supports the stride parameter to be 1 currently.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 dilation=1,
-                 groups=1,
-                 bias=True):
-        super(MaskedConv2d,
-              self).__init__(in_channels, out_channels, kernel_size, stride,
-                             padding, dilation, groups, bias)
-
-    def forward(self, input, mask=None):
-        if mask is None:  # fallback to the normal Conv2d
-            return super(MaskedConv2d, self).forward(input)
-        else:
-            return masked_conv2d(input, mask, self.weight, self.bias,
-                                 self.padding)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/utils/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/utils/__init__.py
deleted file mode 100644
index 3d3bdd349b9f2ae499a2fcb2ac1d2e3c77befebe..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/models/utils/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from .drop import DropPath
-from .inverted_residual import InvertedResidual, InvertedResidualV3
-from .make_divisible import make_divisible
-from .res_layer import ResLayer
-from .se_layer import SELayer
-from .self_attention_block import SelfAttentionBlock
-from .up_conv_block import UpConvBlock
-from .weight_init import trunc_normal_
-
-__all__ = [
-    'ResLayer', 'SelfAttentionBlock', 'make_divisible', 'InvertedResidual',
-    'UpConvBlock', 'InvertedResidualV3', 'SELayer', 'DropPath', 'trunc_normal_'
-]
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/cnn/bricks/hswish.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/cnn/bricks/hswish.py
deleted file mode 100644
index 7e0c090ff037c99ee6c5c84c4592e87beae02208..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/cnn/bricks/hswish.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-
-from .registry import ACTIVATION_LAYERS
-
-
-@ACTIVATION_LAYERS.register_module()
-class HSwish(nn.Module):
-    """Hard Swish Module.
-
-    This module applies the hard swish function:
-
-    .. math::
-        Hswish(x) = x * ReLU6(x + 3) / 6
-
-    Args:
-        inplace (bool): can optionally do the operation in-place.
-            Default: False.
-
-    Returns:
-        Tensor: The output tensor.
-    """
-
-    def __init__(self, inplace=False):
-        super(HSwish, self).__init__()
-        self.act = nn.ReLU6(inplace)
-
-    def forward(self, x):
-        return x * self.act(x + 3) / 6
diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/utils/__init__.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/utils/__init__.py
deleted file mode 100644
index f03b1c2bafcd7759cb7e8722a0c6715f201a46dc..0000000000000000000000000000000000000000
--- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/utils/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from .face_utils import align_crop_face_landmarks, compute_increased_bbox, get_valid_bboxes, paste_face_back
-from .misc import img2tensor, load_file_from_url, download_pretrained_models, scandir
-
-__all__ = [
-    'align_crop_face_landmarks', 'compute_increased_bbox', 'get_valid_bboxes', 'load_file_from_url', 
-    'download_pretrained_models', 'paste_face_back', 'img2tensor', 'scandir'
-]
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/implementations/webhdfs.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/implementations/webhdfs.py
deleted file mode 100644
index cc595934f9a0161be24d3e300260fb73d4fd9784..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/implementations/webhdfs.py
+++ /dev/null
@@ -1,447 +0,0 @@
-# https://hadoop.apache.org/docs/r1.0.4/webhdfs.html
-
-import logging
-import os
-import secrets
-import shutil
-import tempfile
-import uuid
-from contextlib import suppress
-from urllib.parse import quote
-
-import requests
-
-from ..spec import AbstractBufferedFile, AbstractFileSystem
-from ..utils import infer_storage_options, tokenize
-
-logger = logging.getLogger("webhdfs")
-
-
-class WebHDFS(AbstractFileSystem):
-    """
-    Interface to HDFS over HTTP using the WebHDFS API. Supports also HttpFS gateways.
-
-    Three auth mechanisms are supported:
-
-    insecure: no auth is done, and the user is assumed to be whoever they
-        say they are (parameter ``user``), or a predefined value such as
-        "dr.who" if not given
-    spnego: when kerberos authentication is enabled, auth is negotiated by
-        requests_kerberos https://github.com/requests/requests-kerberos .
-        This establishes a session based on existing kinit login and/or
-        specified principal/password; parameters are passed with ``kerb_kwargs``
-    token: uses an existing Hadoop delegation token from another secured
-        service. Indeed, this client can also generate such tokens when
-        not insecure. Note that tokens expire, but can be renewed (by a
-        previously specified user) and may allow for proxying.
-
-    """
-
-    tempdir = str(tempfile.gettempdir())
-    protocol = "webhdfs", "webHDFS"
-
-    def __init__(
-        self,
-        host,
-        port=50070,
-        kerberos=False,
-        token=None,
-        user=None,
-        proxy_to=None,
-        kerb_kwargs=None,
-        data_proxy=None,
-        use_https=False,
-        **kwargs,
-    ):
-        """
-        Parameters
-        ----------
-        host: str
-            Name-node address
-        port: int
-            Port for webHDFS
-        kerberos: bool
-            Whether to authenticate with kerberos for this connection
-        token: str or None
-            If given, use this token on every call to authenticate. A user
-            and user-proxy may be encoded in the token and should not be also
-            given
-        user: str or None
-            If given, assert the user name to connect with
-        proxy_to: str or None
-            If given, the user has the authority to proxy, and this value is
-            the user in who's name actions are taken
-        kerb_kwargs: dict
-            Any extra arguments for HTTPKerberosAuth, see
-            `<https://github.com/requests/requests-kerberos/blob/master/requests_kerberos/kerberos_.py>`_
-        data_proxy: dict, callable or None
-            If given, map data-node addresses. This can be necessary if the
-            HDFS cluster is behind a proxy, running on Docker or otherwise has
-            a mismatch between the host-names given by the name-node and the
-            address by which to refer to them from the client. If a dict,
-            maps host names ``host->data_proxy[host]``; if a callable, full
-            URLs are passed, and function must conform to
-            ``url->data_proxy(url)``.
-        use_https: bool
-            Whether to connect to the Name-node using HTTPS instead of HTTP
-        kwargs
-        """
-        if self._cached:
-            return
-        super().__init__(**kwargs)
-        self.url = "{protocol}://{host}:{port}/webhdfs/v1".format(
-            protocol="https" if use_https else "http", host=host, port=port
-        )
-        self.kerb = kerberos
-        self.kerb_kwargs = kerb_kwargs or {}
-        self.pars = {}
-        self.proxy = data_proxy or {}
-        if token is not None:
-            if user is not None or proxy_to is not None:
-                raise ValueError(
-                    "If passing a delegation token, must not set "
-                    "user or proxy_to, as these are encoded in the"
-                    " token"
-                )
-            self.pars["delegation"] = token
-        if user is not None:
-            self.pars["user.name"] = user
-        if proxy_to is not None:
-            self.pars["doas"] = proxy_to
-        if kerberos and user is not None:
-            raise ValueError(
-                "If using Kerberos auth, do not specify the "
-                "user, this is handled by kinit."
-            )
-        self._connect()
-
-        self._fsid = "webhdfs_" + tokenize(host, port)
-
-    @property
-    def fsid(self):
-        return self._fsid
-
-    def _connect(self):
-        self.session = requests.Session()
-        if self.kerb:
-            from requests_kerberos import HTTPKerberosAuth
-
-            self.session.auth = HTTPKerberosAuth(**self.kerb_kwargs)
-
-    def _call(self, op, method="get", path=None, data=None, redirect=True, **kwargs):
-        url = self.url + quote(path or "")
-        args = kwargs.copy()
-        args.update(self.pars)
-        args["op"] = op.upper()
-        logger.debug("sending %s with %s", url, method)
-        out = self.session.request(
-            method=method.upper(),
-            url=url,
-            params=args,
-            data=data,
-            allow_redirects=redirect,
-        )
-        if out.status_code in [400, 401, 403, 404, 500]:
-            try:
-                err = out.json()
-                msg = err["RemoteException"]["message"]
-                exp = err["RemoteException"]["exception"]
-            except (ValueError, KeyError):
-                pass
-            else:
-                if exp in ["IllegalArgumentException", "UnsupportedOperationException"]:
-                    raise ValueError(msg)
-                elif exp in ["SecurityException", "AccessControlException"]:
-                    raise PermissionError(msg)
-                elif exp in ["FileNotFoundException"]:
-                    raise FileNotFoundError(msg)
-                else:
-                    raise RuntimeError(msg)
-        out.raise_for_status()
-        return out
-
-    def _open(
-        self,
-        path,
-        mode="rb",
-        block_size=None,
-        autocommit=True,
-        replication=None,
-        permissions=None,
-        **kwargs,
-    ):
-        """
-
-        Parameters
-        ----------
-        path: str
-            File location
-        mode: str
-            'rb', 'wb', etc.
-        block_size: int
-            Client buffer size for read-ahead or write buffer
-        autocommit: bool
-            If False, writes to temporary file that only gets put in final
-            location upon commit
-        replication: int
-            Number of copies of file on the cluster, write mode only
-        permissions: str or int
-            posix permissions, write mode only
-        kwargs
-
-        Returns
-        -------
-        WebHDFile instance
-        """
-        block_size = block_size or self.blocksize
-        return WebHDFile(
-            self,
-            path,
-            mode=mode,
-            block_size=block_size,
-            tempdir=self.tempdir,
-            autocommit=autocommit,
-            replication=replication,
-            permissions=permissions,
-        )
-
-    @staticmethod
-    def _process_info(info):
-        info["type"] = info["type"].lower()
-        info["size"] = info["length"]
-        return info
-
-    @classmethod
-    def _strip_protocol(cls, path):
-        return infer_storage_options(path)["path"]
-
-    @staticmethod
-    def _get_kwargs_from_urls(urlpath):
-        out = infer_storage_options(urlpath)
-        out.pop("path", None)
-        out.pop("protocol", None)
-        if "username" in out:
-            out["user"] = out.pop("username")
-        return out
-
-    def info(self, path):
-        out = self._call("GETFILESTATUS", path=path)
-        info = out.json()["FileStatus"]
-        info["name"] = path
-        return self._process_info(info)
-
-    def ls(self, path, detail=False):
-        out = self._call("LISTSTATUS", path=path)
-        infos = out.json()["FileStatuses"]["FileStatus"]
-        for info in infos:
-            self._process_info(info)
-            info["name"] = path.rstrip("/") + "/" + info["pathSuffix"]
-        if detail:
-            return sorted(infos, key=lambda i: i["name"])
-        else:
-            return sorted(info["name"] for info in infos)
-
-    def content_summary(self, path):
-        """Total numbers of files, directories and bytes under path"""
-        out = self._call("GETCONTENTSUMMARY", path=path)
-        return out.json()["ContentSummary"]
-
-    def ukey(self, path):
-        """Checksum info of file, giving method and result"""
-        out = self._call("GETFILECHECKSUM", path=path, redirect=False)
-        if "Location" in out.headers:
-            location = self._apply_proxy(out.headers["Location"])
-            out2 = self.session.get(location)
-            out2.raise_for_status()
-            return out2.json()["FileChecksum"]
-        else:
-            out.raise_for_status()
-            return out.json()["FileChecksum"]
-
-    def home_directory(self):
-        """Get user's home directory"""
-        out = self._call("GETHOMEDIRECTORY")
-        return out.json()["Path"]
-
-    def get_delegation_token(self, renewer=None):
-        """Retrieve token which can give the same authority to other uses
-
-        Parameters
-        ----------
-        renewer: str or None
-            User who may use this token; if None, will be current user
-        """
-        if renewer:
-            out = self._call("GETDELEGATIONTOKEN", renewer=renewer)
-        else:
-            out = self._call("GETDELEGATIONTOKEN")
-        t = out.json()["Token"]
-        if t is None:
-            raise ValueError("No token available for this user/security context")
-        return t["urlString"]
-
-    def renew_delegation_token(self, token):
-        """Make token live longer. Returns new expiry time"""
-        out = self._call("RENEWDELEGATIONTOKEN", method="put", token=token)
-        return out.json()["long"]
-
-    def cancel_delegation_token(self, token):
-        """Stop the token from being useful"""
-        self._call("CANCELDELEGATIONTOKEN", method="put", token=token)
-
-    def chmod(self, path, mod):
-        """Set the permission at path
-
-        Parameters
-        ----------
-        path: str
-            location to set (file or directory)
-        mod: str or int
-            posix epresentation or permission, give as oct string, e.g, '777'
-            or 0o777
-        """
-        self._call("SETPERMISSION", method="put", path=path, permission=mod)
-
-    def chown(self, path, owner=None, group=None):
-        """Change owning user and/or group"""
-        kwargs = {}
-        if owner is not None:
-            kwargs["owner"] = owner
-        if group is not None:
-            kwargs["group"] = group
-        self._call("SETOWNER", method="put", path=path, **kwargs)
-
-    def set_replication(self, path, replication):
-        """
-        Set file replication factor
-
-        Parameters
-        ----------
-        path: str
-            File location (not for directories)
-        replication: int
-            Number of copies of file on the cluster. Should be smaller than
-            number of data nodes; normally 3 on most systems.
-        """
-        self._call("SETREPLICATION", path=path, method="put", replication=replication)
-
-    def mkdir(self, path, **kwargs):
-        self._call("MKDIRS", method="put", path=path)
-
-    def makedirs(self, path, exist_ok=False):
-        if exist_ok is False and self.exists(path):
-            raise FileExistsError(path)
-        self.mkdir(path)
-
-    def mv(self, path1, path2, **kwargs):
-        self._call("RENAME", method="put", path=path1, destination=path2)
-
-    def rm(self, path, recursive=False, **kwargs):
-        self._call(
-            "DELETE",
-            method="delete",
-            path=path,
-            recursive="true" if recursive else "false",
-        )
-
-    def rm_file(self, path, **kwargs):
-        self.rm(path)
-
-    def cp_file(self, lpath, rpath, **kwargs):
-        with self.open(lpath) as lstream:
-            tmp_fname = "/".join([self._parent(rpath), f".tmp.{secrets.token_hex(16)}"])
-            # Perform an atomic copy (stream to a temporary file and
-            # move it to the actual destination).
-            try:
-                with self.open(tmp_fname, "wb") as rstream:
-                    shutil.copyfileobj(lstream, rstream)
-                self.mv(tmp_fname, rpath)
-            except BaseException:  # noqa
-                with suppress(FileNotFoundError):
-                    self.rm(tmp_fname)
-                raise
-
-    def _apply_proxy(self, location):
-        if self.proxy and callable(self.proxy):
-            location = self.proxy(location)
-        elif self.proxy:
-            # as a dict
-            for k, v in self.proxy.items():
-                location = location.replace(k, v, 1)
-        return location
-
-
-class WebHDFile(AbstractBufferedFile):
-    """A file living in HDFS over webHDFS"""
-
-    def __init__(self, fs, path, **kwargs):
-        super().__init__(fs, path, **kwargs)
-        kwargs = kwargs.copy()
-        if kwargs.get("permissions", None) is None:
-            kwargs.pop("permissions", None)
-        if kwargs.get("replication", None) is None:
-            kwargs.pop("replication", None)
-        self.permissions = kwargs.pop("permissions", 511)
-        tempdir = kwargs.pop("tempdir")
-        if kwargs.pop("autocommit", False) is False:
-            self.target = self.path
-            self.path = os.path.join(tempdir, str(uuid.uuid4()))
-
-    def _upload_chunk(self, final=False):
-        """Write one part of a multi-block file upload
-
-        Parameters
-        ==========
-        final: bool
-            This is the last block, so should complete file, if
-            self.autocommit is True.
-        """
-        out = self.fs.session.post(
-            self.location,
-            data=self.buffer.getvalue(),
-            headers={"content-type": "application/octet-stream"},
-        )
-        out.raise_for_status()
-        return True
-
-    def _initiate_upload(self):
-        """Create remote file/upload"""
-        kwargs = self.kwargs.copy()
-        if "a" in self.mode:
-            op, method = "APPEND", "POST"
-        else:
-            op, method = "CREATE", "PUT"
-            kwargs["overwrite"] = "true"
-        out = self.fs._call(op, method, self.path, redirect=False, **kwargs)
-        location = self.fs._apply_proxy(out.headers["Location"])
-        if "w" in self.mode:
-            # create empty file to append to
-            out2 = self.fs.session.put(
-                location, headers={"content-type": "application/octet-stream"}
-            )
-            out2.raise_for_status()
-            # after creating empty file, change location to append to
-            out2 = self.fs._call("APPEND", "POST", self.path, redirect=False, **kwargs)
-            self.location = self.fs._apply_proxy(out2.headers["Location"])
-
-    def _fetch_range(self, start, end):
-        start = max(start, 0)
-        end = min(self.size, end)
-        if start >= end or start >= self.size:
-            return b""
-        out = self.fs._call(
-            "OPEN", path=self.path, offset=start, length=end - start, redirect=False
-        )
-        out.raise_for_status()
-        if "Location" in out.headers:
-            location = out.headers["Location"]
-            out2 = self.fs.session.get(self.fs._apply_proxy(location))
-            return out2.content
-        else:
-            return out.content
-
-    def commit(self):
-        self.fs.mv(self.path, self.target)
-
-    def discard(self):
-        self.fs.rm(self.path)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/test_compatibilty_files.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/test_compatibilty_files.py
deleted file mode 100644
index 13ad0dfb21a1d5b7fb91f2419b78b9bdf90f0ec3..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/importlib_resources/tests/test_compatibilty_files.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import io
-import unittest
-
-import importlib_resources as resources
-
-from importlib_resources._adapters import (
-    CompatibilityFiles,
-    wrap_spec,
-)
-
-from . import util
-
-
-class CompatibilityFilesTests(unittest.TestCase):
-    @property
-    def package(self):
-        bytes_data = io.BytesIO(b'Hello, world!')
-        return util.create_package(
-            file=bytes_data,
-            path='some_path',
-            contents=('a', 'b', 'c'),
-        )
-
-    @property
-    def files(self):
-        return resources.files(self.package)
-
-    def test_spec_path_iter(self):
-        self.assertEqual(
-            sorted(path.name for path in self.files.iterdir()),
-            ['a', 'b', 'c'],
-        )
-
-    def test_child_path_iter(self):
-        self.assertEqual(list((self.files / 'a').iterdir()), [])
-
-    def test_orphan_path_iter(self):
-        self.assertEqual(list((self.files / 'a' / 'a').iterdir()), [])
-        self.assertEqual(list((self.files / 'a' / 'a' / 'a').iterdir()), [])
-
-    def test_spec_path_is(self):
-        self.assertFalse(self.files.is_file())
-        self.assertFalse(self.files.is_dir())
-
-    def test_child_path_is(self):
-        self.assertTrue((self.files / 'a').is_file())
-        self.assertFalse((self.files / 'a').is_dir())
-
-    def test_orphan_path_is(self):
-        self.assertFalse((self.files / 'a' / 'a').is_file())
-        self.assertFalse((self.files / 'a' / 'a').is_dir())
-        self.assertFalse((self.files / 'a' / 'a' / 'a').is_file())
-        self.assertFalse((self.files / 'a' / 'a' / 'a').is_dir())
-
-    def test_spec_path_name(self):
-        self.assertEqual(self.files.name, 'testingpackage')
-
-    def test_child_path_name(self):
-        self.assertEqual((self.files / 'a').name, 'a')
-
-    def test_orphan_path_name(self):
-        self.assertEqual((self.files / 'a' / 'b').name, 'b')
-        self.assertEqual((self.files / 'a' / 'b' / 'c').name, 'c')
-
-    def test_spec_path_open(self):
-        self.assertEqual(self.files.read_bytes(), b'Hello, world!')
-        self.assertEqual(self.files.read_text(encoding='utf-8'), 'Hello, world!')
-
-    def test_child_path_open(self):
-        self.assertEqual((self.files / 'a').read_bytes(), b'Hello, world!')
-        self.assertEqual(
-            (self.files / 'a').read_text(encoding='utf-8'), 'Hello, world!'
-        )
-
-    def test_orphan_path_open(self):
-        with self.assertRaises(FileNotFoundError):
-            (self.files / 'a' / 'b').read_bytes()
-        with self.assertRaises(FileNotFoundError):
-            (self.files / 'a' / 'b' / 'c').read_bytes()
-
-    def test_open_invalid_mode(self):
-        with self.assertRaises(ValueError):
-            self.files.open('0')
-
-    def test_orphan_path_invalid(self):
-        with self.assertRaises(ValueError):
-            CompatibilityFiles.OrphanPath()
-
-    def test_wrap_spec(self):
-        spec = wrap_spec(self.package)
-        self.assertIsInstance(spec.loader.get_resource_reader(None), CompatibilityFiles)
-
-
-class CompatibilityFilesNoReaderTests(unittest.TestCase):
-    @property
-    def package(self):
-        return util.create_package_from_loader(None)
-
-    @property
-    def files(self):
-        return resources.files(self.package)
-
-    def test_spec_path_joinpath(self):
-        self.assertIsInstance(self.files / 'a', CompatibilityFiles.OrphanPath)
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/utils/dummy_torch_and_scipy_objects.py b/spaces/declare-lab/tango/diffusers/src/diffusers/utils/dummy_torch_and_scipy_objects.py
deleted file mode 100644
index a1ff25863822b04971d2c6dfdc17f5b28774cf05..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/utils/dummy_torch_and_scipy_objects.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..utils import DummyObject, requires_backends
-
-
-class LMSDiscreteScheduler(metaclass=DummyObject):
-    _backends = ["torch", "scipy"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "scipy"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "scipy"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "scipy"])
diff --git a/spaces/deepaksarika01/youtube-video-qa-lamini/main.py b/spaces/deepaksarika01/youtube-video-qa-lamini/main.py
deleted file mode 100644
index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000
--- a/spaces/deepaksarika01/youtube-video-qa-lamini/main.py
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/spaces/deepwisdom/MetaGPT/metagpt/actions/add_requirement.py b/spaces/deepwisdom/MetaGPT/metagpt/actions/add_requirement.py
deleted file mode 100644
index 7dc09d0620039fc93c662da4729067f83b56b097..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/metagpt/actions/add_requirement.py
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/20 17:46
-@Author  : alexanderwu
-@File    : add_requirement.py
-"""
-from metagpt.actions import Action
-
-
-class BossRequirement(Action):
-    """Boss Requirement without any implementation details"""
-    async def run(self, *args, **kwargs):
-        raise NotImplementedError
diff --git a/spaces/deepwisdom/MetaGPT/tests/metagpt/actions/test_ui_design.py b/spaces/deepwisdom/MetaGPT/tests/metagpt/actions/test_ui_design.py
deleted file mode 100644
index dedd0b30eff02f5d75cb0215cfd05b6a39299551..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/tests/metagpt/actions/test_ui_design.py
+++ /dev/null
@@ -1,189 +0,0 @@
-# -*- coding: utf-8 -*-
-# @Date    : 2023/7/22 02:40
-# @Author  : stellahong (stellahong@fuzhi.ai)
-#
-from tests.metagpt.roles.ui_role import UIDesign
-
-llm_resp = '''
-    # UI Design Description
-```The user interface for the snake game will be designed in a way that is simple, clean, and intuitive. The main elements of the game such as the game grid, snake, food, score, and game over message will be clearly defined and easy to understand. The game grid will be centered on the screen with the score displayed at the top. The game controls will be intuitive and easy to use. The design will be modern and minimalist with a pleasing color scheme.```
-
-## Selected Elements
-
-Game Grid: The game grid will be a rectangular area in the center of the screen where the game will take place. It will be defined by a border and will have a darker background color.
-
-Snake: The snake will be represented by a series of connected blocks that move across the grid. The color of the snake will be different from the background color to make it stand out.
-
-Food: The food will be represented by small objects that are a different color from the snake and the background. The food will be randomly placed on the grid.
-
-Score: The score will be displayed at the top of the screen. The score will increase each time the snake eats a piece of food.
-
-Game Over: When the game is over, a message will be displayed in the center of the screen. The player will be given the option to restart the game.
-
-## HTML Layout
-```html
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Snake Game</title>
-    <link rel="stylesheet" href="styles.css">
-</head>
-<body>
-    <div class="score">Score: 0</div>
-    <div class="game-grid">
-        <!-- Snake and food will be dynamically generated here using JavaScript -->
-    </div>
-    <div class="game-over">Game Over</div>
-</body>
-</html>
-```
-
-## CSS Styles (styles.css)
-```css
-body {
-    display: flex;
-    flex-direction: column;
-    justify-content: center;
-    align-items: center;
-    height: 100vh;
-    margin: 0;
-    background-color: #f0f0f0;
-}
-
-.score {
-    font-size: 2em;
-    margin-bottom: 1em;
-}
-
-.game-grid {
-    width: 400px;
-    height: 400px;
-    display: grid;
-    grid-template-columns: repeat(20, 1fr);
-    grid-template-rows: repeat(20, 1fr);
-    gap: 1px;
-    background-color: #222;
-    border: 1px solid #555;
-}
-
-.snake-segment {
-    background-color: #00cc66;
-}
-
-.food {
-    background-color: #cc3300;
-}
-
-.control-panel {
-    display: flex;
-    justify-content: space-around;
-    width: 400px;
-    margin-top: 1em;
-}
-
-.control-button {
-    padding: 1em;
-    font-size: 1em;
-    border: none;
-    background-color: #555;
-    color: #fff;
-    cursor: pointer;
-}
-
-.game-over {
-    position: absolute;
-    top: 50%;
-    left: 50%;
-    transform: translate(-50%, -50%);
-    font-size: 3em;
-    '''
-
-
-def test_ui_design_parse_css():
-    ui_design_work = UIDesign(name="UI design action")
-
-    css = '''
-    body {
-    display: flex;
-    flex-direction: column;
-    justify-content: center;
-    align-items: center;
-    height: 100vh;
-    margin: 0;
-    background-color: #f0f0f0;
-}
-
-.score {
-    font-size: 2em;
-    margin-bottom: 1em;
-}
-
-.game-grid {
-    width: 400px;
-    height: 400px;
-    display: grid;
-    grid-template-columns: repeat(20, 1fr);
-    grid-template-rows: repeat(20, 1fr);
-    gap: 1px;
-    background-color: #222;
-    border: 1px solid #555;
-}
-
-.snake-segment {
-    background-color: #00cc66;
-}
-
-.food {
-    background-color: #cc3300;
-}
-
-.control-panel {
-    display: flex;
-    justify-content: space-around;
-    width: 400px;
-    margin-top: 1em;
-}
-
-.control-button {
-    padding: 1em;
-    font-size: 1em;
-    border: none;
-    background-color: #555;
-    color: #fff;
-    cursor: pointer;
-}
-
-.game-over {
-    position: absolute;
-    top: 50%;
-    left: 50%;
-    transform: translate(-50%, -50%);
-    font-size: 3em;
-    '''
-    assert ui_design_work.parse_css_code(context=llm_resp) == css
-
-
-def test_ui_design_parse_html():
-    ui_design_work = UIDesign(name="UI design action")
-
-    html = '''
-    <!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Snake Game</title>
-    <link rel="stylesheet" href="styles.css">
-</head>
-<body>
-    <div class="score">Score: 0</div>
-    <div class="game-grid">
-        <!-- Snake and food will be dynamically generated here using JavaScript -->
-    </div>
-    <div class="game-over">Game Over</div>
-</body>
-</html>
-    '''
-    assert ui_design_work.parse_css_code(context=llm_resp) == html
diff --git a/spaces/derek-thomas/disc-golf-simulator/setup.py b/spaces/derek-thomas/disc-golf-simulator/setup.py
deleted file mode 100644
index 9abb7dcfdf5965b442b3429561dce3b90f52940b..0000000000000000000000000000000000000000
--- a/spaces/derek-thomas/disc-golf-simulator/setup.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from setuptools import setup, find_packages
-setup(
-    name="shotshaper",
-    version="0.1.0",
-    packages=find_packages(),
-    include_package_data=True
-)
diff --git a/spaces/diacanFperku/AutoGPT/HD Online Player (thuppakki VERIFIED Full Movie Hd 1080p Blu Ra).md b/spaces/diacanFperku/AutoGPT/HD Online Player (thuppakki VERIFIED Full Movie Hd 1080p Blu Ra).md
deleted file mode 100644
index 103afe80ff8c6643cbf4fd38a6f96d5401d5f3de..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/HD Online Player (thuppakki VERIFIED Full Movie Hd 1080p Blu Ra).md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>HD Online Player (thuppakki full movie hd 1080p blu ra)</h2><br /><p><b><b>DOWNLOAD</b> === <a href="https://gohhs.com/2uFTvf">https://gohhs.com/2uFTvf</a></b></p><br /><br />
-
- 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/text/symbols.py b/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/text/symbols.py
deleted file mode 100644
index 9dfae4e633829f20c4fd767b1c7a9198911ed801..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Bufeiyan-a-Bert-VITS2/text/symbols.py
+++ /dev/null
@@ -1,51 +0,0 @@
-punctuation = ['!', '?', '…', ",", ".", "'", '-']
-pu_symbols = punctuation + ["SP", "UNK"]
-pad = '_'
-
-# chinese
-zh_symbols = ['E', 'En', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h',
-       'i', 'i0', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'ir', 'iu', 'j', 'k', 'l', 'm', 'n', 'o',
-       'ong',
-       'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn',
-       'w', 'x', 'y', 'z', 'zh',
-        "AA", "EE", "OO"]
-num_zh_tones = 6
-
-# japanese
-ja_symbols = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j', 'k', 'ky',
-              'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z']
-num_ja_tones = 1
-
-# English
-en_symbols = ['aa', 'ae', 'ah', 'ao', 'aw', 'ay', 'b', 'ch', 'd', 'dh', 'eh', 'er', 'ey', 'f', 'g', 'hh', 'ih', 'iy',
-              'jh', 'k', 'l', 'm', 'n', 'ng', 'ow', 'oy', 'p', 'r', 's',
-              'sh', 't', 'th', 'uh', 'uw', 'V', 'w', 'y', 'z', 'zh']
-num_en_tones = 4
-
-# combine all symbols
-normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
-symbols = [pad] + normal_symbols + pu_symbols
-sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
-
-# combine all tones
-num_tones = num_zh_tones + num_ja_tones + num_en_tones
-
-# language maps
-language_id_map = {
-    'ZH': 0,
-    "JA": 1,
-    "EN": 2
-}
-num_languages = len(language_id_map.keys())
-
-language_tone_start_map = {
-    'ZH': 0,
-    "JA": num_zh_tones,
-    "EN": num_zh_tones + num_ja_tones
-}
-
-if __name__ == '__main__':
-    a = set(zh_symbols)
-    b = set(en_symbols)
-    print(sorted(a&b))
-
diff --git a/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/bert_gen.py b/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/bert_gen.py
deleted file mode 100644
index 467655b2c4171608ad690fe7dec350db85f84f1b..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/bert_gen.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import torch
-from torch.utils.data import DataLoader
-from multiprocessing import Pool
-import commons
-import utils
-from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
-from tqdm import tqdm
-import warnings
-
-from text import cleaned_text_to_sequence, get_bert
-
-config_path = 'configs/config.json'
-hps = utils.get_hparams_from_file(config_path)
-
-def process_line(line):
-    _id, spk, language_str, text, phones, tone, word2ph = line.strip().split("|")
-    phone = phones.split(" ")
-    tone = [int(i) for i in tone.split(" ")]
-    word2ph = [int(i) for i in word2ph.split(" ")]
-    w2pho = [i for i in word2ph]
-    word2ph = [i for i in word2ph]
-    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
-    if hps.data.add_blank:
-        phone = commons.intersperse(phone, 0)
-        tone = commons.intersperse(tone, 0)
-        language = commons.intersperse(language, 0)
-        for i in range(len(word2ph)):
-            word2ph[i] = word2ph[i] * 2
-        word2ph[0] += 1
-    wav_path = f'{_id}'
-
-    bert_path = wav_path.replace(".wav", ".bert.pt")
-    try:
-        bert = torch.load(bert_path)
-        assert bert.shape[-1] == len(phone)
-    except:
-        bert = get_bert(text, word2ph, language_str)
-        assert bert.shape[-1] == len(phone)
-        torch.save(bert, bert_path)
-
-
-if __name__ == '__main__':
-    lines = []
-    with open(hps.data.training_files, encoding='utf-8' ) as f:
-        lines.extend(f.readlines())
-
-   # with open(hps.data.validation_files, encoding='utf-8' ) as f:
-       # lines.extend(f.readlines())
-
-    with Pool(processes=2) as pool: #A100 40GB suitable config,if coom,please decrease the processess number.
-        for _ in tqdm(pool.imap_unordered(process_line, lines)):
-            pass
diff --git a/spaces/digitalxingtong/Xingtong-Read-Dongmuchang-Bert-VITS2/attentions.py b/spaces/digitalxingtong/Xingtong-Read-Dongmuchang-Bert-VITS2/attentions.py
deleted file mode 100644
index ecbdbc8be941a962046fc11fd6739b093112123e..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Xingtong-Read-Dongmuchang-Bert-VITS2/attentions.py
+++ /dev/null
@@ -1,343 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-from torch.nn.utils import weight_norm, remove_weight_norm
-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)
-
- 
-   
-@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
-
-class Encoder(nn.Module):
-   def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, isflow = True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    if isflow:
-      cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
-      self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
-      self.cond_layer = weight_norm(cond_layer, name='weight')
-      self.gin_channels = 256
-    self.cond_layer_idx = self.n_layers
-    if 'gin_channels' in kwargs:
-      self.gin_channels = kwargs['gin_channels']
-      if self.gin_channels != 0:
-        self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
-        # vits2 says 3rd block, so idx is 2 by default
-        self.cond_layer_idx = kwargs['cond_layer_idx'] if 'cond_layer_idx' in kwargs else 2
-        print(self.gin_channels, self.cond_layer_idx)
-        assert self.cond_layer_idx < self.n_layers, 'cond_layer_idx should be less than n_layers'
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-   def forward(self, x, x_mask, g=None):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      if i == self.cond_layer_idx and g is not None:
-        g = self.spk_emb_linear(g.transpose(1, 2))
-        g = g.transpose(1, 2)
-        x = x + g
-        x = x * x_mask
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/dineshreddy/WALT/mmdet/models/backbones/detectors_resnet.py b/spaces/dineshreddy/WALT/mmdet/models/backbones/detectors_resnet.py
deleted file mode 100644
index 519db464493c7c7b60fc34be1d21add2235ec341..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/backbones/detectors_resnet.py
+++ /dev/null
@@ -1,305 +0,0 @@
-import torch.nn as nn
-import torch.utils.checkpoint as cp
-from mmcv.cnn import build_conv_layer, build_norm_layer, constant_init
-
-from ..builder import BACKBONES
-from .resnet import Bottleneck as _Bottleneck
-from .resnet import ResNet
-
-
-class Bottleneck(_Bottleneck):
-    r"""Bottleneck for the ResNet backbone in `DetectoRS
-    <https://arxiv.org/pdf/2006.02334.pdf>`_.
-
-    This bottleneck allows the users to specify whether to use
-    SAC (Switchable Atrous Convolution) and RFP (Recursive Feature Pyramid).
-
-    Args:
-         inplanes (int): The number of input channels.
-         planes (int): The number of output channels before expansion.
-         rfp_inplanes (int, optional): The number of channels from RFP.
-             Default: None. If specified, an additional conv layer will be
-             added for ``rfp_feat``. Otherwise, the structure is the same as
-             base class.
-         sac (dict, optional): Dictionary to construct SAC. Default: None.
-    """
-    expansion = 4
-
-    def __init__(self,
-                 inplanes,
-                 planes,
-                 rfp_inplanes=None,
-                 sac=None,
-                 **kwargs):
-        super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
-
-        assert sac is None or isinstance(sac, dict)
-        self.sac = sac
-        self.with_sac = sac is not None
-        if self.with_sac:
-            self.conv2 = build_conv_layer(
-                self.sac,
-                planes,
-                planes,
-                kernel_size=3,
-                stride=self.conv2_stride,
-                padding=self.dilation,
-                dilation=self.dilation,
-                bias=False)
-
-        self.rfp_inplanes = rfp_inplanes
-        if self.rfp_inplanes:
-            self.rfp_conv = build_conv_layer(
-                None,
-                self.rfp_inplanes,
-                planes * self.expansion,
-                1,
-                stride=1,
-                bias=True)
-        self.init_weights()
-
-    def init_weights(self):
-        """Initialize the weights."""
-        if self.rfp_inplanes:
-            constant_init(self.rfp_conv, 0)
-
-    def rfp_forward(self, x, rfp_feat):
-        """The forward function that also takes the RFP features as input."""
-
-        def _inner_forward(x):
-            identity = x
-
-            out = self.conv1(x)
-            out = self.norm1(out)
-            out = self.relu(out)
-
-            if self.with_plugins:
-                out = self.forward_plugin(out, self.after_conv1_plugin_names)
-
-            out = self.conv2(out)
-            out = self.norm2(out)
-            out = self.relu(out)
-
-            if self.with_plugins:
-                out = self.forward_plugin(out, self.after_conv2_plugin_names)
-
-            out = self.conv3(out)
-            out = self.norm3(out)
-
-            if self.with_plugins:
-                out = self.forward_plugin(out, self.after_conv3_plugin_names)
-
-            if self.downsample is not None:
-                identity = self.downsample(x)
-
-            out += identity
-
-            return out
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(_inner_forward, x)
-        else:
-            out = _inner_forward(x)
-
-        if self.rfp_inplanes:
-            rfp_feat = self.rfp_conv(rfp_feat)
-            out = out + rfp_feat
-
-        out = self.relu(out)
-
-        return out
-
-
-class ResLayer(nn.Sequential):
-    """ResLayer to build ResNet style backbone for RPF in detectoRS.
-
-    The difference between this module and base class is that we pass
-    ``rfp_inplanes`` to the first block.
-
-    Args:
-        block (nn.Module): block used to build ResLayer.
-        inplanes (int): inplanes of block.
-        planes (int): planes of block.
-        num_blocks (int): number of blocks.
-        stride (int): stride of the first block. Default: 1
-        avg_down (bool): Use AvgPool instead of stride conv when
-            downsampling in the bottleneck. Default: False
-        conv_cfg (dict): dictionary to construct and config conv layer.
-            Default: None
-        norm_cfg (dict): dictionary to construct and config norm layer.
-            Default: dict(type='BN')
-        downsample_first (bool): Downsample at the first block or last block.
-            False for Hourglass, True for ResNet. Default: True
-        rfp_inplanes (int, optional): The number of channels from RFP.
-            Default: None. If specified, an additional conv layer will be
-            added for ``rfp_feat``. Otherwise, the structure is the same as
-            base class.
-    """
-
-    def __init__(self,
-                 block,
-                 inplanes,
-                 planes,
-                 num_blocks,
-                 stride=1,
-                 avg_down=False,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 downsample_first=True,
-                 rfp_inplanes=None,
-                 **kwargs):
-        self.block = block
-        assert downsample_first, f'downsample_first={downsample_first} is ' \
-                                 'not supported in DetectoRS'
-
-        downsample = None
-        if stride != 1 or inplanes != planes * block.expansion:
-            downsample = []
-            conv_stride = stride
-            if avg_down and stride != 1:
-                conv_stride = 1
-                downsample.append(
-                    nn.AvgPool2d(
-                        kernel_size=stride,
-                        stride=stride,
-                        ceil_mode=True,
-                        count_include_pad=False))
-            downsample.extend([
-                build_conv_layer(
-                    conv_cfg,
-                    inplanes,
-                    planes * block.expansion,
-                    kernel_size=1,
-                    stride=conv_stride,
-                    bias=False),
-                build_norm_layer(norm_cfg, planes * block.expansion)[1]
-            ])
-            downsample = nn.Sequential(*downsample)
-
-        layers = []
-        layers.append(
-            block(
-                inplanes=inplanes,
-                planes=planes,
-                stride=stride,
-                downsample=downsample,
-                conv_cfg=conv_cfg,
-                norm_cfg=norm_cfg,
-                rfp_inplanes=rfp_inplanes,
-                **kwargs))
-        inplanes = planes * block.expansion
-        for _ in range(1, num_blocks):
-            layers.append(
-                block(
-                    inplanes=inplanes,
-                    planes=planes,
-                    stride=1,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=norm_cfg,
-                    **kwargs))
-
-        super(ResLayer, self).__init__(*layers)
-
-
-@BACKBONES.register_module()
-class DetectoRS_ResNet(ResNet):
-    """ResNet backbone for DetectoRS.
-
-    Args:
-        sac (dict, optional): Dictionary to construct SAC (Switchable Atrous
-            Convolution). Default: None.
-        stage_with_sac (list): Which stage to use sac. Default: (False, False,
-            False, False).
-        rfp_inplanes (int, optional): The number of channels from RFP.
-            Default: None. If specified, an additional conv layer will be
-            added for ``rfp_feat``. Otherwise, the structure is the same as
-            base class.
-        output_img (bool): If ``True``, the input image will be inserted into
-            the starting position of output. Default: False.
-        pretrained (str, optional): The pretrained model to load.
-    """
-
-    arch_settings = {
-        50: (Bottleneck, (3, 4, 6, 3)),
-        101: (Bottleneck, (3, 4, 23, 3)),
-        152: (Bottleneck, (3, 8, 36, 3))
-    }
-
-    def __init__(self,
-                 sac=None,
-                 stage_with_sac=(False, False, False, False),
-                 rfp_inplanes=None,
-                 output_img=False,
-                 pretrained=None,
-                 **kwargs):
-        self.sac = sac
-        self.stage_with_sac = stage_with_sac
-        self.rfp_inplanes = rfp_inplanes
-        self.output_img = output_img
-        self.pretrained = pretrained
-        super(DetectoRS_ResNet, self).__init__(**kwargs)
-
-        self.inplanes = self.stem_channels
-        self.res_layers = []
-        for i, num_blocks in enumerate(self.stage_blocks):
-            stride = self.strides[i]
-            dilation = self.dilations[i]
-            dcn = self.dcn if self.stage_with_dcn[i] else None
-            sac = self.sac if self.stage_with_sac[i] else None
-            if self.plugins is not None:
-                stage_plugins = self.make_stage_plugins(self.plugins, i)
-            else:
-                stage_plugins = None
-            planes = self.base_channels * 2**i
-            res_layer = self.make_res_layer(
-                block=self.block,
-                inplanes=self.inplanes,
-                planes=planes,
-                num_blocks=num_blocks,
-                stride=stride,
-                dilation=dilation,
-                style=self.style,
-                avg_down=self.avg_down,
-                with_cp=self.with_cp,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                dcn=dcn,
-                sac=sac,
-                rfp_inplanes=rfp_inplanes if i > 0 else None,
-                plugins=stage_plugins)
-            self.inplanes = planes * self.block.expansion
-            layer_name = f'layer{i + 1}'
-            self.add_module(layer_name, res_layer)
-            self.res_layers.append(layer_name)
-
-        self._freeze_stages()
-
-    def make_res_layer(self, **kwargs):
-        """Pack all blocks in a stage into a ``ResLayer`` for DetectoRS."""
-        return ResLayer(**kwargs)
-
-    def forward(self, x):
-        """Forward function."""
-        outs = list(super(DetectoRS_ResNet, self).forward(x))
-        if self.output_img:
-            outs.insert(0, x)
-        return tuple(outs)
-
-    def rfp_forward(self, x, rfp_feats):
-        """Forward function for RFP."""
-        if self.deep_stem:
-            x = self.stem(x)
-        else:
-            x = self.conv1(x)
-            x = self.norm1(x)
-            x = self.relu(x)
-        x = self.maxpool(x)
-        outs = []
-        for i, layer_name in enumerate(self.res_layers):
-            res_layer = getattr(self, layer_name)
-            rfp_feat = rfp_feats[i] if i > 0 else None
-            for layer in res_layer:
-                x = layer.rfp_forward(x, rfp_feat)
-            if i in self.out_indices:
-                outs.append(x)
-        return tuple(outs)
diff --git a/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/recog_models/abinet.py b/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/recog_models/abinet.py
deleted file mode 100644
index 19c6b66731f0b205741037ece8d6b49f91d0110b..0000000000000000000000000000000000000000
--- a/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/recog_models/abinet.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# num_chars depends on the configuration of label_convertor. The actual
-# dictionary size is 36 + 1 (<BOS/EOS>).
-# TODO: Automatically update num_chars based on the configuration of
-# label_convertor
-num_chars = 37
-max_seq_len = 26
-
-label_convertor = dict(
-    type='ABIConvertor',
-    dict_type='DICT36',
-    with_unknown=False,
-    with_padding=False,
-    lower=True,
-)
-
-model = dict(
-    type='ABINet',
-    backbone=dict(type='ResNetABI'),
-    encoder=dict(
-        type='ABIVisionModel',
-        encoder=dict(
-            type='TransformerEncoder',
-            n_layers=3,
-            n_head=8,
-            d_model=512,
-            d_inner=2048,
-            dropout=0.1,
-            max_len=8 * 32,
-        ),
-        decoder=dict(
-            type='ABIVisionDecoder',
-            in_channels=512,
-            num_channels=64,
-            attn_height=8,
-            attn_width=32,
-            attn_mode='nearest',
-            use_result='feature',
-            num_chars=num_chars,
-            max_seq_len=max_seq_len,
-            init_cfg=dict(type='Xavier', layer='Conv2d')),
-    ),
-    decoder=dict(
-        type='ABILanguageDecoder',
-        d_model=512,
-        n_head=8,
-        d_inner=2048,
-        n_layers=4,
-        dropout=0.1,
-        detach_tokens=True,
-        use_self_attn=False,
-        pad_idx=num_chars - 1,
-        num_chars=num_chars,
-        max_seq_len=max_seq_len,
-        init_cfg=None),
-    fuser=dict(
-        type='ABIFuser',
-        d_model=512,
-        num_chars=num_chars,
-        init_cfg=None,
-        max_seq_len=max_seq_len,
-    ),
-    loss=dict(
-        type='ABILoss',
-        enc_weight=1.0,
-        dec_weight=1.0,
-        fusion_weight=1.0,
-        num_classes=num_chars),
-    label_convertor=label_convertor,
-    max_seq_len=max_seq_len,
-    iter_size=3)
diff --git a/spaces/dinhminh20521597/OCR_DEMO/configs/textdet/drrg/README.md b/spaces/dinhminh20521597/OCR_DEMO/configs/textdet/drrg/README.md
deleted file mode 100644
index 2f2beb1b757ccbf2dd2e41a70769d963b098264d..0000000000000000000000000000000000000000
--- a/spaces/dinhminh20521597/OCR_DEMO/configs/textdet/drrg/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# DRRG
-
-> [Deep relational reasoning graph network for arbitrary shape text detection](https://arxiv.org/abs/2003.07493)
-
-<!-- [ALGORITHM] -->
-
-## Abstract
-
-Arbitrary shape text detection is a challenging task due to the high variety and complexity of scenes texts. In this paper, we propose a novel unified relational reasoning graph network for arbitrary shape text detection. In our method, an innovative local graph bridges a text proposal model via Convolutional Neural Network (CNN) and a deep relational reasoning network via Graph Convolutional Network (GCN), making our network end-to-end trainable. To be concrete, every text instance will be divided into a series of small rectangular components, and the geometry attributes (e.g., height, width, and orientation) of the small components will be estimated by our text proposal model. Given the geometry attributes, the local graph construction model can roughly establish linkages between different text components. For further reasoning and deducing the likelihood of linkages between the component and its neighbors, we adopt a graph-based network to perform deep relational reasoning on local graphs. Experiments on public available datasets demonstrate the state-of-the-art performance of our method.
-
-<div align=center>
-<img src="https://user-images.githubusercontent.com/22607038/142791777-f282300a-fb83-4b5a-a7d4-29f308949f11.png"/>
-</div>
-
-## Results and models
-
-### CTW1500
-
-|                       Method                        | Pretrained Model | Training set  |   Test set   | #epochs | Test size |    Recall     |   Precision   |     Hmean     |                       Download                        |
-| :-------------------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :-----------: | :-----------: | :-----------: | :---------------------------------------------------: |
-| [DRRG](configs/textdet/drrg/drrg_r50_fpn_unet_1200e_ctw1500.py) |     ImageNet     | CTW1500 Train | CTW1500 Test |  1200   |    640    | 0.822 (0.791) | 0.858 (0.862) | 0.840 (0.825) | [model](https://download.openmmlab.com/mmocr/textdet/drrg/drrg_r50_fpn_unet_1200e_ctw1500_20211022-fb30b001.pth) \\ [log](https://download.openmmlab.com/mmocr/textdet/drrg/20210511_234719.log) |
-
-```{note}
-We've upgraded our IoU backend from `Polygon3` to `shapely`. There are some performance differences for some models due to the backends' different logics to handle invalid polygons (more info [here](https://github.com/open-mmlab/mmocr/issues/465)). **New evaluation result is presented in brackets** and new logs will be uploaded soon.
-```
-
-## Citation
-
-```bibtex
-@article{zhang2020drrg,
-  title={Deep relational reasoning graph network for arbitrary shape text detection},
-  author={Zhang, Shi-Xue and Zhu, Xiaobin and Hou, Jie-Bo and Liu, Chang and Yang, Chun and Wang, Hongfa and Yin, Xu-Cheng},
-  booktitle={CVPR},
-  pages={9699-9708},
-  year={2020}
-}
-```
diff --git a/spaces/dolceschokolade/chatbot-mini/utils/app/prompts.ts b/spaces/dolceschokolade/chatbot-mini/utils/app/prompts.ts
deleted file mode 100644
index 64a8b2fd71ad78983d1bdd7d988b95b1a34ea7f9..0000000000000000000000000000000000000000
--- a/spaces/dolceschokolade/chatbot-mini/utils/app/prompts.ts
+++ /dev/null
@@ -1,22 +0,0 @@
-import { Prompt } from '@/types/prompt';
-
-export const updatePrompt = (updatedPrompt: Prompt, allPrompts: Prompt[]) => {
-  const updatedPrompts = allPrompts.map((c) => {
-    if (c.id === updatedPrompt.id) {
-      return updatedPrompt;
-    }
-
-    return c;
-  });
-
-  savePrompts(updatedPrompts);
-
-  return {
-    single: updatedPrompt,
-    all: updatedPrompts,
-  };
-};
-
-export const savePrompts = (prompts: Prompt[]) => {
-  localStorage.setItem('prompts', JSON.stringify(prompts));
-};
diff --git a/spaces/dorkai/ChatUIPro/i18n/lang/app.en.ts b/spaces/dorkai/ChatUIPro/i18n/lang/app.en.ts
deleted file mode 100644
index 5530b2064a128dd3a23537052a48c28845afc384..0000000000000000000000000000000000000000
--- a/spaces/dorkai/ChatUIPro/i18n/lang/app.en.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-const translation = {
-  common: {
-    welcome: "Welcome to use",
-    appUnavailable: "App is unavailable",
-    appUnkonwError: "App is unavailable"
-  },
-  chat: {
-    newChat: "New chat",
-    newChatDefaultName: "New conversation",
-    openingStatementTitle: "Opening statement",
-    powerBy: "Powered by",
-    prompt: "Prompt",
-    privatePromptConfigTitle: "Conversation settings",
-    publicPromptConfigTitle: "Initial Prompt",
-    configStatusDes: "Before start, you can modify conversation settings",
-    configDisabled:
-      "Previous session settings have been used for this session.",
-    startChat: "Start Chat",
-    privacyPolicyLeft:
-      "Please read the ",
-    privacyPolicyMiddle:
-      "privacy policy",
-    privacyPolicyRight:
-      " provided by the app developer.",
-  },
-  errorMessage: {
-    valueOfVarRequired: "Variables value can not be empty",
-    waitForResponse:
-      "Please wait for the response to the previous message to complete.",
-  },
-};
-
-export default translation;
diff --git a/spaces/dotmet/Real-ESRGAN-Enhanced-Anime-Diffusion/style.css b/spaces/dotmet/Real-ESRGAN-Enhanced-Anime-Diffusion/style.css
deleted file mode 100644
index 79d03d4905e60bebf2f357a1ba97ffedd0f0d5f5..0000000000000000000000000000000000000000
--- a/spaces/dotmet/Real-ESRGAN-Enhanced-Anime-Diffusion/style.css
+++ /dev/null
@@ -1,22 +0,0 @@
-.finetuned-diffusion-div div{
-    display:inline-flex;
-    align-items:center;
-    gap:.8rem;
-    font-size:1.75rem
-    }
-
-.finetuned-diffusion-div div h1{
-    font-weight:900;
-    margin-bottom:7px
-    }
-
-.finetuned-diffusion-div p{
-    margin-bottom:10px;
-    font-size:94%}
-
-a{text-decoration:underline}
-
-.tabs{margin-top:0;
-      margin-bottom:0}
-
-#gallery{min-height:20rem}
diff --git a/spaces/dynamicstude/RHYTHMflowise/README.md b/spaces/dynamicstude/RHYTHMflowise/README.md
deleted file mode 100644
index 28d35ca323be97d5ddfafe662e4bffeae59b55ac..0000000000000000000000000000000000000000
--- a/spaces/dynamicstude/RHYTHMflowise/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: RHYTHMflowise
-emoji: 🌖
-colorFrom: yellow
-colorTo: pink
-sdk: docker
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/editing-images/project/static/css/fontawesome.all.min.css b/spaces/editing-images/project/static/css/fontawesome.all.min.css
deleted file mode 100644
index 656a50745f7224b3eca827869677851c705b26c9..0000000000000000000000000000000000000000
--- a/spaces/editing-images/project/static/css/fontawesome.all.min.css
+++ /dev/null
@@ -1,5 +0,0 @@
-/*!
- * Font Awesome Free 5.15.1 by @fontawesome - https://fontawesome.com
- * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
- */
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.15em}.fa-pull-left{float:left}.fa-pull-right{float:right}.fa.fa-pull-left,.fab.fa-pull-left,.fal.fa-pull-left,.far.fa-pull-left,.fas.fa-pull-left{margin-right:.3em}.fa.fa-pull-right,.fab.fa-pull-right,.fal.fa-pull-right,.far.fa-pull-right,.fas.fa-pull-right{margin-left:.3em}.fa-spin{-webkit-animation:fa-spin 2s linear infinite;animation:fa-spin 2s linear infinite}.fa-pulse{-webkit-animation:fa-spin 1s steps(8) infinite;animation:fa-spin 1s steps(8) infinite}@-webkit-keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(1turn);transform:rotate(1turn)}}@keyframes 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ontent:"\f0e2"}.fa-undo-alt:before{content:"\f2ea"}.fa-uniregistry:before{content:"\f404"}.fa-unity:before{content:"\e049"}.fa-universal-access:before{content:"\f29a"}.fa-university:before{content:"\f19c"}.fa-unlink:before{content:"\f127"}.fa-unlock:before{content:"\f09c"}.fa-unlock-alt:before{content:"\f13e"}.fa-unsplash:before{content:"\e07c"}.fa-untappd:before{content:"\f405"}.fa-upload:before{content:"\f093"}.fa-ups:before{content:"\f7e0"}.fa-usb:before{content:"\f287"}.fa-user:before{content:"\f007"}.fa-user-alt:before{content:"\f406"}.fa-user-alt-slash:before{content:"\f4fa"}.fa-user-astronaut:before{content:"\f4fb"}.fa-user-check:before{content:"\f4fc"}.fa-user-circle:before{content:"\f2bd"}.fa-user-clock:before{content:"\f4fd"}.fa-user-cog:before{content:"\f4fe"}.fa-user-edit:before{content:"\f4ff"}.fa-user-friends:before{content:"\f500"}.fa-user-graduate:before{content:"\f501"}.fa-user-injured:before{content:"\f728"}.fa-user-lock:before{content:"\f502"}.fa-user-md:before{content:"\f0f0"}.fa-user-minus:before{content:"\f503"}.fa-user-ninja:before{content:"\f504"}.fa-user-nurse:before{content:"\f82f"}.fa-user-plus:before{content:"\f234"}.fa-user-secret:before{content:"\f21b"}.fa-user-shield:before{content:"\f505"}.fa-user-slash:before{content:"\f506"}.fa-user-tag:before{content:"\f507"}.fa-user-tie:before{content:"\f508"}.fa-user-times:before{content:"\f235"}.fa-users:before{content:"\f0c0"}.fa-users-cog:before{content:"\f509"}.fa-users-slash:before{content:"\e073"}.fa-usps:before{content:"\f7e1"}.fa-ussunnah:before{content:"\f407"}.fa-utensil-spoon:before{content:"\f2e5"}.fa-utensils:before{content:"\f2e7"}.fa-vaadin:before{content:"\f408"}.fa-vector-square:before{content:"\f5cb"}.fa-venus:before{content:"\f221"}.fa-venus-double:before{content:"\f226"}.fa-venus-mars:before{content:"\f228"}.fa-vest:before{content:"\e085"}.fa-vest-patches:before{content:"\e086"}.fa-viacoin:before{content:"\f237"}.fa-viadeo:before{content:"\f2a9"}.fa-viadeo-square:before{content:"\f2aa"}.fa-vial:before{content:"\f492"}.fa-vials:before{content:"\f493"}.fa-viber:before{content:"\f409"}.fa-video:before{content:"\f03d"}.fa-video-slash:before{content:"\f4e2"}.fa-vihara:before{content:"\f6a7"}.fa-vimeo:before{content:"\f40a"}.fa-vimeo-square:before{content:"\f194"}.fa-vimeo-v:before{content:"\f27d"}.fa-vine:before{content:"\f1ca"}.fa-virus:before{content:"\e074"}.fa-virus-slash:before{content:"\e075"}.fa-viruses:before{content:"\e076"}.fa-vk:before{content:"\f189"}.fa-vnv:before{content:"\f40b"}.fa-voicemail:before{content:"\f897"}.fa-volleyball-ball:before{content:"\f45f"}.fa-volume-down:before{content:"\f027"}.fa-volume-mute:before{content:"\f6a9"}.fa-volume-off:before{content:"\f026"}.fa-volume-up:before{content:"\f028"}.fa-vote-yea:before{content:"\f772"}.fa-vr-cardboard:before{content:"\f729"}.fa-vuejs:before{content:"\f41f"}.fa-walking:before{content:"\f554"}.fa-wallet:before{content:"\f555"}.fa-warehouse:before{content:"\f494"}.fa-watchman-monitoring:before{content:"\e087"}.fa-water:before{content:"\f773"}.fa-wave-square:before{content:"\f83e"}.fa-waze:before{content:"\f83f"}.fa-weebly:before{content:"\f5cc"}.fa-weibo:before{content:"\f18a"}.fa-weight:before{content:"\f496"}.fa-weight-hanging:before{content:"\f5cd"}.fa-weixin:before{content:"\f1d7"}.fa-whatsapp:before{content:"\f232"}.fa-whatsapp-square:before{content:"\f40c"}.fa-wheelchair:before{content:"\f193"}.fa-whmcs:before{content:"\f40d"}.fa-wifi:before{content:"\f1eb"}.fa-wikipedia-w:before{content:"\f266"}.fa-wind:before{content:"\f72e"}.fa-window-close:before{content:"\f410"}.fa-window-maximize:before{content:"\f2d0"}.fa-window-minimize:before{content:"\f2d1"}.fa-window-restore:before{content:"\f2d2"}.fa-windows:before{content:"\f17a"}.fa-wine-bottle:before{content:"\f72f"}.fa-wine-glass:before{content:"\f4e3"}.fa-wine-glass-alt:before{content:"\f5ce"}.fa-wix:before{content:"\f5cf"}.fa-wizards-of-the-coast:before{content:"\f730"}.fa-wodu:before{content:"\e088"}.fa-wolf-pack-battalion:before{content:"\f514"}.fa-won-sign:before{content:"\f159"}.fa-wordpress:before{content:"\f19a"}.fa-wordpress-simple:before{content:"\f411"}.fa-wpbeginner:before{content:"\f297"}.fa-wpexplorer:before{content:"\f2de"}.fa-wpforms:before{content:"\f298"}.fa-wpressr:before{content:"\f3e4"}.fa-wrench:before{content:"\f0ad"}.fa-x-ray:before{content:"\f497"}.fa-xbox:before{content:"\f412"}.fa-xing:before{content:"\f168"}.fa-xing-square:before{content:"\f169"}.fa-y-combinator:before{content:"\f23b"}.fa-yahoo:before{content:"\f19e"}.fa-yammer:before{content:"\f840"}.fa-yandex:before{content:"\f413"}.fa-yandex-international:before{content:"\f414"}.fa-yarn:before{content:"\f7e3"}.fa-yelp:before{content:"\f1e9"}.fa-yen-sign:before{content:"\f157"}.fa-yin-yang:before{content:"\f6ad"}.fa-yoast:before{content:"\f2b1"}.fa-youtube:before{content:"\f167"}.fa-youtube-square:before{content:"\f431"}.fa-zhihu:before{content:"\f63f"}.sr-only{border:0;clip:rect(0,0,0,0);height:1px;margin:-1px;overflow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\ No newline at end of file
diff --git a/spaces/ericmichael/openai-playground-utrgv/app/db.py b/spaces/ericmichael/openai-playground-utrgv/app/db.py
deleted file mode 100644
index f4b2372dea196c2ce3c268ff6d021f378ffc1560..0000000000000000000000000000000000000000
--- a/spaces/ericmichael/openai-playground-utrgv/app/db.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from typing import AsyncGenerator
-
-from fastapi import Depends
-from fastapi_users.db import SQLAlchemyBaseUserTableUUID, SQLAlchemyUserDatabase
-from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
-from sqlalchemy.orm import DeclarativeBase
-from dotenv import load_dotenv
-import os
-
-# Get the current environment from the environment variable
-current_environment = os.getenv("APP_ENV", "dev")
-
-# Load the appropriate .env file based on the current environment
-if current_environment == "dev":
-    load_dotenv(".env.dev")
-elif current_environment == "test":
-    load_dotenv(".env.test")
-elif current_environment == "prod":
-    load_dotenv(".env.prod")
-else:
-    raise ValueError("Invalid environment specified")
-
-db_connection_string = os.getenv("DB_CONNECTION_STRING")
-
-DATABASE_URL = db_connection_string
-
-
-class Base(DeclarativeBase):
-    pass
-
-
-class User(SQLAlchemyBaseUserTableUUID, Base):
-    pass
-
-
-engine = create_async_engine(DATABASE_URL)
-async_session_maker = async_sessionmaker(engine, expire_on_commit=False)
-
-
-async def create_db_and_tables():
-    async with engine.begin() as conn:
-        await conn.run_sync(Base.metadata.create_all)
-
-
-async def get_async_session() -> AsyncGenerator[AsyncSession, None]:
-    async with async_session_maker() as session:
-        yield session
-
-
-async def get_user_db(session: AsyncSession = Depends(get_async_session)):
-    yield SQLAlchemyUserDatabase(session, User)
diff --git a/spaces/eskayML/mask_segmentation/app.py b/spaces/eskayML/mask_segmentation/app.py
deleted file mode 100644
index 576f5ca23a7d94edf14893d119e8a51d502e4c7d..0000000000000000000000000000000000000000
--- a/spaces/eskayML/mask_segmentation/app.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import gradio as gr
-import torch
-import random
-import numpy as np
-from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
-
-device = torch.device("cpu")
-model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-tiny-ade").to(device)
-model.eval()
-preprocessor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-tiny-ade")
-
-def visualize_instance_seg_mask(mask):
-    image = np.zeros((mask.shape[0], mask.shape[1], 3))
-    labels = np.unique(mask)
-    label2color = {label: (random.randint(0, 1), random.randint(0, 255), random.randint(0, 255)) for label in labels}
-    for i in range(image.shape[0]):
-      for j in range(image.shape[1]):
-        image[i, j, :] = label2color[mask[i, j]]
-    image = image / 255
-    return image
-
-def query_image(img):
-    target_size = (img.shape[0], img.shape[1])
-    inputs = preprocessor(images=img, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model(**inputs)
-    outputs.class_queries_logits = outputs.class_queries_logits.cpu()
-    outputs.masks_queries_logits = outputs.masks_queries_logits.cpu()
-    results = preprocessor.post_process_segmentation(outputs=outputs, target_size=target_size)[0].cpu().detach()
-    results = torch.argmax(results, dim=0).numpy()
-    results = visualize_instance_seg_mask(results)
-    return results
-
-demo = gr.Interface(
-    query_image,
-    inputs=[gr.Image()],
-    outputs="image",
-    title="Image Segmentation using Maskformer",
-)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/eson/tokenizer-arena/vocab/baichuan2/__init__.py b/spaces/eson/tokenizer-arena/vocab/baichuan2/__init__.py
deleted file mode 100644
index 8f1ed960e61065a444fc791122a8ab5430511ca3..0000000000000000000000000000000000000000
--- a/spaces/eson/tokenizer-arena/vocab/baichuan2/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from transformers import AutoTokenizer
-from vocab import TokenizerType
-
-tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan2-7B-Chat", trust_remote_code=True)
-
-
-# byte-bpe  sentencepiece
-tokenizer.type = TokenizerType.ByteBPE
-
-tokenizer.comments = "expand the vocqbulary size from 64000 in Baichuan1 to 125696"
diff --git a/spaces/fakezeta/pdfchat/ingest_data.py b/spaces/fakezeta/pdfchat/ingest_data.py
deleted file mode 100644
index 03ef31679a6429faa146464ad301b167d546f28e..0000000000000000000000000000000000000000
--- a/spaces/fakezeta/pdfchat/ingest_data.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.document_loaders import PyPDFLoader
-from langchain.vectorstores import Chroma
-from langchain.embeddings import HuggingFaceEmbeddings, SentenceTransformerEmbeddings
-import os
-import time
-import streamlit as st
-
-def embed_doc(filename):
-    if len(os.listdir("."))>0:
-        loader=PyPDFLoader(filename)        
-        start = time.time()
-        raw_documents = loader.load()
-        # Split text
-
-        text_splitter = RecursiveCharacterTextSplitter(
-            chunk_size=1000,
-            chunk_overlap=0,
-            length_function=len
-
-        )
-
-        documents = text_splitter.split_documents(raw_documents)
-        end = time.time()
-        st.text("Load and split text: "+str(round(end - start,1)))
-
-
-        start = time.time()
-        embeddings = HuggingFaceEmbeddings(model_name="intfloat/e5-base")
-        end = time.time()
-        st.text("Embedding time: "+str(round(end - start,1)))
-        start = time.time()
-        vectorstore = Chroma.from_documents(documents, embeddings)
-        end = time.time()
-        st.text("Vectorizing time: "+str(round(end - start,1)))
-        return vectorstore
-
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deleted file mode 100644
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index bec14e136e34d5e9d0ee943c1d655debc10815a3..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Duplicate Cleaner Pro 4.1.4 Crack With License Key !!INSTALL!! Download.md	
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--- a/spaces/falterWliame/Face_Mask_Detection/Nomad Factory Blue Tubes Analog TrackBox V1 3 VST RTAS PC Clipart Matita Invaders Theme Archite.md	
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diff --git a/spaces/fatiXbelha/sd/American Marksman How to Hunt and Survive in the Wild with Your Friends.md b/spaces/fatiXbelha/sd/American Marksman How to Hunt and Survive in the Wild with Your Friends.md
deleted file mode 100644
index 4ccbaa96cf54d3cf0512c5a7d7ac0bdda5ce411d..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/American Marksman How to Hunt and Survive in the Wild with Your Friends.md	
+++ /dev/null
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-
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- <h3>How to find and track animals</h3>
- <p>One of the most important skills in hunting is finding and tracking animals. You have to use your eyes, ears, and nose to locate them. You can also use tools such as binoculars, rangefinder, trail camera, etc. to help you. You have to look for signs of animal presence, such as tracks, droppings, scents, sounds, etc. You have to follow these clues and predict where the animal is heading. You also have to be aware of the wind direction and avoid being detected by the animal's sense of smell.</p>
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- <p>Another important skill in hunting is choosing the best gun for each animal. You have to consider the size, speed, distance, and behavior of the animal. You also have to consider the terrain, weather, and visibility of the location. You have to use a gun that can deliver a clean and ethical shot that will kill the animal quickly and humanely. You also have to use a gun that you are comfortable and confident with. Here is a table that shows some examples of guns that are suitable for different animals:</p>
- <table>
-<tr>
-<th>Animal</th>
-<th>Gun</th>
-<th>Reason</th>
-</tr>
-<tr>
-<td>Deer</td>
-<td>Rifle</td>
-<td>A rifle can provide a long-range and accurate shot that can penetrate the deer's vital organs.</td>
-</tr>
-<tr>
-<td>Bear</td>
-<td>Shotgun</td>
-<td>A shotgun can deliver a powerful and close-range shot that can stop the bear's charge.</td>
-</tr>
-<tr>
-<td>Turkey</td>
-<td>Bow</td>
-<td>A bow can offer a silent and stealthy shot that can avoid spooking the turkey.</td>
-</tr>
-<tr>
-<td>Duck</td>
-<td>Pistol</td>
-<td>A pistol can provide a fast and easy shot that can hit the duck in flight.</td>
-</tr>
-<tr>
-<td>Fox</td>
-<td>Crossbow</td>
-<td>A crossbow can offer a precise and quiet shot that can target the fox's small body.</td>
-</tr>
-</table>
- <h3>How to make more money by selling trophies</h3>
- <p>One of the ways to make money in the game is by selling trophies. Trophies are parts of animals that you can collect after killing them, such as antlers, horns, skulls, pelts, etc. You can sell these trophies to buyers who will pay you based on their quality and rarity. You can also mount and display your trophies in your farm or house. Here are some tips on how to make more money by selling trophies:</p>
- - Aim for the head or neck of the animal to preserve the trophy quality. - Use a weapon that can cause minimal damage to the trophy part. - Hunt rare or exotic animals that have higher trophy value. - Compare prices and deals from different buyers before selling. - Upgrade your skills and equipment to improve your hunting efficiency. <h3>How to compare prices and deals for guns</h3>
- <p>Another way to make money in the game is by buying and selling guns. Guns are essential tools for hunting that you can purchase from shops or other players. You can also sell your guns that you no longer need or want. Here are some tips on how to compare prices and deals for guns:</p>
- - Check the stats and features of each gun before buying or selling. - Look for discounts or offers from shops or other players. - Negotiate with other players for a fair price or trade. - Sell your guns when they are in high demand or low supply. - Buy your guns when they are in low demand or high supply. <h3>How to enjoy other modes and activities</h3>
- <p>American Marksman is not only about hunting. It also offers other modes and activities that you can enjoy with or without other players. Here are some examples of other modes and activities:</p>
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- <p>American Marksman is a game that will appeal to hunting enthusiasts and outdoor lovers. It offers realistic graphics, various hunting modes, multiplayer and roleplay options, customizable farm and terrain, diverse animals and weapons, and more. It also allows you to download it for free from the official website or other sources. You can follow the link below to get your copy of the game and start your hunting adventure.</p>
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- <h3>What are the system requirements for American Marksman?</h3>
- <p>The minimum system requirements for American Marksman are as follows:</p>
- - OS: Windows 7/8/10 (64-bit) - Processor: Intel Core i3-2100 or AMD FX-6300 - Memory: 4 GB RAM - Graphics: NVIDIA GeForce GTX 750 Ti or AMD Radeon HD 7850 - DirectX: Version 11 - Storage: 20 GB available space <h3>How do I join or create a multiplayer session?</h3>
- <p>To join or create a multiplayer session, you have to follow these steps:</p>
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- <p>To modify the terrain and place objects, you have to follow these steps:</p>
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diff --git a/spaces/fatiXbelha/sd/BEST.md b/spaces/fatiXbelha/sd/BEST.md
deleted file mode 100644
index 9d8fa449e5be082a79f4cbf1c368f5db5bc27ff8..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/BEST.md
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deleted file mode 100644
index cbb99d4702e2c3f23395446efb546736af98f0b9..0000000000000000000000000000000000000000
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- <ul>
-<li><b>John Constantine</b> (played by Matt Ryan): The protagonist of the series, a cynical and witty occult detective who has a troubled past and a dubious morality. He uses his knowledge of the dark arts and his supernatural abilities to fight against the rising darkness, while also trying to save his own soul and the soul of Astra Logue, a young girl he accidentally damned to hell.</li>
-<li><b>Chas Chandler</b> (played by Charles Halford): Constantine's loyal friend and companion, who has a knack for surviving deadly situations. He has a mysterious ability that allows him to resurrect himself after death, but at the cost of losing one of his limited number of lives. He is also a cab driver and a family man who cares for his wife and daughter.</li>
-<li><b>Zed Martin</b> (played by Angélica Celaya): A psychic artist who has visions related to Constantine's cases. She joins Constantine's team after meeting him in Atlanta, where she was hiding from her abusive father and his cult. She has a strong connection to Constantine and the rising darkness, and also develops feelings for him.</li>
-<li><b>Manny</b> (played by Harold Perrineau): An angel who is assigned to watch over Constantine and guide him. He can only communicate with Constantine when he possesses a human body, and he often appears in times of crisis or danger. He is also part of a larger plan that involves Constantine and the rising darkness, but he keeps his true motives hidden.</li>
-<li><b>Papa Midnite</b> (played by Michael James Shaw): A voodoo priest and crime lord who operates in New York. He has a history with Constantine, as they were once allies but later became enemies. He is also a rival in the occult business, as he offers his services to anyone who can pay him. He has access to various magical artifacts and resources, such as the Ace of Winchesters (a cursed gun that can kill anything) and the Lacroix Mansion (a haunted house that contains a portal to hell).</li>
-<li><b>Ritchie Simpson</b> (played by Jeremy Davies): A hacker and former friend of Constantine, who was part of the Newcastle crew. He blames Constantine for what happened to Astra Logue, and he suffers from survivor's guilt and depression. He is also a master of cyber-magic, which allows him to manipulate digital information and enter virtual realities.</li>
-<li><b>Anne Marie Flynn</b> (played by Claire van der Boom): A nun and former lover of Constantine, who was also part of the Newcastle crew. She left Constantine after the Astra Logue incident, and she joined a convent in Mexico. She is an expert in demonology and exorcism, and she still cares for Constantine despite their complicated history.</li>
-<li><b>Jim Corrigan</b> (played by Emmett J. Scanlan): A detective who works in New Orleans. He crosses paths with Constantine when he investigates a series of murders that are linked to the rising darkness. He is also destined to become the Spectre, a powerful spirit of vengeance who punishes the wicked.</li>
-<li><b>Felix Faust</b> (played by Mark Margolis): A powerful sorcerer and nemesis of Constantine, who is behind some of the cases that involve the rising darkness. He is an old and cunning villain who seeks to gain more power and knowledge by any means necessary. He also has a connection to Manny and his plan.</li>
-</ul>
- <h3>The reception and legacy of Constantine s1</h3>
- <p>The reception and legacy of Constantine s1 are mixed. The series received generally positive reviews from critics and fans, who praised the performance of Matt Ryan as John Constantine, the faithful adaptation of the comic book source material, the dark and gritty tone, the horror elements, and the potential for future stories . However, the series also faced some challenges, such as low ratings, network interference, scheduling issues, budget constraints, and creative differences . The series was canceled by NBC after one season, leaving many fans disappointed and angry. However, the series also gained a cult following and a loyal fanbase, who launched various campaigns to save the show or revive it on another platform. The series also had an impact on the Arrowverse franchise, as Matt Ryan reprised his role as John Constantine in several guest appearances on Arrow, Legends of Tomorrow, and The Flash. The character also received his own animated web series on CW Seed, titled Constantine: City of Demons <h2>What is 480p resolution and why do you need it?</h2>
- <p>480p resolution is a term that refers to the quality and size of a video or image. It is also known as standard definition (SD) or enhanced definition (ED). It means that the video or image has a width of 720 pixels and a height of 480 pixels, and that it displays 60 frames per second (fps) in the NTSC format or 50 fps in the PAL format. It is one of the most common resolutions used for digital media, especially for DVDs, web videos, and older TV shows and movies.</p>
- <h3>The definition and features of 480p resolution</h3>
- <p>480p resolution is defined by the number of pixels (or dots) that make up a video or image. Pixels are the smallest units of color and light that can be displayed on a screen. The more pixels a video or image has, the more detail and clarity it can show. However, the number of pixels also affects the file size and the bandwidth required to stream or download a video or image. Therefore, there is a trade-off between quality and efficiency when choosing a resolution.</p>
- <p>480p resolution has some features that distinguish it from other resolutions, such as:</p>
- <ul>
-<li>It has an aspect ratio of 4:3, which means that the width is four times the height. This is the standard shape for older TV screens and monitors. However, some 480p videos and images can also have an aspect ratio of 16:9, which means that the width is 16 times the height. This is the standard shape for modern widescreen TV screens and monitors.</li>
-<li>It has a pixel density of 37 pixels per inch (ppi), which means that there are 37 pixels in every inch of the screen. This is a low pixel density compared to other resolutions, which means that the pixels are more visible and less smooth. However, this also depends on the size and distance of the screen.</li>
-<li>It has a bit rate of about 1 megabit per second (Mbps), which means that it transfers about 1 million bits of data every second. This is a low bit rate compared to other resolutions, which means that it consumes less bandwidth and storage space. However, this also means that it has less color depth and dynamic range.</li>
-</ul>
- <h3>The advantages and disadvantages of 480p resolution</h3>
- <p>480p resolution has some advantages and disadvantages that make it suitable or unsuitable for different purposes and preferences. Some of the advantages are:</p>
- <ul>
-<li>It is compatible with most devices and platforms, such as DVD players, computers, smartphones, tablets, game consoles, streaming services, and websites. It is also easy to convert to other resolutions if needed.</li>
-<li>It is fast and efficient to stream or download, as it requires less bandwidth and storage space than higher resolutions. It also reduces buffering and loading times, which can improve the viewing experience.</li>
-<li>It is nostalgic and retro, as it reminds us of the classic TV shows and movies that we grew up with or enjoyed in the past. It can also create a sense of immersion and realism, as it mimics how we perceive the world with our eyes.</li>
-</ul>
- <p>Some of the disadvantages are:</p>
- <ul>
-<li>It is low-quality and blurry, as it lacks detail and clarity compared to higher resolutions. It can also cause pixelation, aliasing, noise, artifacts, and distortion, which can ruin the aesthetic and artistic value of a video or image.</li>
-<li>It is outdated and obsolete, as it does not match the standards and expectations of modern technology and media. It can also create a sense of frustration and dissatisfaction, as it prevents us from enjoying the full potential and beauty of a video or image.</li>
-<li>It is incompatible with some devices and platforms, such as HD TVs, monitors, projectors, Blu-ray players, cameras, camcorders, and software. It can also cause black bars, stretching, cropping, or letterboxing, which can affect the aspect ratio and composition of a video or image.</li>
-</ul>
- <h3>The comparison of 480p resolution with other resolutions</h3>
- <p>480p resolution can be compared with other resolutions to understand how they differ in terms of quality and efficiency. Some of the most common resolutions are:</p>
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- <table>
-<tr>
-<th>Resolution</th>
-<th>Width x Height (pixels)</th>
-<th>Aspect Ratio</th>
-<th>Pixel Density (ppi)</th>
-<th>Bit Rate (Mbps)</th>
-</tr>
-<tr>
-<td>240p</td>
-<td>426 x 240</td>
-<td>16:9</td>
-<td>18</td>
-<td>0.5</td>
-</tr>
-<tr> <tr>
-<td>360p</td>
-<td>640 x 360</td>
-<td>16:9</td>
-<td>29</td>
-<td>1</td>
-</tr>
-<tr>
-<td><b>480p</b></td>
-<td><b>720 x 480</b></td>
-<td><b>4:3 or 16:9</b></td>
-<td><b>37</b></td>
-<td><b>1</b></td>
-</tr>
-<tr>
-<td>720p</td>
-<td>1280 x 720</td>
-<td>16:9</td>
-<td>58</td>
-<td>2.5</td>
-</tr>
-<tr>
-<td>1080p</td>
-<td>1920 x 1080</td>
-<td>16:9</td>
-<td>88</td>
-<td>5</td>
-</tr>
-<tr>
-<td>4K</td>
-<td>3840 x 2160</td>
-<td>16:9</td>
-<td>176</td>
-<td>20</td>
-</tr>
-<tr>
-<td>8K</td>
-<td>7680 x 4320</td>
-<td>16:9</td>
-<td>352</td>
-<td>80</td>
-</tr>
- <p>The table shows that 480p resolution is lower than 720p, 1080p, 4K, and 8K resolutions in terms of width, height, pixel density, and bit rate. However, it is higher than 240p and 360p resolutions in these aspects. Therefore, 480p resolution can be considered as a middle ground between low-quality and high-quality resolutions.</p>
- <h2>How to download Constantine s1 in 480p resolution?</h2>
- <p>If you want to watch Constantine s1 in 480p resolution, you might be wondering how to download it from the internet. However, before you do that, you should be aware of some legal and ethical issues that might arise from downloading Constantine s1 in 480p resolution. You should also know the best sources and websites to download Constantine s1 in 480p resolution, and the steps and tips to download Constantine s1 in 480p resolution.</p>
- <h3>The legal and ethical issues of downloading Constantine s1 in 480p resolution</h3>
- <p>The legal and ethical issues of downloading Constantine s1 in 480p resolution are related to the concepts of intellectual property rights, piracy, and fair use. Intellectual property rights are the rights that creators and owners of original works have over their works, such as the right to reproduce, distribute, display, perform, or modify their works. Piracy is the unauthorized copying, distribution, or use of someone else's intellectual property without their permission or consent. Fair use is a legal doctrine that allows limited use of someone else's intellectual property for purposes such as criticism, comment, news reporting, teaching, scholarship, or research.</p>
- <p>The problem with downloading Constantine s1 in 480p resolution is that it might violate the intellectual property rights of the creators and owners of the series, such as DC Comics, Warner Bros., NBC, and the writers, producers, actors, and other contributors. It might also constitute piracy, as it might involve obtaining Constantine s1 in 480p resolution from illegal or unauthorized sources or websites that do not have the rights or licenses to distribute the series. It might also not fall under fair use, as it might not have a legitimate purpose or benefit for downloading Constantine s1 in 480p resolution.</p>
- <p>The consequences of downloading Constantine s1 in 480p resolution might include legal actions, such as lawsuits, fines, or criminal charges from the creators and owners of the series or their representatives. It might also include ethical implications, such as harming the reputation and income of the creators and owners of the series or their representatives. It might also include moral dilemmas, such as feeling guilty or ashamed for stealing someone else's work or depriving them of their deserved recognition or reward.</p>
- <p>The solution to downloading Constantine s1 in 480p resolution is to respect the intellectual property rights of the creators and owners of the series or their representatives. It is also to avoid piracy, by obtaining Constantine s1 in 480p resolution from legal or authorized sources or websites that have the rights or licenses to distribute the series. It is also to follow fair use, by having a legitimate purpose or benefit for downloading Constantine s1 in 480p resolution.</p>
- <h3>The best sources and websites to download Constantine s1 in 480p resolution</h3>
- <p>The best sources and websites to download Constantine s1 in 480p resolution are those that are legal or authorized to distribute the series. These sources and websites might include:</p>
- <ul>
-<li><b , and social media. However, they also have some drawbacks, such as limited content, registration requirements, and pop-ups.</li>
-</ul>
- <p>The best sources and websites to download Constantine s1 in 480p resolution are those that are legal or authorized to distribute the series. These sources and websites might vary depending on your location, device, preference, and budget. Therefore, you should do some research and comparison before choosing the best source or website for you.</p>
- <h3>The steps and tips to download Constantine s1 in 480p resolution</h3>
- <p>The steps and tips to download Constantine s1 in 480p resolution are as follows:</p>
- <ol>
-<li><b>Choose a source or website</b>: The first step is to choose a source or website that has Constantine s1 in 480p resolution. You can use the list above as a reference, or you can search for other sources or websites that suit your needs. You should also check the reviews, ratings, and feedback of the source or website to ensure its quality and reliability.</li>
-<li><b>Sign up or log in</b>: The second step is to sign up or log in to the source or website that you have chosen. You might need to provide some personal information, such as your name, email, password, payment method, and address. You should also read the terms and conditions, privacy policy, and refund policy of the source or website to understand your rights and responsibilities.</li>
-<li><b>Select an episode or season</b>: The third step is to select an episode or season of Constantine s1 that you want to download in 480p resolution. You can browse through the catalog or search bar of the source or website to find the episode or season that you are looking for. You should also check the description, summary, trailer, and screenshots of the episode or season to make sure that it is what you want.</li>
-<li><b>Download or stream</b>: The fourth step is to download or stream the episode or season of Constantine s1 in 480p resolution. You can choose to download the episode or season to your device for offline viewing, or you can choose to stream the episode or season online. You should also check the download or streaming options, such as the format, size, speed, and quality of the episode or season. You should also make sure that you have enough storage space and internet connection for the download or streaming process.</li>
-<li><b>Enjoy</b>: The fifth step is to enjoy watching Constantine s1 in 480p resolution. You can watch the episode or season on your device using a media player or a web browser. You can also watch the episode or season on your TV using a HDMI cable or a casting device. You can also adjust the settings, such as the volume, brightness, contrast, and subtitles of the episode or season to enhance your viewing experience.</li>
-</ol>
- <p>Some tips to download Constantine s1 in 480p resolution are:</p>
- <ul>
-<li><b>Use a VPN</b>: A VPN (virtual private network) is a service that encrypts your internet traffic and changes your IP address. This can help you access geo-restricted content, bypass censorship, protect your privacy, and avoid malware. You can use a VPN to download Constantine s1 in 480p resolution from sources or websites that are not available in your region.</li>
-<li><b>Use a downloader</b>: A downloader is a software or an app that allows you to download videos from various sources or websites. This can help you save time, bandwidth, and storage space. You can use a downloader to download Constantine s1 in 480p resolution from sources or websites that do not have a direct download option.</li>
-<li><b>Use a converter</b>: A converter is a software or an app that allows you to change the format, size, quality, and other aspects of a video. This can help you optimize your video for your device, preference, and purpose. You can use a converter to download Constantine s1 in 480p resolution from sources or websites that do not have a 480p option.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, Constantine s1 is a TV series based on the DC Comics character John Constantine, an exorcist and occult detective who battles the forces of darkness. The series aired for one season on NBC from 2014 to 2015, and has since gained a cult following and a place in the Arrowverse franchise. 480p resolution is a term that refers to the quality and size of a video or image. It is also known as standard definition (SD) or enhanced definition (ED). It means that the video or image has a width of 720 pixels and a height of 480 pixels , and that it displays 60 frames per second (fps) in the NTSC format or 50 fps in the PAL format. It is one of the most common resolutions used for digital media, especially for DVDs, web videos, and older TV shows and movies. If you want to watch Constantine s1 in 480p resolution, you should be aware of some legal and ethical issues that might arise from downloading it from the internet. You should also know the best sources and websites to download Constantine s1 in 480p resolution, and the steps and tips to download Constantine s1 in 480p resolution. We hope that this article has helped you learn more about Constantine s1, 480p resolution, and how to download Constantine s1 in 480p resolution.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions (FAQs) about Constantine s1, 480p resolution, and how to download Constantine s1 in 480p resolution:</p>
- <ol>
-<li><b>Is Constantine s1 worth watching?</b></li>
-<p>Constantine s1 is worth watching if you are a fan of supernatural and comic book adaptations, or if you enjoy dark and gritty stories with horror elements and witty humor. The series has a faithful adaptation of the comic book source material, a compelling performance by Matt Ryan as John Constantine, and a potential for future stories. However, the series also has some flaws, such as low ratings, network interference, budget constraints, and creative differences. The series was canceled by NBC after one season, leaving many fans disappointed and angry.</p>
-<li><b>Is 480p resolution good enough?</b></li>
-<p>480p resolution is good enough if you have a small or medium-sized screen, a limited internet connection, or a preference for nostalgia and retro. The resolution is compatible with most devices and platforms, fast and efficient to stream or download, and nostalgic and retro. However, the resolution is not good enough if you have a large or high-definition screen, a fast internet connection, or a preference for quality and modernity. The resolution is low-quality and blurry, outdated and obsolete, and incompatible with some devices and platforms.</p>
-<li><b>How can I watch Constantine s1 in HD or 4K resolution?</b></li>
-<p>You can watch Constantine s1 in HD or 4K resolution by using streaming services, digital stores, or official websites that offer these resolutions. Some of the streaming services that have Constantine s1 in HD or 4K resolution are Netflix, Hulu, Amazon Prime Video, and HBO Max. Some of the digital stores that have Constantine s1 in HD or 4K resolution are iTunes, Google Play, YouTube, and Vudu. Some of the official websites that have Constantine s1 in HD or 4K resolution are DC Comics, Warner Bros., NBC , and CW Seed. However, you should also be aware that watching Constantine s1 in HD or 4K resolution might require more bandwidth and storage space, as well as a compatible device and platform.</p>
-<li><b>Is Constantine s1 connected to the Arrowverse?</b></li>
-<p>Constantine s1 is connected to the Arrowverse, a shared universe of TV shows based on DC Comics characters that air on The CW network. The connection was established when Matt Ryan reprised his role as John Constantine in a guest appearance on Arrow season 4 episode 5, titled "Haunted". In this episode, Constantine helps Oliver Queen and his team to restore the soul of Sara Lance, who was resurrected by the Lazarus Pit. Constantine also reveals that he knows Oliver from a previous encounter on Lian Yu, the island where Oliver was stranded for five years. Since then, Constantine has appeared in several other episodes of Arrow, Legends of Tomorrow, and The Flash, as well as in his own animated web series on CW Seed, titled Constantine: City of Demons.</p>
-<li><b>Will there be a Constantine s2?</b></li>
-<p>There is no official confirmation or announcement about a Constantine s2. However, there are some rumors and speculations that suggest that a Constantine s2 might be possible in the future. Some of the rumors and speculations are:</p>
- <ul>
-<li>There is a petition on Change.org that has over 100,000 signatures from fans who want NBC to renew Constantine for a second season or another network to pick it up.</li>
-<li>There is a report from We Got This Covered that claims that HBO Max is interested in reviving Constantine for a second season, with Matt Ryan returning as John Constantine and J.J. Abrams producing the show.</li>
-<li>There is a tweet from Matt Ryan that hints that he is working on something related to Constantine, with the hashtag #SaveConstantine.</li>
-</ul>
- <p>However, these rumors and speculations are not verified or confirmed by any official sources or representatives. Therefore, they should be taken with a grain of salt and not be considered as facts or evidence. Until there is an official confirmation or announcement about a Constantine s2, we can only hope and wait for the best.</p> 401be4b1e0<br />
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-<br />
-<h1>The Legend of Heroes: Trails from Zero - How to Download and Play the English Patched Version on PC</h1>
- <p>If you are a fan of the <em>The Legend of Heroes</em> series, you might have heard of <strong>The Legend of Heroes: Trails from Zero</strong>, a spin-off game that takes place in the city-state of Crossbell. This game was originally released in Japan for the PSP in 2010, and later ported to other platforms such as PS Vita, PS4, and PC. However, it never got an official localization in English, leaving many fans unable to experience this amazing story.</p>
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- <p>Fortunately, thanks to the efforts of some dedicated fan translators, there is now a way to play <strong>The Legend of Heroes: Trails from Zero</strong> in English on your PC. In this article, we will show you how to download and install the English patch for this game, as well as some tips and tricks to enjoy it to the fullest.</p>
- <h2>Introduction</h2>
- <h3>What is The Legend of Heroes: Trails from Zero?</h3>
- <p><strong>The Legend of Heroes: Trails from Zero</strong> is a role-playing game developed by Nihon Falcom, and part of the <em>Trails</em> sub-series within the <em>The Legend of Heroes</em> franchise. It is the first game in the <em>Crossbell arc</em>, which follows the adventures of Lloyd Bannings, a rookie police officer who joins the Special Support Section, a new division that handles odd jobs and minor requests. Along with his new teammates, he uncovers the dark secrets and corruption that plague his city.</p>
- <p>The game features a turn-based combat system that allows you to use various skills, items, and crafts. It also has a unique feature called the <em>Quartz System</em>, which lets you customize your characters' abilities by equipping different types of quartz crystals. The game also has a rich and detailed story that is connected to the other games in the series, as well as many side quests, mini-games, and hidden content.</p>
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-The legend of heroes trails from zero lore and history<br />
-The legend of heroes trails from zero wiki and database<br />
-The legend of heroes trails from zero reddit and community<br />
-The legend of heroes trails from zero youtube and videos<br />
-The legend of heroes trails from zero discord and chat<br />
-The legend of heroes trails from zero memes and jokes<br />
-The legend of heroes trails from zero quotes and dialogues<br />
-The legend of heroes trails from zero fanfiction and stories</p>
- <h3>Why is the English patch important?</h3>
- <p>As mentioned earlier, <strong>The Legend of Heroes: Trails from Zero</strong> never got an official localization in English, despite being one of the most popular and critically acclaimed games in the series. This means that many fans outside Japan were unable to play this game and enjoy its story and gameplay. Moreover, since this game is part of a larger narrative that spans multiple games, missing out on this game would make it harder to understand and appreciate the other games in the series.</p>
- <p>That's why some fan translators decided to take matters into their own hands and create an English patch for this game. The patch is based on the PC version of the game, which has improved graphics, sound, and performance compared to the original PSP version. The patch also incorporates elements from the PS Vita version, which added voice acting, new illustrations, and extra content. The patch also fixes some bugs and errors that were present in the original game.</p>
- <p>The English patch for <strong>The Legend of Heroes: Trails from Zero</strong> is a labor of love that took years of hard work and dedication. It is not an official product endorsed by Nihon Falcom or any other company. It is a fan-made project that aims to share this wonderful game with more people around the world.</p>
- <h2>How to download and install the English patch</h2>
- <h3>Requirements and sources</h3>
- <p>In order to play <strong>The Legend of Heroes: Trails from Zero</strong> with the English patch, you will need:</p>
-<ul>
-<li>A PC that meets the minimum system requirements for running the game.</li>
-<li>A copy of the original Japanese PC version of <strong>The Legend of Heroes: Trails from Zero</ <li>A copy of the English patch, which you can download from the official website of the fan translation team. The patch is free and legal, but you will need to register an account on the website to access the download link.</li>
-<li>A program that can extract compressed files, such as WinRAR or 7-Zip.</li>
-<li>A program that can mount disc images, such as Daemon Tools or Virtual CloneDrive.</li>
-</ul>
- <p>Once you have all the necessary files and programs, you can proceed to the next step.</p>
- <h3>Steps to patch the game</h3>
- <p>Here are the steps to patch <strong>The Legend of Heroes: Trails from Zero</strong> with the English patch:</p>
-<ol>
-<li>Extract the contents of the English patch file to a folder on your PC. You should see a folder called <em>Trails from Zero</em> and a file called <em>setup.exe</em>.</li>
-<li>Mount the disc image of the original Japanese PC version of <strong>The Legend of Heroes: Trails from Zero</strong> using your preferred program. You should see a virtual drive appear on your PC.</li>
-<li>Run the <em>setup.exe</em> file from the English patch folder. Follow the instructions on the screen and select the virtual drive as the installation destination. The patch will automatically detect and overwrite the original Japanese files with the English ones.</li>
-<li>Wait for the installation to finish. You should see a message saying that the patching was successful.</li>
-<li>Eject the disc image of the original Japanese PC version of <strong>The Legend of Heroes: Trails from Zero</strong>. You will no longer need it.</li>
-<li>Run the game from the shortcut on your desktop or from the <em>Trails from Zero</em> folder. You should see the game start in English.</li>
-</ol>
- <p>Congratulations! You have successfully patched <strong>The Legend of Heroes: Trails from Zero</strong> with the English patch. You can now enjoy this amazing game on your PC.</p>
- <h2>How to enjoy the game</h2>
- <h3>Features and improvements of the patch</h3>
- <p>The English patch for <strong>The Legend of Heroes: Trails from Zero</strong> is not just a simple translation. It also adds and improves many aspects of the game, such as:</p>
-<ul>
-<li><strong>Voice acting:</strong> The patch includes voice acting for all major characters and scenes, as well as some minor ones. The voice acting is based on the PS Vita version, which featured more than 10,000 lines of voiced dialogue. The voice actors are mostly professional and well-known in the industry, such as Kana Hanazawa, Hiroshi Kamiya, and Rie Kugimiya. The voice acting adds more emotion and personality to the characters and enhances the immersion of the game.</li>
-<li><strong>New illustrations:</strong> The patch also includes new illustrations for some scenes and events, as well as some character portraits. The illustrations are based on the PS Vita version, which added more than 100 new illustrations by various artists. The illustrations add more detail and beauty to the game's visuals and complement the story well.</li>
-<li><strong>Extra content:</strong> The patch also includes some extra content that was added in later versions of the game, such as new quests, items, costumes, mini-games, and achievements. The extra content adds more variety and fun to the game and gives you more reasons to explore and replay it.</li>
-<li><strong>Bug fixes and enhancements:</strong> The patch also fixes some bugs and errors that were present in the original game, such as typos, glitches, crashes, and compatibility issues. The patch also enhances some aspects of the game, such as loading times, resolution, frame rate, and user interface. The patch makes sure that the game runs smoothly and flawlessly on your PC.</li>
-</ul>
- <p>The English patch for <strong>The Legend of Heroes: Trails from Zero</strong> is a comprehensive and high-quality project that improves every aspect of the game. It is a testament to the passion and dedication of the fan translators and their respect for Nihon Falcom's work.</p>
- <h3>Tips and tricks for playing the game</h3>
- <p>If you are new to <strong>The Legend of Heroes: Trails from Zero</strong>, or to <em>The Legend of Heroes</em> series in general, you might find some aspects of the game challenging or confusing. Here are some tips and tricks that will help you enjoy the game more:</p>
-<ul>
-<li><strong>Save often:</strong> The game has a generous save system that allows you to save anytime and anywhere, except during battles and cutscenes. You should take advantage of this feature and save often, especially before entering a new area, facing a boss, or making a choice. The game can be unpredictable and challenging at times, and you don't want to lose your progress or miss out on anything.</li>
-<li><strong>Explore and talk:</strong> The game has a vast and detailed world that is full of secrets and surprises. You should explore every corner and talk to every NPC you encounter. You might find hidden items, quests, events, or information that will enrich your experience and reward you with useful resources. The game also has a dynamic NPC system that changes their dialogue and behavior depending on the time of day, the weather, and the story progression. You should talk to them often to see how they react and what they have to say.</li>
-<li><strong>Customize and experiment:</strong> The game has a deep and flexible customization system that allows you to tailor your characters' abilities and equipment to your liking. You should use the <em>Quartz System</em> to equip different types of quartz crystals that grant you various skills, stats, and effects. You should also use the <em>Orbment System</em> to arrange your quartz in different slots and lines that affect their compatibility and synergy. You should experiment with different combinations and see what works best for you.</li>
-<li><strong>Use your skills and items:</strong> The game has a strategic and challenging combat system that requires you to use your skills and items wisely. You should use your <em>Crafts</em>, which are unique skills that consume CP (Craft Points), to perform powerful attacks, buffs, debuffs, or healing. You should also use your <em>Arts</em>, which are elemental skills that consume EP (Energy Points), to exploit the enemies' weaknesses, create combos, or alter the battlefield. You should also use your items, such as healing potions, status cures, or attack boosters, to aid you in battle.</li>
-<li><strong>Pay attention to the indicators:</strong> The game has several indicators that help you understand the combat situation and plan your actions accordingly. You should pay attention to the <em>Turn Order</em>, which shows the order of actions for both allies and enemies, as well as the bonuses or penalties that apply to each turn. You should also pay attention to the <em>Break Gauge</em>, which shows the enemies' resistance to being interrupted or stunned by your attacks. You should also pay attention to the <em>S-Craft Gauge</em>, which shows when you can use your <em>S-Crafts</em>, which are ultimate skills that consume all your CP and can be used at any time.</li>
-</ul>
- <p>The game has many more features and mechanics that you will discover and learn as you play. The game is designed to be accessible for newcomers, but also rewarding for veterans. The game has multiple difficulty levels that you can choose from or change at any time. The game also has a comprehensive tutorial system that explains everything you need to know.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the article</h3>
- <p>In this article, we have shown you how to download and install the English patch for <strong>The Legend of Heroes: Trails from Zero</strong>, a spin-off game in <em>The Legend of Heroes</em> series that was never officially localized in English. We have also shown you some features and improvements of the patch, as well as some tips and tricks for playing the game.</p>
- <p><strong>The Legend of Heroes: Trails from Zero</strong> is a fantastic game that deserves more attention and appreciation from fans around the world. It has a captivating story, engaging gameplay, beautiful graphics, and memorable music. It is a game that will make you laugh, cry, think, and feel. It is a game that will stay with you for a long time.</p>
- <h3>Call to action</h3>
- <p>If you are interested in playing <strong>The Legend of Heroes: Trails from Zero</strong>, you can download the English patch from the official website of the fan translation team here: [https://geofront.esterior.net/]. You will need a copy of the original Japanese PC version of the game, which you can buy from online stores such as [https://www.dlsite.com/soft/work/=/product_id/VJ009178.html] or [https://www.amazon.co.jp/-/en/dp/B00B5U8JZ4].</p>
- <p>If you enjoy <strong>The Legend of Heroes: Trails from Zero</strong>, you might also want to check out its sequel, <strong>The Legend of Heroes: Trails to Azure</strong>, which is also being translated by the same fan team. You can follow their progress and updates on their website or social media accounts.</p>
- <p>If you want to support Nihon Falcom and their amazing games, you can buy their other titles from their official website [https://www.falcom.co.jp/] or from other platforms such as Steam or GOG. You can also follow them on their social media accounts or join their fan communities.</p>
- <p>We hope that this article has helped you to play and enjoy <strong>The Legend of Heroes: Trails from Zero</strong> in English on your PC. If you have any questions, comments, or feedback, please feel free to leave them below. We would love to hear from you.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about <strong>The Legend of Heroes: Trails from Zero</strong> and the English patch:</p>
- <h3>Q: Do I need to play the other games in <em>The Legend of Heroes</em> series before playing <strong>The Legend of Heroes: Trails from Zero</strong>?</h3>
-<p>A: No, you don't need to play the other games in the series before playing this game. However, it is highly recommended that you do so, as this game is connected to the other games in terms of story, characters, and world-building. The game also contains references, spoilers, and easter eggs that will make more sense if you have played the other games. The recommended order of playing the games is as follows:</p>
-<ol>
-<li><em>The Legend of Heroes: Trails in the Sky</em></li>
-<li><em>The Legend of Heroes: Trails in the Sky SC</em></li>
-<li><em>The Legend of Heroes: Trails in the Sky the 3rd</em></li>
-<li><strong>The Legend of Heroes: Trails from Zero</strong></li>
-<li><strong>The Legend of Heroes: Trails to Azure</strong></li>
-<li><em>The Legend of Heroes: Trails of Cold Steel</em></li>
-<li><em>The Legend of Heroes: Trails of Cold Steel II</em></li>
-<li><em>The Legend of Heroes: Trails of Cold Steel III</em></li>
-<li><em>The Legend of Heroes: Trails of Cold Steel IV</em></li>
-<li><em>The Legend of Heroes: Hajimari no Kiseki</em></li>
-</ol>
- <h3>Q: How long does it take to finish <strong>The Legend of Heroes: Trails from Zero</strong>?</h3>
-<p>A: It depends on how you play the game and how much content you want to see. The game has a main story that takes about 40 to 50 hours to complete, depending on your difficulty level and pace. The game also has many side quests, mini-games, and hidden content that can add another 10 to 20 hours to your playtime. The game also has a New Game Plus mode that lets you carry over your progress and unlock more content. The game has a lot of replay value and content for you to enjoy.</p>
- <h3>Q: Is the English patch compatible with mods or cheats?</h3>
-<p>A: The English patch is compatible with most mods or cheats that do not alter the game's files or code. However, some mods or cheats may cause conflicts or errors with the patch, so use them at your own risk. The patch team is not responsible for any issues or damages caused by using mods or cheats with the patch.</p>
- <h3>Q: How can I support the fan translation team?</h3>
-<p>A: The fan translation team is a group of volunteers who work on this project for free and for fun. They do not accept any donations or payments for their work. However, you can support them by spreading the word about their project, giving them feedback and suggestions, reporting any bugs or errors you find, and thanking them for their hard work. You can also support them by supporting Nihon Falcom and buying their official games.</p>
- <h3>Q: Where can I find more information about <strong>The Legend of Heroes: Trails from Zero</strong> and the English patch?</h3>
-<p>A: You can find more information about <strong>The Legend of Heroes: Trails from Zero</strong> and the English patch on the following sources:</p>
-<ul>
-<li>The official website of the fan translation team [https://geofront.esterior.net/]</li>
-<li>The official website of Nihon Falcom [https://www.falcom.co.jp/]</li>
-<li>The official wiki of <em>The Legend of Heroes</em> series [https://kiseki.fandom.com/wiki/Main_Page]</li>
-<li>The official subreddit of <em>The Legend of Heroes</em> series [https://www.reddit.com/r/Falcom/]</li>
-<li>The official Discord server of <em>The Legend of Heroes</em> series [https://discord.gg/falcom]</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fclong/summary/fengshen/models/deltalm/tokenizer_deltalm.py b/spaces/fclong/summary/fengshen/models/deltalm/tokenizer_deltalm.py
deleted file mode 100644
index dcc81acffeb15bea28c0dd5bb10287fc897cd55d..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/models/deltalm/tokenizer_deltalm.py
+++ /dev/null
@@ -1,323 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-import os
-import re
-import warnings
-from shutil import copyfile
-from typing import Any, Dict, List, Optional, Tuple
-
-import sentencepiece as spm
-
-from transformers.tokenization_utils import PreTrainedTokenizer
-from transformers.utils import logging
-
-
-SPIECE_UNDERLINE = "▁"
-
-VOCAB_FILES_NAMES = {"vocab_file": "spm.model"}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {"IDEA-CCNL/deltalm": "https://huggingface.co/IDEA-CCNL/Randeng-Deltalm-362M-En-Zn/resolve/main/spm.model"}
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "IDEA-CCNL/deltalm": 512,
-}
-
-
-logger = logging.get_logger(__name__)
-
-
-class DeltalmTokenizer(PreTrainedTokenizer):
-    """
-    Construct a T5 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
-    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
-    this superclass for more information regarding those methods.
-    Args:
-        vocab_file (`str`):
-            [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
-            contains the vocabulary necessary to instantiate a tokenizer.
-        eos_token (`str`, *optional*, defaults to `"</s>"`):
-            The end of sequence token.
-            <Tip>
-            When building a sequence using special tokens, this is not the token that is used for the end of sequence.
-            The token used is the `sep_token`.
-            </Tip>
-        unk_token (`str`, *optional*, defaults to `"<unk>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        pad_token (`str`, *optional*, defaults to `"<pad>"`):
-            The token used for padding, for example when batching sequences of different lengths.
-        extra_ids (`int`, *optional*, defaults to 100):
-            Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
-            accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
-            indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary
-            like in T5 preprocessing see
-            [here](https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117)).
-        additional_special_tokens (`List[str]`, *optional*):
-            Additional special tokens used by the tokenizer.
-        sp_model_kwargs (`dict`, *optional*):
-            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
-            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
-            to set:
-            - `enable_sampling`: Enable subword regularization.
-            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
-              - `nbest_size = {0,1}`: No sampling is performed.
-              - `nbest_size > 1`: samples from the nbest_size results.
-              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
-                using forward-filtering-and-backward-sampling algorithm.
-            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
-              BPE-dropout.
-    Attributes:
-        sp_model (`SentencePieceProcessor`):
-            The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["input_ids", "attention_mask"]
-
-    def __init__(
-        self,
-        vocab_file,
-        bos_token="<s>",
-        eos_token="</s>",
-        unk_token="<unk>",
-        pad_token="<pad>",
-        extra_ids=0,
-        additional_special_tokens=None,
-        sp_model_kwargs: Optional[Dict[str, Any]] = None,
-        **kwargs
-    ) -> None:
-        # Add extra_ids to the special token list
-        if extra_ids > 0 and additional_special_tokens is None:
-            additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
-        elif extra_ids > 0 and additional_special_tokens is not None:
-            # Check that we have the right number of extra_id special tokens
-            extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens)))
-            if extra_tokens != extra_ids:
-                raise ValueError(
-                    f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
-                    " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
-                    " tokens"
-                )
-
-        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
-        super().__init__(
-            bos_token=bos_token,
-            eos_token=eos_token,
-            unk_token=unk_token,
-            pad_token=pad_token,
-            additional_special_tokens=additional_special_tokens,
-            extra_ids=extra_ids,
-            sp_model_kwargs=self.sp_model_kwargs,
-            **kwargs,
-        )
-
-        self.vocab_file = vocab_file
-        self.offset = 1
-        self._extra_ids = extra_ids
-
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.Load(vocab_file)
-
-        self.encoder: Dict[int, str] = {
-            0: self.bos_token,
-            1: self.pad_token,
-            2: self.eos_token,
-            3: self.unk_token,
-        }
-
-        self.decoder: Dict[str, int] = {v: k for k, v in self.encoder.items()}
-
-    @staticmethod
-    def _eventually_correct_t5_max_length(pretrained_model_name_or_path, max_model_length, init_max_model_length):
-        if pretrained_model_name_or_path in DeltalmTokenizer.max_model_input_sizes:
-            deprecated_max_model_length = DeltalmTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
-            if init_max_model_length is not None and init_max_model_length != max_model_length:
-                return init_max_model_length
-            elif init_max_model_length is None:
-                warnings.warn(
-                    "This tokenizer was incorrectly instantiated with a model max length of"
-                    f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
-                    " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
-                    " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
-                    f" {pretrained_model_name_or_path} automatically truncating your input to"
-                    f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
-                    f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
-                    " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
-                    " instantiate this tokenizer with `model_max_length` set to your preferred value.",
-                    FutureWarning,
-                )
-
-        return max_model_length
-
-    @property
-    def vocab_size(self):
-        return self.sp_model.get_piece_size()  # + self._extra_ids
-
-    def get_vocab(self):
-        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
-        vocab.update(self.added_tokens_encoder)
-        return vocab
-
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieve 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` method.
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-        Returns:
-            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-        if already_has_special_tokens:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
-            )
-
-        # normal case: some special tokens
-        if token_ids_1 is None:
-            return ([0] * len(token_ids_0)) + [1]
-        return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
-
-    def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]:
-        """Do not add eos again if user already added it."""
-        if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
-            warnings.warn(
-                f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
-                " eos tokens being added."
-            )
-            return token_ids
-        else:
-            return token_ids + [self.eos_token_id]
-
-    def create_token_type_ids_from_sequences(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
-        use of token type ids, therefore a list of zeros is returned.
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-        Returns:
-            `List[int]`: List of zeros.
-        """
-        eos = [self.eos_token_id]
-
-        if token_ids_1 is None:
-            return len(token_ids_0 + eos) * [0]
-        return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
-
-    def build_inputs_with_special_tokens(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. A sequence has the following format:
-        - single sequence: `X </s>`
-        - pair of sequences: `A </s> B </s>`
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs to which the special tokens will be added.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-        Returns:
-            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
-        """
-        token_ids_0 = self._add_eos_if_not_present(token_ids_0)
-        if token_ids_1 is None:
-            return token_ids_0
-        else:
-            token_ids_1 = self._add_eos_if_not_present(token_ids_1)
-            return token_ids_0 + token_ids_1
-
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        state["sp_model"] = None
-        return state
-
-    def __setstate__(self, d):
-        self.__dict__ = d
-
-        # for backward compatibility
-        if not hasattr(self, "sp_model_kwargs"):
-            self.sp_model_kwargs = {}
-
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.Load(self.vocab_file)
-
-    def _tokenize(self, text: str) -> List[str]:
-        """Take as input a string and return a list of strings (tokens) for words/sub-words"""
-        return self.sp_model.encode(text, out_type=str)
-
-    def _convert_token_to_id(self, token):
-        """Converts a token (str) in an id using the vocab."""
-        if token.startswith("<extra_id_"):
-            match = re.match(r"<extra_id_(\d+)>", token)
-            num = int(match.group(1))
-            return self.vocab_size - num - 1
-        elif token in self.decoder:
-            return self.decoder[token]
-
-        sp_id = self.sp_model.piece_to_id(token)
-        return sp_id + self.offset
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        # if index < self.sp_model.get_piece_size():
-        #     token = self.sp_model.IdToPiece(index)
-        # else:
-        #     token = f"<extra_id_{self.vocab_size - 1 - index}>"
-        # return token
-        if index in self.encoder:
-            return self.encoder[index]
-        elif index in self.added_tokens_encoder:
-            return self.added_tokens_encoder[index]
-        elif index < self.sp_model.get_piece_size() + 4:
-            token = self.sp_model.IdToPiece(index-self.offset)
-        else:
-            token = f"<extra_id_{self.vocab_size - 1 - index}>"
-        return token
-
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (string) in a single string."""
-        current_sub_tokens = []
-        out_string = ""
-        for token in tokens:
-            # make sure that special tokens are not decoded using sentencepiece model
-            if token in self.all_special_tokens:
-                out_string += self.sp_model.decode_pieces(current_sub_tokens) + token + " "
-                current_sub_tokens = []
-            else:
-                current_sub_tokens.append(token)
-        out_string += self.sp_model.decode_pieces(current_sub_tokens)
-        return out_string.strip()
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-        out_vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-
-        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
-            copyfile(self.vocab_file, out_vocab_file)
-        elif not os.path.isfile(self.vocab_file):
-            with open(out_vocab_file, "wb") as fi:
-                content_spiece_model = self.sp_model.serialized_model_proto()
-                fi.write(content_spiece_model)
-
-        return (out_vocab_file,)
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Bubble Shooter 5 APK A Challenging and Fun Game for All Ages.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Bubble Shooter 5 APK A Challenging and Fun Game for All Ages.md
deleted file mode 100644
index 12b68daa12f2a2b10231c12bc6aa7eee2def1a5a..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Bubble Shooter 5 APK A Challenging and Fun Game for All Ages.md	
+++ /dev/null
@@ -1,159 +0,0 @@
-
-<h1>Bubble Shooter 5 APK: A Classic Game with a New Twist</h1>
-<p>If you are looking for a fun and addictive game that you can play anytime and anywhere, you might want to check out Bubble Shooter 5 APK. This is a new version of the classic puzzle game that has been around for decades, but with improved graphics, sounds, and gameplay. In this article, we will tell you everything you need to know about Bubble Shooter 5 APK, including what it is, how to download and install it, why you should play it, and some tips and tricks to help you master it.</p>
- <h2>What is Bubble Shooter 5 APK?</h2>
-<p>Bubble Shooter 5 APK is an Android game that belongs to the genre of bubble shooter games. These are games where you have to shoot colored bubbles from a cannon at the bottom of the screen and match them with other bubbles of the same color at the top of the screen. When you create a group of three or more bubbles of the same color, they pop and disappear, clearing some space on the screen. The goal is to clear all the bubbles from the screen before they reach the bottom.</p>
-<h2>bubble shooter 5 apk</h2><br /><p><b><b>Download</b> &#10027; <a href="https://gohhs.com/2uPoez">https://gohhs.com/2uPoez</a></b></p><br /><br />
- <h3>The history of Bubble Shooter games</h3>
-<p>Bubble shooter games have a long and interesting history that dates back to the late 1980s. The first bubble shooter game was called Puzzle Bobble, also known as Bust-a-Move, and it was developed by Taito Corporation in Japan. It was released as an arcade game in 1989 and as a home console game in 1994. It became very popular and spawned many sequels and spin-offs over the years.</p>
-<p>The most famous bubble shooter game, however, is probably Bubble Shooter, which was created by Absolutist Games in 2002. It was a web-based game that could be played on any browser, and it had simple but addictive gameplay. It also had many variations and clones that were developed by different developers and publishers. One of these variations is Bubble Shooter 5 APK, which is based on the original Bubble Shooter game but with some improvements and enhancements.</p>
- <h3>The features of Bubble Shooter 5 APK</h3>
-<p>Bubble Shooter 5 APK has many features that make it stand out from other bubble shooter games. Some of these features are:</p>
-<ul>
-<li>It has over 3000 levels of varying difficulty and complexity, so you will never run out of challenges.</li>
-<li>It has stunning graphics and animations that make the game more colorful and lively.</li>
-<li>It has realistic physics and smooth controls that make the game more fun and easy to play.</li>
-<li>It has cool sound effects and music that create a relaxing and enjoyable atmosphere.</li>
-<li>It has a leaderboard and achievements system that let you compete with other players and track your progress.</li>
-<li>It has a daily bonus feature that rewards you with coins and boosters every day.</li>
-<li>It has a shop feature that lets you buy more coins and boosters with real money or watch ads to get them for free.</li>
-<li>It has an offline mode that lets you play the game without an internet connection.</li>
-</ul>
- <h3>How to download and install Bubble Shooter 5 APK</h3>
-<p>If you want to play Bubble Shooter 5 APK on your Android device, you will need to download and install it first. Here are the steps to do so:</p>
-<ol>
-<li>Go to [this link](^1^) or [this link](^2^) on your device's browser.</li>
-<li>Tap on the download button and wait for the file to be downloaded.</li>
-<li>Once the file is downloaded, tap on it to open it.</li>
-OK" and then tap on "Install anyway".</li>
-<li>If you see a message that says "For your security, your phone is not allowed to install unknown apps from this source", tap on "Settings" and then enable the option that says "Allow from this source".</li>
-<li>Wait for the installation to finish and then tap on "Open" to launch the game.</li>
-</ol>
-<p>Congratulations, you have successfully downloaded and installed Bubble Shooter 5 APK on your device. Now you can enjoy playing this amazing game anytime and anywhere.</p>
- <h2>Why should you play Bubble Shooter 5 APK?</h2>
-<p>Bubble Shooter 5 APK is not just a game, it is also a great way to relax, have fun, and exercise your brain. Here are some of the reasons why you should play this game:</p>
-<p>bubble shooter 5 apk download<br />
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- <h3>The benefits of playing Bubble Shooter games</h3>
-<p>Bubble Shooter games are not only entertaining, but also beneficial for your mental health and well-being. Some of the benefits are:</p>
-<ul>
-<li>They improve your concentration and focus, as you have to aim and shoot accurately and quickly.</li>
-<li>They enhance your memory and cognitive skills, as you have to remember the colors and patterns of the bubbles and plan your moves ahead.</li>
-<li>They stimulate your creativity and imagination, as you have to come up with different strategies and solutions to clear the levels.</li>
-<li>They reduce your stress and anxiety, as you have to immerse yourself in the game and forget about your worries.</li>
-<li>They boost your mood and happiness, as you have to enjoy the colorful graphics, cheerful sounds, and satisfying pops.</li>
-</ul>
- <h3>The challenges and rewards of Bubble Shooter 5 APK</h3>
-<p>Bubble Shooter 5 APK is not a game that you can easily master. It has many challenges that will test your skills and patience. Some of the challenges are:</p>
-<ul>
-<li>The bubbles move down faster and faster as you progress through the levels, giving you less time to think and act.</li>
-<li>The bubbles come in different shapes and sizes, making it harder to match them and pop them.</li>
-<li>The bubbles have different effects and abilities, such as bombs, stars, magnets, rainbows, and more, adding more variety and complexity to the game.</li>
-<li>The levels have different layouts and obstacles, such as walls, spikes, bubbles with locks, bubbles with chains, and more, requiring more strategy and precision to clear them.</li>
-</ul>
-<p>However, Bubble Shooter 5 APK also has many rewards that will motivate you to keep playing. Some of the rewards are:</p>
-<ul>
-<li>You can earn coins by clearing levels, popping special bubbles, completing achievements, and collecting daily bonuses. You can use these coins to buy more boosters or unlock more levels.</li>
-<li>You can use boosters to help you clear the levels faster and easier. Some of the boosters are fireballs, color bombs, lightning bolts, hammers, swaps, and more.</li>
-<li>You can unlock new themes and backgrounds by clearing certain levels or collecting stars. These themes and backgrounds will make the game more visually appealing and diverse.</li>
-<li>You can compete with other players around the world on the leaderboard. You can see your rank, score, level, and achievements compared to others. You can also challenge your friends and family to beat your score.</li>
-</ul>
- <h3>The tips and tricks for Bubble Shooter 5 APK</h3>
-<p>If you want to become a pro at Bubble Shooter 5 APK, you will need some tips and tricks to help you improve your game. Here are some of them:</p>
-<ul>
-<li>Aim carefully before you shoot. You can use the dotted line as a guide to see where your bubble will go. You can also bounce your bubble off the walls to reach tricky spots.</li>
-<li>Try to pop as many bubbles as possible with one shot. This will give you more points and coins. You can also create combos by popping several groups of bubbles in a row.</li>
-<li>Pay attention to the colors of the bubbles at the bottom of the screen. These are the next bubbles that you will shoot. You can use them to plan your moves ahead.</li>
-<li>Use the swap feature wisely. You can swap the current bubble with the next one by tapping on them. This can help you avoid wasting bubbles or getting stuck.</li>
-<li>Use the boosters sparingly. You have a limited number of boosters that you can use per level. Save them for when you really need them or when they can make a big difference.</li>
-have five lives that you can use per day. You can also buy more lives with coins or watch ads to get them for free.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Bubble Shooter 5 APK is a game that you should definitely try if you love bubble shooter games or puzzle games in general. It has everything you need to have a great time: beautiful graphics, relaxing sounds, addictive gameplay, challenging levels, rewarding boosters, and more. You can download and install it for free on your Android device and play it offline or online. You can also share your scores and achievements with other players and challenge your friends and family. Bubble Shooter 5 APK is a game that will keep you entertained for hours and hours.</p>
- <h3>Summary of the main points</h3>
-<p>Here are the main points that we covered in this article:</p>
-<ul>
-<li>Bubble Shooter 5 APK is a new version of the classic bubble shooter game that has been around for decades.</li>
-<li>It has over 3000 levels of varying difficulty and complexity, stunning graphics and animations, realistic physics and smooth controls, cool sound effects and music, a leaderboard and achievements system, a daily bonus feature, a shop feature, and an offline mode.</li>
-<li>It is beneficial for your mental health and well-being, as it improves your concentration, memory, creativity, mood, and happiness.</li>
-<li>It is challenging and rewarding, as it has different shapes, sizes, effects, abilities, layouts, and obstacles of bubbles, as well as coins, boosters, themes, backgrounds, and competitions.</li>
-<li>It is easy to download and install on your Android device, and you can play it anytime and anywhere.</li>
-</ul>
- <h3>Call to action</h3>
-<p>What are you waiting for? Download Bubble Shooter 5 APK now and enjoy this amazing game. You will not regret it. Just click on the button below and start popping some bubbles.</p>
-<p><a href="^1^" >Download Bubble Shooter 5 APK</a></p>
- <h3>FAQs</h3>
-<p>Here are some of the frequently asked questions about Bubble Shooter 5 APK:</p>
-<ol>
-<li><b>Is Bubble Shooter 5 APK safe to download and install?</b></li>
-<p>Yes, Bubble Shooter 5 APK is safe to download and install. It does not contain any viruses or malware that can harm your device or data. However, you should always download it from a trusted source like [this link] or [this link] to avoid any problems.</p>
-<li><b>Is Bubble Shooter 5 APK free to play?</b></li>
-<p>Yes, Bubble Shooter 5 APK is free to play. You do not have to pay anything to download or install it. However, it does have some in-app purchases that let you buy more coins and boosters with real money. You can also watch ads to get them for free.</p>
-<li><b>How can I contact the developer of Bubble Shooter 5 APK?</b></li>
-<p>If you have any questions, feedback, or suggestions about Bubble Shooter 5 APK, you can contact the developer by sending an email to [this address](mailto:bubbleshooter5apk@gmail.com). You can also visit their [Facebook page](https://www.facebook.com/bubbleshooter5apk) or their [website](https://www.bubbleshooter5apk.com) for more information.</p>
-<li><b>How can I update Bubble Shooter 5 APK?</b></li>
-<p>If you want to update Bubble Shooter 5 APK to the latest version, you can do so by following these steps:</p>
-<ol type="a">
-<li>Go to [this link] or [this link] on your device's browser.</li>
-<li>Tap on the download button and wait for the file to be downloaded.</li>
-<li>Once the file is downloaded, tap on it to open it.</li>
-<li>If you see a message that says "Do you want to install an update to this existing application?", tap on "Install".</li>
-<li>Wait for the installation to finish and then tap on "Open" to launch the game.</li>
-</ol>
-<p>Congratulations, you have successfully updated Bubble Shooter 5 APK to the latest version.</p>
-<li><b>How can I uninstall Bubble Shooter 5 APK?</b></li>
-<p>If you want to uninstall Bubble Shooter 5 APK from your device, you can do so by following these steps:</p>
-<ol type="a">
-<li>Go to your device's settings and tap on "Apps" or "Applications".</li>
-<li>Find and tap on "Bubble Shooter 5 APK" from the list of apps.</li>
-<li>Tap on "Uninstall" and then confirm by tapping on "OK".</li>
-<li>Wait for the uninstallation to finish and then tap on "Done".</li>
-</ol>
-<p>Congratulations, you have successfully uninstalled Bubble Shooter 5 APK from your device.</p>
-</ol>
- <p>I hope you enjoyed this article and learned something new about Bubble Shooter 5 APK. If you did, please share it with your friends and family who might also be interested in this game. Thank you for reading and have a wonderful day.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/ff4214/Newbing/Dockerfile b/spaces/ff4214/Newbing/Dockerfile
deleted file mode 100644
index 3698c7cb7938e025afc53b18a571ae2961fbdffe..0000000000000000000000000000000000000000
--- a/spaces/ff4214/Newbing/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="kJs8hD92ncMzLaoQWYtX5rG6bE3fZ4iO"
-
-# 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/fffiloni/SplitTrack2MusicGen/audiocraft/data/__init__.py b/spaces/fffiloni/SplitTrack2MusicGen/audiocraft/data/__init__.py
deleted file mode 100644
index 708a3dcead8dda89374a021177481dacae9f7fe9..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/SplitTrack2MusicGen/audiocraft/data/__init__.py
+++ /dev/null
@@ -1,8 +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.
-
-# flake8: noqa
-from . import audio, audio_dataset
diff --git a/spaces/flax-community/spanish-gpt2/README.md b/spaces/flax-community/spanish-gpt2/README.md
deleted file mode 100644
index 61160005da1b8ac3abc3b717aabd55000c32ff69..0000000000000000000000000000000000000000
--- a/spaces/flax-community/spanish-gpt2/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: Spanish GPT-2
-emoji: 🔥
-colorFrom: yellow
-colorTo: red
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/foduucom/table-extraction-yolov8/app.py b/spaces/foduucom/table-extraction-yolov8/app.py
deleted file mode 100644
index 4d0c8ea718af7fdde562dbf1f0659e37a9ad378e..0000000000000000000000000000000000000000
--- a/spaces/foduucom/table-extraction-yolov8/app.py
+++ /dev/null
@@ -1,66 +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, render_result
-
-# Images
-torch.hub.download_url_to_file('https://huggingface.co/spaces/foduucom/table-extraction-yolov8/resolve/main/test/table1.jpg', 'document1.jpg')
-torch.hub.download_url_to_file('https://huggingface.co/spaces/foduucom/table-extraction-yolov8/resolve/main/test/table2.jpg', 'document2.jpg')
-torch.hub.download_url_to_file('https://huggingface.co/spaces/foduucom/table-extraction-yolov8/resolve/main/test/table3.jpg', 'document3.jpg')
-
-def yolov8_inference(
-    image: gr.inputs.Image = None,
-    model_path: gr.inputs.Dropdown = None,
-    image_size: gr.inputs.Slider = 640,
-    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.overrides['conf'] = conf_threshold
-    model.overrides['iou']= iou_threshold
-    model.overrides['agnostic_nms'] = False  # NMS class-agnostic
-    model.overrides['max_det'] = 1000 
-    image = read_image(image)
-    results = model.predict(image)
-    render = render_result(model=model, image=image, result=results[0])
-    
-    return render
-        
-
-inputs = [
-    gr.inputs.Image(type="filepath", label="Input Image"),
-    gr.inputs.Dropdown(["foduucom/table-detection-and-extraction"], 
-                       default="foduucom/table-detection-and-extraction", label="Model"),
-    gr.inputs.Slider(minimum=320, maximum=1280, default=640, 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 = "YoloTableExtract: Efficient Table Detection"
-
-description = "🔍 YoloTableExtract is a powerful space that utilizes YOLOv8s for accurate table detection and extraction. Whether tables are bordered or borderless, this space can effectively identify and extract them from images. For further assistance and support related to documentation or data-related issues, feel free to contact info@foduu.com. If you find this space helpful, please show your appreciation by liking it. ❤️👍🏼"
-examples = [['document1.jpg', 'foduucom/table-detection-and-extraction', 640, 0.25, 0.45], ['document2.jpg', 'foduucom/table-detection-and-extraction', 640, 0.25, 0.45], ['document3.jpg', 'foduucom/table-detection-and-extraction', 1280, 0.25, 0.45]]
-demo_app = gr.Interface(
-    fn=yolov8_inference,
-    inputs=inputs,
-    outputs=outputs,
-    title=title,
-    description=description,
-    examples=examples,
-    cache_examples=True,
-    theme='huggingface',
-)
-demo_app.launch(debug=True, enable_queue=True)
\ No newline at end of file
diff --git a/spaces/fuckyoudeki/AutoGPT/main.py b/spaces/fuckyoudeki/AutoGPT/main.py
deleted file mode 100644
index 160addc390b94a8b143a3a2e18991a560f9b032e..0000000000000000000000000000000000000000
--- a/spaces/fuckyoudeki/AutoGPT/main.py
+++ /dev/null
@@ -1 +0,0 @@
-from autogpt import main
diff --git a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/decoders/pan/model.py b/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/decoders/pan/model.py
deleted file mode 100644
index 8086d024ea9768832cbcd81addd1196cfb34c3cf..0000000000000000000000000000000000000000
--- a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/decoders/pan/model.py
+++ /dev/null
@@ -1,100 +0,0 @@
-from typing import Optional, Union
-
-from segmentation_models_pytorch.encoders import get_encoder
-from segmentation_models_pytorch.base import (
-    SegmentationModel,
-    SegmentationHead,
-    ClassificationHead,
-)
-from .decoder import PANDecoder
-
-
-class PAN(SegmentationModel):
-    """Implementation of PAN_ (Pyramid Attention Network).
-
-    Note:
-        Currently works with shape of input tensor >= [B x C x 128 x 128] for pytorch <= 1.1.0
-        and with shape of input tensor >= [B x C x 256 x 256] for pytorch == 1.3.1
-
-    Args:
-        encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
-            to extract features of different spatial resolution
-        encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and
-            other pretrained weights (see table with available weights for each encoder_name)
-        encoder_output_stride: 16 or 32, if 16 use dilation in encoder last layer.
-            Doesn't work with ***ception***, **vgg***, **densenet*`** backbones.Default is 16.
-        decoder_channels: A number of convolution layer filters in decoder blocks
-        in_channels: A number of input channels for the model, default is 3 (RGB images)
-        classes: A number of classes for output mask (or you can think as a number of channels of output mask)
-        activation: An activation function to apply after the final convolution layer.
-            Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**,
-                **callable** and **None**.
-            Default is **None**
-        upsampling: Final upsampling factor. Default is 4 to preserve input-output spatial shape identity
-        aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build
-            on top of encoder if **aux_params** is not **None** (default). Supported params:
-                - classes (int): A number of classes
-                - pooling (str): One of "max", "avg". Default is "avg"
-                - dropout (float): Dropout factor in [0, 1)
-                - activation (str): An activation function to apply "sigmoid"/"softmax"
-                    (could be **None** to return logits)
-
-    Returns:
-        ``torch.nn.Module``: **PAN**
-
-    .. _PAN:
-        https://arxiv.org/abs/1805.10180
-
-    """
-
-    def __init__(
-        self,
-        encoder_name: str = "resnet34",
-        encoder_weights: Optional[str] = "imagenet",
-        encoder_output_stride: int = 16,
-        decoder_channels: int = 32,
-        in_channels: int = 3,
-        classes: int = 1,
-        activation: Optional[Union[str, callable]] = None,
-        upsampling: int = 4,
-        aux_params: Optional[dict] = None,
-    ):
-        super().__init__()
-
-        if encoder_output_stride not in [16, 32]:
-            raise ValueError(
-                "PAN support output stride 16 or 32, got {}".format(
-                    encoder_output_stride
-                )
-            )
-
-        self.encoder = get_encoder(
-            encoder_name,
-            in_channels=in_channels,
-            depth=5,
-            weights=encoder_weights,
-            output_stride=encoder_output_stride,
-        )
-
-        self.decoder = PANDecoder(
-            encoder_channels=self.encoder.out_channels,
-            decoder_channels=decoder_channels,
-        )
-
-        self.segmentation_head = SegmentationHead(
-            in_channels=decoder_channels,
-            out_channels=classes,
-            activation=activation,
-            kernel_size=3,
-            upsampling=upsampling,
-        )
-
-        if aux_params is not None:
-            self.classification_head = ClassificationHead(
-                in_channels=self.encoder.out_channels[-1], **aux_params
-            )
-        else:
-            self.classification_head = None
-
-        self.name = "pan-{}".format(encoder_name)
-        self.initialize()
diff --git a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/encoders/efficientnet.py b/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/encoders/efficientnet.py
deleted file mode 100644
index 0216e9010b9fcc1d8c94d6d8ef150bc95d2746e8..0000000000000000000000000000000000000000
--- a/spaces/ghlee94/MEDIAR/segmentation_models_pytorch/encoders/efficientnet.py
+++ /dev/null
@@ -1,178 +0,0 @@
-"""Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
-
-Attributes:
-
-    _out_channels (list of int): specify number of channels for each encoder feature tensor
-    _depth (int): specify number of stages in decoder (in other words number of downsampling operations)
-    _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
-
-Methods:
-
-    forward(self, x: torch.Tensor)
-        produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
-        shape NCHW (features should be sorted in descending order according to spatial resolution, starting
-        with resolution same as input `x` tensor).
-
-        Input: `x` with shape (1, 3, 64, 64)
-        Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
-                [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
-                (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
-
-        also should support number of features according to specified depth, e.g. if depth = 5,
-        number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
-        depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
-"""
-import torch.nn as nn
-from efficientnet_pytorch import EfficientNet
-from efficientnet_pytorch.utils import url_map, url_map_advprop, get_model_params
-
-from ._base import EncoderMixin
-
-
-class EfficientNetEncoder(EfficientNet, EncoderMixin):
-    def __init__(self, stage_idxs, out_channels, model_name, depth=5):
-
-        blocks_args, global_params = get_model_params(model_name, override_params=None)
-        super().__init__(blocks_args, global_params)
-
-        self._stage_idxs = stage_idxs
-        self._out_channels = out_channels
-        self._depth = depth
-        self._in_channels = 3
-
-        del self._fc
-
-    def get_stages(self):
-        return [
-            nn.Identity(),
-            nn.Sequential(self._conv_stem, self._bn0, self._swish),
-            self._blocks[: self._stage_idxs[0]],
-            self._blocks[self._stage_idxs[0] : self._stage_idxs[1]],
-            self._blocks[self._stage_idxs[1] : self._stage_idxs[2]],
-            self._blocks[self._stage_idxs[2] :],
-        ]
-
-    def forward(self, x):
-        stages = self.get_stages()
-
-        block_number = 0.0
-        drop_connect_rate = self._global_params.drop_connect_rate
-
-        features = []
-        for i in range(self._depth + 1):
-
-            # Identity and Sequential stages
-            if i < 2:
-                x = stages[i](x)
-
-            # Block stages need drop_connect rate
-            else:
-                for module in stages[i]:
-                    drop_connect = drop_connect_rate * block_number / len(self._blocks)
-                    block_number += 1.0
-                    x = module(x, drop_connect)
-
-            features.append(x)
-
-        return features
-
-    def load_state_dict(self, state_dict, **kwargs):
-        state_dict.pop("_fc.bias", None)
-        state_dict.pop("_fc.weight", None)
-        super().load_state_dict(state_dict, **kwargs)
-
-
-def _get_pretrained_settings(encoder):
-    pretrained_settings = {
-        "imagenet": {
-            "mean": [0.485, 0.456, 0.406],
-            "std": [0.229, 0.224, 0.225],
-            "url": url_map[encoder],
-            "input_space": "RGB",
-            "input_range": [0, 1],
-        },
-        "advprop": {
-            "mean": [0.5, 0.5, 0.5],
-            "std": [0.5, 0.5, 0.5],
-            "url": url_map_advprop[encoder],
-            "input_space": "RGB",
-            "input_range": [0, 1],
-        },
-    }
-    return pretrained_settings
-
-
-efficient_net_encoders = {
-    "efficientnet-b0": {
-        "encoder": EfficientNetEncoder,
-        "pretrained_settings": _get_pretrained_settings("efficientnet-b0"),
-        "params": {
-            "out_channels": (3, 32, 24, 40, 112, 320),
-            "stage_idxs": (3, 5, 9, 16),
-            "model_name": "efficientnet-b0",
-        },
-    },
-    "efficientnet-b1": {
-        "encoder": EfficientNetEncoder,
-        "pretrained_settings": _get_pretrained_settings("efficientnet-b1"),
-        "params": {
-            "out_channels": (3, 32, 24, 40, 112, 320),
-            "stage_idxs": (5, 8, 16, 23),
-            "model_name": "efficientnet-b1",
-        },
-    },
-    "efficientnet-b2": {
-        "encoder": EfficientNetEncoder,
-        "pretrained_settings": _get_pretrained_settings("efficientnet-b2"),
-        "params": {
-            "out_channels": (3, 32, 24, 48, 120, 352),
-            "stage_idxs": (5, 8, 16, 23),
-            "model_name": "efficientnet-b2",
-        },
-    },
-    "efficientnet-b3": {
-        "encoder": EfficientNetEncoder,
-        "pretrained_settings": _get_pretrained_settings("efficientnet-b3"),
-        "params": {
-            "out_channels": (3, 40, 32, 48, 136, 384),
-            "stage_idxs": (5, 8, 18, 26),
-            "model_name": "efficientnet-b3",
-        },
-    },
-    "efficientnet-b4": {
-        "encoder": EfficientNetEncoder,
-        "pretrained_settings": _get_pretrained_settings("efficientnet-b4"),
-        "params": {
-            "out_channels": (3, 48, 32, 56, 160, 448),
-            "stage_idxs": (6, 10, 22, 32),
-            "model_name": "efficientnet-b4",
-        },
-    },
-    "efficientnet-b5": {
-        "encoder": EfficientNetEncoder,
-        "pretrained_settings": _get_pretrained_settings("efficientnet-b5"),
-        "params": {
-            "out_channels": (3, 48, 40, 64, 176, 512),
-            "stage_idxs": (8, 13, 27, 39),
-            "model_name": "efficientnet-b5",
-        },
-    },
-    "efficientnet-b6": {
-        "encoder": EfficientNetEncoder,
-        "pretrained_settings": _get_pretrained_settings("efficientnet-b6"),
-        "params": {
-            "out_channels": (3, 56, 40, 72, 200, 576),
-            "stage_idxs": (9, 15, 31, 45),
-            "model_name": "efficientnet-b6",
-        },
-    },
-    "efficientnet-b7": {
-        "encoder": EfficientNetEncoder,
-        "pretrained_settings": _get_pretrained_settings("efficientnet-b7"),
-        "params": {
-            "out_channels": (3, 64, 48, 80, 224, 640),
-            "stage_idxs": (11, 18, 38, 55),
-            "model_name": "efficientnet-b7",
-        },
-    },
-}
diff --git a/spaces/gligen/demo/gligen/trainer.py b/spaces/gligen/demo/gligen/trainer.py
deleted file mode 100644
index 0815b7b25579001be44674e6fa2afa2a7d9e79b0..0000000000000000000000000000000000000000
--- a/spaces/gligen/demo/gligen/trainer.py
+++ /dev/null
@@ -1,456 +0,0 @@
-import torch
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.plms import PLMSSampler
-from ldm.util import instantiate_from_config
-import numpy as np
-import random
-import time 
-from dataset.concat_dataset import ConCatDataset #, collate_fn
-from torch.utils.data.distributed import  DistributedSampler
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import os 
-import shutil
-import torchvision
-import math
-from torch.nn.parallel import DistributedDataParallel as DDP
-from tqdm import tqdm
-from distributed import get_rank, synchronize, get_world_size
-from transformers import get_cosine_schedule_with_warmup, get_constant_schedule_with_warmup
-from copy import deepcopy
-try:
-    from apex import amp 
-except:
-    pass 
-# = = = = = = = = = = = = = = = = = = useful functions = = = = = = = = = = = = = = = = = #
-
-class ImageCaptionSaver:
-    def __init__(self, base_path, nrow=8, normalize=True, scale_each=True, range=(-1,1) ):
-        self.base_path = base_path 
-        self.nrow = nrow
-        self.normalize = normalize
-        self.scale_each = scale_each
-        self.range = range
-
-    def __call__(self, images, real, captions, seen):
-        
-        save_path = os.path.join(self.base_path, str(seen).zfill(8)+'.png')
-        torchvision.utils.save_image( images, save_path, nrow=self.nrow, normalize=self.normalize, scale_each=self.scale_each, range=self.range )
-        
-        save_path = os.path.join(self.base_path, str(seen).zfill(8)+'_real.png')
-        torchvision.utils.save_image( real, save_path, nrow=self.nrow)
-
-        assert images.shape[0] == len(captions)
-
-        save_path = os.path.join(self.base_path, 'captions.txt')
-        with open(save_path, "a") as f:
-            f.write( str(seen).zfill(8) + ':\n' )    
-            for cap in captions:
-                f.write( cap + '\n' )  
-            f.write( '\n' ) 
-
-
-
-def read_official_ckpt(ckpt_path):      
-    "Read offical pretrained ckpt and convert into my style" 
-    state_dict = torch.load(ckpt_path, map_location="cpu")["state_dict"]
-    out = {}
-    out["model"] = {}
-    out["text_encoder"] = {}
-    out["autoencoder"] = {}
-    out["unexpected"] = {}
-    out["diffusion"] = {}
-
-    for k,v in state_dict.items():
-        if k.startswith('model.diffusion_model'):
-            out["model"][k.replace("model.diffusion_model.", "")] = v 
-        elif k.startswith('cond_stage_model'):
-            out["text_encoder"][k.replace("cond_stage_model.", "")] = v 
-        elif k.startswith('first_stage_model'):
-            out["autoencoder"][k.replace("first_stage_model.", "")] = v 
-        elif k in ["model_ema.decay", "model_ema.num_updates"]:
-            out["unexpected"][k] = v  
-        else:
-            out["diffusion"][k] = v     
-    return out 
-
-
-def batch_to_device(batch, device):
-    for k in batch:
-        if isinstance(batch[k], torch.Tensor):
-            batch[k] = batch[k].to(device)
-    return batch
-
-
-def sub_batch(batch, num=1):
-    # choose first num in given batch 
-    num = num if num > 1 else 1 
-    for k in batch:
-        batch[k] = batch[k][0:num]
-    return batch
-
-
-def wrap_loader(loader):
-    while True:
-        for batch in loader:  # TODO: it seems each time you have the same order for all epoch?? 
-            yield batch
-
-
-def disable_grads(model):
-    for p in model.parameters():
-        p.requires_grad = False
-
-
-def count_params(params):
-    total_trainable_params_count = 0 
-    for p in params:
-        total_trainable_params_count += p.numel()
-    print("total_trainable_params_count is: ", total_trainable_params_count)
-
-
-def update_ema(target_params, source_params, rate=0.99):
-    for targ, src in zip(target_params, source_params):
-        targ.detach().mul_(rate).add_(src, alpha=1 - rate)
-
-           
-def create_expt_folder_with_auto_resuming(OUTPUT_ROOT, name):
-    #curr_folder_name = os.getcwd().split("/")[-1]
-    name = os.path.join( OUTPUT_ROOT, name )
-    writer = None
-    checkpoint = None
-
-    if os.path.exists(name):
-        all_tags = os.listdir(name)
-        all_existing_tags = [ tag for tag in all_tags if tag.startswith('tag')    ]
-        all_existing_tags.sort()
-        all_existing_tags = all_existing_tags[::-1]
-        for previous_tag in all_existing_tags:
-            potential_ckpt = os.path.join( name, previous_tag, 'checkpoint_latest.pth' )
-            if os.path.exists(potential_ckpt):
-                checkpoint = potential_ckpt
-                if get_rank() == 0:
-                    print('ckpt found '+ potential_ckpt)
-                break 
-        curr_tag = 'tag'+str(len(all_existing_tags)).zfill(2)
-        name = os.path.join( name, curr_tag ) # output/name/tagxx
-    else:
-        name = os.path.join( name, 'tag00' ) # output/name/tag00
-
-    if get_rank() == 0:
-        os.makedirs(name) 
-        os.makedirs(  os.path.join(name,'Log')  ) 
-        writer = SummaryWriter( os.path.join(name,'Log')  )
-
-    return name, writer, checkpoint
-
-
-
-# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # 
-# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # 
-# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # 
-
-
-
-
-
-
-class Trainer:
-    def __init__(self, config):
-
-        self.config = config
-        self.device = torch.device("cuda")
-
-        self.l_simple_weight = 1
-        self.name, self.writer, checkpoint = create_expt_folder_with_auto_resuming(config.OUTPUT_ROOT, config.name)
-        if get_rank() == 0:
-            shutil.copyfile(config.yaml_file, os.path.join(self.name, "train_config_file.yaml")  )
-            torch.save(  vars(config),  os.path.join(self.name, "config_dict.pth")     )
-
-        # = = = = = = = = = = create model and diffusion = = = = = = = = = = #
-        self.model = instantiate_from_config(config.model).to(self.device)
-        self.autoencoder = instantiate_from_config(config.autoencoder).to(self.device)
-        self.text_encoder = instantiate_from_config(config.text_encoder).to(self.device)
-        self.diffusion = instantiate_from_config(config.diffusion).to(self.device)
-
-
-        state_dict = read_official_ckpt(  os.path.join(config.DATA_ROOT, config.official_ckpt_name)   )
-        missing_keys, unexpected_keys = self.model.load_state_dict( state_dict["model"], strict=False  )
-        assert unexpected_keys == []
-        original_params_names = list( state_dict["model"].keys()  )
-        self.autoencoder.load_state_dict( state_dict["autoencoder"]  )
-        self.text_encoder.load_state_dict( state_dict["text_encoder"]  )
-        self.diffusion.load_state_dict( state_dict["diffusion"]  )
- 
-        self.autoencoder.eval()
-        self.text_encoder.eval()
-        disable_grads(self.autoencoder)
-        disable_grads(self.text_encoder)
-
-
-
-        # = = load from ckpt: (usually second stage whole model finetune) = = #
-        if self.config.ckpt is not None:
-            first_stage_ckpt = torch.load(self.config.ckpt, map_location="cpu")
-            self.model.load_state_dict(first_stage_ckpt["model"])
-
-
-
-
-        # = = = = = = = = = = create opt = = = = = = = = = = #
-        print("  ")
-        print("IMPORTANT: following code decides which params trainable!")
-        print("  ")
-
-        if self.config.whole:
-            print("Entire model is trainable")
-            params = list(self.model.parameters())
-        else:
-            print("Only new added components will be updated")
-            params = []
-            trainable_names = []
-            for name, p in self.model.named_parameters():
-                if ("transformer_blocks" in name) and ("fuser" in name):
-                    params.append(p) 
-                    trainable_names.append(name)
-                elif  "position_net" in name:
-                    params.append(p) 
-                    trainable_names.append(name)
-                else:
-                    # all new added trainable params have to be haddled above
-                    # otherwise it will trigger the following error  
-                    assert name in original_params_names, name  
-            
-            all_params_name = list( self.model.state_dict().keys()  )
-            assert set(all_params_name) == set(trainable_names + original_params_names) 
-
-        self.opt = torch.optim.AdamW(params, lr=config.base_learning_rate, weight_decay=config.weight_decay) 
-        count_params(params)
-        
-        self.master_params = list(self.model.parameters()) # note: you cannot assign above params as master_params since that is only trainable one
-        
-        if config.enable_ema:
-            self.ema = deepcopy(self.model)
-            self.ema_params = list(self.ema.parameters())
-            self.ema.eval()
-
-        # = = = = = = = = = = create scheduler = = = = = = = = = = #
-        if config.scheduler_type == "cosine":
-            self.scheduler = get_cosine_schedule_with_warmup(self.opt, num_warmup_steps=config.warmup_steps, num_training_steps=config.total_iters)
-        elif config.scheduler_type == "constant":
-            self.scheduler = get_constant_schedule_with_warmup(self.opt, num_warmup_steps=config.warmup_steps)
-        else:
-            assert False 
-
-
-
-        # = = = = = = = = = = create data = = = = = = = = = = #  
-        train_dataset_repeats = config.train_dataset_repeats if 'train_dataset_repeats' in config else None
-        dataset_train = ConCatDataset(config.train_dataset_names, config.DATA_ROOT, config.which_embedder, train=True, repeats=train_dataset_repeats)
-        sampler = DistributedSampler(dataset_train) if config.distributed else None 
-        loader_train = DataLoader( dataset_train,  batch_size=config.batch_size, 
-                                                   shuffle=(sampler is None),
-                                                   num_workers=config.workers, 
-                                                   pin_memory=True, 
-                                                   sampler=sampler)
-        self.dataset_train = dataset_train
-        self.loader_train = wrap_loader(loader_train)
-
-        if get_rank() == 0:
-            total_image = dataset_train.total_images()
-            print("Total training images: ", total_image)     
-        
-
-        # = = = = = = = = = = load from autoresuming ckpt = = = = = = = = = = #
-        self.starting_iter = 0  
-        if checkpoint is not None:
-            checkpoint = torch.load(checkpoint, map_location="cpu")
-            self.model.load_state_dict(checkpoint["model"])
-            if config.enable_ema:
-                self.ema.load_state_dict(checkpoint["ema"])
-            self.opt.load_state_dict(checkpoint["opt"])
-            self.scheduler.load_state_dict(checkpoint["scheduler"])
-            self.starting_iter = checkpoint["iters"]
-            if self.starting_iter >= config.total_iters:
-                synchronize()
-                print("Training finished. Start exiting")
-                exit()
-
-
-        # = = = = = misc = = = = = #    
-        if get_rank() == 0:
-            print("Actual total need see images is: ", config.total_iters*config.total_batch_size)
-            print("Equivalent training epoch is: ", (config.total_iters*config.total_batch_size) / len(dataset_train) )         
-            self.image_caption_saver = ImageCaptionSaver(self.name)
-            # self.counter = Counter(config.total_batch_size, config.save_every_images)
-
-        if config.use_o2:
-            self.model, self.opt = amp.initialize(self.model, self.opt, opt_level="O2")
-            self.model.use_o2 = True
-
-
-        # = = = = = wrap into ddp = = = = = #
-        if config.distributed:
-            self.model = DDP( self.model, device_ids=[config.local_rank], output_device=config.local_rank, broadcast_buffers=False )
-
-
-
-
-
-    @torch.no_grad()
-    def get_input(self, batch):
-
-        z = self.autoencoder.encode( batch["image"] )
-
-        context = self.text_encoder.encode( batch["caption"]  )
-
-        _t = torch.rand(z.shape[0]).to(z.device)
-        t = (torch.pow(_t, self.config.resample_step_gamma) * 1000).long()
-        t = torch.where(t!=1000, t, 999) # if 1000, then replace it with 999
-        
-        return z, t, context 
-
-
-    def run_one_step(self, batch):
-        x_start, t, context = self.get_input(batch)
-        noise = torch.randn_like(x_start)
-        x_noisy = self.diffusion.q_sample(x_start=x_start, t=t, noise=noise)
-
-        input = dict(x = x_noisy, 
-                     timesteps = t, 
-                     context = context, 
-                     boxes = batch['boxes'],
-                     masks = batch['masks'], 
-                     text_masks = batch['text_masks'],
-                     image_masks = batch['image_masks'], 
-                     text_embeddings = batch["text_embeddings"], 
-                     image_embeddings = batch["image_embeddings"]  )
-        model_output = self.model(input)
-        
-        loss = torch.nn.functional.mse_loss(model_output, noise) * self.l_simple_weight
-
-        self.loss_dict = {"loss": loss.item()}
-
-        return loss 
-        
-
-
-    def start_training(self):
-
-        if not self.config.use_o2:
-            # use pytorch mixed training which is similar to o1 but faster
-            scaler = torch.cuda.amp.GradScaler()
-
-
-        iterator = tqdm(range(self.starting_iter, self.config.total_iters), desc='Training progress',  disable=get_rank() != 0 )
-        self.model.train()
-        for iter_idx in iterator: # note: iter_idx is not from 0 if resume training
-            self.iter_idx = iter_idx
-
-            self.opt.zero_grad()
-            batch = next(self.loader_train)
-            batch_to_device(batch, self.device)
-
-            if self.config.use_o2:
-                loss = self.run_one_step(batch)
-                with amp.scale_loss(loss, self.opt) as scaled_loss:
-                    scaled_loss.backward()
-                self.opt.step()
-            else:
-                enabled = True if self.config.use_mixed else False
-                with torch.cuda.amp.autocast(enabled=enabled):  # with torch.autocast(enabled=True):
-                    loss = self.run_one_step(batch)
-                scaler.scale(loss).backward() 
-                scaler.step(self.opt)
-                scaler.update()
-
-
-            self.scheduler.step()
-
-            if self.config.enable_ema:
-                update_ema(self.ema_params, self.master_params, self.config.ema_rate)
-
-
-            if (get_rank() == 0):
-                if (iter_idx % 10 == 0):
-                    self.log_loss() 
-                if (iter_idx == 0)  or  ( iter_idx % self.config.save_every_iters == 0 )  or  (iter_idx == self.config.total_iters-1):
-                    self.save_ckpt_and_result()
-            synchronize()
-
-        
-        synchronize()
-        print("Training finished. Start exiting")
-        exit()
-
-
-    def log_loss(self):
-        for k, v in self.loss_dict.items():
-            self.writer.add_scalar(  k, v, self.iter_idx+1  )  # we add 1 as the actual name
-    
-
-    @torch.no_grad()
-    def save_ckpt_and_result(self):
-
-        model_wo_wrapper = self.model.module if self.config.distributed else self.model
-
-        iter_name = self.iter_idx + 1     # we add 1 as the actual name
-
-        if not self.config.disable_inference_in_training:
-            # Do a quick inference on one training batch 
-            batch_here = self.config.batch_size
-            batch = sub_batch( next(self.loader_train), batch_here)
-            batch_to_device(batch, self.device)
-
-            
-            real_images_with_box_drawing = [] # we save this durining trianing for better visualization
-            for i in range(batch_here):
-                temp_data = {"image": batch["image"][i], "boxes":batch["boxes"][i]}
-                im = self.dataset_train.datasets[0].vis_getitem_data(out=temp_data, return_tensor=True, print_caption=False)
-                real_images_with_box_drawing.append(im)
-            real_images_with_box_drawing = torch.stack(real_images_with_box_drawing)
-
-            
-            uc = self.text_encoder.encode( batch_here*[""] )
-            context = self.text_encoder.encode(  batch["caption"]  )
-            
-            ddim_sampler = PLMSSampler(self.diffusion, model_wo_wrapper)      
-            shape = (batch_here, model_wo_wrapper.in_channels, model_wo_wrapper.image_size, model_wo_wrapper.image_size)
-            input = dict( x = None, 
-                          timesteps = None, 
-                          context = context, 
-                          boxes = batch['boxes'], 
-                          masks = batch['masks'],
-                          text_masks = batch['text_masks'], 
-                          image_masks = batch['image_masks'], 
-                          text_embeddings = batch["text_embeddings"], 
-                          image_embeddings = batch["image_embeddings"] )
-            samples = ddim_sampler.sample(S=50, shape=shape, input=input, uc=uc, guidance_scale=5)
-            
-            # old 
-            # autoencoder_wo_wrapper = self.autoencoder # Note itself is without wrapper since we do not train that. 
-            # autoencoder_wo_wrapper = autoencoder_wo_wrapper.cpu() # To save GPU 
-            # samples = autoencoder_wo_wrapper.decode(samples.cpu())
-            # autoencoder_wo_wrapper = autoencoder_wo_wrapper.to(self.device)
-
-            # new 
-            autoencoder_wo_wrapper = self.autoencoder # Note itself is without wrapper since we do not train that. 
-            samples = autoencoder_wo_wrapper.decode(samples).cpu()
-
-            self.image_caption_saver(samples, real_images_with_box_drawing,  batch["caption"], iter_name)
-
-        ckpt = dict(model = model_wo_wrapper.state_dict(),
-                    opt = self.opt.state_dict(),
-                    scheduler= self.scheduler.state_dict(),
-                    iters = self.iter_idx+1 )
-        if self.config.enable_ema:
-            ckpt["ema"] = self.ema.state_dict()
-        torch.save( ckpt, os.path.join(self.name, "checkpoint_"+str(iter_name).zfill(8)+".pth") )
-        torch.save( ckpt, os.path.join(self.name, "checkpoint_latest.pth") )
-
-
-
-
-
-
-
-
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Mfc 9420cn Driver Download Compatible with Windows Mac and Linux Operating Systems.md b/spaces/gotiQspiryo/whisper-ui/examples/Mfc 9420cn Driver Download Compatible with Windows Mac and Linux Operating Systems.md
deleted file mode 100644
index 62820fe13140e4a3b928073e2b0060acf859ef19..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Mfc 9420cn Driver Download Compatible with Windows Mac and Linux Operating Systems.md	
+++ /dev/null
@@ -1,11 +0,0 @@
-
-<p><b>All PPD files of this driver entry are available in the PostScript Printer Application</b> Under this entry Brother's PPD files for their PostScript printers are hosted. PostScript printers are supported natively in Linux and Unix environments, so you only need the PPD file for your printer, no driver executables, to get access to all printing functionality. The PPD contains all information about properties and user-settable options which are specific to the printer and makes printing dialogs show these options and allow the user to control all the printer's functionality. The PPDs only support the printing functionality of the printers. To use additional functionality, like scanning and faxing you will need additional drivers from Brother's Linux driver web site. There you find also new and updated PPDs for PostScript printers and printer drivers for non-PostScript printers. Note that these printers work, like all other PostScript printers, with generic PostScript PPD files, but then you will not get access to all the printer specific functionality. <br /></p>
-<h2>Mfc 9420cn Driver Download</h2><br /><p><b><b>Download</b> &#10031;&#10031;&#10031; <a href="https://urlgoal.com/2uyM3u">https://urlgoal.com/2uyM3u</a></b></p><br /><br />
-<p>For machines that scan, the built-in driver does not offer ControlCenter or PaperPort. Scanning to .PDF format is not available using the built-in scanner driver and built-in scanning applications. You will need to have a third-party scanning application that supports the .PDF format.</p>
-<p><br /><br /> An Internet connection may be required to install built-in drivers. If your computer is not connected to the Internet, then the built-in driver may not be installed properly.<br /><br /> - USB Users<br /> - Network Users (For machines with a built-in network interface)<br /><br /><br />USB Users - The built-in drivers that you install via USB connection support the printing and scanning*1 functions.<br />(*1 Available only for Brother machines with scanner function.)<br /><br />To install the driver, connect your machine to your computer with a USB cable and turn the machine on. The built-in drivers will be installed automatically.<br /><br /></p>
-<p><br /><br />4. Click View devices and printers.<br /><br />5. Click Add a printer.<br /><br />6. Select your Brother printer from the list and then click Next. Go to step 12.<br /><br /><br /><br /><br /> If your machine doesn't appear in the list, it may not be communicating with your computer. Make sure that your computer and your machine are connected to the same network.<br />To manually add your printer, complete the following steps:<br /><br /> a. Click The printer that I want isn't listed.<br /><br /> b. Select Add a printer using a TCP/IP address or hostname and then click Next.<br /><br /> c. Type the IP address of the Brother machine in the Hostname or IP address field, and check the Query the printer and automatically select the driver to use option.<br /><br /> d. Click Next. Go to step 7.<br /><br />7. On the "Install the printer driver" screen, choose Brother from the Manufacturer list. <br /><br />8. Choose your model from the Printers list and then click Next.<br /><br /> If your model is not listed, click the Windows Update button. This will take a few minutes to contact the Microsoft server and download the additional drivers. Your computer must be connected to the Internet.<br /><br />9. If a previous driver was already installed, click Replace the current driver, and then click Next.<br /><br />10. We recommend that you leave the printer name as listed and then click Next.<br /><br />11. Choose an option for sharing and then click Next. Typically a network-connected printer does not need to be shared since it is already on a network.<br /><br />12. You'll receive a confirmation once the driver has been installed.<br /><br /> - You may choose to set your printer as the default.<br /> - You may choose to print a test page.<br /><br />13. Click Finish.<br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /></p>
-<p>Download Source Package brother-lpr-drivers-laser1: <ul> <li>[brother-lpr-drivers-laser1_1.0.0-3-0ubuntu6.dsc]</li> <li>[brother-lpr-drivers-laser1_1.0.0-3.orig.tar.gz]</li> <li>[brother-lpr-drivers-laser1_1.0.0-3-0ubuntu6.diff.gz]</li> </ul> Maintainer:<ul> <li>Ubuntu MOTU Team (Mail Archive)</li></ul>Please consider filing a bug or asking a question via Launchpad before contacting the maintainer directly.</p>
-<p></p>
-<p>Other Packages Related to brother-lpr-drivers-laser1 <li>depends</li></ul> <li>recommends</li></ul> <li>suggests</li></ul> <li>enhances</li></ul> <li> dep:a2ps GNU a2ps - 'Anything to PostScript' converter and pretty-printer <li> dep:brother-cups-wrapper-laser1 Cups Wrapper drivers for laser1 brother printers <li> dep:brother-lpr-drivers-common Common files for brother-lpr-drivers packages <li> dep:libc6-i386 (>= 2.1.3) GNU C Library: 32-bit shared libraries for AMD64 </ul> Download brother-lpr-drivers-laser1 Download for all available architectures ArchitecturePackage SizeInstalled SizeFiles amd6439.1 kB262.0 kB [list of files] This page is also available in the following languages:</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/gradio/HuBERT/examples/speech_recognition/w2l_decoder.py b/spaces/gradio/HuBERT/examples/speech_recognition/w2l_decoder.py
deleted file mode 100644
index aef44815932fb2d33bae2cf2d8777c762b9f8182..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/speech_recognition/w2l_decoder.py
+++ /dev/null
@@ -1,464 +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.
-
-"""
-Flashlight decoders.
-"""
-
-import gc
-import itertools as it
-import os.path as osp
-import warnings
-from collections import deque, namedtuple
-
-import numpy as np
-import torch
-from examples.speech_recognition.data.replabels import unpack_replabels
-from fairseq import tasks
-from fairseq.utils import apply_to_sample
-from omegaconf import open_dict
-from fairseq.dataclass.utils import convert_namespace_to_omegaconf
-
-
-try:
-    from flashlight.lib.text.dictionary import create_word_dict, load_words
-    from flashlight.lib.sequence.criterion import CpuViterbiPath, get_data_ptr_as_bytes
-    from flashlight.lib.text.decoder import (
-        CriterionType,
-        LexiconDecoderOptions,
-        KenLM,
-        LM,
-        LMState,
-        SmearingMode,
-        Trie,
-        LexiconDecoder,
-    )
-except:
-    warnings.warn(
-        "flashlight python bindings are required to use this functionality. Please install from https://github.com/facebookresearch/flashlight/tree/master/bindings/python"
-    )
-    LM = object
-    LMState = object
-
-
-class W2lDecoder(object):
-    def __init__(self, args, tgt_dict):
-        self.tgt_dict = tgt_dict
-        self.vocab_size = len(tgt_dict)
-        self.nbest = args.nbest
-
-        # criterion-specific init
-        self.criterion_type = CriterionType.CTC
-        self.blank = (
-            tgt_dict.index("<ctc_blank>")
-            if "<ctc_blank>" in tgt_dict.indices
-            else tgt_dict.bos()
-        )
-        if "<sep>" in tgt_dict.indices:
-            self.silence = tgt_dict.index("<sep>")
-        elif "|" in tgt_dict.indices:
-            self.silence = tgt_dict.index("|")
-        else:
-            self.silence = tgt_dict.eos()
-        self.asg_transitions = None
-
-    def generate(self, models, sample, **unused):
-        """Generate a batch of inferences."""
-        # model.forward normally channels prev_output_tokens into the decoder
-        # separately, but SequenceGenerator directly calls model.encoder
-        encoder_input = {
-            k: v for k, v in sample["net_input"].items() if k != "prev_output_tokens"
-        }
-        emissions = self.get_emissions(models, encoder_input)
-        return self.decode(emissions)
-
-    def get_emissions(self, models, encoder_input):
-        """Run encoder and normalize emissions"""
-        model = models[0]
-        encoder_out = model(**encoder_input)
-        if hasattr(model, "get_logits"):
-            emissions = model.get_logits(encoder_out) # no need to normalize emissions
-        else:
-            emissions = model.get_normalized_probs(encoder_out, log_probs=True)
-        return emissions.transpose(0, 1).float().cpu().contiguous()
-
-    def get_tokens(self, idxs):
-        """Normalize tokens by handling CTC blank, ASG replabels, etc."""
-        idxs = (g[0] for g in it.groupby(idxs))
-        idxs = filter(lambda x: x != self.blank, idxs)
-        return torch.LongTensor(list(idxs))
-
-
-class W2lViterbiDecoder(W2lDecoder):
-    def __init__(self, args, tgt_dict):
-        super().__init__(args, tgt_dict)
-
-    def decode(self, emissions):
-        B, T, N = emissions.size()
-        hypos = []
-        if self.asg_transitions is None:
-            transitions = torch.FloatTensor(N, N).zero_()
-        else:
-            transitions = torch.FloatTensor(self.asg_transitions).view(N, N)
-        viterbi_path = torch.IntTensor(B, T)
-        workspace = torch.ByteTensor(CpuViterbiPath.get_workspace_size(B, T, N))
-        CpuViterbiPath.compute(
-            B,
-            T,
-            N,
-            get_data_ptr_as_bytes(emissions),
-            get_data_ptr_as_bytes(transitions),
-            get_data_ptr_as_bytes(viterbi_path),
-            get_data_ptr_as_bytes(workspace),
-        )
-        return [
-            [{"tokens": self.get_tokens(viterbi_path[b].tolist()), "score": 0}]
-            for b in range(B)
-        ]
-
-
-class W2lKenLMDecoder(W2lDecoder):
-    def __init__(self, args, tgt_dict):
-        super().__init__(args, tgt_dict)
-
-        self.unit_lm = getattr(args, "unit_lm", False)
-
-        if args.lexicon:
-            self.lexicon = load_words(args.lexicon)
-            self.word_dict = create_word_dict(self.lexicon)
-            self.unk_word = self.word_dict.get_index("<unk>")
-
-            self.lm = KenLM(args.kenlm_model, self.word_dict)
-            self.trie = Trie(self.vocab_size, self.silence)
-
-            start_state = self.lm.start(False)
-            for i, (word, spellings) in enumerate(self.lexicon.items()):
-                word_idx = self.word_dict.get_index(word)
-                _, score = self.lm.score(start_state, word_idx)
-                for spelling in spellings:
-                    spelling_idxs = [tgt_dict.index(token) for token in spelling]
-                    assert (
-                        tgt_dict.unk() not in spelling_idxs
-                    ), f"{spelling} {spelling_idxs}"
-                    self.trie.insert(spelling_idxs, word_idx, score)
-            self.trie.smear(SmearingMode.MAX)
-
-            self.decoder_opts = LexiconDecoderOptions(
-                beam_size=args.beam,
-                beam_size_token=int(getattr(args, "beam_size_token", len(tgt_dict))),
-                beam_threshold=args.beam_threshold,
-                lm_weight=args.lm_weight,
-                word_score=args.word_score,
-                unk_score=args.unk_weight,
-                sil_score=args.sil_weight,
-                log_add=False,
-                criterion_type=self.criterion_type,
-            )
-
-            if self.asg_transitions is None:
-                N = 768
-                # self.asg_transitions = torch.FloatTensor(N, N).zero_()
-                self.asg_transitions = []
-
-            self.decoder = LexiconDecoder(
-                self.decoder_opts,
-                self.trie,
-                self.lm,
-                self.silence,
-                self.blank,
-                self.unk_word,
-                self.asg_transitions,
-                self.unit_lm,
-            )
-        else:
-            assert args.unit_lm, "lexicon free decoding can only be done with a unit language model"
-            from flashlight.lib.text.decoder import LexiconFreeDecoder, LexiconFreeDecoderOptions
-
-            d = {w: [[w]] for w in tgt_dict.symbols}
-            self.word_dict = create_word_dict(d)
-            self.lm = KenLM(args.kenlm_model, self.word_dict)
-            self.decoder_opts = LexiconFreeDecoderOptions(
-                beam_size=args.beam,
-                beam_size_token=int(getattr(args, "beam_size_token", len(tgt_dict))),
-                beam_threshold=args.beam_threshold,
-                lm_weight=args.lm_weight,
-                sil_score=args.sil_weight,
-                log_add=False,
-                criterion_type=self.criterion_type,
-            )
-            self.decoder = LexiconFreeDecoder(
-                self.decoder_opts, self.lm, self.silence, self.blank, []
-            )
-
-
-    def decode(self, emissions):
-        B, T, N = emissions.size()
-        hypos = []
-        for b in range(B):
-            emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0)
-            results = self.decoder.decode(emissions_ptr, T, N)
-
-            nbest_results = results[: self.nbest]
-            hypos.append(
-                [
-                    {
-                        "tokens": self.get_tokens(result.tokens),
-                        "score": result.score,
-                        "words": [
-                            self.word_dict.get_entry(x) for x in result.words if x >= 0
-                        ],
-                    }
-                    for result in nbest_results
-                ]
-            )
-        return hypos
-
-
-FairseqLMState = namedtuple("FairseqLMState", ["prefix", "incremental_state", "probs"])
-
-
-class FairseqLM(LM):
-    def __init__(self, dictionary, model):
-        LM.__init__(self)
-        self.dictionary = dictionary
-        self.model = model
-        self.unk = self.dictionary.unk()
-
-        self.save_incremental = False  # this currently does not work properly
-        self.max_cache = 20_000
-
-        model.cuda()
-        model.eval()
-        model.make_generation_fast_()
-
-        self.states = {}
-        self.stateq = deque()
-
-    def start(self, start_with_nothing):
-        state = LMState()
-        prefix = torch.LongTensor([[self.dictionary.eos()]])
-        incremental_state = {} if self.save_incremental else None
-        with torch.no_grad():
-            res = self.model(prefix.cuda(), incremental_state=incremental_state)
-            probs = self.model.get_normalized_probs(res, log_probs=True, sample=None)
-
-        if incremental_state is not None:
-            incremental_state = apply_to_sample(lambda x: x.cpu(), incremental_state)
-        self.states[state] = FairseqLMState(
-            prefix.numpy(), incremental_state, probs[0, -1].cpu().numpy()
-        )
-        self.stateq.append(state)
-
-        return state
-
-    def score(self, state: LMState, token_index: int, no_cache: bool = False):
-        """
-        Evaluate language model based on the current lm state and new word
-        Parameters:
-        -----------
-        state: current lm state
-        token_index: index of the word
-                     (can be lexicon index then you should store inside LM the
-                      mapping between indices of lexicon and lm, or lm index of a word)
-
-        Returns:
-        --------
-        (LMState, float): pair of (new state, score for the current word)
-        """
-        curr_state = self.states[state]
-
-        def trim_cache(targ_size):
-            while len(self.stateq) > targ_size:
-                rem_k = self.stateq.popleft()
-                rem_st = self.states[rem_k]
-                rem_st = FairseqLMState(rem_st.prefix, None, None)
-                self.states[rem_k] = rem_st
-
-        if curr_state.probs is None:
-            new_incremental_state = (
-                curr_state.incremental_state.copy()
-                if curr_state.incremental_state is not None
-                else None
-            )
-            with torch.no_grad():
-                if new_incremental_state is not None:
-                    new_incremental_state = apply_to_sample(
-                        lambda x: x.cuda(), new_incremental_state
-                    )
-                elif self.save_incremental:
-                    new_incremental_state = {}
-
-                res = self.model(
-                    torch.from_numpy(curr_state.prefix).cuda(),
-                    incremental_state=new_incremental_state,
-                )
-                probs = self.model.get_normalized_probs(
-                    res, log_probs=True, sample=None
-                )
-
-                if new_incremental_state is not None:
-                    new_incremental_state = apply_to_sample(
-                        lambda x: x.cpu(), new_incremental_state
-                    )
-
-                curr_state = FairseqLMState(
-                    curr_state.prefix, new_incremental_state, probs[0, -1].cpu().numpy()
-                )
-
-            if not no_cache:
-                self.states[state] = curr_state
-                self.stateq.append(state)
-
-        score = curr_state.probs[token_index].item()
-
-        trim_cache(self.max_cache)
-
-        outstate = state.child(token_index)
-        if outstate not in self.states and not no_cache:
-            prefix = np.concatenate(
-                [curr_state.prefix, torch.LongTensor([[token_index]])], -1
-            )
-            incr_state = curr_state.incremental_state
-
-            self.states[outstate] = FairseqLMState(prefix, incr_state, None)
-
-        if token_index == self.unk:
-            score = float("-inf")
-
-        return outstate, score
-
-    def finish(self, state: LMState):
-        """
-        Evaluate eos for language model based on the current lm state
-
-        Returns:
-        --------
-        (LMState, float): pair of (new state, score for the current word)
-        """
-        return self.score(state, self.dictionary.eos())
-
-    def empty_cache(self):
-        self.states = {}
-        self.stateq = deque()
-        gc.collect()
-
-
-class W2lFairseqLMDecoder(W2lDecoder):
-    def __init__(self, args, tgt_dict):
-        super().__init__(args, tgt_dict)
-
-        self.unit_lm = getattr(args, "unit_lm", False)
-
-        self.lexicon = load_words(args.lexicon) if args.lexicon else None
-        self.idx_to_wrd = {}
-
-        checkpoint = torch.load(args.kenlm_model, map_location="cpu")
-
-        if "cfg" in checkpoint and checkpoint["cfg"] is not None:
-            lm_args = checkpoint["cfg"]
-        else:
-            lm_args = convert_namespace_to_omegaconf(checkpoint["args"])
-
-        with open_dict(lm_args.task):
-            lm_args.task.data = osp.dirname(args.kenlm_model)
-
-        task = tasks.setup_task(lm_args.task)
-        model = task.build_model(lm_args.model)
-        model.load_state_dict(checkpoint["model"], strict=False)
-
-        self.trie = Trie(self.vocab_size, self.silence)
-
-        self.word_dict = task.dictionary
-        self.unk_word = self.word_dict.unk()
-        self.lm = FairseqLM(self.word_dict, model)
-
-        if self.lexicon:
-            start_state = self.lm.start(False)
-            for i, (word, spellings) in enumerate(self.lexicon.items()):
-                if self.unit_lm:
-                    word_idx = i
-                    self.idx_to_wrd[i] = word
-                    score = 0
-                else:
-                    word_idx = self.word_dict.index(word)
-                    _, score = self.lm.score(start_state, word_idx, no_cache=True)
-
-                for spelling in spellings:
-                    spelling_idxs = [tgt_dict.index(token) for token in spelling]
-                    assert (
-                        tgt_dict.unk() not in spelling_idxs
-                    ), f"{spelling} {spelling_idxs}"
-                    self.trie.insert(spelling_idxs, word_idx, score)
-            self.trie.smear(SmearingMode.MAX)
-
-            self.decoder_opts = LexiconDecoderOptions(
-                beam_size=args.beam,
-                beam_size_token=int(getattr(args, "beam_size_token", len(tgt_dict))),
-                beam_threshold=args.beam_threshold,
-                lm_weight=args.lm_weight,
-                word_score=args.word_score,
-                unk_score=args.unk_weight,
-                sil_score=args.sil_weight,
-                log_add=False,
-                criterion_type=self.criterion_type,
-            )
-
-            self.decoder = LexiconDecoder(
-                self.decoder_opts,
-                self.trie,
-                self.lm,
-                self.silence,
-                self.blank,
-                self.unk_word,
-                [],
-                self.unit_lm,
-            )
-        else:
-            assert args.unit_lm, "lexicon free decoding can only be done with a unit language model"
-            from flashlight.lib.text.decoder import LexiconFreeDecoder, LexiconFreeDecoderOptions
-
-            d = {w: [[w]] for w in tgt_dict.symbols}
-            self.word_dict = create_word_dict(d)
-            self.lm = KenLM(args.kenlm_model, self.word_dict)
-            self.decoder_opts = LexiconFreeDecoderOptions(
-                beam_size=args.beam,
-                beam_size_token=int(getattr(args, "beam_size_token", len(tgt_dict))),
-                beam_threshold=args.beam_threshold,
-                lm_weight=args.lm_weight,
-                sil_score=args.sil_weight,
-                log_add=False,
-                criterion_type=self.criterion_type,
-            )
-            self.decoder = LexiconFreeDecoder(
-                self.decoder_opts, self.lm, self.silence, self.blank, []
-            )
-
-    def decode(self, emissions):
-        B, T, N = emissions.size()
-        hypos = []
-
-        def idx_to_word(idx):
-            if self.unit_lm:
-                return self.idx_to_wrd[idx]
-            else:
-                return self.word_dict[idx]
-
-        def make_hypo(result):
-            hypo = {"tokens": self.get_tokens(result.tokens), "score": result.score}
-            if self.lexicon:
-                hypo["words"] = [idx_to_word(x) for x in result.words if x >= 0]
-            return hypo
-
-        for b in range(B):
-            emissions_ptr = emissions.data_ptr() + 4 * b * emissions.stride(0)
-            results = self.decoder.decode(emissions_ptr, T, N)
-
-            nbest_results = results[: self.nbest]
-            hypos.append([make_hypo(result) for result in nbest_results])
-            self.lm.empty_cache()
-
-        return hypos
diff --git a/spaces/gradio/calculator-flagging-options/README.md b/spaces/gradio/calculator-flagging-options/README.md
deleted file mode 100644
index 01f3f766974fbcb3d51204267a57b9128d5244f1..0000000000000000000000000000000000000000
--- a/spaces/gradio/calculator-flagging-options/README.md
+++ /dev/null
@@ -1,46 +0,0 @@
----
-title: Calculator Flagging Options
-emoji: 🐨
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.1.0
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `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/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/ops/bias_act.h b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/ops/bias_act.h
deleted file mode 100644
index d0246aa06c3dcd5919111fdc914136014b9044b5..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/ops/bias_act.h
+++ /dev/null
@@ -1,40 +0,0 @@
-// Copyright (c) SenseTime Research. All rights reserved.
-
-// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct bias_act_kernel_params
-{
-    const void* x;      // [sizeX]
-    const void* b;      // [sizeB] or NULL
-    const void* xref;   // [sizeX] or NULL
-    const void* yref;   // [sizeX] or NULL
-    const void* dy;     // [sizeX] or NULL
-    void*       y;      // [sizeX]
-
-    int         grad;
-    int         act;
-    float       alpha;
-    float       gain;
-    float       clamp;
-
-    int         sizeX;
-    int         sizeB;
-    int         stepB;
-    int         loopX;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T> void* choose_bias_act_kernel(const bias_act_kernel_params& p);
-
-//------------------------------------------------------------------------
diff --git a/spaces/haakohu/deep_privacy2/dp2/data/datasets/fdf128_wds.py b/spaces/haakohu/deep_privacy2/dp2/data/datasets/fdf128_wds.py
deleted file mode 100644
index 5af477de9b1d2e2670bfae24930c9e698e95b975..0000000000000000000000000000000000000000
--- a/spaces/haakohu/deep_privacy2/dp2/data/datasets/fdf128_wds.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import torch
-import tops
-import numpy as np
-import io
-import webdataset as wds
-import os
-from ..utils import png_decoder, get_num_workers, collate_fn
-
-
-def kp_decoder(x):
-    # Keypoints are between [0, 1] for webdataset
-    keypoints = torch.from_numpy(np.load(io.BytesIO(x))).float().view(7, 2).clamp(0, 1)
-    keypoints = torch.cat((keypoints, torch.ones((7, 1))), dim=-1)
-    return keypoints
-
-
-def bbox_decoder(x):
-    return torch.from_numpy(np.load(io.BytesIO(x))).float().view(4)
-
-
-class BBoxToMask:
-
-    def __call__(self, sample):
-        imsize = sample["image.png"].shape[-1]
-        bbox = sample["bounding_box.npy"] * imsize
-        x0, y0, x1, y1 = np.round(bbox).astype(np.int64)
-        mask = torch.ones((1, imsize, imsize), dtype=torch.bool)
-        mask[:, y0:y1, x0:x1] = 0
-        sample["mask"] = mask
-        return sample
-
-
-def get_dataloader_fdf_wds(
-        path,
-        batch_size: int,
-        num_workers: int,
-        transform: torch.nn.Module,
-        gpu_transform: torch.nn.Module,
-        infinite: bool,
-        shuffle: bool,
-        partial_batches: bool,
-        sample_shuffle=10_000,
-        tar_shuffle=100,
-        channels_last=False,
-    ):
-    # Need to set this for split_by_node to work.
-    os.environ["RANK"] = str(tops.rank())
-    os.environ["WORLD_SIZE"] = str(tops.world_size())
-    if infinite:
-        pipeline = [wds.ResampledShards(str(path))]
-    else:
-        pipeline = [wds.SimpleShardList(str(path))]
-    if shuffle:
-        pipeline.append(wds.shuffle(tar_shuffle))
-    pipeline.extend([
-        wds.split_by_node,
-        wds.split_by_worker,
-    ])
-    if shuffle:
-        pipeline.append(wds.shuffle(sample_shuffle))
-
-    decoder = [
-        wds.handle_extension("image.png", png_decoder),
-        wds.handle_extension("keypoints.npy", kp_decoder),
-    ]
-
-    rename_keys = [
-        ["img", "image.png"],
-        ["keypoints", "keypoints.npy"],
-        ["__key__", "__key__"],
-        ["mask", "mask"]
-    ]
-
-    pipeline.extend([
-        wds.tarfile_to_samples(),
-        wds.decode(*decoder),
-    ])
-    pipeline.append(wds.map(BBoxToMask()))
-    pipeline.extend([
-        wds.batched(batch_size, collation_fn=collate_fn, partial=partial_batches),
-        wds.rename_keys(*rename_keys),
-    ])
-
-    if transform is not None:
-        pipeline.append(wds.map(transform))
-    pipeline = wds.DataPipeline(*pipeline)
-    if infinite:
-        pipeline = pipeline.repeat(nepochs=1000000)
-
-    loader = wds.WebLoader(
-        pipeline, batch_size=None, shuffle=False,
-        num_workers=get_num_workers(num_workers),
-        persistent_workers=True,
-    )
-    loader = tops.DataPrefetcher(loader, gpu_transform, channels_last=channels_last, to_float=False)
-    return loader
diff --git a/spaces/hamelcubsfan/AutoGPT/ui/api.py b/spaces/hamelcubsfan/AutoGPT/ui/api.py
deleted file mode 100644
index 3b46ad32148b23f06c6eb64c88708fc2bf92e4dc..0000000000000000000000000000000000000000
--- a/spaces/hamelcubsfan/AutoGPT/ui/api.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import os, sys
-import utils
-import uuid
-import json
-import subprocess, threading
-
-FILE_DIR = os.path.dirname(os.path.abspath(__file__))
-REPO_DIR = os.path.dirname(FILE_DIR)
-STATE_DIR = os.path.join(FILE_DIR, "state") 
-sys.path.append(REPO_DIR)
-if not os.path.exists(STATE_DIR):
-    os.mkdir(STATE_DIR)
-import time
-
-
-def get_openai_api_key():
-    return os.getenv("OPENAI_API_KEY")
-
-
-running_apis = []
-
-
-def get_state(state_file):
-    with open(state_file, "r") as f:
-        state = json.load(f)
-    return state
-
-
-def set_state(state_file, state):
-    with open(state_file, "w") as f:
-        json.dump(state, f)
-
-
-class AutoAPI:
-    def __init__(self, openai_key, ai_name, ai_role, top_5_goals):
-        self.openai_key = openai_key
-        hex = uuid.uuid4().hex
-        print(hex)
-        self.state_file = os.path.join(STATE_DIR, f"state_{hex}.json")
-        self.log_file = os.path.join(STATE_DIR, f"log_{hex}.json")
-
-        newline = "\n"
-        with open(os.path.join(REPO_DIR, "ai_settings.yaml"), "w") as f:
-            f.write(
-                f"""ai_goals:
-{newline.join([f'- {goal[0]}' for goal in top_5_goals if goal[0]])}
-ai_name: {ai_name}
-ai_role: {ai_role}
-"""
-            )
-        state = {
-            "pending_input": None,
-            "awaiting_input": False,
-            "messages": [],
-            "last_message_read_index": -1,
-        }
-        set_state(self.state_file, state)
-
-        with open(self.log_file, "w") as f:
-            subprocess.Popen(
-                [
-                    "python",
-                    os.path.join(REPO_DIR, "ui", "api.py"),
-                    openai_key,
-                    self.state_file,
-                ],
-                cwd=REPO_DIR,
-                stdout=f,
-                stderr=f,
-            )
-
-    def send_message(self, message="Y"):
-        state = get_state(self.state_file)
-        state["pending_input"] = message
-        state["awaiting_input"] = False
-        set_state(self.state_file, state)
-
-    def get_chatbot_response(self):
-        while True:
-            state = get_state(self.state_file)
-            if (
-                state["awaiting_input"]
-                and state["last_message_read_index"] >= len(state["messages"]) - 1
-            ):
-                break
-            if state["last_message_read_index"] >= len(state["messages"]) - 1:
-                time.sleep(1)
-            else:
-                state["last_message_read_index"] += 1
-                title, content = state["messages"][state["last_message_read_index"]]
-                yield (f"**{title.strip()}** " if title else "") + utils.remove_color(
-                    content
-                ).replace("\n", "<br />")
-                set_state(self.state_file, state)
-
-
-if __name__ == "__main__":
-    print(sys.argv)
-    _, openai_key, state_file = sys.argv
-    os.environ["OPENAI_API_KEY"] = openai_key
-    import autogpt.config.config
-    from autogpt.logs import logger
-    from autogpt.cli import main
-    import autogpt.utils
-    from autogpt.spinner import Spinner
-
-    def add_message(title, content):
-        state = get_state(state_file)
-        state["messages"].append((title, content))
-        set_state(state_file, state)
-
-    def typewriter_log(title="", title_color="", content="", *args, **kwargs):
-        add_message(title, content)
-
-    def warn(message, title="", *args, **kwargs):
-        add_message(title, message)
-
-    def error(title, message="", *args, **kwargs):
-        add_message(title, message)
-
-    def clean_input(prompt=""):
-        add_message(None, prompt)
-        state = get_state(state_file)
-        state["awaiting_input"] = True
-        set_state(state_file, state)
-        while state["pending_input"] is None:
-            state = get_state(state_file)
-            print("Waiting for input...")
-            time.sleep(1)
-        print("Got input")
-        pending_input = state["pending_input"]
-        state["pending_input"] = None
-        set_state(state_file, state)
-        return pending_input
-
-    def spinner_start():
-        add_message(None, "Thinking...")
-
-    logger.typewriter_log = typewriter_log
-    logger.warn = warn
-    logger.error = error
-    autogpt.utils.clean_input = clean_input
-    Spinner.spin = spinner_start
-
-    sys.argv = sys.argv[:1]
-    main()
diff --git a/spaces/hamzapehlivan/StyleRes/models/torch_utils/training_stats.py b/spaces/hamzapehlivan/StyleRes/models/torch_utils/training_stats.py
deleted file mode 100644
index 5de4134f1943e7c3104bbc926b2abaf828626525..0000000000000000000000000000000000000000
--- a/spaces/hamzapehlivan/StyleRes/models/torch_utils/training_stats.py
+++ /dev/null
@@ -1,268 +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.
-
-"""Facilities for reporting and collecting training statistics across
-multiple processes and devices. The interface is designed to minimize
-synchronization overhead as well as the amount of boilerplate in user
-code."""
-
-import re
-import numpy as np
-import torch
-import dnnlib
-
-from . import misc
-
-#----------------------------------------------------------------------------
-
-_num_moments    = 3             # [num_scalars, sum_of_scalars, sum_of_squares]
-_reduce_dtype   = torch.float32 # Data type to use for initial per-tensor reduction.
-_counter_dtype  = torch.float64 # Data type to use for the internal counters.
-_rank           = 0             # Rank of the current process.
-_sync_device    = None          # Device to use for multiprocess communication. None = single-process.
-_sync_called    = False         # Has _sync() been called yet?
-_counters       = dict()        # Running counters on each device, updated by report(): name => device => torch.Tensor
-_cumulative     = dict()        # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
-
-#----------------------------------------------------------------------------
-
-def init_multiprocessing(rank, sync_device):
-    r"""Initializes `torch_utils.training_stats` for collecting statistics
-    across multiple processes.
-
-    This function must be called after
-    `torch.distributed.init_process_group()` and before `Collector.update()`.
-    The call is not necessary if multi-process collection is not needed.
-
-    Args:
-        rank:           Rank of the current process.
-        sync_device:    PyTorch device to use for inter-process
-                        communication, or None to disable multi-process
-                        collection. Typically `torch.device('cuda', rank)`.
-    """
-    global _rank, _sync_device
-    assert not _sync_called
-    _rank = rank
-    _sync_device = sync_device
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def report(name, value):
-    r"""Broadcasts the given set of scalars to all interested instances of
-    `Collector`, across device and process boundaries.
-
-    This function is expected to be extremely cheap and can be safely
-    called from anywhere in the training loop, loss function, or inside a
-    `torch.nn.Module`.
-
-    Warning: The current implementation expects the set of unique names to
-    be consistent across processes. Please make sure that `report()` is
-    called at least once for each unique name by each process, and in the
-    same order. If a given process has no scalars to broadcast, it can do
-    `report(name, [])` (empty list).
-
-    Args:
-        name:   Arbitrary string specifying the name of the statistic.
-                Averages are accumulated separately for each unique name.
-        value:  Arbitrary set of scalars. Can be a list, tuple,
-                NumPy array, PyTorch tensor, or Python scalar.
-
-    Returns:
-        The same `value` that was passed in.
-    """
-    if name not in _counters:
-        _counters[name] = dict()
-
-    elems = torch.as_tensor(value)
-    if elems.numel() == 0:
-        return value
-
-    elems = elems.detach().flatten().to(_reduce_dtype)
-    moments = torch.stack([
-        torch.ones_like(elems).sum(),
-        elems.sum(),
-        elems.square().sum(),
-    ])
-    assert moments.ndim == 1 and moments.shape[0] == _num_moments
-    moments = moments.to(_counter_dtype)
-
-    device = moments.device
-    if device not in _counters[name]:
-        _counters[name][device] = torch.zeros_like(moments)
-    _counters[name][device].add_(moments)
-    return value
-
-#----------------------------------------------------------------------------
-
-def report0(name, value):
-    r"""Broadcasts the given set of scalars by the first process (`rank = 0`),
-    but ignores any scalars provided by the other processes.
-    See `report()` for further details.
-    """
-    report(name, value if _rank == 0 else [])
-    return value
-
-#----------------------------------------------------------------------------
-
-class Collector:
-    r"""Collects the scalars broadcasted by `report()` and `report0()` and
-    computes their long-term averages (mean and standard deviation) over
-    user-defined periods of time.
-
-    The averages are first collected into internal counters that are not
-    directly visible to the user. They are then copied to the user-visible
-    state as a result of calling `update()` and can then be queried using
-    `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the
-    internal counters for the next round, so that the user-visible state
-    effectively reflects averages collected between the last two calls to
-    `update()`.
-
-    Args:
-        regex:          Regular expression defining which statistics to
-                        collect. The default is to collect everything.
-        keep_previous:  Whether to retain the previous averages if no
-                        scalars were collected on a given round
-                        (default: True).
-    """
-    def __init__(self, regex='.*', keep_previous=True):
-        self._regex = re.compile(regex)
-        self._keep_previous = keep_previous
-        self._cumulative = dict()
-        self._moments = dict()
-        self.update()
-        self._moments.clear()
-
-    def names(self):
-        r"""Returns the names of all statistics broadcasted so far that
-        match the regular expression specified at construction time.
-        """
-        return [name for name in _counters if self._regex.fullmatch(name)]
-
-    def update(self):
-        r"""Copies current values of the internal counters to the
-        user-visible state and resets them for the next round.
-
-        If `keep_previous=True` was specified at construction time, the
-        operation is skipped for statistics that have received no scalars
-        since the last update, retaining their previous averages.
-
-        This method performs a number of GPU-to-CPU transfers and one
-        `torch.distributed.all_reduce()`. It is intended to be called
-        periodically in the main training loop, typically once every
-        N training steps.
-        """
-        if not self._keep_previous:
-            self._moments.clear()
-        for name, cumulative in _sync(self.names()):
-            if name not in self._cumulative:
-                self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
-            delta = cumulative - self._cumulative[name]
-            self._cumulative[name].copy_(cumulative)
-            if float(delta[0]) != 0:
-                self._moments[name] = delta
-
-    def _get_delta(self, name):
-        r"""Returns the raw moments that were accumulated for the given
-        statistic between the last two calls to `update()`, or zero if
-        no scalars were collected.
-        """
-        assert self._regex.fullmatch(name)
-        if name not in self._moments:
-            self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
-        return self._moments[name]
-
-    def num(self, name):
-        r"""Returns the number of scalars that were accumulated for the given
-        statistic between the last two calls to `update()`, or zero if
-        no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        return int(delta[0])
-
-    def mean(self, name):
-        r"""Returns the mean of the scalars that were accumulated for the
-        given statistic between the last two calls to `update()`, or NaN if
-        no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        if int(delta[0]) == 0:
-            return float('nan')
-        return float(delta[1] / delta[0])
-
-    def std(self, name):
-        r"""Returns the standard deviation of the scalars that were
-        accumulated for the given statistic between the last two calls to
-        `update()`, or NaN if no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
-            return float('nan')
-        if int(delta[0]) == 1:
-            return float(0)
-        mean = float(delta[1] / delta[0])
-        raw_var = float(delta[2] / delta[0])
-        return np.sqrt(max(raw_var - np.square(mean), 0))
-
-    def as_dict(self):
-        r"""Returns the averages accumulated between the last two calls to
-        `update()` as an `dnnlib.EasyDict`. The contents are as follows:
-
-            dnnlib.EasyDict(
-                NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT),
-                ...
-            )
-        """
-        stats = dnnlib.EasyDict()
-        for name in self.names():
-            stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name))
-        return stats
-
-    def __getitem__(self, name):
-        r"""Convenience getter.
-        `collector[name]` is a synonym for `collector.mean(name)`.
-        """
-        return self.mean(name)
-
-#----------------------------------------------------------------------------
-
-def _sync(names):
-    r"""Synchronize the global cumulative counters across devices and
-    processes. Called internally by `Collector.update()`.
-    """
-    if len(names) == 0:
-        return []
-    global _sync_called
-    _sync_called = True
-
-    # Collect deltas within current rank.
-    deltas = []
-    device = _sync_device if _sync_device is not None else torch.device('cpu')
-    for name in names:
-        delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device)
-        for counter in _counters[name].values():
-            delta.add_(counter.to(device))
-            counter.copy_(torch.zeros_like(counter))
-        deltas.append(delta)
-    deltas = torch.stack(deltas)
-
-    # Sum deltas across ranks.
-    if _sync_device is not None:
-        torch.distributed.all_reduce(deltas)
-
-    # Update cumulative values.
-    deltas = deltas.cpu()
-    for idx, name in enumerate(names):
-        if name not in _cumulative:
-            _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
-        _cumulative[name].add_(deltas[idx])
-
-    # Return name-value pairs.
-    return [(name, _cumulative[name]) for name in names]
-
-#----------------------------------------------------------------------------
diff --git a/spaces/haonanzhang/ChatGPT-BOT/run_Windows.bat b/spaces/haonanzhang/ChatGPT-BOT/run_Windows.bat
deleted file mode 100644
index 4c18f9ccaeea0af972301ffdf48778641221f76d..0000000000000000000000000000000000000000
--- a/spaces/haonanzhang/ChatGPT-BOT/run_Windows.bat
+++ /dev/null
@@ -1,5 +0,0 @@
-@echo off
-echo Opening ChuanhuChatGPT...
-
-REM Open powershell via bat
-start powershell.exe -NoExit -Command "python ./ChuanhuChatbot.py"
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/solver/lr_scheduler.py b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/solver/lr_scheduler.py
deleted file mode 100644
index 6148d86785dae03ed2611792fb28da387d1103b8..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/detectron2/solver/lr_scheduler.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import math
-from bisect import bisect_right
-from typing import List
-import torch
-
-# NOTE: PyTorch's LR scheduler interface uses names that assume the LR changes
-# only on epoch boundaries. We typically use iteration based schedules instead.
-# As a result, "epoch" (e.g., as in self.last_epoch) should be understood to mean
-# "iteration" instead.
-
-# FIXME: ideally this would be achieved with a CombinedLRScheduler, separating
-# MultiStepLR with WarmupLR but the current LRScheduler design doesn't allow it.
-
-
-class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
-    def __init__(
-        self,
-        optimizer: torch.optim.Optimizer,
-        milestones: List[int],
-        gamma: float = 0.1,
-        warmup_factor: float = 0.001,
-        warmup_iters: int = 1000,
-        warmup_method: str = "linear",
-        last_epoch: int = -1,
-    ):
-        if not list(milestones) == sorted(milestones):
-            raise ValueError(
-                "Milestones should be a list of" " increasing integers. Got {}", milestones
-            )
-        self.milestones = milestones
-        self.gamma = gamma
-        self.warmup_factor = warmup_factor
-        self.warmup_iters = warmup_iters
-        self.warmup_method = warmup_method
-        super().__init__(optimizer, last_epoch)
-
-    def get_lr(self) -> List[float]:
-        warmup_factor = _get_warmup_factor_at_iter(
-            self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor
-        )
-        return [
-            base_lr * warmup_factor * self.gamma ** bisect_right(self.milestones, self.last_epoch)
-            for base_lr in self.base_lrs
-        ]
-
-    def _compute_values(self) -> List[float]:
-        # The new interface
-        return self.get_lr()
-
-
-class WarmupCosineLR(torch.optim.lr_scheduler._LRScheduler):
-    def __init__(
-        self,
-        optimizer: torch.optim.Optimizer,
-        max_iters: int,
-        warmup_factor: float = 0.001,
-        warmup_iters: int = 1000,
-        warmup_method: str = "linear",
-        last_epoch: int = -1,
-    ):
-        self.max_iters = max_iters
-        self.warmup_factor = warmup_factor
-        self.warmup_iters = warmup_iters
-        self.warmup_method = warmup_method
-        super().__init__(optimizer, last_epoch)
-
-    def get_lr(self) -> List[float]:
-        warmup_factor = _get_warmup_factor_at_iter(
-            self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor
-        )
-        # Different definitions of half-cosine with warmup are possible. For
-        # simplicity we multiply the standard half-cosine schedule by the warmup
-        # factor. An alternative is to start the period of the cosine at warmup_iters
-        # instead of at 0. In the case that warmup_iters << max_iters the two are
-        # very close to each other.
-        return [
-            base_lr
-            * warmup_factor
-            * 0.5
-            * (1.0 + math.cos(math.pi * self.last_epoch / self.max_iters))
-            for base_lr in self.base_lrs
-        ]
-
-    def _compute_values(self) -> List[float]:
-        # The new interface
-        return self.get_lr()
-
-
-def _get_warmup_factor_at_iter(
-    method: str, iter: int, warmup_iters: int, warmup_factor: float
-) -> float:
-    """
-    Return the learning rate warmup factor at a specific iteration.
-    See :paper:`in1k1h` for more details.
-
-    Args:
-        method (str): warmup method; either "constant" or "linear".
-        iter (int): iteration at which to calculate the warmup factor.
-        warmup_iters (int): the number of warmup iterations.
-        warmup_factor (float): the base warmup factor (the meaning changes according
-            to the method used).
-
-    Returns:
-        float: the effective warmup factor at the given iteration.
-    """
-    if iter >= warmup_iters:
-        return 1.0
-
-    if method == "constant":
-        return warmup_factor
-    elif method == "linear":
-        alpha = iter / warmup_iters
-        return warmup_factor * (1 - alpha) + alpha
-    else:
-        raise ValueError("Unknown warmup method: {}".format(method))
diff --git a/spaces/helliun/beism/README.md b/spaces/helliun/beism/README.md
deleted file mode 100644
index b2fefb479c634c75aacb52f3fce1d6eb1904dd9b..0000000000000000000000000000000000000000
--- a/spaces/helliun/beism/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: PersonalChatGPT
-emoji: 📈
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-duplicated_from: helliun/personalChatGPT
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/housexu123/bingo-2.0/src/pages/api/image.ts b/spaces/housexu123/bingo-2.0/src/pages/api/image.ts
deleted file mode 100644
index f35b101ebd43726ffeae7442b618e55c7f3cb3e0..0000000000000000000000000000000000000000
--- a/spaces/housexu123/bingo-2.0/src/pages/api/image.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { debug } from '@/lib/isomorphic'
-import { createHeaders } from '@/lib/utils'
-import { createImage } from '@/lib/bots/bing/utils'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  const { prompt, id } = req.query
-  if (!prompt) {
-    return res.json({
-      result: {
-        value: 'Image',
-        message: 'No Prompt'
-      }
-    })
-  }
-  try {
-    const headers = createHeaders(req.cookies)
-
-    debug('headers', headers)
-    const response = await createImage(String(prompt), String(id), headers)
-    res.writeHead(200, {
-      'Content-Type': 'text/plain; charset=UTF-8',
-    })
-    return res.end(response)
-  } catch (e) {
-    return res.json({
-      result: {
-        value: 'Error',
-        message: `${e}`
-      }
-    })
-  }
-}
diff --git a/spaces/hra/chatgpt-stock-news-snapshots/app.py b/spaces/hra/chatgpt-stock-news-snapshots/app.py
deleted file mode 100644
index bea9273808878cfa1b8116f3c43224d767f64655..0000000000000000000000000000000000000000
--- a/spaces/hra/chatgpt-stock-news-snapshots/app.py
+++ /dev/null
@@ -1,152 +0,0 @@
-from llama_index  import GPTSimpleVectorIndex, SimpleDirectoryReader, LLMPredictor
-from langchain.llms import OpenAIChat
-from llama_index import download_loader
-import gradio as gr
-import pandas as pd
-import openai
-
-import datetime
-from datetime import datetime, date, time, timedelta
-import os
-import regex
-import requests
-import json
-
-HRA_TOKEN=os.getenv("HRA_TOKEN")
-
-listofcategories=["Earnings Announcements", "Overall","Automotive","Energy","Healthcare","Retail","Technology"]
-
-headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
-url_hraprompts='https://us-central1-createinsightsproject.cloudfunctions.net/gethrahfprompts'
-
-data={"prompt_type":'chatgpt_stock_news_snapshot_sector',"hra_token":HRA_TOKEN}
-try:
-    r = requests.post(url_hraprompts, data=json.dumps(data), headers=headers)
-except requests.exceptions.ReadTimeout as e:
-    print(e)
-#print(r.content)
-
-
-sector_prompt_text=str(r.content, 'UTF-8').split('UNIQUE_SEPERATOR')
-print(sector_prompt_text)
-
-data={"prompt_type":'chatgpt_stock_news_snapshot_earnings',"hra_token":HRA_TOKEN}
-try:
-    r = requests.post(url_hraprompts, data=json.dumps(data), headers=headers)
-except requests.exceptions.ReadTimeout as e:
-    print(e)
-#print(r.content)
-
-
-earnings_prompt_text=str(r.content, 'UTF-8').split('UNIQUE_SEPERATOR')
-print(earnings_prompt_text)
-
-def getstuff(openapikey,category_selector):
-    dateforfilesave=datetime.today().strftime("%d-%m-%Y %I:%M%p")
-    print(category_selector)
-    print(dateforfilesave)
-    if openapikey=='':
-        return pd.DataFrame(["Please provide OpenAPI Key"],columns=['ERROR']),pd.DataFrame(["Please provide OpenAPI Key"],columns=['ERROR']),'Error: Please provide OpenAPI key','Error: Please provide OpenAPI key'
-    
-    os.environ['OPENAI_API_KEY'] = str(openapikey)
-    
-    RssReader = download_loader("RssReader")
-    reader = RssReader()
-    whichone=listofcategories[listofcategories.index(category_selector)]
-
-    querylist=sector_prompt_text
-    
-    if whichone=="Overall":
-        rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=100003114"
-    elif whichone=="Automotive":
-        rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=10000101"
-    elif whichone=="Retail":
-        rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=10000116"
-    elif whichone=="Technology":
-        rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=19854910"
-    elif whichone=="Healthcare":
-        rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=10000108"
-    elif whichone=="Energy":
-        rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=19836768"
-    elif whichone=="Media":
-        rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=10000110"
-    elif whichone=='Earnings Announcements':
-        rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=15839135"
-        querylist=earnings_prompt_text
-    else:
-        rssurl="https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=15839135" ###should not come here but using earnings url
-        querylist=earnings_prompt_text
-    
-    documents = reader.load_data([rssurl])
-    index = GPTSimpleVectorIndex(documents)
-    llm_predictor = LLMPredictor(llm=OpenAIChat(temperature=0, model_name="gpt-3.5-turbo"))
-
-    answerlist=[]
-    for i in range(len(querylist)):
-        print(i,"Query: ",querylist[i])
-        response = index.query(
-        querylist[i], 
-        llm_predictor=llm_predictor,
-        response_mode="tree_summarize",
-        similarity_top_k=int(len(documents)/3)
-        )
-        print(response.response)
-        if 'dataframe' in querylist[i]:
-            try:
-                pattern = regex.compile(r'\{(?:[^{}]|(?R))*\}')
-                jsonextract=pattern.findall(response.response)[0]
-                #print("json extract\n",jsonextract)
-                df_tmp=pd.read_json(jsonextract)
-                if len(df_tmp.columns)<=1:
-                    df=pd.DataFrame(df_tmp[df_tmp.columns[0]].tolist())
-                else:
-                    df=df_tmp
-            except:
-                df=pd.DataFrame()
-                df['message']=['Data insufficient to decipher']
-                df['action']=['try again in a few hours']
-            answerlist.append(df)
-        else:
-            answerlist.append(response.response)
-
-    print('Came to return statement')
-    return answerlist
-
-with gr.Blocks() as demo:
-    gr.Markdown("<h1><center>ChatGPT Stock News Snapshots</center></h1>")
-    gr.Markdown(
-        """What are the sectors with positive momentum? What are the macro trends? Which companies have momentum? Sector summaries and much more. \n\nThis is a demo & showcases ChatGPT integrated with real data. It shows how to get real-time data and marry it with ChatGPT capabilities. This demonstrates 'Chain of Thought' thinking using ChatGPT.\n\n4 snapshots are provided for illustration (trends, sector outlook, news summary email, macro trends email)\n\nNote: llama-index & gpt-3.5-turbo are used. The analysis takes roughly 120 secs & may not always be consistent. If ChatGPT API is overloaded you will get an error\n ![visitors](https://visitor-badge.glitch.me/badge?page_id=hra.chatgpt-stock-news-snapshots)"""
-        )
-    
-    with gr.Row() as row:
-        with gr.Column():
-            category_selector=gr.Dropdown(
-                listofcategories, label="Sector Options", info="Select the snapshot you want..."
-                )
-        with gr.Column():
-            textboxopenapi = gr.Textbox(placeholder="Enter OpenAPI Key...", lines=1,label='OpenAPI Key')
-        
-    with gr.Column():
-        btn = gr.Button("Generate \nSnapshot")
-        
-    with gr.Row() as row:
-        table1=gr.Dataframe(
-            #headers=["Item", "Cost"],
-            #datatype=["str", "str","str"],
-            label="Snapshot 1",
-        )
-    with gr.Row() as row:
-        table2=gr.Dataframe(
-            #headers=["Item", "Cost"],
-            #datatype=["str", "str","str"],
-            label="Snapshot 2",
-        )
-    with gr.Row() as row:
-        output1 = gr.Textbox(placeholder='', lines=4,label='Snapshot 3')
-    with gr.Row() as row:
-        output2 = gr.Textbox(placeholder='', lines=4,label='Snapshot 4')
-
-    btn.click(getstuff, inputs=[textboxopenapi,category_selector],outputs=[table1,table2,output1,output2])
-    
-    
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/huaiji3y/bingo-Public/src/components/chat-notification.tsx b/spaces/huaiji3y/bingo-Public/src/components/chat-notification.tsx
deleted file mode 100644
index 3474e522992c43a4d1d0eadcf205a9760d5b930b..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/components/chat-notification.tsx
+++ /dev/null
@@ -1,91 +0,0 @@
-import { useEffect } from 'react'
-import Image from 'next/image'
-
-import IconWarning from '@/assets/images/warning.svg'
-import { ChatError, ErrorCode, ChatMessageModel } from '@/lib/bots/bing/types'
-import { ExternalLink } from './external-link'
-import { useBing } from '@/lib/hooks/use-bing'
-
-export interface ChatNotificationProps extends Pick<ReturnType<typeof useBing>, 'bot'> {
-  message?: ChatMessageModel
-}
-
-function getAction(error: ChatError, reset: () => void) {
-  if (error.code === ErrorCode.THROTTLE_LIMIT) {
-    reset()
-    return (
-      <div>
-        你已达到每日最大发送消息次数，请<a href={`#dialog="settings"`}>更换账号</a>或隔一天后重试
-      </div>
-    )
-  }
-  if (error.code === ErrorCode.BING_IP_FORBIDDEN) {
-    return (
-      <ExternalLink href="https://github.com/weaigc/bingo/issues">
-        你的服务器或代理已被封禁，请更换服务器或使用代理重试
-      </ExternalLink>
-    )
-  }
-  if (error.code === ErrorCode.BING_TRY_LATER) {
-    return (
-      <ExternalLink href="/">
-        创建会话失败，请稍候重试
-      </ExternalLink>
-    )
-  }
-  if (error.code === ErrorCode.BING_FORBIDDEN) {
-    return (
-      <ExternalLink href="https://bing.com/new">
-        你的账号已在黑名单，请尝试更换账号及申请解封
-      </ExternalLink>
-    )
-  }
-  if (error.code === ErrorCode.CONVERSATION_LIMIT) {
-    return (
-      <div>
-        当前话题已中止，请点
-        <a href={`#dialog="reset"`}>重新开始</a>
-        开启新的对话
-      </div>
-    )
-  }
-  if (error.code === ErrorCode.BING_CAPTCHA) {
-    return (
-      <ExternalLink href="https://www.bing.com/turing/captcha/challenge">
-        点击通过人机验证
-      </ExternalLink>
-    )
-  }
-  if (error.code === ErrorCode.BING_UNAUTHORIZED) {
-    reset()
-    return (
-      <a href={`#dialog="settings"`}>没有获取到身份信息或身份信息失效，点此重新设置</a>
-    )
-  }
-  return error.message
-}
-
-export function ChatNotification({ message, bot }: ChatNotificationProps) {
-  useEffect(() => {
-    window.scrollBy(0, 2000)
-  }, [message])
-
-  if (!message?.error) return
-
-  return (
-    <div
-      className="notification-container"
-    >
-      <div className="bottom-notifications">
-        <div className="inline-type with-decorative-line">
-          <div className="text-container mt-1">
-            <div className="title inline-flex items-start">
-              <Image alt="error" src={IconWarning} width={20} className="mr-1 mt-1" />
-              {getAction(message.error, () => bot.resetConversation())}
-            </div>
-          </div>
-        </div>
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/huak95/personaGPT_custom/frontend/components/ChatPage.tsx b/spaces/huak95/personaGPT_custom/frontend/components/ChatPage.tsx
deleted file mode 100644
index e8872feb59dfb64082c188c0eaad5b7341598d6d..0000000000000000000000000000000000000000
--- a/spaces/huak95/personaGPT_custom/frontend/components/ChatPage.tsx
+++ /dev/null
@@ -1,148 +0,0 @@
-import axios, { AxiosError } from "axios";
-import emojiRegex from "emoji-regex";
-import { useMemo, useState } from "react";
-import Bubble from "./Bubble";
-import ChatInput from "./ChatInput";
-
-const SHOW_TYPING_AFTER = 2500; // ms
-const INITIAL_DIALOG_HISTORY = [[5303, 50256]];
-
-export interface Status {
-  status: 'ready' | 'loading' | 'error',
-  errorMessage?: string,
-}
-
-export interface Message {
-  timestamp: number,
-  from: string,
-  text: string,
-}
-
-export default function ChatPage() {
-  const [messages, setMessages] = useState<Array<Message>>([]);
-  const [inputText, setInputText] = useState<string>('');
-  const [inputHistory, setInputHistory] = useState<any>(INITIAL_DIALOG_HISTORY);
-  const [status, setStatus] = useState<Status>({ status: 'ready' });
-  const [typingCheckHook, checkTyping] = useState<number>(0);
-  const isTyping: boolean = useMemo((): boolean => {
-    if (messages.length === 0) return false;
-    const { from, timestamp } = messages.at(-1) as Message;
-    const isShow = status.status === 'loading'
-      && from === 'me'
-      && Date.now() - timestamp > SHOW_TYPING_AFTER;
-    if (isShow) scrollToBottom();
-    return isShow;
-  }, [status, messages, typingCheckHook]);
-
-  const addMessage = (msg: Message) => {
-    setMessages(lastMessages => [...lastMessages, msg]);
-    scrollToBottom();
-  };
-
-  const onSend = async () => {
-    try {
-      const cleanedText = cleanText(inputText);
-      addMessage({
-        timestamp: Date.now(),
-        from: 'me',
-        text: inputText,
-      });
-      setInputText('');
-      setTimeout(() => checkTyping(Date.now()), SHOW_TYPING_AFTER + 100);
-      setStatus({ status: 'loading' });
-      const res = await getResponse(cleanedText, inputHistory);
-      if (res.error) {
-        setStatus({ status: 'error', errorMessage: res.message });
-      } else {
-        setStatus({ status: 'ready' });
-        setInputHistory(res.history);
-        addMessage({
-          timestamp: Date.now(),
-          from: 'cat',
-          text: res.text,
-        });
-      }
-    } catch (e) {
-      const err = e as Error;
-      setStatus({ status: 'error', errorMessage: err.message });
-    }
-  };
-
-  return <>
-    <div className="container bootstrap snippets">
-      <div className="">
-        <div className="panel" id="chat">
-          <div className="panel-heading">
-            <h3 className="panel-title">
-              🐱 Chat
-            </h3>
-          </div>
-          <div className="panel-body">
-            <div className="chats" id="chat-messages-container">
-
-              {messages.length === 0 && <p className="guide">
-                Say something to the cat 🐱<br />
-                <small>(For now, the cat can only understand <b>english</b>)</small>
-              </p>}
-
-              {messages.map(({ timestamp, from, text }) =>
-                <Bubble key={timestamp} left={from === 'cat'} text={text} />
-              )}
-
-              {isTyping && <img src="/typing_dots.gif" alt="typing" className="typing-dots" />}
-
-            </div>
-          </div>
-          <div className="panel-footer">
-            <ChatInput text={inputText}
-              onTextChanged={setInputText} onSend={onSend}
-              disabled={status.status === 'loading'}
-              errorText={status.status === 'error' ? status.errorMessage : ''}
-            />
-          </div>
-        </div>
-      </div>
-    </div>
-  </>;
-}
-
-function scrollToBottom() {
-  setTimeout(() => {
-    const elem = document.getElementById('chat-messages-container') as any;
-    elem.scrollTop = elem?.scrollHeight;
-  }, 20);
-}
-
-async function getResponse(userMessageText: string, dialogHistory: any): Promise<any> {
-  try {
-    const BACKEND_URL = window.location.href.match(/localhost/)
-      ? 'https://ngxson-poet-cat.hf.space/ask' : '/ask';
-    const { data } = await axios.post(BACKEND_URL, {
-      text_input: userMessageText,
-      dialog_history: dialogHistory,
-    }, {
-      timeout: 60000,
-    });
-    return {
-      text: data.bot_response_text,
-      history: data.dialog_history,
-    };
-  } catch (err) {
-    console.error(err);
-    const errors = err as Error | AxiosError;
-    return { error: true, message: errors.message };
-  }
-}
-
-const VIETNAMESE_REGEX = /["ảàáãạăằẳẵắặâầẩẫấậẢÃÀÁẠĂẰẲẴẮẶÂẦẨẪẤẬđĐẻẽẹêềểễếệẺẼẸÊỀỂỄẾỆìỉĩíịÌỈĨÍỊòỏõóọôồổỗốộơờởỡớợÒỎÕÓỌÔỒỔỖỐỘƠỜỞỠỚỢủũúụưừửữứựỦŨÚỤƯỪỬỮỨỰỳỷỹýỵỲỶỸÝỴ"]+/g;
-const EMOJI_REGEX = emojiRegex();
-function cleanText(text: string): string {
-  if (text.match(VIETNAMESE_REGEX)) {
-    throw new Error('Sorry, for now, the cat can only understand english');
-  } else {
-    return text
-      .replace(EMOJI_REGEX, ' :) ')
-      .replace(/\s{2,100}/g, ' ')
-      .trim();
-  }
-}
diff --git a/spaces/huggan/crypto-gan/README.md b/spaces/huggan/crypto-gan/README.md
deleted file mode 100644
index 46766b779843e8964e2d22e072e0cef0742d543d..0000000000000000000000000000000000000000
--- a/spaces/huggan/crypto-gan/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Crypto Gan
-emoji: 👀
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.8.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/hzrr/dal_audio_inference/monotonic_align/__init__.py b/spaces/hzrr/dal_audio_inference/monotonic_align/__init__.py
deleted file mode 100644
index 3d7009c40fea3a98168e3e3bc9ae061e91327422..0000000000000000000000000000000000000000
--- a/spaces/hzrr/dal_audio_inference/monotonic_align/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import numpy as np
-import torch
-from .monotonic_align.core import maximum_path_c
-
-
-def maximum_path(neg_cent, mask):
-  """ Cython optimized version.
-  neg_cent: [b, t_t, t_s]
-  mask: [b, t_t, t_s]
-  """
-  device = neg_cent.device
-  dtype = neg_cent.dtype
-  neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
-  path = np.zeros(neg_cent.shape, dtype=np.int32)
-
-  t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
-  t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
-  maximum_path_c(path, neg_cent, t_t_max, t_s_max)
-  return torch.from_numpy(path).to(device=device, dtype=dtype)
diff --git a/spaces/imperialwool/funapi/routes/helpers.py b/spaces/imperialwool/funapi/routes/helpers.py
deleted file mode 100644
index 1c9dbfe0e8bc069c02732acbe43273107999423c..0000000000000000000000000000000000000000
--- a/spaces/imperialwool/funapi/routes/helpers.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import os
-import json
-import random
-import string
-
-# Deleting audio
-def deleteAudio(path: str, waitInSeconds: int = 0):
-    config = configFile()
-    os.system("rm {}/{}".format(config['static-path'], path))
-
-# Config file reading
-def configFile():
-    with open("/app/routes/config.json", "r") as file:
-        config = json.loads(file.read())
-    return config
-
-# Hook for yt-dlp
-def thisIsHook(d):
-    try:
-        if d['total_bytes'] > 52428800:
-            print("\nFILE IS BIG, ABORT!!!\n")
-            raise Exception(f"Too long file (recieved {d['total_bytes']/1048576}MB, max is 50MB)")
-    except: pass
-
-# Get variable from request
-def getFromRequest(request, thing: str):
-    return request.json.get(thing) if request.is_json else request.form.get(thing)
-
-def randString(len: int = 16):
-    return ''.join(random.SystemRandom().choice(string.ascii_uppercase + string.ascii_lowercase + string.digits) for _ in range(len))
\ No newline at end of file
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/3D Equalizer 4 Version 3B2 Win X64.md b/spaces/inplisQlawa/anything-midjourney-v4-1/3D Equalizer 4 Version 3B2 Win X64.md
deleted file mode 100644
index 2feebfaba340f88fdd53957a2fea842ee1941c5f..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/3D Equalizer 4 Version 3B2 Win X64.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-<br />
-<p>MarketResearch, Inc. is the independent provider of unbiased market research reports and data covering over 2000 market segments in over 100 industries. We are the world’s largest market research firm offering marketing and advertising professionals a complete and comprehensive source of premium market research reports, subscription and one-month free trials. For more information on the MarketResearch, Inc. portfolio of more than 2000 market research industry reports please visit our website at http://www.marketresearchreports.com or call (866) 620-6581. https://www.marketresearchreports.com/cryp-series-crypto-trading-indices-by-marketresearch-inc-2018.htmlhttps://www.cryp.to/assets/MarketResearchReports/The-Market-for-Crypto-Currency-Trading-Indices.html#downloadhttps://www.marketresearchreports.com/cryp-series-crypto-trading-indices-by-marketresearch-inc-2018.htmlhttp://www.cryp.to/research/2019/01/3d-equalizer-4000-v2-02.htmlhttps://www.marketresearchreports.biz/similar/1/https://www.cryp.to/assets/MarketResearchReports/The-Market-for-Crypto-Currency-Trading-Indices.html#downloadhttps://www.marketresearchreports.biz/similar/1/https://www.cryp.to/research/2019/01/3d-equalizer-4000-v2-02.htmlhttp://www.cryp.to/research/2019/01/3d-equalizer-4000-v2-02.htmlhttps://www.cryp.to/assets/MarketResearchReports/The-Market-for-Crypto-Currency-Trading-Indices.html#downloadhttps://www.marketresearchreports.biz/similar/1/https://www.cryp.to/research/2019/01/3d-equalizer-4000-v2-02.htmlhttp://www.cryp.to/research/2019/01/3d-equalizer-4000-v2-02.htmlhttps://www.cryp.to/assets/MarketResearchReports/The-Market-for-Crypto-Currency-Trading-Indices.html#downloadhttps://www.marketresearchreports.biz/similar/1/https://www.cryp.to/research/2019/01/3d-equalizer-4000-v2-02.htmlhttp://www.cryp.to/research/2019/01/3d-equalizer-4000-v2-02.htmlhttps://www.cryp.</p>
-<h2>3D Equalizer 4 Version 3B2 Win X64</h2><br /><p><b><b>DOWNLOAD</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://urlin.us/2uExkY">https://urlin.us/2uExkY</a></b></p><br /><br /> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Do Aankhen Barah Haath 1 Full Movie Free Download.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Do Aankhen Barah Haath 1 Full Movie Free Download.md
deleted file mode 100644
index 8b0031c11b7fe35b2874548765d08102a95a6697..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Do Aankhen Barah Haath 1 Full Movie Free Download.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-<h2>Do Aankhen Barah Haath 1 Full Movie Free Download</h2><br /><p><b><b>Download File</b> &#10022; <a href="https://urlin.us/2uEvvA">https://urlin.us/2uEvvA</a></b></p><br /><br />
-<br />
-Director: Kirti Kumar Producer: Shashi Prabhu Music: Bappi Lahiri Main cast: Govinda, Ishrat Ali, Sadashiv. ‘‘Raja Narayan’ is a story about the adventures of a young man named Raja Narayan who is searching for the truth and his place in life.
-The plot of the film is based on a real story about how a young man lost his family when he was only 12 years old.
-This story happened to a young man who lived in India.
-Raja Narayan, like the hero of the film, was an orphan.
-The young man lived in a small room in which he huddled on a wooden framed bed that was attached to the wall. 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Facegen Modeller 3.5 Full.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Facegen Modeller 3.5 Full.md
deleted file mode 100644
index 5d8a92cb7ac0451e9aa0dc42a0e0d139c0781bcb..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Facegen Modeller 3.5 Full.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>facegen modeller 3.5 full</h2><br /><p><b><b>Download</b> &#9734;&#9734;&#9734;&#9734;&#9734; <a href="https://urlin.us/2uEvqA">https://urlin.us/2uEvqA</a></b></p><br /><br />
-
-FaceGen Modeller uses OpenGL 1.1 graphics acceleration. Viewport controls. Detailed texture. Adds fine details to the statistically reconstructed color map. Using OpenGL allows you to create graphic effects that are not available in Direct3D, but Direct3D is easier to use. In this section, we will look at the main features of the Geany program, as well as some of the non-standard features of the program. The following program can be cited as an illustration of the main features of the program. The program is similar to the TextureFetcher program. It allows you to search for texture maps and load them into graphics programs. 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/seed.py b/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/seed.py
deleted file mode 100644
index 49b8f704355f93c3977f72bb1b7751b5b138a525..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/seed.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import random
-
-def next_seed(args):
-    if args.seed_behavior == 'iter':
-        if args.seed_internal % args.seed_iter_N == 0:
-            args.seed += 1
-        args.seed_internal += 1
-    elif args.seed_behavior == 'ladder':
-        if args.seed_internal == 0:
-            args.seed += 2
-            args.seed_internal = 1
-        else:
-            args.seed -= 1
-            args.seed_internal = 0
-    elif args.seed_behavior == 'alternate':
-        if args.seed_internal == 0:
-            args.seed += 1
-            args.seed_internal = 1
-        else:
-            args.seed -= 1
-            args.seed_internal = 0
-    elif args.seed_behavior == 'fixed':
-        pass # always keep seed the same
-    else:
-        args.seed = random.randint(0, 2**32 - 1)
-    return args.seed
\ No newline at end of file
diff --git a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/midas/transforms.py b/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/midas/transforms.py
deleted file mode 100644
index 350cbc11662633ad7f8968eb10be2e7de6e384e9..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/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/james-oldfield/PandA/networks/stylegan3/gui_utils/imgui_window.py b/spaces/james-oldfield/PandA/networks/stylegan3/gui_utils/imgui_window.py
deleted file mode 100644
index 30d539a1382def526050c83978d1118348ac77ad..0000000000000000000000000000000000000000
--- a/spaces/james-oldfield/PandA/networks/stylegan3/gui_utils/imgui_window.py
+++ /dev/null
@@ -1,103 +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 os
-import imgui
-import imgui.integrations.glfw
-
-from . import glfw_window
-from . import imgui_utils
-from . import text_utils
-
-#----------------------------------------------------------------------------
-
-class ImguiWindow(glfw_window.GlfwWindow):
-    def __init__(self, *, title='ImguiWindow', font=None, font_sizes=range(14,24), **glfw_kwargs):
-        if font is None:
-            font = text_utils.get_default_font()
-        font_sizes = {int(size) for size in font_sizes}
-        super().__init__(title=title, **glfw_kwargs)
-
-        # Init fields.
-        self._imgui_context  = None
-        self._imgui_renderer = None
-        self._imgui_fonts    = None
-        self._cur_font_size  = max(font_sizes)
-
-        # Delete leftover imgui.ini to avoid unexpected behavior.
-        if os.path.isfile('imgui.ini'):
-            os.remove('imgui.ini')
-
-        # Init ImGui.
-        self._imgui_context = imgui.create_context()
-        self._imgui_renderer = _GlfwRenderer(self._glfw_window)
-        self._attach_glfw_callbacks()
-        imgui.get_io().ini_saving_rate = 0 # Disable creating imgui.ini at runtime.
-        imgui.get_io().mouse_drag_threshold = 0 # Improve behavior with imgui_utils.drag_custom().
-        self._imgui_fonts = {size: imgui.get_io().fonts.add_font_from_file_ttf(font, size) for size in font_sizes}
-        self._imgui_renderer.refresh_font_texture()
-
-    def close(self):
-        self.make_context_current()
-        self._imgui_fonts = None
-        if self._imgui_renderer is not None:
-            self._imgui_renderer.shutdown()
-            self._imgui_renderer = None
-        if self._imgui_context is not None:
-            #imgui.destroy_context(self._imgui_context) # Commented out to avoid creating imgui.ini at the end.
-            self._imgui_context = None
-        super().close()
-
-    def _glfw_key_callback(self, *args):
-        super()._glfw_key_callback(*args)
-        self._imgui_renderer.keyboard_callback(*args)
-
-    @property
-    def font_size(self):
-        return self._cur_font_size
-
-    @property
-    def spacing(self):
-        return round(self._cur_font_size * 0.4)
-
-    def set_font_size(self, target): # Applied on next frame.
-        self._cur_font_size = min((abs(key - target), key) for key in self._imgui_fonts.keys())[1]
-
-    def begin_frame(self):
-        # Begin glfw frame.
-        super().begin_frame()
-
-        # Process imgui events.
-        self._imgui_renderer.mouse_wheel_multiplier = self._cur_font_size / 10
-        if self.content_width > 0 and self.content_height > 0:
-            self._imgui_renderer.process_inputs()
-
-        # Begin imgui frame.
-        imgui.new_frame()
-        imgui.push_font(self._imgui_fonts[self._cur_font_size])
-        imgui_utils.set_default_style(spacing=self.spacing, indent=self.font_size, scrollbar=self.font_size+4)
-
-    def end_frame(self):
-        imgui.pop_font()
-        imgui.render()
-        imgui.end_frame()
-        self._imgui_renderer.render(imgui.get_draw_data())
-        super().end_frame()
-
-#----------------------------------------------------------------------------
-# Wrapper class for GlfwRenderer to fix a mouse wheel bug on Linux.
-
-class _GlfwRenderer(imgui.integrations.glfw.GlfwRenderer):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.mouse_wheel_multiplier = 1
-
-    def scroll_callback(self, window, x_offset, y_offset):
-        self.io.mouse_wheel += y_offset * self.mouse_wheel_multiplier
-
-#----------------------------------------------------------------------------
diff --git a/spaces/jbilcke-hf/ai-clip-factory/src/components/ui/accordion.tsx b/spaces/jbilcke-hf/ai-clip-factory/src/components/ui/accordion.tsx
deleted file mode 100644
index 937620af27e5d8ef577f0baca229a9b753ebd017..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-clip-factory/src/components/ui/accordion.tsx
+++ /dev/null
@@ -1,60 +0,0 @@
-"use client"
-
-import * as React from "react"
-import * as AccordionPrimitive from "@radix-ui/react-accordion"
-import { ChevronDown } from "lucide-react"
-
-import { cn } from "@/lib/utils"
-
-const Accordion = AccordionPrimitive.Root
-
-const AccordionItem = React.forwardRef<
-  React.ElementRef<typeof AccordionPrimitive.Item>,
-  React.ComponentPropsWithoutRef<typeof AccordionPrimitive.Item>
->(({ className, ...props }, ref) => (
-  <AccordionPrimitive.Item
-    ref={ref}
-    className={cn("border-b", className)}
-    {...props}
-  />
-))
-AccordionItem.displayName = "AccordionItem"
-
-const AccordionTrigger = React.forwardRef<
-  React.ElementRef<typeof AccordionPrimitive.Trigger>,
-  React.ComponentPropsWithoutRef<typeof AccordionPrimitive.Trigger>
->(({ className, children, ...props }, ref) => (
-  <AccordionPrimitive.Header className="flex">
-    <AccordionPrimitive.Trigger
-      ref={ref}
-      className={cn(
-        "flex flex-1 items-center justify-between py-4 font-medium transition-all hover:underline [&[data-state=open]>svg]:rotate-180",
-        className
-      )}
-      {...props}
-    >
-      {children}
-      <ChevronDown className="h-4 w-4 shrink-0 transition-transform duration-200" />
-    </AccordionPrimitive.Trigger>
-  </AccordionPrimitive.Header>
-))
-AccordionTrigger.displayName = AccordionPrimitive.Trigger.displayName
-
-const AccordionContent = React.forwardRef<
-  React.ElementRef<typeof AccordionPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof AccordionPrimitive.Content>
->(({ className, children, ...props }, ref) => (
-  <AccordionPrimitive.Content
-    ref={ref}
-    className={cn(
-      "overflow-hidden text-sm transition-all data-[state=closed]:animate-accordion-up data-[state=open]:animate-accordion-down",
-      className
-    )}
-    {...props}
-  >
-    <div className="pb-4 pt-0">{children}</div>
-  </AccordionPrimitive.Content>
-))
-AccordionContent.displayName = AccordionPrimitive.Content.displayName
-
-export { Accordion, AccordionItem, AccordionTrigger, AccordionContent }
diff --git a/spaces/jbilcke-hf/ai-comic-factory/src/app/interface/maintenance/index.tsx b/spaces/jbilcke-hf/ai-comic-factory/src/app/interface/maintenance/index.tsx
deleted file mode 100644
index 459bc1c5b6cdde1641919751a5e202706970f4a9..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-comic-factory/src/app/interface/maintenance/index.tsx
+++ /dev/null
@@ -1,20 +0,0 @@
-import { fonts } from "@/lib/fonts"
-import { cn } from "@/lib/utils"
-
-export function Maintenance() {
-  return (
-    <div className="z-20 fixed inset-0 w-screen h-screen bg-white text-stone-800 flex flex-col items-center justify-center">
-    <div className={cn(
-      fonts.actionman.className,
-      "text-center"
-    )}>
-      <p className="text-4xl">🚧 Maintenance in progress 🚧</p>
-      <p className="text-3xl mt-12 mb-8">See the <a
-        href="https://huggingface.co/spaces/jbilcke-hf/ai-comic-factory/discussions/339"
-        className="underline text-yellow-500"
-        >announcement here</a> <img src="/quick-and-dirty-emoji.png" className="inline w-10 h-10"></img></p>
-      <p className="text-2xl">This shouldn&apos;t last long, so stay tuned!</p>
-    </div>
-  </div>
-  )
-}
\ No newline at end of file
diff --git a/spaces/jennysun/jwsun-multisubject-render-model/dataset/utils.py b/spaces/jennysun/jwsun-multisubject-render-model/dataset/utils.py
deleted file mode 100644
index 0ceafd04bc6860eaccfe5a480fb452f00792dac4..0000000000000000000000000000000000000000
--- a/spaces/jennysun/jwsun-multisubject-render-model/dataset/utils.py
+++ /dev/null
@@ -1,116 +0,0 @@
-#!/usr/bin/python
-#
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import PIL
-import torch
-import torchvision.transforms as T
-
-
-IMAGENET_MEAN = [0.485, 0.456, 0.406]
-IMAGENET_STD = [0.229, 0.224, 0.225]
-
-INV_IMAGENET_MEAN = [-m for m in IMAGENET_MEAN]
-INV_IMAGENET_STD = [1.0 / s for s in IMAGENET_STD]
-
-
-def imagenet_preprocess():
-  return T.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
-
-
-def rescale(x):
-  lo, hi = x.min(), x.max()
-  return x.sub(lo).div(hi - lo)
-
-
-def imagenet_deprocess(rescale_image=True):
-  transforms = [
-    T.Normalize(mean=[0, 0, 0], std=INV_IMAGENET_STD),
-    T.Normalize(mean=INV_IMAGENET_MEAN, std=[1.0, 1.0, 1.0]),
-  ]
-  if rescale_image:
-    transforms.append(rescale)
-  return T.Compose(transforms)
-
-
-def imagenet_deprocess_batch(imgs, rescale=True):
-  """
-  Input:
-  - imgs: FloatTensor of shape (N, C, H, W) giving preprocessed images
-
-  Output:
-  - imgs_de: ByteTensor of shape (N, C, H, W) giving deprocessed images
-    in the range [0, 255]
-  """
-  if isinstance(imgs, torch.autograd.Variable):
-    imgs = imgs.data
-  imgs = imgs.cpu().clone()
-  deprocess_fn = imagenet_deprocess(rescale_image=rescale)
-  imgs_de = []
-  for i in range(imgs.size(0)):
-    img_de = deprocess_fn(imgs[i])[None]
-    img_de = img_de.mul(255).clamp(0, 255).byte()
-    imgs_de.append(img_de)
-  imgs_de = torch.cat(imgs_de, dim=0)
-  return imgs_de
-
-
-class Resize(object):
-  def __init__(self, size, interp=PIL.Image.BILINEAR):
-    if isinstance(size, tuple):
-      H, W = size
-      self.size = (W, H)
-    else:
-      self.size = (size, size)
-    self.interp = interp
-
-  def __call__(self, img):
-    return img.resize(self.size, self.interp)
-
-
-def unpack_var(v):
-  if isinstance(v, torch.autograd.Variable):
-    return v.data
-  return v
-
-
-def split_graph_batch(triples, obj_data, obj_to_img, triple_to_img):
-  triples = unpack_var(triples)
-  obj_data = [unpack_var(o) for o in obj_data]
-  obj_to_img = unpack_var(obj_to_img)
-  triple_to_img = unpack_var(triple_to_img)
-
-  triples_out = []
-  obj_data_out = [[] for _ in obj_data]
-  obj_offset = 0
-  N = obj_to_img.max() + 1
-  for i in range(N):
-    o_idxs = (obj_to_img == i).nonzero().view(-1)
-    t_idxs = (triple_to_img == i).nonzero().view(-1)
-
-    cur_triples = triples[t_idxs].clone()
-    cur_triples[:, 0] -= obj_offset
-    cur_triples[:, 2] -= obj_offset
-    triples_out.append(cur_triples)
-
-    for j, o_data in enumerate(obj_data):
-      cur_o_data = None
-      if o_data is not None:
-        cur_o_data = o_data[o_idxs]
-      obj_data_out[j].append(cur_o_data)
-
-    obj_offset += o_idxs.size(0)
-
-  return triples_out, obj_data_out
diff --git a/spaces/jiedong-yang/Speech-Summarization-with-Whisper/utils.py b/spaces/jiedong-yang/Speech-Summarization-with-Whisper/utils.py
deleted file mode 100644
index d5c4ce5756df891f3d538a5f347437466c89df69..0000000000000000000000000000000000000000
--- a/spaces/jiedong-yang/Speech-Summarization-with-Whisper/utils.py
+++ /dev/null
@@ -1,21 +0,0 @@
-demo_css = """
-    .footer {
-                margin-bottom: 40px;
-                margin-top: 45px;
-                text-align: center;
-                border-bottom: 1px solid #e5e5e5;
-            }
-"""
-
-footer_html = """
-<div class="footer">
-        <p>Whisper by <a href="https://github.com/openai/whisper" 
-        style="text-decoration: underline;" 
-        target="_blank">OpenAI</a> - BART by <a href="https://huggingface.co/facebook/bart-large-cnn" 
-        style="text-decoration: underline;" 
-        target="_blank">Facebook</a> - Conformer by <a href="https://github.com/espnet/espnet_model_zoo" 
-        style="text-decoration: underline;" 
-        target="_blank">ESPNet</a>
-        </p>
-</div>
-"""
\ No newline at end of file
diff --git a/spaces/jiejiejie0420/bingo/src/components/ui/textarea.tsx b/spaces/jiejiejie0420/bingo/src/components/ui/textarea.tsx
deleted file mode 100644
index e25af722c7a5dc1121a9ab58d6716952f9f76081..0000000000000000000000000000000000000000
--- a/spaces/jiejiejie0420/bingo/src/components/ui/textarea.tsx
+++ /dev/null
@@ -1,24 +0,0 @@
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-
-export interface TextareaProps
-  extends React.TextareaHTMLAttributes<HTMLTextAreaElement> {}
-
-const Textarea = React.forwardRef<HTMLTextAreaElement, TextareaProps>(
-  ({ className, ...props }, ref) => {
-    return (
-      <textarea
-        className={cn(
-          'flex min-h-[80px] w-full rounded-md border border-input bg-transparent px-3 py-2 text-sm ring-offset-background placeholder:text-muted-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50',
-          className
-        )}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Textarea.displayName = 'Textarea'
-
-export { Textarea }
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Util/py3compat.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Util/py3compat.py
deleted file mode 100644
index 087550d4b1dc4d1b2dda9f86c60f3f18d7ffeeb1..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/Util/py3compat.py
+++ /dev/null
@@ -1,185 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-#  Util/py3compat.py : Compatibility code for handling Py3k / Python 2.x
-#
-# Written in 2010 by Thorsten Behrens
-#
-# ===================================================================
-# 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.
-# ===================================================================
-
-"""Compatibility code for handling string/bytes changes from Python 2.x to Py3k
-
-In Python 2.x, strings (of type ''str'') contain binary data, including encoded
-Unicode text (e.g. UTF-8).  The separate type ''unicode'' holds Unicode text.
-Unicode literals are specified via the u'...' prefix.  Indexing or slicing
-either type always produces a string of the same type as the original.
-Data read from a file is always of '''str'' type.
-
-In Python 3.x, strings (type ''str'') may only contain Unicode text. The u'...'
-prefix and the ''unicode'' type are now redundant.  A new type (called
-''bytes'') has to be used for binary data (including any particular
-''encoding'' of a string).  The b'...' prefix allows one to specify a binary
-literal.  Indexing or slicing a string produces another string.  Slicing a byte
-string produces another byte string, but the indexing operation produces an
-integer.  Data read from a file is of '''str'' type if the file was opened in
-text mode, or of ''bytes'' type otherwise.
-
-Since PyCrypto aims at supporting both Python 2.x and 3.x, the following helper
-functions are used to keep the rest of the library as independent as possible
-from the actual Python version.
-
-In general, the code should always deal with binary strings, and use integers
-instead of 1-byte character strings.
-
-b(s)
-    Take a text string literal (with no prefix or with u'...' prefix) and
-    make a byte string.
-bchr(c)
-    Take an integer and make a 1-character byte string.
-bord(c)
-    Take the result of indexing on a byte string and make an integer.
-tobytes(s)
-    Take a text string, a byte string, or a sequence of character taken from
-    a byte string and make a byte string.
-"""
-
-import sys
-import abc
-
-
-if sys.version_info[0] == 2:
-    def b(s):
-        return s
-    def bchr(s):
-        return chr(s)
-    def bstr(s):
-        return str(s)
-    def bord(s):
-        return ord(s)
-    def tobytes(s, encoding="latin-1"):
-        if isinstance(s, unicode):
-            return s.encode(encoding)
-        elif isinstance(s, str):
-            return s
-        elif isinstance(s, bytearray):
-            return bytes(s)
-        elif isinstance(s, memoryview):
-            return s.tobytes()
-        else:
-            return ''.join(s)
-    def tostr(bs):
-        return bs
-    def byte_string(s):
-        return isinstance(s, str)
-
-    # In Python 2, a memoryview does not support concatenation
-    def concat_buffers(a, b):
-        if isinstance(a, memoryview):
-            a = a.tobytes()
-        if isinstance(b, memoryview):
-            b = b.tobytes()
-        return a + b
-
-    from StringIO import StringIO
-    BytesIO = StringIO
-
-    from sys import maxint
-
-    iter_range = xrange
-
-    def is_native_int(x):
-        return isinstance(x, (int, long))
-
-    def is_string(x):
-        return isinstance(x, basestring)
-
-    def is_bytes(x):
-        return isinstance(x, str) or \
-                isinstance(x, bytearray) or \
-                isinstance(x, memoryview)
-
-    ABC = abc.ABCMeta('ABC', (object,), {'__slots__': ()})
-
-    FileNotFoundError = IOError
-
-else:
-    def b(s):
-       return s.encode("latin-1") # utf-8 would cause some side-effects we don't want
-    def bchr(s):
-        return bytes([s])
-    def bstr(s):
-        if isinstance(s,str):
-            return bytes(s,"latin-1")
-        else:
-            return bytes(s)
-    def bord(s):
-        return s
-    def tobytes(s, encoding="latin-1"):
-        if isinstance(s, bytes):
-            return s
-        elif isinstance(s, bytearray):
-            return bytes(s)
-        elif isinstance(s,str):
-            return s.encode(encoding)
-        elif isinstance(s, memoryview):
-            return s.tobytes()
-        else:
-            return bytes([s])
-    def tostr(bs):
-        return bs.decode("latin-1")
-    def byte_string(s):
-        return isinstance(s, bytes)
-
-    def concat_buffers(a, b):
-        return a + b
-
-    from io import BytesIO
-    from io import StringIO
-    from sys import maxsize as maxint
-
-    iter_range = range
-
-    def is_native_int(x):
-        return isinstance(x, int)
-
-    def is_string(x):
-        return isinstance(x, str)
-
-    def is_bytes(x):
-        return isinstance(x, bytes) or \
-                isinstance(x, bytearray) or \
-                isinstance(x, memoryview)
-
-    from abc import ABC
-
-    FileNotFoundError = FileNotFoundError
-
-
-def _copy_bytes(start, end, seq):
-    """Return an immutable copy of a sequence (byte string, byte array, memoryview)
-    in a certain interval [start:seq]"""
-
-    if isinstance(seq, memoryview):
-        return seq[start:end].tobytes()
-    elif isinstance(seq, bytearray):
-        return bytes(seq[start:end])
-    else:
-        return seq[start:end]
-
-del sys
-del abc
diff --git a/spaces/julien-c/sveltekit-demo/src/routes/todos/index.json.ts b/spaces/julien-c/sveltekit-demo/src/routes/todos/index.json.ts
deleted file mode 100644
index 57d68a91dc2fc1f67281002a8ad73c4530f045ee..0000000000000000000000000000000000000000
--- a/spaces/julien-c/sveltekit-demo/src/routes/todos/index.json.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-import { api } from './_api';
-import type { RequestHandler } from '@sveltejs/kit';
-import type { Locals } from '$lib/types';
-
-// GET /todos.json
-export const get: RequestHandler<Locals> = async (request) => {
-	// request.locals.userid comes from src/hooks.js
-	const response = await api(request, `todos/${request.locals.userid}`);
-
-	if (response.status === 404) {
-		// user hasn't created a todo list.
-		// start with an empty array
-		return { body: [] };
-	}
-
-	return response;
-};
-
-// POST /todos.json
-export const post: RequestHandler<Locals, FormData> = async (request) => {
-	const response = await api(request, `todos/${request.locals.userid}`, {
-		// because index.svelte posts a FormData object,
-		// request.body is _also_ a (readonly) FormData
-		// object, which allows us to get form data
-		// with the `body.get(key)` method
-		text: request.body.get('text')
-	});
-
-	return response;
-};
diff --git a/spaces/justest/gpt4free/g4f/.v1/testing/gptworldai_test.py b/spaces/justest/gpt4free/g4f/.v1/testing/gptworldai_test.py
deleted file mode 100644
index 3dfb32ce17b645e21991d07124421b3dc11cbfb1..0000000000000000000000000000000000000000
--- a/spaces/justest/gpt4free/g4f/.v1/testing/gptworldai_test.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import gptworldAi
-
-# single completion
-for chunk in gptworldAi.Completion.create("你是谁", "127.0.0.1:7890"):
-    print(chunk, end="", flush=True)
-print()
-
-# chat completion
-message = []
-while True:
-    prompt = input("请输入问题：")
-    message.append({"role": "user", "content": prompt})
-    text = ""
-    for chunk in gptworldAi.ChatCompletion.create(message, '127.0.0.1:7890'):
-        text = text + chunk
-        print(chunk, end="", flush=True)
-    print()
-    message.append({"role": "assistant", "content": text})
diff --git a/spaces/juuxn/SimpleRVC/infer_pack/attentions.py b/spaces/juuxn/SimpleRVC/infer_pack/attentions.py
deleted file mode 100644
index 09aef8621e1bcd153526d56b6990c73f9fc90d78..0000000000000000000000000000000000000000
--- a/spaces/juuxn/SimpleRVC/infer_pack/attentions.py
+++ /dev/null
@@ -1,413 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from infer_pack import commons
-from infer_pack.modules import LayerNorm
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=10,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    window_size=window_size,
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        proximal_bias=False,
-        proximal_init=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-
-        self.drop = nn.Dropout(p_dropout)
-        self.self_attn_layers = nn.ModuleList()
-        self.norm_layers_0 = nn.ModuleList()
-        self.encdec_attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.self_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    proximal_bias=proximal_bias,
-                    proximal_init=proximal_init,
-                )
-            )
-            self.norm_layers_0.append(LayerNorm(hidden_channels))
-            self.encdec_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                    causal=True,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, h, h_mask):
-        """
-        x: decoder input
-        h: encoder output
-        """
-        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
-            device=x.device, dtype=x.dtype
-        )
-        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.self_attn_layers[i](x, x, self_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_0[i](x + y)
-
-            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        n_heads,
-        p_dropout=0.0,
-        window_size=None,
-        heads_share=True,
-        block_length=None,
-        proximal_bias=False,
-        proximal_init=False,
-    ):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels**-0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-        if proximal_init:
-            with torch.no_grad():
-                self.conv_k.weight.copy_(self.conv_q.weight)
-                self.conv_k.bias.copy_(self.conv_q.bias)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(
-                query / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-        causal=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-
-        if causal:
-            self.padding = self._causal_padding
-        else:
-            self.padding = self._same_padding
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(self.padding(x * x_mask))
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(self.padding(x * x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
diff --git a/spaces/karolmajek/YOLOR/scripts/get_coco.sh b/spaces/karolmajek/YOLOR/scripts/get_coco.sh
deleted file mode 100644
index cb3c14a8a53f34c31300a520f50e243d69679f66..0000000000000000000000000000000000000000
--- a/spaces/karolmajek/YOLOR/scripts/get_coco.sh
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/bin/bash
-# Script credit to https://github.com/ultralytics/yolov5
-# COCO 2017 dataset http://cocodataset.org
-# Download command: bash scripts/get_coco.sh
-# Default dataset location is next to /yolor:
-#   /parent_folder
-#     /coco
-#     /yolor
-
-# Download/unzip labels
-d='../' # unzip directory
-url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
-echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
-
-# Download/unzip images
-d='../coco/images' # unzip directory
-url=http://images.cocodataset.org/zips/
-f1='train2017.zip' # 19G, 118k images
-f2='val2017.zip'   # 1G, 5k images
-f3='test2017.zip'  # 7G, 41k images (optional)
-for f in $f1 $f2 $f3; do
-  echo 'Downloading' $url$f '...'
-  curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
-done
-wait # finish background tasks
diff --git a/spaces/kdrkdrkdr/ShirokoTTS/utils.py b/spaces/kdrkdrkdr/ShirokoTTS/utils.py
deleted file mode 100644
index 4cb5b43d0ca2bae496e7871b2094f2ffb26ab642..0000000000000000000000000000000000000000
--- a/spaces/kdrkdrkdr/ShirokoTTS/utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-    iteration = checkpoint_dict['iteration']
-    learning_rate = checkpoint_dict['learning_rate']
-    if optimizer is not None:
-        optimizer.load_state_dict(checkpoint_dict['optimizer'])
-    saved_state_dict = checkpoint_dict['model']
-    if hasattr(model, 'module'):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():
-        try:
-            new_state_dict[k] = saved_state_dict[k]
-        except:
-            logger.info("%s is not in the checkpoint" % k)
-            new_state_dict[k] = v
-    if hasattr(model, 'module'):
-        model.module.load_state_dict(new_state_dict)
-    else:
-        model.load_state_dict(new_state_dict)
-    logger.info("Loaded checkpoint '{}' (iteration {})".format(
-        checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-    plt.xlabel("Frames")
-    plt.ylabel("Channels")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                   interpolation='none')
-    fig.colorbar(im, ax=ax)
-    xlabel = 'Decoder timestep'
-    if info is not None:
-        xlabel += '\n\n' + info
-    plt.xlabel(xlabel)
-    plt.ylabel('Encoder timestep')
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def load_wav_to_torch(full_path):
-    sampling_rate, data = read(full_path)
-    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-    with open(filename, encoding='utf-8') as f:
-        filepaths_and_text = [line.strip().split(split) for line in f]
-    return filepaths_and_text
-
-
-def get_hparams(init=True):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                        help='JSON file for configuration')
-    parser.add_argument('-m', '--model', type=str, required=True,
-                        help='Model name')
-
-    args = parser.parse_args()
-    model_dir = os.path.join("./logs", args.model)
-
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-
-    config_path = args.config
-    config_save_path = os.path.join(model_dir, "config.json")
-    if init:
-        with open(config_path, "r") as f:
-            data = f.read()
-        with open(config_save_path, "w") as f:
-            f.write(data)
-    else:
-        with open(config_save_path, "r") as f:
-            data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_dir(model_dir):
-    config_save_path = os.path.join(model_dir, "config.json")
-    with open(config_save_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_file(config_path):
-    with open(config_path, "r", encoding="utf-8") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def check_git_hash(model_dir):
-    source_dir = os.path.dirname(os.path.realpath(__file__))
-    if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-            source_dir
-        ))
-        return
-
-    cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-    path = os.path.join(model_dir, "githash")
-    if os.path.exists(path):
-        saved_hash = open(path).read()
-        if saved_hash != cur_hash:
-            logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-                saved_hash[:8], cur_hash[:8]))
-    else:
-        open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-    global logger
-    logger = logging.getLogger(os.path.basename(model_dir))
-    logger.setLevel(logging.DEBUG)
-
-    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    h = logging.FileHandler(os.path.join(model_dir, filename))
-    h.setLevel(logging.DEBUG)
-    h.setFormatter(formatter)
-    logger.addHandler(h)
-    return logger
-
-
-class HParams():
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if type(v) == dict:
-                v = HParams(**v)
-            self[k] = v
-
-    def keys(self):
-        return self.__dict__.keys()
-
-    def items(self):
-        return self.__dict__.items()
-
-    def values(self):
-        return self.__dict__.values()
-
-    def __len__(self):
-        return len(self.__dict__)
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-    def __setitem__(self, key, value):
-        return setattr(self, key, value)
-
-    def __contains__(self, key):
-        return key in self.__dict__
-
-    def __repr__(self):
-        return self.__dict__.__repr__()
diff --git a/spaces/keneonyeachonam/punctuation-Token-Classification/README.md b/spaces/keneonyeachonam/punctuation-Token-Classification/README.md
deleted file mode 100644
index f891b13253c875897cb3693768ed110b9a95c454..0000000000000000000000000000000000000000
--- a/spaces/keneonyeachonam/punctuation-Token-Classification/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Punctuation Token Classification
-emoji: 🐨
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/keras-dreambooth/dreambooth-bioshock/README.md b/spaces/keras-dreambooth/dreambooth-bioshock/README.md
deleted file mode 100644
index 4224c23ccba7bf7c881e0725821c4477eca001c4..0000000000000000000000000000000000000000
--- a/spaces/keras-dreambooth/dreambooth-bioshock/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: Dreambooth Bioshock
-emoji: 📊
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-tags:
-- keras-dreambooth
-- scifi
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kevinwang676/Voice-Cloning-Demo/README.md b/spaces/kevinwang676/Voice-Cloning-Demo/README.md
deleted file mode 100644
index ec664ceee7fda462b2a5a56ae16bc7af219cc18a..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/Voice-Cloning-Demo/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Voice Cloning
-emoji: ⚡
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.33.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: Kevin676/Voice-Cloning
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/kevinwang676/VoiceChangers/src/audio2pose_models/cvae.py b/spaces/kevinwang676/VoiceChangers/src/audio2pose_models/cvae.py
deleted file mode 100644
index d017ce865a03bae40dfe066dbcd82e29839d89dc..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChangers/src/audio2pose_models/cvae.py
+++ /dev/null
@@ -1,149 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-from src.audio2pose_models.res_unet import ResUnet
-
-def class2onehot(idx, class_num):
-
-    assert torch.max(idx).item() < class_num
-    onehot = torch.zeros(idx.size(0), class_num).to(idx.device)
-    onehot.scatter_(1, idx, 1)
-    return onehot
-
-class CVAE(nn.Module):
-    def __init__(self, cfg):
-        super().__init__()
-        encoder_layer_sizes = cfg.MODEL.CVAE.ENCODER_LAYER_SIZES
-        decoder_layer_sizes = cfg.MODEL.CVAE.DECODER_LAYER_SIZES
-        latent_size = cfg.MODEL.CVAE.LATENT_SIZE
-        num_classes = cfg.DATASET.NUM_CLASSES
-        audio_emb_in_size = cfg.MODEL.CVAE.AUDIO_EMB_IN_SIZE
-        audio_emb_out_size = cfg.MODEL.CVAE.AUDIO_EMB_OUT_SIZE
-        seq_len = cfg.MODEL.CVAE.SEQ_LEN
-
-        self.latent_size = latent_size
-
-        self.encoder = ENCODER(encoder_layer_sizes, latent_size, num_classes,
-                                audio_emb_in_size, audio_emb_out_size, seq_len)
-        self.decoder = DECODER(decoder_layer_sizes, latent_size, num_classes,
-                                audio_emb_in_size, audio_emb_out_size, seq_len)
-    def reparameterize(self, mu, logvar):
-        std = torch.exp(0.5 * logvar)
-        eps = torch.randn_like(std)
-        return mu + eps * std
-
-    def forward(self, batch):
-        batch = self.encoder(batch)
-        mu = batch['mu']
-        logvar = batch['logvar']
-        z = self.reparameterize(mu, logvar)
-        batch['z'] = z
-        return self.decoder(batch)
-
-    def test(self, batch):
-        '''
-        class_id = batch['class']
-        z = torch.randn([class_id.size(0), self.latent_size]).to(class_id.device)
-        batch['z'] = z
-        '''
-        return self.decoder(batch)
-
-class ENCODER(nn.Module):
-    def __init__(self, layer_sizes, latent_size, num_classes, 
-                audio_emb_in_size, audio_emb_out_size, seq_len):
-        super().__init__()
-
-        self.resunet = ResUnet()
-        self.num_classes = num_classes
-        self.seq_len = seq_len
-
-        self.MLP = nn.Sequential()
-        layer_sizes[0] += latent_size + seq_len*audio_emb_out_size + 6
-        for i, (in_size, out_size) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):
-            self.MLP.add_module(
-                name="L{:d}".format(i), module=nn.Linear(in_size, out_size))
-            self.MLP.add_module(name="A{:d}".format(i), module=nn.ReLU())
-
-        self.linear_means = nn.Linear(layer_sizes[-1], latent_size)
-        self.linear_logvar = nn.Linear(layer_sizes[-1], latent_size)
-        self.linear_audio = nn.Linear(audio_emb_in_size, audio_emb_out_size)
-
-        self.classbias = nn.Parameter(torch.randn(self.num_classes, latent_size))
-
-    def forward(self, batch):
-        class_id = batch['class']
-        pose_motion_gt = batch['pose_motion_gt']                             #bs seq_len 6
-        ref = batch['ref']                             #bs 6
-        bs = pose_motion_gt.shape[0]
-        audio_in = batch['audio_emb']                          # bs seq_len audio_emb_in_size
-
-        #pose encode
-        pose_emb = self.resunet(pose_motion_gt.unsqueeze(1))          #bs 1 seq_len 6 
-        pose_emb = pose_emb.reshape(bs, -1)                    #bs seq_len*6
-
-        #audio mapping
-        print(audio_in.shape)
-        audio_out = self.linear_audio(audio_in)                # bs seq_len audio_emb_out_size
-        audio_out = audio_out.reshape(bs, -1)
-
-        class_bias = self.classbias[class_id]                  #bs latent_size
-        x_in = torch.cat([ref, pose_emb, audio_out, class_bias], dim=-1) #bs seq_len*(audio_emb_out_size+6)+latent_size
-        x_out = self.MLP(x_in)
-
-        mu = self.linear_means(x_out)
-        logvar = self.linear_means(x_out)                      #bs latent_size 
-
-        batch.update({'mu':mu, 'logvar':logvar})
-        return batch
-
-class DECODER(nn.Module):
-    def __init__(self, layer_sizes, latent_size, num_classes, 
-                audio_emb_in_size, audio_emb_out_size, seq_len):
-        super().__init__()
-
-        self.resunet = ResUnet()
-        self.num_classes = num_classes
-        self.seq_len = seq_len
-
-        self.MLP = nn.Sequential()
-        input_size = latent_size + seq_len*audio_emb_out_size + 6
-        for i, (in_size, out_size) in enumerate(zip([input_size]+layer_sizes[:-1], layer_sizes)):
-            self.MLP.add_module(
-                name="L{:d}".format(i), module=nn.Linear(in_size, out_size))
-            if i+1 < len(layer_sizes):
-                self.MLP.add_module(name="A{:d}".format(i), module=nn.ReLU())
-            else:
-                self.MLP.add_module(name="sigmoid", module=nn.Sigmoid())
-        
-        self.pose_linear = nn.Linear(6, 6)
-        self.linear_audio = nn.Linear(audio_emb_in_size, audio_emb_out_size)
-
-        self.classbias = nn.Parameter(torch.randn(self.num_classes, latent_size))
-
-    def forward(self, batch):
-
-        z = batch['z']                                          #bs latent_size
-        bs = z.shape[0]
-        class_id = batch['class']
-        ref = batch['ref']                             #bs 6
-        audio_in = batch['audio_emb']                           # bs seq_len audio_emb_in_size
-        #print('audio_in: ', audio_in[:, :, :10])
-
-        audio_out = self.linear_audio(audio_in)                 # bs seq_len audio_emb_out_size
-        #print('audio_out: ', audio_out[:, :, :10])
-        audio_out = audio_out.reshape([bs, -1])                 # bs seq_len*audio_emb_out_size
-        class_bias = self.classbias[class_id]                   #bs latent_size
-
-        z = z + class_bias
-        x_in = torch.cat([ref, z, audio_out], dim=-1)
-        x_out = self.MLP(x_in)                                  # bs layer_sizes[-1]
-        x_out = x_out.reshape((bs, self.seq_len, -1))
-
-        #print('x_out: ', x_out)
-
-        pose_emb = self.resunet(x_out.unsqueeze(1))             #bs 1 seq_len 6
-
-        pose_motion_pred = self.pose_linear(pose_emb.squeeze(1))       #bs seq_len 6
-
-        batch.update({'pose_motion_pred':pose_motion_pred})
-        return batch
diff --git a/spaces/kevinwang676/VoiceChangers/src/face3d/models/arcface_torch/utils/utils_amp.py b/spaces/kevinwang676/VoiceChangers/src/face3d/models/arcface_torch/utils/utils_amp.py
deleted file mode 100644
index 9ac2a03f4212faa129faed447a8f4519c0a00a8b..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChangers/src/face3d/models/arcface_torch/utils/utils_amp.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from typing import Dict, List
-
-import torch
-
-if torch.__version__ < '1.9':
-    Iterable = torch._six.container_abcs.Iterable
-else:
-    import collections
-
-    Iterable = collections.abc.Iterable
-from torch.cuda.amp import GradScaler
-
-
-class _MultiDeviceReplicator(object):
-    """
-    Lazily serves copies of a tensor to requested devices.  Copies are cached per-device.
-    """
-
-    def __init__(self, master_tensor: torch.Tensor) -> None:
-        assert master_tensor.is_cuda
-        self.master = master_tensor
-        self._per_device_tensors: Dict[torch.device, torch.Tensor] = {}
-
-    def get(self, device) -> torch.Tensor:
-        retval = self._per_device_tensors.get(device, None)
-        if retval is None:
-            retval = self.master.to(device=device, non_blocking=True, copy=True)
-            self._per_device_tensors[device] = retval
-        return retval
-
-
-class MaxClipGradScaler(GradScaler):
-    def __init__(self, init_scale, max_scale: float, growth_interval=100):
-        GradScaler.__init__(self, init_scale=init_scale, growth_interval=growth_interval)
-        self.max_scale = max_scale
-
-    def scale_clip(self):
-        if self.get_scale() == self.max_scale:
-            self.set_growth_factor(1)
-        elif self.get_scale() < self.max_scale:
-            self.set_growth_factor(2)
-        elif self.get_scale() > self.max_scale:
-            self._scale.fill_(self.max_scale)
-            self.set_growth_factor(1)
-
-    def scale(self, outputs):
-        """
-        Multiplies ('scales') a tensor or list of tensors by the scale factor.
-
-        Returns scaled outputs.  If this instance of :class:`GradScaler` is not enabled, outputs are returned
-        unmodified.
-
-        Arguments:
-            outputs (Tensor or iterable of Tensors):  Outputs to scale.
-        """
-        if not self._enabled:
-            return outputs
-        self.scale_clip()
-        # Short-circuit for the common case.
-        if isinstance(outputs, torch.Tensor):
-            assert outputs.is_cuda
-            if self._scale is None:
-                self._lazy_init_scale_growth_tracker(outputs.device)
-            assert self._scale is not None
-            return outputs * self._scale.to(device=outputs.device, non_blocking=True)
-
-        # Invoke the more complex machinery only if we're treating multiple outputs.
-        stash: List[_MultiDeviceReplicator] = []  # holds a reference that can be overwritten by apply_scale
-
-        def apply_scale(val):
-            if isinstance(val, torch.Tensor):
-                assert val.is_cuda
-                if len(stash) == 0:
-                    if self._scale is None:
-                        self._lazy_init_scale_growth_tracker(val.device)
-                    assert self._scale is not None
-                    stash.append(_MultiDeviceReplicator(self._scale))
-                return val * stash[0].get(val.device)
-            elif isinstance(val, Iterable):
-                iterable = map(apply_scale, val)
-                if isinstance(val, list) or isinstance(val, tuple):
-                    return type(val)(iterable)
-                else:
-                    return iterable
-            else:
-                raise ValueError("outputs must be a Tensor or an iterable of Tensors")
-
-        return apply_scale(outputs)
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/encoder/preprocess.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/encoder/preprocess.py
deleted file mode 100644
index 69986bb3bb0a2d8a0e352d1cb330a375d55f7e2c..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/encoder/preprocess.py
+++ /dev/null
@@ -1,184 +0,0 @@
-from multiprocess.pool import ThreadPool
-from encoder.params_data import *
-from encoder.config import librispeech_datasets, anglophone_nationalites
-from datetime import datetime
-from encoder import audio
-from pathlib import Path
-from tqdm import tqdm
-import numpy as np
-
-
-class DatasetLog:
-    """
-    Registers metadata about the dataset in a text file.
-    """
-    def __init__(self, root, name):
-        self.text_file = open(Path(root, "Log_%s.txt" % name.replace("/", "_")), "w")
-        self.sample_data = dict()
-        
-        start_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
-        self.write_line("Creating dataset %s on %s" % (name, start_time))
-        self.write_line("-----")
-        self._log_params()
-        
-    def _log_params(self):
-        from encoder import params_data
-        self.write_line("Parameter values:")
-        for param_name in (p for p in dir(params_data) if not p.startswith("__")):
-            value = getattr(params_data, param_name)
-            self.write_line("\t%s: %s" % (param_name, value))
-        self.write_line("-----")
-    
-    def write_line(self, line):
-        self.text_file.write("%s\n" % line)
-        
-    def add_sample(self, **kwargs):
-        for param_name, value in kwargs.items():
-            if not param_name in self.sample_data:
-                self.sample_data[param_name] = []
-            self.sample_data[param_name].append(value)
-            
-    def finalize(self):
-        self.write_line("Statistics:")
-        for param_name, values in self.sample_data.items():
-            self.write_line("\t%s:" % param_name)
-            self.write_line("\t\tmin %.3f, max %.3f" % (np.min(values), np.max(values)))
-            self.write_line("\t\tmean %.3f, median %.3f" % (np.mean(values), np.median(values)))
-        self.write_line("-----")
-        end_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M"))
-        self.write_line("Finished on %s" % end_time)
-        self.text_file.close()
-       
-        
-def _init_preprocess_dataset(dataset_name, datasets_root, out_dir) -> (Path, DatasetLog):
-    dataset_root = datasets_root.joinpath(dataset_name)
-    if not dataset_root.exists():
-        print("Couldn\'t find %s, skipping this dataset." % dataset_root)
-        return None, None
-    return dataset_root, DatasetLog(out_dir, dataset_name)
-
-
-def _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, extension,
-                             skip_existing, logger):
-    print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs)))
-    
-    # Function to preprocess utterances for one speaker
-    def preprocess_speaker(speaker_dir: Path):
-        # Give a name to the speaker that includes its dataset
-        speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts)
-        
-        # Create an output directory with that name, as well as a txt file containing a 
-        # reference to each source file.
-        speaker_out_dir = out_dir.joinpath(speaker_name)
-        speaker_out_dir.mkdir(exist_ok=True)
-        sources_fpath = speaker_out_dir.joinpath("_sources.txt")
-        
-        # There's a possibility that the preprocessing was interrupted earlier, check if 
-        # there already is a sources file.
-        if sources_fpath.exists():
-            try:
-                with sources_fpath.open("r") as sources_file:
-                    existing_fnames = {line.split(",")[0] for line in sources_file}
-            except:
-                existing_fnames = {}
-        else:
-            existing_fnames = {}
-        
-        # Gather all audio files for that speaker recursively
-        sources_file = sources_fpath.open("a" if skip_existing else "w")
-        for in_fpath in speaker_dir.glob("**/*.%s" % extension):
-            # Check if the target output file already exists
-            out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts)
-            out_fname = out_fname.replace(".%s" % extension, ".npy")
-            if skip_existing and out_fname in existing_fnames:
-                continue
-                
-            # Load and preprocess the waveform
-            wav = audio.preprocess_wav(in_fpath)
-            if len(wav) == 0:
-                continue
-            
-            # Create the mel spectrogram, discard those that are too short
-            frames = audio.wav_to_mel_spectrogram(wav)
-            if len(frames) < partials_n_frames:
-                continue
-            
-            out_fpath = speaker_out_dir.joinpath(out_fname)
-            np.save(out_fpath, frames)
-            logger.add_sample(duration=len(wav) / sampling_rate)
-            sources_file.write("%s,%s\n" % (out_fname, in_fpath))
-        
-        sources_file.close()
-    
-    # Process the utterances for each speaker
-    with ThreadPool(8) as pool:
-        list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs),
-                  unit="speakers"))
-    logger.finalize()
-    print("Done preprocessing %s.\n" % dataset_name)
-
-def preprocess_aidatatang_200zh(datasets_root: Path, out_dir: Path, skip_existing=False):
-    dataset_name = "aidatatang_200zh"
-    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-    if not dataset_root:
-        return 
-    # Preprocess all speakers
-    speaker_dirs = list(dataset_root.joinpath("corpus", "train").glob("*"))
-    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "wav",
-                                skip_existing, logger)
-
-def preprocess_librispeech(datasets_root: Path, out_dir: Path, skip_existing=False):
-    for dataset_name in librispeech_datasets["train"]["other"]:
-        # Initialize the preprocessing
-        dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-        if not dataset_root:
-            return 
-        
-        # Preprocess all speakers
-        speaker_dirs = list(dataset_root.glob("*"))
-        _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "flac",
-                                 skip_existing, logger)
-
-
-def preprocess_voxceleb1(datasets_root: Path, out_dir: Path, skip_existing=False):
-    # Initialize the preprocessing
-    dataset_name = "VoxCeleb1"
-    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-    if not dataset_root:
-        return
-
-    # Get the contents of the meta file
-    with dataset_root.joinpath("vox1_meta.csv").open("r") as metafile:
-        metadata = [line.split("\t") for line in metafile][1:]
-    
-    # Select the ID and the nationality, filter out non-anglophone speakers
-    nationalities = {line[0]: line[3] for line in metadata}
-    keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items() if 
-                        nationality.lower() in anglophone_nationalites]
-    print("VoxCeleb1: using samples from %d (presumed anglophone) speakers out of %d." % 
-          (len(keep_speaker_ids), len(nationalities)))
-    
-    # Get the speaker directories for anglophone speakers only
-    speaker_dirs = dataset_root.joinpath("wav").glob("*")
-    speaker_dirs = [speaker_dir for speaker_dir in speaker_dirs if
-                    speaker_dir.name in keep_speaker_ids]
-    print("VoxCeleb1: found %d anglophone speakers on the disk, %d missing (this is normal)." % 
-          (len(speaker_dirs), len(keep_speaker_ids) - len(speaker_dirs)))
-
-    # Preprocess all speakers
-    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "wav",
-                             skip_existing, logger)
-
-
-def preprocess_voxceleb2(datasets_root: Path, out_dir: Path, skip_existing=False):
-    # Initialize the preprocessing
-    dataset_name = "VoxCeleb2"
-    dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir)
-    if not dataset_root:
-        return
-    
-    # Get the speaker directories
-    # Preprocess all speakers
-    speaker_dirs = list(dataset_root.joinpath("dev", "aac").glob("*"))
-    _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "m4a",
-                             skip_existing, logger)
diff --git a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg_extractor/encoder/layer_norm.py b/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg_extractor/encoder/layer_norm.py
deleted file mode 100644
index db8be30ff70554edb179109037665e51c04510ec..0000000000000000000000000000000000000000
--- a/spaces/kira4424/Tacotron-zero-short-voice-clone/ppg_extractor/encoder/layer_norm.py
+++ /dev/null
@@ -1,33 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Shigeki Karita
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Layer normalization module."""
-
-import torch
-
-
-class LayerNorm(torch.nn.LayerNorm):
-    """Layer normalization module.
-
-    :param int nout: output dim size
-    :param int dim: dimension to be normalized
-    """
-
-    def __init__(self, nout, dim=-1):
-        """Construct an LayerNorm object."""
-        super(LayerNorm, self).__init__(nout, eps=1e-12)
-        self.dim = dim
-
-    def forward(self, x):
-        """Apply layer normalization.
-
-        :param torch.Tensor x: input tensor
-        :return: layer normalized tensor
-        :rtype torch.Tensor
-        """
-        if self.dim == -1:
-            return super(LayerNorm, self).forward(x)
-        return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1)
diff --git a/spaces/kira4424/VITS-fast-fine-tuning/long_audio_transcribe.py b/spaces/kira4424/VITS-fast-fine-tuning/long_audio_transcribe.py
deleted file mode 100644
index e2292bbdb8847a1e09468118953cea39b2dad98a..0000000000000000000000000000000000000000
--- a/spaces/kira4424/VITS-fast-fine-tuning/long_audio_transcribe.py
+++ /dev/null
@@ -1,71 +0,0 @@
-from moviepy.editor import AudioFileClip
-import whisper
-import os
-import torchaudio
-import librosa
-import torch
-import argparse
-parent_dir = "./denoised_audio/"
-filelist = list(os.walk(parent_dir))[0][2]
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--languages", default="CJE")
-    parser.add_argument("--whisper_size", default="medium")
-    args = parser.parse_args()
-    if args.languages == "CJE":
-        lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-            "en": "[EN]",
-        }
-    elif args.languages == "CJ":
-        lang2token = {
-            'zh': "[ZH]",
-            'ja': "[JA]",
-        }
-    elif args.languages == "C":
-        lang2token = {
-            'zh': "[ZH]",
-        }
-    assert(torch.cuda.is_available()), "Please enable GPU in order to run Whisper!"
-    model = whisper.load_model(args.whisper_size)
-    speaker_annos = []
-    for file in filelist:
-        print(f"transcribing {parent_dir + file}...\n")
-        options = dict(beam_size=5, best_of=5)
-        transcribe_options = dict(task="transcribe", **options)
-        result = model.transcribe(parent_dir + file, **transcribe_options)
-        segments = result["segments"]
-        # result = model.transcribe(parent_dir + file)
-        lang = result['language']
-        if result['language'] not in list(lang2token.keys()):
-            print(f"{lang} not supported, ignoring...\n")
-            continue
-        # segment audio based on segment results
-        character_name = file.rstrip(".wav").split("_")[0]
-        code = file.rstrip(".wav").split("_")[1]
-        if not os.path.exists("./segmented_character_voice/" + character_name):
-            os.mkdir("./segmented_character_voice/" + character_name)
-        wav, sr = torchaudio.load(parent_dir + file, frame_offset=0, num_frames=-1, normalize=True,
-                                  channels_first=True)
-
-        for i, seg in enumerate(result['segments']):
-            start_time = seg['start']
-            end_time = seg['end']
-            text = seg['text']
-            text = lang2token[lang] + text.replace("\n", "") + lang2token[lang]
-            text = text + "\n"
-            wav_seg = wav[:, int(start_time*sr):int(end_time*sr)]
-            wav_seg_name = f"{character_name}_{code}_{i}.wav"
-            savepth = "./segmented_character_voice/" + character_name + "/" + wav_seg_name
-            speaker_annos.append(savepth + "|" + character_name + "|" + text)
-            print(f"Transcribed segment: {speaker_annos[-1]}")
-            # trimmed_wav_seg = librosa.effects.trim(wav_seg.squeeze().numpy())
-            # trimmed_wav_seg = torch.tensor(trimmed_wav_seg[0]).unsqueeze(0)
-            torchaudio.save(savepth, wav_seg, 22050, channels_first=True)
-    if len(speaker_annos) == 0:
-        print("Warning: no long audios & videos found, this IS expected if you have only uploaded short audios")
-        print("this IS NOT expected if you have uploaded any long audios, videos or video links. Please check your file structure or make sure your audio/video language is supported.")
-    with open("long_character_anno.txt", 'w', encoding='utf-8') as f:
-        for line in speaker_annos:
-            f.write(line)
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmseg/models/losses/__init__.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmseg/models/losses/__init__.py
deleted file mode 100644
index beca72045694273d63465bac2f27dbc6672271db..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmseg/models/losses/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from .accuracy import Accuracy, accuracy
-from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
-                                 cross_entropy, mask_cross_entropy)
-from .dice_loss import DiceLoss
-from .lovasz_loss import LovaszLoss
-from .utils import reduce_loss, weight_reduce_loss, weighted_loss
-
-__all__ = [
-    'accuracy', 'Accuracy', 'cross_entropy', 'binary_cross_entropy',
-    'mask_cross_entropy', 'CrossEntropyLoss', 'reduce_loss',
-    'weight_reduce_loss', 'weighted_loss', 'LovaszLoss', 'DiceLoss'
-]
diff --git a/spaces/kquote03/lama-video-watermark-remover/bin/predict.py b/spaces/kquote03/lama-video-watermark-remover/bin/predict.py
deleted file mode 100644
index 9e3e98124d35d167d39796ed514b3c4095d25427..0000000000000000000000000000000000000000
--- a/spaces/kquote03/lama-video-watermark-remover/bin/predict.py
+++ /dev/null
@@ -1,89 +0,0 @@
-#!/usr/bin/env python3
-
-# Example command:
-# ./bin/predict.py \
-#       model.path=<path to checkpoint, prepared by make_checkpoint.py> \
-#       indir=<path to input data> \
-#       outdir=<where to store predicts>
-
-import logging
-import os
-import sys
-import traceback
-
-from saicinpainting.evaluation.utils import move_to_device
-
-os.environ['OMP_NUM_THREADS'] = '1'
-os.environ['OPENBLAS_NUM_THREADS'] = '1'
-os.environ['MKL_NUM_THREADS'] = '1'
-os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
-os.environ['NUMEXPR_NUM_THREADS'] = '1'
-
-import cv2
-import hydra
-import numpy as np
-import torch
-import tqdm
-import yaml
-from omegaconf import OmegaConf
-from torch.utils.data._utils.collate import default_collate
-
-from saicinpainting.training.data.datasets import make_default_val_dataset
-from saicinpainting.training.trainers import load_checkpoint
-from saicinpainting.utils import register_debug_signal_handlers
-
-LOGGER = logging.getLogger(__name__)
-
-
-@hydra.main(config_path='../configs/prediction', config_name='default.yaml')
-def main(predict_config: OmegaConf):
-    try:
-        register_debug_signal_handlers()  # kill -10 <pid> will result in traceback dumped into log
-
-        device = torch.device(predict_config.device)
-
-        train_config_path = os.path.join(predict_config.model.path, 'config.yaml')
-        with open(train_config_path, 'r') as f:
-            train_config = OmegaConf.create(yaml.safe_load(f))
-        
-        train_config.training_model.predict_only = True
-
-        out_ext = predict_config.get('out_ext', '.png')
-
-        checkpoint_path = os.path.join(predict_config.model.path, 
-                                       'models', 
-                                       predict_config.model.checkpoint)
-        model = load_checkpoint(train_config, checkpoint_path, strict=False, map_location='cpu')
-        model.freeze()
-        model.to(device)
-
-        if not predict_config.indir.endswith('/'):
-            predict_config.indir += '/'
-
-        dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset)
-        with torch.no_grad():
-            for img_i in tqdm.trange(len(dataset)):
-                mask_fname = dataset.mask_filenames[img_i]
-                cur_out_fname = os.path.join(
-                    predict_config.outdir, 
-                    os.path.splitext(mask_fname[len(predict_config.indir):])[0] + out_ext
-                )
-                os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
-
-                batch = move_to_device(default_collate([dataset[img_i]]), device)
-                batch['mask'] = (batch['mask'] > 0) * 1
-                batch = model(batch)
-                cur_res = batch[predict_config.out_key][0].permute(1, 2, 0).detach().cpu().numpy()
-
-                cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
-                cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
-                cv2.imwrite(cur_out_fname, cur_res)
-    except KeyboardInterrupt:
-        LOGGER.warning('Interrupted by user')
-    except Exception as ex:
-        LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}')
-        sys.exit(1)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/kquote03/lama-video-watermark-remover/models/ade20k/segm_lib/nn/modules/comm.py b/spaces/kquote03/lama-video-watermark-remover/models/ade20k/segm_lib/nn/modules/comm.py
deleted file mode 100644
index b64bf6ba3b3e7abbab375c6dd4a87d8239e62138..0000000000000000000000000000000000000000
--- a/spaces/kquote03/lama-video-watermark-remover/models/ade20k/segm_lib/nn/modules/comm.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : comm.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import queue
-import collections
-import threading
-
-__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster']
-
-
-class FutureResult(object):
-    """A thread-safe future implementation. Used only as one-to-one pipe."""
-
-    def __init__(self):
-        self._result = None
-        self._lock = threading.Lock()
-        self._cond = threading.Condition(self._lock)
-
-    def put(self, result):
-        with self._lock:
-            assert self._result is None, 'Previous result has\'t been fetched.'
-            self._result = result
-            self._cond.notify()
-
-    def get(self):
-        with self._lock:
-            if self._result is None:
-                self._cond.wait()
-
-            res = self._result
-            self._result = None
-            return res
-
-
-_MasterRegistry = collections.namedtuple('MasterRegistry', ['result'])
-_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result'])
-
-
-class SlavePipe(_SlavePipeBase):
-    """Pipe for master-slave communication."""
-
-    def run_slave(self, msg):
-        self.queue.put((self.identifier, msg))
-        ret = self.result.get()
-        self.queue.put(True)
-        return ret
-
-
-class SyncMaster(object):
-    """An abstract `SyncMaster` object.
-
-    - During the replication, as the data parallel will trigger an callback of each module, all slave devices should
-    call `register(id)` and obtain an `SlavePipe` to communicate with the master.
-    - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected,
-    and passed to a registered callback.
-    - After receiving the messages, the master device should gather the information and determine to message passed
-    back to each slave devices.
-    """
-
-    def __init__(self, master_callback):
-        """
-
-        Args:
-            master_callback: a callback to be invoked after having collected messages from slave devices.
-        """
-        self._master_callback = master_callback
-        self._queue = queue.Queue()
-        self._registry = collections.OrderedDict()
-        self._activated = False
-
-    def register_slave(self, identifier):
-        """
-        Register an slave device.
-
-        Args:
-            identifier: an identifier, usually is the device id.
-
-        Returns: a `SlavePipe` object which can be used to communicate with the master device.
-
-        """
-        if self._activated:
-            assert self._queue.empty(), 'Queue is not clean before next initialization.'
-            self._activated = False
-            self._registry.clear()
-        future = FutureResult()
-        self._registry[identifier] = _MasterRegistry(future)
-        return SlavePipe(identifier, self._queue, future)
-
-    def run_master(self, master_msg):
-        """
-        Main entry for the master device in each forward pass.
-        The messages were first collected from each devices (including the master device), and then
-        an callback will be invoked to compute the message to be sent back to each devices
-        (including the master device).
-
-        Args:
-            master_msg: the message that the master want to send to itself. This will be placed as the first
-            message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example.
-
-        Returns: the message to be sent back to the master device.
-
-        """
-        self._activated = True
-
-        intermediates = [(0, master_msg)]
-        for i in range(self.nr_slaves):
-            intermediates.append(self._queue.get())
-
-        results = self._master_callback(intermediates)
-        assert results[0][0] == 0, 'The first result should belongs to the master.'
-
-        for i, res in results:
-            if i == 0:
-                continue
-            self._registry[i].result.put(res)
-
-        for i in range(self.nr_slaves):
-            assert self._queue.get() is True
-
-        return results[0][1]
-
-    @property
-    def nr_slaves(self):
-        return len(self._registry)
diff --git a/spaces/kunishou/Japanese-Alpaca-LoRA-7b-DEMO/app.py b/spaces/kunishou/Japanese-Alpaca-LoRA-7b-DEMO/app.py
deleted file mode 100644
index 51e5aaba4891c856634e02a7586c620a0ba1ea3b..0000000000000000000000000000000000000000
--- a/spaces/kunishou/Japanese-Alpaca-LoRA-7b-DEMO/app.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import gradio as gr
-
-with gr.Blocks() as demo:
-
-    gr.Markdown(
-        """
-        # デモ公開期間は終了しました
-        たくさん触っていただき誠にありがとうございました！
-        """)
-    
-if __name__ == "__main__":
-    demo.launch()
\ No newline at end of file
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_p_o_s_t.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_p_o_s_t.py
deleted file mode 100644
index dba637117a0ac148af65c75853dd3bffbbbd1154..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_p_o_s_t.py
+++ /dev/null
@@ -1,308 +0,0 @@
-from fontTools import ttLib
-from fontTools.ttLib.standardGlyphOrder import standardGlyphOrder
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import bytechr, byteord, tobytes, tostr, safeEval, readHex
-from . import DefaultTable
-import sys
-import struct
-import array
-import logging
-
-log = logging.getLogger(__name__)
-
-postFormat = """
-	>
-	formatType:			16.16F
-	italicAngle:		16.16F		# italic angle in degrees
-	underlinePosition:	h
-	underlineThickness:	h
-	isFixedPitch:		L
-	minMemType42:		L			# minimum memory if TrueType font is downloaded
-	maxMemType42:		L			# maximum memory if TrueType font is downloaded
-	minMemType1:		L			# minimum memory if Type1 font is downloaded
-	maxMemType1:		L			# maximum memory if Type1 font is downloaded
-"""
-
-postFormatSize = sstruct.calcsize(postFormat)
-
-
-class table__p_o_s_t(DefaultTable.DefaultTable):
-    def decompile(self, data, ttFont):
-        sstruct.unpack(postFormat, data[:postFormatSize], self)
-        data = data[postFormatSize:]
-        if self.formatType == 1.0:
-            self.decode_format_1_0(data, ttFont)
-        elif self.formatType == 2.0:
-            self.decode_format_2_0(data, ttFont)
-        elif self.formatType == 3.0:
-            self.decode_format_3_0(data, ttFont)
-        elif self.formatType == 4.0:
-            self.decode_format_4_0(data, ttFont)
-        else:
-            # supported format
-            raise ttLib.TTLibError(
-                "'post' table format %f not supported" % self.formatType
-            )
-
-    def compile(self, ttFont):
-        data = sstruct.pack(postFormat, self)
-        if self.formatType == 1.0:
-            pass  # we're done
-        elif self.formatType == 2.0:
-            data = data + self.encode_format_2_0(ttFont)
-        elif self.formatType == 3.0:
-            pass  # we're done
-        elif self.formatType == 4.0:
-            data = data + self.encode_format_4_0(ttFont)
-        else:
-            # supported format
-            raise ttLib.TTLibError(
-                "'post' table format %f not supported" % self.formatType
-            )
-        return data
-
-    def getGlyphOrder(self):
-        """This function will get called by a ttLib.TTFont instance.
-        Do not call this function yourself, use TTFont().getGlyphOrder()
-        or its relatives instead!
-        """
-        if not hasattr(self, "glyphOrder"):
-            raise ttLib.TTLibError("illegal use of getGlyphOrder()")
-        glyphOrder = self.glyphOrder
-        del self.glyphOrder
-        return glyphOrder
-
-    def decode_format_1_0(self, data, ttFont):
-        self.glyphOrder = standardGlyphOrder[: ttFont["maxp"].numGlyphs]
-
-    def decode_format_2_0(self, data, ttFont):
-        (numGlyphs,) = struct.unpack(">H", data[:2])
-        numGlyphs = int(numGlyphs)
-        if numGlyphs > ttFont["maxp"].numGlyphs:
-            # Assume the numGlyphs field is bogus, so sync with maxp.
-            # I've seen this in one font, and if the assumption is
-            # wrong elsewhere, well, so be it: it's hard enough to
-            # work around _one_ non-conforming post format...
-            numGlyphs = ttFont["maxp"].numGlyphs
-        data = data[2:]
-        indices = array.array("H")
-        indices.frombytes(data[: 2 * numGlyphs])
-        if sys.byteorder != "big":
-            indices.byteswap()
-        data = data[2 * numGlyphs :]
-        maxIndex = max(indices)
-        self.extraNames = extraNames = unpackPStrings(data, maxIndex - 257)
-        self.glyphOrder = glyphOrder = [""] * int(ttFont["maxp"].numGlyphs)
-        for glyphID in range(numGlyphs):
-            index = indices[glyphID]
-            if index > 257:
-                try:
-                    name = extraNames[index - 258]
-                except IndexError:
-                    name = ""
-            else:
-                # fetch names from standard list
-                name = standardGlyphOrder[index]
-            glyphOrder[glyphID] = name
-        self.build_psNameMapping(ttFont)
-
-    def build_psNameMapping(self, ttFont):
-        mapping = {}
-        allNames = {}
-        for i in range(ttFont["maxp"].numGlyphs):
-            glyphName = psName = self.glyphOrder[i]
-            if glyphName == "":
-                glyphName = "glyph%.5d" % i
-            if glyphName in allNames:
-                # make up a new glyphName that's unique
-                n = allNames[glyphName]
-                while (glyphName + "#" + str(n)) in allNames:
-                    n += 1
-                allNames[glyphName] = n + 1
-                glyphName = glyphName + "#" + str(n)
-
-            self.glyphOrder[i] = glyphName
-            allNames[glyphName] = 1
-            if glyphName != psName:
-                mapping[glyphName] = psName
-
-        self.mapping = mapping
-
-    def decode_format_3_0(self, data, ttFont):
-        # Setting self.glyphOrder to None will cause the TTFont object
-        # try and construct glyph names from a Unicode cmap table.
-        self.glyphOrder = None
-
-    def decode_format_4_0(self, data, ttFont):
-        from fontTools import agl
-
-        numGlyphs = ttFont["maxp"].numGlyphs
-        indices = array.array("H")
-        indices.frombytes(data)
-        if sys.byteorder != "big":
-            indices.byteswap()
-        # In some older fonts, the size of the post table doesn't match
-        # the number of glyphs. Sometimes it's bigger, sometimes smaller.
-        self.glyphOrder = glyphOrder = [""] * int(numGlyphs)
-        for i in range(min(len(indices), numGlyphs)):
-            if indices[i] == 0xFFFF:
-                self.glyphOrder[i] = ""
-            elif indices[i] in agl.UV2AGL:
-                self.glyphOrder[i] = agl.UV2AGL[indices[i]]
-            else:
-                self.glyphOrder[i] = "uni%04X" % indices[i]
-        self.build_psNameMapping(ttFont)
-
-    def encode_format_2_0(self, ttFont):
-        numGlyphs = ttFont["maxp"].numGlyphs
-        glyphOrder = ttFont.getGlyphOrder()
-        assert len(glyphOrder) == numGlyphs
-        indices = array.array("H")
-        extraDict = {}
-        extraNames = self.extraNames = [
-            n for n in self.extraNames if n not in standardGlyphOrder
-        ]
-        for i in range(len(extraNames)):
-            extraDict[extraNames[i]] = i
-        for glyphID in range(numGlyphs):
-            glyphName = glyphOrder[glyphID]
-            if glyphName in self.mapping:
-                psName = self.mapping[glyphName]
-            else:
-                psName = glyphName
-            if psName in extraDict:
-                index = 258 + extraDict[psName]
-            elif psName in standardGlyphOrder:
-                index = standardGlyphOrder.index(psName)
-            else:
-                index = 258 + len(extraNames)
-                extraDict[psName] = len(extraNames)
-                extraNames.append(psName)
-            indices.append(index)
-        if sys.byteorder != "big":
-            indices.byteswap()
-        return (
-            struct.pack(">H", numGlyphs) + indices.tobytes() + packPStrings(extraNames)
-        )
-
-    def encode_format_4_0(self, ttFont):
-        from fontTools import agl
-
-        numGlyphs = ttFont["maxp"].numGlyphs
-        glyphOrder = ttFont.getGlyphOrder()
-        assert len(glyphOrder) == numGlyphs
-        indices = array.array("H")
-        for glyphID in glyphOrder:
-            glyphID = glyphID.split("#")[0]
-            if glyphID in agl.AGL2UV:
-                indices.append(agl.AGL2UV[glyphID])
-            elif len(glyphID) == 7 and glyphID[:3] == "uni":
-                indices.append(int(glyphID[3:], 16))
-            else:
-                indices.append(0xFFFF)
-        if sys.byteorder != "big":
-            indices.byteswap()
-        return indices.tobytes()
-
-    def toXML(self, writer, ttFont):
-        formatstring, names, fixes = sstruct.getformat(postFormat)
-        for name in names:
-            value = getattr(self, name)
-            writer.simpletag(name, value=value)
-            writer.newline()
-        if hasattr(self, "mapping"):
-            writer.begintag("psNames")
-            writer.newline()
-            writer.comment(
-                "This file uses unique glyph names based on the information\n"
-                "found in the 'post' table. Since these names might not be unique,\n"
-                "we have to invent artificial names in case of clashes. In order to\n"
-                "be able to retain the original information, we need a name to\n"
-                "ps name mapping for those cases where they differ. That's what\n"
-                "you see below.\n"
-            )
-            writer.newline()
-            items = sorted(self.mapping.items())
-            for name, psName in items:
-                writer.simpletag("psName", name=name, psName=psName)
-                writer.newline()
-            writer.endtag("psNames")
-            writer.newline()
-        if hasattr(self, "extraNames"):
-            writer.begintag("extraNames")
-            writer.newline()
-            writer.comment(
-                "following are the name that are not taken from the standard Mac glyph order"
-            )
-            writer.newline()
-            for name in self.extraNames:
-                writer.simpletag("psName", name=name)
-                writer.newline()
-            writer.endtag("extraNames")
-            writer.newline()
-        if hasattr(self, "data"):
-            writer.begintag("hexdata")
-            writer.newline()
-            writer.dumphex(self.data)
-            writer.endtag("hexdata")
-            writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if name not in ("psNames", "extraNames", "hexdata"):
-            setattr(self, name, safeEval(attrs["value"]))
-        elif name == "psNames":
-            self.mapping = {}
-            for element in content:
-                if not isinstance(element, tuple):
-                    continue
-                name, attrs, content = element
-                if name == "psName":
-                    self.mapping[attrs["name"]] = attrs["psName"]
-        elif name == "extraNames":
-            self.extraNames = []
-            for element in content:
-                if not isinstance(element, tuple):
-                    continue
-                name, attrs, content = element
-                if name == "psName":
-                    self.extraNames.append(attrs["name"])
-        else:
-            self.data = readHex(content)
-
-
-def unpackPStrings(data, n):
-    # extract n Pascal strings from data.
-    # if there is not enough data, use ""
-
-    strings = []
-    index = 0
-    dataLen = len(data)
-
-    for _ in range(n):
-        if dataLen <= index:
-            length = 0
-        else:
-            length = byteord(data[index])
-        index += 1
-
-        if dataLen <= index + length - 1:
-            name = ""
-        else:
-            name = tostr(data[index : index + length], encoding="latin1")
-        strings.append(name)
-        index += length
-
-    if index < dataLen:
-        log.warning("%d extra bytes in post.stringData array", dataLen - index)
-
-    elif dataLen < index:
-        log.warning("not enough data in post.stringData array")
-
-    return strings
-
-
-def packPStrings(strings):
-    data = b""
-    for s in strings:
-        data = data + bytechr(len(s)) + tobytes(s, encoding="latin1")
-    return data
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/tests/test_utils.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/tests/test_utils.py
deleted file mode 100644
index 4e542b9628d2572c3f43da40c46b2a0b13ac7421..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/tests/test_utils.py
+++ /dev/null
@@ -1,124 +0,0 @@
-from unittest import TestCase
-
-from jsonschema._utils import equal
-
-
-class TestEqual(TestCase):
-    def test_none(self):
-        self.assertTrue(equal(None, None))
-
-
-class TestDictEqual(TestCase):
-    def test_equal_dictionaries(self):
-        dict_1 = {"a": "b", "c": "d"}
-        dict_2 = {"c": "d", "a": "b"}
-        self.assertTrue(equal(dict_1, dict_2))
-
-    def test_missing_key(self):
-        dict_1 = {"a": "b", "c": "d"}
-        dict_2 = {"c": "d", "x": "b"}
-        self.assertFalse(equal(dict_1, dict_2))
-
-    def test_additional_key(self):
-        dict_1 = {"a": "b", "c": "d"}
-        dict_2 = {"c": "d", "a": "b", "x": "x"}
-        self.assertFalse(equal(dict_1, dict_2))
-
-    def test_missing_value(self):
-        dict_1 = {"a": "b", "c": "d"}
-        dict_2 = {"c": "d", "a": "x"}
-        self.assertFalse(equal(dict_1, dict_2))
-
-    def test_empty_dictionaries(self):
-        dict_1 = {}
-        dict_2 = {}
-        self.assertTrue(equal(dict_1, dict_2))
-
-    def test_one_none(self):
-        dict_1 = None
-        dict_2 = {"a": "b", "c": "d"}
-        self.assertFalse(equal(dict_1, dict_2))
-
-    def test_same_item(self):
-        dict_1 = {"a": "b", "c": "d"}
-        self.assertTrue(equal(dict_1, dict_1))
-
-    def test_nested_equal(self):
-        dict_1 = {"a": {"a": "b", "c": "d"}, "c": "d"}
-        dict_2 = {"c": "d", "a": {"a": "b", "c": "d"}}
-        self.assertTrue(equal(dict_1, dict_2))
-
-    def test_nested_dict_unequal(self):
-        dict_1 = {"a": {"a": "b", "c": "d"}, "c": "d"}
-        dict_2 = {"c": "d", "a": {"a": "b", "c": "x"}}
-        self.assertFalse(equal(dict_1, dict_2))
-
-    def test_mixed_nested_equal(self):
-        dict_1 = {"a": ["a", "b", "c", "d"], "c": "d"}
-        dict_2 = {"c": "d", "a": ["a", "b", "c", "d"]}
-        self.assertTrue(equal(dict_1, dict_2))
-
-    def test_nested_list_unequal(self):
-        dict_1 = {"a": ["a", "b", "c", "d"], "c": "d"}
-        dict_2 = {"c": "d", "a": ["b", "c", "d", "a"]}
-        self.assertFalse(equal(dict_1, dict_2))
-
-
-class TestListEqual(TestCase):
-    def test_equal_lists(self):
-        list_1 = ["a", "b", "c"]
-        list_2 = ["a", "b", "c"]
-        self.assertTrue(equal(list_1, list_2))
-
-    def test_unsorted_lists(self):
-        list_1 = ["a", "b", "c"]
-        list_2 = ["b", "b", "a"]
-        self.assertFalse(equal(list_1, list_2))
-
-    def test_first_list_larger(self):
-        list_1 = ["a", "b", "c"]
-        list_2 = ["a", "b"]
-        self.assertFalse(equal(list_1, list_2))
-
-    def test_second_list_larger(self):
-        list_1 = ["a", "b"]
-        list_2 = ["a", "b", "c"]
-        self.assertFalse(equal(list_1, list_2))
-
-    def test_list_with_none_unequal(self):
-        list_1 = ["a", "b", None]
-        list_2 = ["a", "b", "c"]
-        self.assertFalse(equal(list_1, list_2))
-
-        list_1 = ["a", "b", None]
-        list_2 = [None, "b", "c"]
-        self.assertFalse(equal(list_1, list_2))
-
-    def test_list_with_none_equal(self):
-        list_1 = ["a", None, "c"]
-        list_2 = ["a", None, "c"]
-        self.assertTrue(equal(list_1, list_2))
-
-    def test_empty_list(self):
-        list_1 = []
-        list_2 = []
-        self.assertTrue(equal(list_1, list_2))
-
-    def test_one_none(self):
-        list_1 = None
-        list_2 = []
-        self.assertFalse(equal(list_1, list_2))
-
-    def test_same_list(self):
-        list_1 = ["a", "b", "c"]
-        self.assertTrue(equal(list_1, list_1))
-
-    def test_equal_nested_lists(self):
-        list_1 = ["a", ["b", "c"], "d"]
-        list_2 = ["a", ["b", "c"], "d"]
-        self.assertTrue(equal(list_1, list_2))
-
-    def test_unequal_nested_lists(self):
-        list_1 = ["a", ["b", "c"], "d"]
-        list_2 = ["a", [], "c"]
-        self.assertFalse(equal(list_1, list_2))
diff --git a/spaces/leogabraneth/text-generation-webui-main/extensions/superboogav2/utils.py b/spaces/leogabraneth/text-generation-webui-main/extensions/superboogav2/utils.py
deleted file mode 100644
index 89b367eacc9e520912327cff0b7893113381aada..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/extensions/superboogav2/utils.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""
-This module contains common functions across multiple other modules.
-"""
-
-import extensions.superboogav2.parameters as parameters
-
-# Create the context using the prefix + data_separator + postfix from parameters.
-def create_context_text(results):
-    context = parameters.get_prefix() + parameters.get_data_separator().join(results) + parameters.get_postfix()
-
-    return context
-
-
-# Create metadata with the specified source
-def create_metadata_source(source: str):
-    return {'source': source}
\ No newline at end of file
diff --git a/spaces/leogabraneth/text-generation-webui-main/modules/logits.py b/spaces/leogabraneth/text-generation-webui-main/modules/logits.py
deleted file mode 100644
index e356a98605c9c29b051ad44ae139c3ebc923e4f6..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/modules/logits.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import torch
-from transformers import is_torch_xpu_available
-
-from modules import sampler_hijack, shared
-from modules.logging_colors import logger
-from modules.text_generation import generate_reply
-
-global_scores = None
-
-
-def get_next_logits(prompt, state, use_samplers, previous):
-    if shared.model is None:
-        logger.error("No model is loaded! Select one in the Model tab.")
-        return 'Error: No model is loaded1 Select one in the Model tab.', previous
-
-    is_non_hf_exllamav2 = shared.model.__class__.__name__ == 'Exllamav2Model'
-    is_non_hf_exllamav1 = shared.model.__class__.__name__ == 'ExllamaModel'
-    is_non_hf_llamacpp = shared.model.__class__.__name__ == 'LlamaCppModel'
-
-    if use_samplers:
-        if any([is_non_hf_exllamav2, is_non_hf_exllamav1, is_non_hf_llamacpp]):
-            logger.error("Sampler hijacking is not supported non-Huggingface loaders.")
-            # sampling is all done in c for exllama, so it is really hard to hijack
-            # it should be possible to hijack llamacpp sampler by hijacking all their sampling methods,
-            # but it is not implemented yet
-            return 'Error: Sampler hijacking is not supported non-Huggingface loaders. Please disable the "Use samplers" option.', previous
-
-        state['max_new_tokens'] = 1
-        state['auto_max_new_tokens'] = False
-        for _ in generate_reply(prompt, state):
-            pass
-
-        scores = sampler_hijack.global_scores[-1]
-    else:
-        if is_non_hf_exllamav2 or is_non_hf_exllamav1:
-            if is_torch_xpu_available():
-                tokens = shared.tokenizer.encode(prompt).to("xpu:0")
-            else:
-                tokens = shared.tokenizer.encode(prompt).cuda()
-            scores = shared.model.get_logits(tokens)[-1][-1]
-        elif is_non_hf_llamacpp:
-            tokens = shared.tokenizer.encode(prompt)
-            scores = shared.model.get_logits(tokens)[-1][-1]
-        else:
-            if is_torch_xpu_available():
-                tokens = shared.tokenizer.encode(prompt, return_tensors='pt').to("xpu:0")
-            else:
-                tokens = shared.tokenizer.encode(prompt, return_tensors='pt').cuda()
-            output = shared.model(input_ids=tokens)
-            scores = output['logits'][-1][-1]
-
-    probs = torch.softmax(scores, dim=-1, dtype=torch.float)
-    topk_values, topk_indices = torch.topk(probs, k=50, largest=True, sorted=True)
-    topk_values = [f"{float(i):.5f}" for i in topk_values]
-    if is_non_hf_exllamav1 or is_non_hf_llamacpp:
-        topk_indices = [i.expand((1, 1)) for i in topk_indices]
-
-    tokens = [shared.tokenizer.decode(i) for i in topk_indices]
-    output = ''
-    for row in list(zip(topk_values, tokens)):
-        output += f"{row[0]}  -  {repr(row[1])}\n"
-
-    return output, previous
diff --git a/spaces/lewiswu1209/MockingBird/encoder/visualizations.py b/spaces/lewiswu1209/MockingBird/encoder/visualizations.py
deleted file mode 100644
index 980c74f95f1f7df41ebccc983600b2713c0b0502..0000000000000000000000000000000000000000
--- a/spaces/lewiswu1209/MockingBird/encoder/visualizations.py
+++ /dev/null
@@ -1,178 +0,0 @@
-from encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataset
-from datetime import datetime
-from time import perf_counter as timer
-import matplotlib.pyplot as plt
-import numpy as np
-# import webbrowser
-import visdom
-import umap
-
-colormap = np.array([
-    [76, 255, 0],
-    [0, 127, 70],
-    [255, 0, 0],
-    [255, 217, 38],
-    [0, 135, 255],
-    [165, 0, 165],
-    [255, 167, 255],
-    [0, 255, 255],
-    [255, 96, 38],
-    [142, 76, 0],
-    [33, 0, 127],
-    [0, 0, 0],
-    [183, 183, 183],
-], dtype=np.float) / 255 
-
-
-class Visualizations:
-    def __init__(self, env_name=None, update_every=10, server="http://localhost", disabled=False):
-        # Tracking data
-        self.last_update_timestamp = timer()
-        self.update_every = update_every
-        self.step_times = []
-        self.losses = []
-        self.eers = []
-        print("Updating the visualizations every %d steps." % update_every)
-        
-        # If visdom is disabled TODO: use a better paradigm for that
-        self.disabled = disabled    
-        if self.disabled:
-            return 
-        
-        # Set the environment name
-        now = str(datetime.now().strftime("%d-%m %Hh%M"))
-        if env_name is None:
-            self.env_name = now
-        else:
-            self.env_name = "%s (%s)" % (env_name, now)
-        
-        # Connect to visdom and open the corresponding window in the browser
-        try:
-            self.vis = visdom.Visdom(server, env=self.env_name, raise_exceptions=True)
-        except ConnectionError:
-            raise Exception("No visdom server detected. Run the command \"visdom\" in your CLI to "
-                            "start it.")
-        # webbrowser.open("http://localhost:8097/env/" + self.env_name)
-        
-        # Create the windows
-        self.loss_win = None
-        self.eer_win = None
-        # self.lr_win = None
-        self.implementation_win = None
-        self.projection_win = None
-        self.implementation_string = ""
-        
-    def log_params(self):
-        if self.disabled:
-            return 
-        from encoder import params_data
-        from encoder import params_model
-        param_string = "<b>Model parameters</b>:<br>"
-        for param_name in (p for p in dir(params_model) if not p.startswith("__")):
-            value = getattr(params_model, param_name)
-            param_string += "\t%s: %s<br>" % (param_name, value)
-        param_string += "<b>Data parameters</b>:<br>"
-        for param_name in (p for p in dir(params_data) if not p.startswith("__")):
-            value = getattr(params_data, param_name)
-            param_string += "\t%s: %s<br>" % (param_name, value)
-        self.vis.text(param_string, opts={"title": "Parameters"})
-        
-    def log_dataset(self, dataset: SpeakerVerificationDataset):
-        if self.disabled:
-            return 
-        dataset_string = ""
-        dataset_string += "<b>Speakers</b>: %s\n" % len(dataset.speakers)
-        dataset_string += "\n" + dataset.get_logs()
-        dataset_string = dataset_string.replace("\n", "<br>")
-        self.vis.text(dataset_string, opts={"title": "Dataset"})
-        
-    def log_implementation(self, params):
-        if self.disabled:
-            return 
-        implementation_string = ""
-        for param, value in params.items():
-            implementation_string += "<b>%s</b>: %s\n" % (param, value)
-            implementation_string = implementation_string.replace("\n", "<br>")
-        self.implementation_string = implementation_string
-        self.implementation_win = self.vis.text(
-            implementation_string, 
-            opts={"title": "Training implementation"}
-        )
-
-    def update(self, loss, eer, step):
-        # Update the tracking data
-        now = timer()
-        self.step_times.append(1000 * (now - self.last_update_timestamp))
-        self.last_update_timestamp = now
-        self.losses.append(loss)
-        self.eers.append(eer)
-        print(".", end="")
-        
-        # Update the plots every <update_every> steps
-        if step % self.update_every != 0:
-            return
-        time_string = "Step time:  mean: %5dms  std: %5dms" % \
-                      (int(np.mean(self.step_times)), int(np.std(self.step_times)))
-        print("\nStep %6d   Loss: %.4f   EER: %.4f   %s" %
-              (step, np.mean(self.losses), np.mean(self.eers), time_string))
-        if not self.disabled:
-            self.loss_win = self.vis.line(
-                [np.mean(self.losses)],
-                [step],
-                win=self.loss_win,
-                update="append" if self.loss_win else None,
-                opts=dict(
-                    legend=["Avg. loss"],
-                    xlabel="Step",
-                    ylabel="Loss",
-                    title="Loss",
-                )
-            )
-            self.eer_win = self.vis.line(
-                [np.mean(self.eers)],
-                [step],
-                win=self.eer_win,
-                update="append" if self.eer_win else None,
-                opts=dict(
-                    legend=["Avg. EER"],
-                    xlabel="Step",
-                    ylabel="EER",
-                    title="Equal error rate"
-                )
-            )
-            if self.implementation_win is not None:
-                self.vis.text(
-                    self.implementation_string + ("<b>%s</b>" % time_string), 
-                    win=self.implementation_win,
-                    opts={"title": "Training implementation"},
-                )
-
-        # Reset the tracking
-        self.losses.clear()
-        self.eers.clear()
-        self.step_times.clear()
-        
-    def draw_projections(self, embeds, utterances_per_speaker, step, out_fpath=None,
-                         max_speakers=10):
-        max_speakers = min(max_speakers, len(colormap))
-        embeds = embeds[:max_speakers * utterances_per_speaker]
-        
-        n_speakers = len(embeds) // utterances_per_speaker
-        ground_truth = np.repeat(np.arange(n_speakers), utterances_per_speaker)
-        colors = [colormap[i] for i in ground_truth]
-        
-        reducer = umap.UMAP()
-        projected = reducer.fit_transform(embeds)
-        plt.scatter(projected[:, 0], projected[:, 1], c=colors)
-        plt.gca().set_aspect("equal", "datalim")
-        plt.title("UMAP projection (step %d)" % step)
-        if not self.disabled:
-            self.projection_win = self.vis.matplot(plt, win=self.projection_win)
-        if out_fpath is not None:
-            plt.savefig(out_fpath)
-        plt.clf()
-        
-    def save(self):
-        if not self.disabled:
-            self.vis.save([self.env_name])
-        
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/1st Studio Siberian Mouse Masha And Veronika Babko Hard Avi.md b/spaces/lincquiQcaudo/Top-20-Diffusion/1st Studio Siberian Mouse Masha And Veronika Babko Hard Avi.md
deleted file mode 100644
index 435681f0886c643e3aeb19ed4651493819865286..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/1st Studio Siberian Mouse Masha And Veronika Babko Hard Avi.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>1st Studio Siberian Mouse Masha And Veronika Babko Hard Avi</h2><br /><p><b><b>Download Zip</b> &#10038;&#10038;&#10038; <a href="https://bytlly.com/2uGyis">https://bytlly.com/2uGyis</a></b></p><br /><br />
-
-Here you can find 1st studio siberian mouse hd 95 shared files. ... Listen to 1st Studio Siberian Mouse Masha And Veronika Babko Hard Avi ... 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Bcl Easyconverter Desktop 3 Word Version Crack Or Serial 43 TOP.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Bcl Easyconverter Desktop 3 Word Version Crack Or Serial 43 TOP.md
deleted file mode 100644
index b967ef9dcc602289b335f2b590398f67f278a1e8..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Bcl Easyconverter Desktop 3 Word Version Crack Or Serial 43 TOP.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Bcl Easyconverter Desktop 3 Word Version Crack Or Serial 43</h2><br /><p><b><b>Download</b> &#9889; <a href="https://bytlly.com/2uGvyj">https://bytlly.com/2uGvyj</a></b></p><br /><br />
-<br />
-Bitdefender Antivirus Plus 2019 3 PC, 1 ao (Clave-cdigo de activacin) . ... Bcl Easyconverter Desktop 3 Word Version Crack Or Serial 43 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/liuyuchen777/DanDanGPT/app.py b/spaces/liuyuchen777/DanDanGPT/app.py
deleted file mode 100644
index b29a1aa07cdeaa4498d66039b18614a63d2ba3b6..0000000000000000000000000000000000000000
--- a/spaces/liuyuchen777/DanDanGPT/app.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import gradio as gr
-# import openai
-import os
-import sys
-from utils import *
-from presets import *
-
-my_api_key = ""    # 在这里输入你的 API 密钥
-HIDE_MY_KEY = False # 如果你想在UI中隐藏你的 API 密钥，将此值设置为 True
-
-gr.Chatbot.postprocess = postprocess
-
-#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":
-        print("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 os.path.exists("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
-
-with gr.Blocks(css=customCSS) as demo:
-    gr.HTML(title)
-    keyTxt = gr.Textbox(show_label=True, placeholder=f"在这里输入你的OpenAI API-key...",
-                        value=my_api_key, label="API Key", type="password", visible=not HIDE_MY_KEY).style(container=True)
-    chatbot = gr.Chatbot()  # .style(color_map=("#1D51EE", "#585A5B"))
-    history = gr.State([])
-    promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2))
-    TRUECOMSTANT = gr.State(True)
-    FALSECONSTANT = gr.State(False)
-    topic = gr.State("未命名对话历史记录")
-
-    with gr.Row():
-        with gr.Column(scale=12):
-            txt = gr.Textbox(show_label=False, placeholder="在这里输入").style(
-                container=False)
-        with gr.Column(min_width=50, scale=1):
-            submitBtn = gr.Button("🚀", variant="primary")
-    with gr.Row():
-        emptyBtn = gr.Button("🧹 新的对话")
-        retryBtn = gr.Button("🔄 重新生成")
-        delLastBtn = gr.Button("🗑️ 删除上条对话")
-        reduceTokenBtn = gr.Button("♻️ 总结对话")
-    statusDisplay = gr.Markdown("status: ready")
-    systemPromptTxt = gr.Textbox(show_label=True, placeholder=f"在这里输入System Prompt...",
-                                 label="System prompt", value=initial_prompt).style(container=True)
-    with gr.Accordion(label="加载Prompt模板", open=False):
-        with gr.Column():
-            with gr.Row():
-                with gr.Column(scale=6):
-                    templateFileSelectDropdown = gr.Dropdown(label="选择Prompt模板集合文件（.csv）", choices=get_template_names(plain=True), multiselect=False, value=get_template_names(plain=True)[0])
-                with gr.Column(scale=1):
-                    templateRefreshBtn = gr.Button("🔄 刷新")
-                    templaeFileReadBtn = gr.Button("📂 读入模板")
-            with gr.Row():
-                with gr.Column(scale=6):
-                    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])
-                with gr.Column(scale=1):
-                    templateApplyBtn = gr.Button("⬇️ 应用")
-    with gr.Accordion(label="保存/加载对话历史记录", open=False):
-        with gr.Column():
-            with gr.Row():
-                with gr.Column(scale=6):
-                    saveFileName = gr.Textbox(
-                        show_label=True, placeholder=f"在这里输入保存的文件名...", 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")
-            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")
-                    historyReadBtn = gr.Button("📂 读入对话")
-    #inputs, top_p, temperature, top_k, repetition_penalty
-    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 (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", )
-    gr.Markdown(description)
-
-
-    txt.submit(predict, [txt, top_p, temperature, keyTxt,
-               chatbot, history, systemPromptTxt], [chatbot, history, statusDisplay])
-    txt.submit(reset_textbox, [], [txt])
-    submitBtn.click(predict, [txt, top_p, temperature, keyTxt, chatbot,
-                    history, systemPromptTxt], [chatbot, history, statusDisplay], show_progress=True)
-    submitBtn.click(reset_textbox, [], [txt])
-    emptyBtn.click(reset_state, outputs=[chatbot, history])
-    retryBtn.click(predict, [txt, top_p, temperature, keyTxt, chatbot, history,
-                   systemPromptTxt, TRUECOMSTANT], [chatbot, history, statusDisplay], show_progress=True)
-    delLastBtn.click(delete_last_conversation, [chatbot, history], [
-                     chatbot, history], show_progress=True)
-    reduceTokenBtn.click(predict, [txt, top_p, temperature, keyTxt, chatbot, history,
-                         systemPromptTxt, FALSECONSTANT, TRUECOMSTANT], [chatbot, history, statusDisplay], show_progress=True)
-    saveHistoryBtn.click(save_chat_history, [
-                  saveFileName, systemPromptTxt, history, chatbot], None, show_progress=True)
-    saveHistoryBtn.click(get_history_names, None, [historyFileSelectDropdown])
-    historyRefreshBtn.click(get_history_names, None, [historyFileSelectDropdown])
-    historyReadBtn.click(load_chat_history, [historyFileSelectDropdown, systemPromptTxt, history, chatbot],  [saveFileName, systemPromptTxt, history, chatbot], show_progress=True)
-    templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown])
-    templaeFileReadBtn.click(load_template, [templateFileSelectDropdown],  [promptTemplates, templateSelectDropdown], show_progress=True)
-    templateApplyBtn.click(get_template_content, [promptTemplates, templateSelectDropdown, systemPromptTxt],  [systemPromptTxt], show_progress=True)
-
-print("访问 http://localhost:7860 查看界面")
-# 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 = "DanDanGPT🐧"
-
-#if running in Docker
-if dockerflag:
-    if authflag:
-        demo.queue().launch(server_name="0.0.0.0", server_port=7860,auth=(username, password))
-    else:
-        demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=False)
-#if not running in Docker
-else:
-    if authflag:
-        demo.queue().launch(share=False, auth=(username, password))
-    else:
-        demo.queue().launch(share=False) # 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/lixq/bingo61/src/components/toaster.tsx b/spaces/lixq/bingo61/src/components/toaster.tsx
deleted file mode 100644
index 4d2693460b61307a1d4c127fd01df9bee16e59ff..0000000000000000000000000000000000000000
--- a/spaces/lixq/bingo61/src/components/toaster.tsx
+++ /dev/null
@@ -1,3 +0,0 @@
-'use client'
-
-export { Toaster } from 'react-hot-toast'
diff --git a/spaces/ljjggr/bingo/src/pages/api/image.ts b/spaces/ljjggr/bingo/src/pages/api/image.ts
deleted file mode 100644
index fbc0c8def432ba212d27347471670d3b6202463d..0000000000000000000000000000000000000000
--- a/spaces/ljjggr/bingo/src/pages/api/image.ts
+++ /dev/null
@@ -1,40 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-import { debug } from '@/lib/isomorphic'
-import { createHeaders } from '@/lib/utils'
-import { createImage } from '@/lib/bots/bing/utils'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  const { prompt, id } = req.query
-  if (!prompt) {
-    return res.json({
-      result: {
-        value: 'Image',
-        message: 'No Prompt'
-      }
-    })
-  }
-  try {
-    const headers = createHeaders(req.cookies, {
-      IMAGE_BING_COOKIE: process.env.IMAGE_BING_COOKIE
-    }, 'image')
-
-    debug('headers', headers)
-    const response = await createImage(String(prompt), String(id), {
-      ...headers,
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-    })
-    res.writeHead(200, {
-      'Content-Type': 'text/plain; charset=UTF-8',
-    })
-    return res.end(response)
-  } catch (e) {
-    return res.json({
-      result: {
-        value: 'Error',
-        message: `${e}`
-      }
-    })
-  }
-}
diff --git a/spaces/llmonitor/benchmarks/next.config.js b/spaces/llmonitor/benchmarks/next.config.js
deleted file mode 100644
index 9b9d6b42dd47bad6be991d0811d159a7ab2378eb..0000000000000000000000000000000000000000
--- a/spaces/llmonitor/benchmarks/next.config.js
+++ /dev/null
@@ -1,11 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-  reactStrictMode: true,
-  output: "standalone",
-
-  experimental: {
-    serverActions: true,
-  },
-}
-
-module.exports = nextConfig
diff --git a/spaces/lunarring/latentblending/ldm/models/diffusion/dpm_solver/dpm_solver.py b/spaces/lunarring/latentblending/ldm/models/diffusion/dpm_solver/dpm_solver.py
deleted file mode 100644
index 095e5ba3ce0b1aa7f4b3f1e2e5d8fff7cfe6dc8c..0000000000000000000000000000000000000000
--- a/spaces/lunarring/latentblending/ldm/models/diffusion/dpm_solver/dpm_solver.py
+++ /dev/null
@@ -1,1154 +0,0 @@
-import torch
-import torch.nn.functional as F
-import math
-from tqdm import tqdm
-
-
-class NoiseScheduleVP:
-    def __init__(
-            self,
-            schedule='discrete',
-            betas=None,
-            alphas_cumprod=None,
-            continuous_beta_0=0.1,
-            continuous_beta_1=20.,
-    ):
-        """Create a wrapper class for the forward SDE (VP type).
-        ***
-        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
-                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
-        ***
-        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
-        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
-        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
-            log_alpha_t = self.marginal_log_mean_coeff(t)
-            sigma_t = self.marginal_std(t)
-            lambda_t = self.marginal_lambda(t)
-        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
-            t = self.inverse_lambda(lambda_t)
-        ===============================================================
-        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
-        1. For discrete-time DPMs:
-            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
-                t_i = (i + 1) / N
-            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
-            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
-            Args:
-                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
-                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
-            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
-            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
-                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
-                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
-                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
-                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
-                and
-                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
-        2. For continuous-time DPMs:
-            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
-            schedule are the default settings in DDPM and improved-DDPM:
-            Args:
-                beta_min: A `float` number. The smallest beta for the linear schedule.
-                beta_max: A `float` number. The largest beta for the linear schedule.
-                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
-                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
-                T: A `float` number. The ending time of the forward process.
-        ===============================================================
-        Args:
-            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
-                    'linear' or 'cosine' for continuous-time DPMs.
-        Returns:
-            A wrapper object of the forward SDE (VP type).
-
-        ===============================================================
-        Example:
-        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', betas=betas)
-        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
-        # For continuous-time DPMs (VPSDE), linear schedule:
-        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
-        """
-
-        if schedule not in ['discrete', 'linear', 'cosine']:
-            raise ValueError(
-                "Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(
-                    schedule))
-
-        self.schedule = schedule
-        if schedule == 'discrete':
-            if betas is not None:
-                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
-            else:
-                assert alphas_cumprod is not None
-                log_alphas = 0.5 * torch.log(alphas_cumprod)
-            self.total_N = len(log_alphas)
-            self.T = 1.
-            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
-            self.log_alpha_array = log_alphas.reshape((1, -1,))
-        else:
-            self.total_N = 1000
-            self.beta_0 = continuous_beta_0
-            self.beta_1 = continuous_beta_1
-            self.cosine_s = 0.008
-            self.cosine_beta_max = 999.
-            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (
-                        1. + self.cosine_s) / math.pi - self.cosine_s
-            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
-            self.schedule = schedule
-            if schedule == 'cosine':
-                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
-                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
-                self.T = 0.9946
-            else:
-                self.T = 1.
-
-    def marginal_log_mean_coeff(self, t):
-        """
-        Compute log(alpha_t) of a given continuous-time label t in [0, T].
-        """
-        if self.schedule == 'discrete':
-            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device),
-                                  self.log_alpha_array.to(t.device)).reshape((-1))
-        elif self.schedule == 'linear':
-            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
-        elif self.schedule == 'cosine':
-            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
-            log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
-            return log_alpha_t
-
-    def marginal_alpha(self, t):
-        """
-        Compute alpha_t of a given continuous-time label t in [0, T].
-        """
-        return torch.exp(self.marginal_log_mean_coeff(t))
-
-    def marginal_std(self, t):
-        """
-        Compute sigma_t of a given continuous-time label t in [0, T].
-        """
-        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
-
-    def marginal_lambda(self, t):
-        """
-        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
-        """
-        log_mean_coeff = self.marginal_log_mean_coeff(t)
-        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
-        return log_mean_coeff - log_std
-
-    def inverse_lambda(self, lamb):
-        """
-        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
-        """
-        if self.schedule == 'linear':
-            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            Delta = self.beta_0 ** 2 + tmp
-            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
-        elif self.schedule == 'discrete':
-            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
-            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]),
-                               torch.flip(self.t_array.to(lamb.device), [1]))
-            return t.reshape((-1,))
-        else:
-            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (
-                        1. + self.cosine_s) / math.pi - self.cosine_s
-            t = t_fn(log_alpha)
-            return t
-
-
-def model_wrapper(
-        model,
-        noise_schedule,
-        model_type="noise",
-        model_kwargs={},
-        guidance_type="uncond",
-        condition=None,
-        unconditional_condition=None,
-        guidance_scale=1.,
-        classifier_fn=None,
-        classifier_kwargs={},
-):
-    """Create a wrapper function for the noise prediction model.
-    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
-    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
-    We support four types of the diffusion model by setting `model_type`:
-        1. "noise": noise prediction model. (Trained by predicting noise).
-        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
-        3. "v": velocity prediction model. (Trained by predicting the velocity).
-            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
-            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
-                arXiv preprint arXiv:2202.00512 (2022).
-            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
-                arXiv preprint arXiv:2210.02303 (2022).
-
-        4. "score": marginal score function. (Trained by denoising score matching).
-            Note that the score function and the noise prediction model follows a simple relationship:
-            ```
-                noise(x_t, t) = -sigma_t * score(x_t, t)
-            ```
-    We support three types of guided sampling by DPMs by setting `guidance_type`:
-        1. "uncond": unconditional sampling by DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-            The input `classifier_fn` has the following format:
-            ``
-                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
-            ``
-            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
-                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
-        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
-            ``
-            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
-            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
-                arXiv preprint arXiv:2207.12598 (2022).
-
-    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
-    or continuous-time labels (i.e. epsilon to T).
-    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
-    ``
-        def model_fn(x, t_continuous) -> noise:
-            t_input = get_model_input_time(t_continuous)
-            return noise_pred(model, x, t_input, **model_kwargs)
-    ``
-    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
-    ===============================================================
-    Args:
-        model: A diffusion model with the corresponding format described above.
-        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-        model_type: A `str`. The parameterization type of the diffusion model.
-                    "noise" or "x_start" or "v" or "score".
-        model_kwargs: A `dict`. A dict for the other inputs of the model function.
-        guidance_type: A `str`. The type of the guidance for sampling.
-                    "uncond" or "classifier" or "classifier-free".
-        condition: A pytorch tensor. The condition for the guided sampling.
-                    Only used for "classifier" or "classifier-free" guidance type.
-        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
-                    Only used for "classifier-free" guidance type.
-        guidance_scale: A `float`. The scale for the guided sampling.
-        classifier_fn: A classifier function. Only used for the classifier guidance.
-        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
-    Returns:
-        A noise prediction model that accepts the noised data and the continuous time as the inputs.
-    """
-
-    def get_model_input_time(t_continuous):
-        """
-        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
-        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
-        For continuous-time DPMs, we just use `t_continuous`.
-        """
-        if noise_schedule.schedule == 'discrete':
-            return (t_continuous - 1. / noise_schedule.total_N) * 1000.
-        else:
-            return t_continuous
-
-    def noise_pred_fn(x, t_continuous, cond=None):
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        t_input = get_model_input_time(t_continuous)
-        if cond is None:
-            output = model(x, t_input, **model_kwargs)
-        else:
-            output = model(x, t_input, cond, **model_kwargs)
-        if model_type == "noise":
-            return output
-        elif model_type == "x_start":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
-        elif model_type == "v":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
-        elif model_type == "score":
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return -expand_dims(sigma_t, dims) * output
-
-    def cond_grad_fn(x, t_input):
-        """
-        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
-        """
-        with torch.enable_grad():
-            x_in = x.detach().requires_grad_(True)
-            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
-            return torch.autograd.grad(log_prob.sum(), x_in)[0]
-
-    def model_fn(x, t_continuous):
-        """
-        The noise predicition model function that is used for DPM-Solver.
-        """
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        if guidance_type == "uncond":
-            return noise_pred_fn(x, t_continuous)
-        elif guidance_type == "classifier":
-            assert classifier_fn is not None
-            t_input = get_model_input_time(t_continuous)
-            cond_grad = cond_grad_fn(x, t_input)
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            noise = noise_pred_fn(x, t_continuous)
-            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
-        elif guidance_type == "classifier-free":
-            if guidance_scale == 1. or unconditional_condition is None:
-                return noise_pred_fn(x, t_continuous, cond=condition)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t_continuous] * 2)
-                c_in = torch.cat([unconditional_condition, condition])
-                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
-                return noise_uncond + guidance_scale * (noise - noise_uncond)
-
-    assert model_type in ["noise", "x_start", "v"]
-    assert guidance_type in ["uncond", "classifier", "classifier-free"]
-    return model_fn
-
-
-class DPM_Solver:
-    def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
-        """Construct a DPM-Solver.
-        We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
-        If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
-        If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
-            In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
-            The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
-        Args:
-            model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
-                ``
-                def model_fn(x, t_continuous):
-                    return noise
-                ``
-            noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-            predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
-            thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
-            max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
-
-        [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
-        """
-        self.model = model_fn
-        self.noise_schedule = noise_schedule
-        self.predict_x0 = predict_x0
-        self.thresholding = thresholding
-        self.max_val = max_val
-
-    def noise_prediction_fn(self, x, t):
-        """
-        Return the noise prediction model.
-        """
-        return self.model(x, t)
-
-    def data_prediction_fn(self, x, t):
-        """
-        Return the data prediction model (with thresholding).
-        """
-        noise = self.noise_prediction_fn(x, t)
-        dims = x.dim()
-        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
-        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
-        if self.thresholding:
-            p = 0.995  # A hyperparameter in the paper of "Imagen" [1].
-            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
-            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
-            x0 = torch.clamp(x0, -s, s) / s
-        return x0
-
-    def model_fn(self, x, t):
-        """
-        Convert the model to the noise prediction model or the data prediction model.
-        """
-        if self.predict_x0:
-            return self.data_prediction_fn(x, t)
-        else:
-            return self.noise_prediction_fn(x, t)
-
-    def get_time_steps(self, skip_type, t_T, t_0, N, device):
-        """Compute the intermediate time steps for sampling.
-        Args:
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            N: A `int`. The total number of the spacing of the time steps.
-            device: A torch device.
-        Returns:
-            A pytorch tensor of the time steps, with the shape (N + 1,).
-        """
-        if skip_type == 'logSNR':
-            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
-            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
-            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
-            return self.noise_schedule.inverse_lambda(logSNR_steps)
-        elif skip_type == 'time_uniform':
-            return torch.linspace(t_T, t_0, N + 1).to(device)
-        elif skip_type == 'time_quadratic':
-            t_order = 2
-            t = torch.linspace(t_T ** (1. / t_order), t_0 ** (1. / t_order), N + 1).pow(t_order).to(device)
-            return t
-        else:
-            raise ValueError(
-                "Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
-
-    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
-        """
-        Get the order of each step for sampling by the singlestep DPM-Solver.
-        We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
-        Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
-            - If order == 1:
-                We take `steps` of DPM-Solver-1 (i.e. DDIM).
-            - If order == 2:
-                - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
-                - If steps % 2 == 0, we use K steps of DPM-Solver-2.
-                - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
-            - If order == 3:
-                - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
-        ============================================
-        Args:
-            order: A `int`. The max order for the solver (2 or 3).
-            steps: A `int`. The total number of function evaluations (NFE).
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            device: A torch device.
-        Returns:
-            orders: A list of the solver order of each step.
-        """
-        if order == 3:
-            K = steps // 3 + 1
-            if steps % 3 == 0:
-                orders = [3, ] * (K - 2) + [2, 1]
-            elif steps % 3 == 1:
-                orders = [3, ] * (K - 1) + [1]
-            else:
-                orders = [3, ] * (K - 1) + [2]
-        elif order == 2:
-            if steps % 2 == 0:
-                K = steps // 2
-                orders = [2, ] * K
-            else:
-                K = steps // 2 + 1
-                orders = [2, ] * (K - 1) + [1]
-        elif order == 1:
-            K = 1
-            orders = [1, ] * steps
-        else:
-            raise ValueError("'order' must be '1' or '2' or '3'.")
-        if skip_type == 'logSNR':
-            # To reproduce the results in DPM-Solver paper
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
-        else:
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[
-                torch.cumsum(torch.tensor([0, ] + orders)).to(device)]
-        return timesteps_outer, orders
-
-    def denoise_to_zero_fn(self, x, s):
-        """
-        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
-        """
-        return self.data_prediction_fn(x, s)
-
-    def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
-        """
-        DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_1 = torch.expm1(-h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-        else:
-            phi_1 = torch.expm1(h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-
-    def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False,
-                                            solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-2 from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the second-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 0.5
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(
-            s1), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
-        alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_1 = torch.expm1(-h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                    expand_dims(sigma_s1 / sigma_s, dims) * x
-                    - expand_dims(alpha_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (
-                                    model_s1 - model_s)
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_1 = torch.expm1(h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                    expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                    - expand_dims(sigma_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
-                )
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1}
-        else:
-            return x_t
-
-    def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2. / 3., model_s=None, model_s1=None,
-                                           return_intermediate=False, solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-3 from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
-                If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 1. / 3.
-        if r2 is None:
-            r2 = 2. / 3.
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        lambda_s2 = lambda_s + r2 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        s2 = ns.inverse_lambda(lambda_s2)
-        log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(
-            s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(
-            s2), ns.marginal_std(t)
-        alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_12 = torch.expm1(-r2 * h)
-            phi_1 = torch.expm1(-h)
-            phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
-            phi_2 = phi_1 / h + 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                        expand_dims(sigma_s1 / sigma_s, dims) * x
-                        - expand_dims(alpha_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                    expand_dims(sigma_s2 / sigma_s, dims) * x
-                    - expand_dims(alpha_s2 * phi_12, dims) * model_s
-                    + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        + expand_dims(alpha_t * phi_2, dims) * D1
-                        - expand_dims(alpha_t * phi_3, dims) * D2
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_12 = torch.expm1(r2 * h)
-            phi_1 = torch.expm1(h)
-            phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
-            phi_2 = phi_1 / h - 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                        expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                        - expand_dims(sigma_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                    expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
-                    - expand_dims(sigma_s2 * phi_12, dims) * model_s
-                    - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - expand_dims(sigma_t * phi_2, dims) * D1
-                        - expand_dims(sigma_t * phi_3, dims) * D2
-                )
-
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
-        else:
-            return x_t
-
-    def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
-        """
-        Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_1, model_prev_0 = model_prev_list
-        t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(
-            t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0 = h_0 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        if self.predict_x0:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(sigma_t / sigma_prev_0, dims) * x
-                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                        - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(sigma_t / sigma_prev_0, dims) * x
-                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                        + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
-                )
-        else:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                        - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                        - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
-                )
-        return x_t
-
-    def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
-        """
-        Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_2, model_prev_1, model_prev_0 = model_prev_list
-        t_prev_2, t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(
-            t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_1 = lambda_prev_1 - lambda_prev_2
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0, r1 = h_0 / h, h_1 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
-        D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
-        D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
-        if self.predict_x0:
-            x_t = (
-                    expand_dims(sigma_t / sigma_prev_0, dims) * x
-                    - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                    + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
-                    - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h ** 2 - 0.5), dims) * D2
-            )
-        else:
-            x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                    - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                    - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
-                    - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h ** 2 - 0.5), dims) * D2
-            )
-        return x_t
-
-    def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None,
-                                     r2=None):
-        """
-        Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-            r1: A `float`. The hyperparameter of the second-order or third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
-        elif order == 2:
-            return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate,
-                                                            solver_type=solver_type, r1=r1)
-        elif order == 3:
-            return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate,
-                                                           solver_type=solver_type, r1=r1, r2=r2)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
-        """
-        Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
-        elif order == 2:
-            return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        elif order == 3:
-            return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5,
-                            solver_type='dpm_solver'):
-        """
-        The adaptive step size solver based on singlestep DPM-Solver.
-        Args:
-            x: A pytorch tensor. The initial value at time `t_T`.
-            order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            h_init: A `float`. The initial step size (for logSNR).
-            atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
-            rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
-            theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
-            t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the
-                current time and `t_0` is less than `t_err`. The default setting is 1e-5.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_0: A pytorch tensor. The approximated solution at time `t_0`.
-        [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
-        """
-        ns = self.noise_schedule
-        s = t_T * torch.ones((x.shape[0],)).to(x)
-        lambda_s = ns.marginal_lambda(s)
-        lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
-        h = h_init * torch.ones_like(s).to(x)
-        x_prev = x
-        nfe = 0
-        if order == 2:
-            r1 = 0.5
-            lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
-                                                                                               solver_type=solver_type,
-                                                                                               **kwargs)
-        elif order == 3:
-            r1, r2 = 1. / 3., 2. / 3.
-            lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
-                                                                                    return_intermediate=True,
-                                                                                    solver_type=solver_type)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2,
-                                                                                              solver_type=solver_type,
-                                                                                              **kwargs)
-        else:
-            raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
-        while torch.abs((s - t_0)).mean() > t_err:
-            t = ns.inverse_lambda(lambda_s + h)
-            x_lower, lower_noise_kwargs = lower_update(x, s, t)
-            x_higher = higher_update(x, s, t, **lower_noise_kwargs)
-            delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
-            norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
-            E = norm_fn((x_higher - x_lower) / delta).max()
-            if torch.all(E <= 1.):
-                x = x_higher
-                s = t
-                x_prev = x_lower
-                lambda_s = ns.marginal_lambda(s)
-            h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
-            nfe += order
-        print('adaptive solver nfe', nfe)
-        return x
-
-    def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
-               method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
-               atol=0.0078, rtol=0.05,
-               ):
-        """
-        Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
-        =====================================================
-        We support the following algorithms for both noise prediction model and data prediction model:
-            - 'singlestep':
-                Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver.
-                We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
-                The total number of function evaluations (NFE) == `steps`.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    - If `order` == 1:
-                        - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
-                        - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
-                        - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                    - If `order` == 3:
-                        - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                        - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
-            - 'multistep':
-                Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
-                We initialize the first `order` values by lower order multistep solvers.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    Denote K = steps.
-                    - If `order` == 1:
-                        - We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
-                    - If `order` == 3:
-                        - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
-            - 'singlestep_fixed':
-                Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
-                We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
-            - 'adaptive':
-                Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
-                We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
-                You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
-                (NFE) and the sample quality.
-                    - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
-                    - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
-        =====================================================
-        Some advices for choosing the algorithm:
-            - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
-                Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
-                            skip_type='time_uniform', method='singlestep')
-            - For **guided sampling with large guidance scale** by DPMs:
-                Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
-                            skip_type='time_uniform', method='multistep')
-        We support three types of `skip_type`:
-            - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
-            - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
-            - 'time_quadratic': quadratic time for the time steps.
-        =====================================================
-        Args:
-            x: A pytorch tensor. The initial value at time `t_start`
-                e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
-            steps: A `int`. The total number of function evaluations (NFE).
-            t_start: A `float`. The starting time of the sampling.
-                If `T` is None, we use self.noise_schedule.T (default is 1.0).
-            t_end: A `float`. The ending time of the sampling.
-                If `t_end` is None, we use 1. / self.noise_schedule.total_N.
-                e.g. if total_N == 1000, we have `t_end` == 1e-3.
-                For discrete-time DPMs:
-                    - We recommend `t_end` == 1. / self.noise_schedule.total_N.
-                For continuous-time DPMs:
-                    - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
-            order: A `int`. The order of DPM-Solver.
-            skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
-            method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
-            denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
-                Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
-                This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
-                score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
-                for diffusion models sampling by diffusion SDEs for low-resolutional images
-                (such as CIFAR-10). However, we observed that such trick does not matter for
-                high-resolutional images. As it needs an additional NFE, we do not recommend
-                it for high-resolutional images.
-            lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
-                Only valid for `method=multistep` and `steps < 15`. We empirically find that
-                this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
-                (especially for steps <= 10). So we recommend to set it to be `True`.
-            solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
-            atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-            rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-        Returns:
-            x_end: A pytorch tensor. The approximated solution at time `t_end`.
-        """
-        t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
-        t_T = self.noise_schedule.T if t_start is None else t_start
-        device = x.device
-        if method == 'adaptive':
-            with torch.no_grad():
-                x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol,
-                                             solver_type=solver_type)
-        elif method == 'multistep':
-            assert steps >= order
-            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
-            assert timesteps.shape[0] - 1 == steps
-            with torch.no_grad():
-                vec_t = timesteps[0].expand((x.shape[0]))
-                model_prev_list = [self.model_fn(x, vec_t)]
-                t_prev_list = [vec_t]
-                # Init the first `order` values by lower order multistep DPM-Solver.
-                for init_order in tqdm(range(1, order), desc="DPM init order"):
-                    vec_t = timesteps[init_order].expand(x.shape[0])
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order,
-                                                         solver_type=solver_type)
-                    model_prev_list.append(self.model_fn(x, vec_t))
-                    t_prev_list.append(vec_t)
-                # Compute the remaining values by `order`-th order multistep DPM-Solver.
-                for step in tqdm(range(order, steps + 1), desc="DPM multistep"):
-                    vec_t = timesteps[step].expand(x.shape[0])
-                    if lower_order_final and steps < 15:
-                        step_order = min(order, steps + 1 - step)
-                    else:
-                        step_order = order
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order,
-                                                         solver_type=solver_type)
-                    for i in range(order - 1):
-                        t_prev_list[i] = t_prev_list[i + 1]
-                        model_prev_list[i] = model_prev_list[i + 1]
-                    t_prev_list[-1] = vec_t
-                    # We do not need to evaluate the final model value.
-                    if step < steps:
-                        model_prev_list[-1] = self.model_fn(x, vec_t)
-        elif method in ['singlestep', 'singlestep_fixed']:
-            if method == 'singlestep':
-                timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order,
-                                                                                              skip_type=skip_type,
-                                                                                              t_T=t_T, t_0=t_0,
-                                                                                              device=device)
-            elif method == 'singlestep_fixed':
-                K = steps // order
-                orders = [order, ] * K
-                timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
-            for i, order in enumerate(orders):
-                t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
-                timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(),
-                                                      N=order, device=device)
-                lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
-                vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
-                h = lambda_inner[-1] - lambda_inner[0]
-                r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
-                r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
-                x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
-        if denoise_to_zero:
-            x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
-        return x
-
-
-#############################################################
-# other utility functions
-#############################################################
-
-def interpolate_fn(x, xp, yp):
-    """
-    A piecewise linear function y = f(x), using xp and yp as keypoints.
-    We implement f(x) in a differentiable way (i.e. applicable for autograd).
-    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
-    Args:
-        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
-        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
-        yp: PyTorch tensor with shape [C, K].
-    Returns:
-        The function values f(x), with shape [N, C].
-    """
-    N, K = x.shape[0], xp.shape[1]
-    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
-    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
-    x_idx = torch.argmin(x_indices, dim=2)
-    cand_start_idx = x_idx - 1
-    start_idx = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(1, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
-    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
-    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
-    start_idx2 = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(0, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
-    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
-    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
-    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
-    return cand
-
-
-def expand_dims(v, dims):
-    """
-    Expand the tensor `v` to the dim `dims`.
-    Args:
-        `v`: a PyTorch tensor with shape [N].
-        `dim`: a `int`.
-    Returns:
-        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
-    """
-    return v[(...,) + (None,) * (dims - 1)]
\ No newline at end of file
diff --git a/spaces/lunarring/latentblending/ldm/modules/image_degradation/__init__.py b/spaces/lunarring/latentblending/ldm/modules/image_degradation/__init__.py
deleted file mode 100644
index 7836cada81f90ded99c58d5942eea4c3477f58fc..0000000000000000000000000000000000000000
--- a/spaces/lunarring/latentblending/ldm/modules/image_degradation/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
-from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
diff --git a/spaces/luxuedong/lxd/src/components/ui/input.tsx b/spaces/luxuedong/lxd/src/components/ui/input.tsx
deleted file mode 100644
index 684a857f3d769b78818fb13de1abaebfb09ca79c..0000000000000000000000000000000000000000
--- a/spaces/luxuedong/lxd/src/components/ui/input.tsx
+++ /dev/null
@@ -1,25 +0,0 @@
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-
-export interface InputProps
-  extends React.InputHTMLAttributes<HTMLInputElement> {}
-
-const Input = React.forwardRef<HTMLInputElement, InputProps>(
-  ({ className, type, ...props }, ref) => {
-    return (
-      <input
-        type={type}
-        className={cn(
-          'flex h-9 w-full rounded-md border border-input bg-transparent px-3 py-2 text-sm shadow-sm ring-offset-background file:border-0 file:bg-transparent file:text-sm file:font-medium placeholder:text-muted-foreground focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50',
-          className
-        )}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Input.displayName = 'Input'
-
-export { Input }
diff --git a/spaces/ma-xu/LIVE/pybind11/.github/ISSUE_TEMPLATE/feature-request.md b/spaces/ma-xu/LIVE/pybind11/.github/ISSUE_TEMPLATE/feature-request.md
deleted file mode 100644
index 5f6ec81ec972b13e38520ef2e37d85022c2642c9..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/pybind11/.github/ISSUE_TEMPLATE/feature-request.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-name: Feature Request
-about: File an issue about adding a feature
-title: "[FEAT] "
----
-
-
-Make sure you've completed the following steps before submitting your issue -- thank you!
-
-1. Check if your feature has already been mentioned / rejected / planned in other issues.
-2. If those resources didn't help, consider asking in the [Gitter chat room][] to see if this is interesting / useful to a larger audience and possible to implement reasonably,
-4. If you have a useful feature that passes the previous items (or not suitable for chat), please fill in the details below.
-
-[Gitter chat room]: https://gitter.im/pybind/Lobby
-
-*After reading, remove this checklist.*
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/temporary_buffer.h b/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/temporary_buffer.h
deleted file mode 100644
index 6b5276141625d61567d3adb06a363682b4df968b..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/cuda/detail/temporary_buffer.h
+++ /dev/null
@@ -1,22 +0,0 @@
-/*
- *  Copyright 2008-2016 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system has no special temporary buffer functions
-
diff --git a/spaces/maksymalist/junk-judge/gen_dataset.py b/spaces/maksymalist/junk-judge/gen_dataset.py
deleted file mode 100644
index 4f2b815fb04e313e5e031242d20ef793988cd0d7..0000000000000000000000000000000000000000
--- a/spaces/maksymalist/junk-judge/gen_dataset.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import torch
-
-MASTER_INDEX = {
-    'biological1': 0, 
-    'cardboard1': 1, 
-    'glass1': 2, 
-    'metal1': 3, 
-    'paper1': 4, 
-    'plastic1': 5, 
-    'trash1': 6,
-    'cardboard2': 7, 
-    'glass2': 8, 
-    'metal2': 9, 
-    'paper2': 10, 
-    'plastic2': 11, 
-    'trash2': 12
-}
-
-def preds_to_data(c1, c2):
-    
-    data = torch.zeros(13)
-    
-    for key in c1.keys():
-        new_key = str(key+"1")
-        data[MASTER_INDEX[new_key]] = c1[str(key)]
-        
-    for key in c2.keys():
-        new_key = str(key+"2")
-        data[MASTER_INDEX[new_key]] = c2[str(key)]
-        
-    return data
-
-def data_to_preds(data):
-    
-    c1 = {}
-    c2 = {}
-    
-    for key in MASTER_INDEX.keys():
-        if key[-1] == "1":
-            c1[key[:-1]] = data[MASTER_INDEX[key]].item()
-        elif key[-1] == "2":
-            c2[key[:-1]] = data[MASTER_INDEX[key]].item()
-            
-    return c1, c2
\ No newline at end of file
diff --git a/spaces/marioboy/neil-breen/vocoder/models/fatchord_version.py b/spaces/marioboy/neil-breen/vocoder/models/fatchord_version.py
deleted file mode 100644
index 70ef1e3f6b99f32cc4fa95f64acfa58268d71ad7..0000000000000000000000000000000000000000
--- a/spaces/marioboy/neil-breen/vocoder/models/fatchord_version.py
+++ /dev/null
@@ -1,434 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from vocoder.distribution import sample_from_discretized_mix_logistic
-from vocoder.display import *
-from vocoder.audio import *
-
-
-class ResBlock(nn.Module):
-    def __init__(self, dims):
-        super().__init__()
-        self.conv1 = nn.Conv1d(dims, dims, kernel_size=1, bias=False)
-        self.conv2 = nn.Conv1d(dims, dims, kernel_size=1, bias=False)
-        self.batch_norm1 = nn.BatchNorm1d(dims)
-        self.batch_norm2 = nn.BatchNorm1d(dims)
-
-    def forward(self, x):
-        residual = x
-        x = self.conv1(x)
-        x = self.batch_norm1(x)
-        x = F.relu(x)
-        x = self.conv2(x)
-        x = self.batch_norm2(x)
-        return x + residual
-
-
-class MelResNet(nn.Module):
-    def __init__(self, res_blocks, in_dims, compute_dims, res_out_dims, pad):
-        super().__init__()
-        k_size = pad * 2 + 1
-        self.conv_in = nn.Conv1d(in_dims, compute_dims, kernel_size=k_size, bias=False)
-        self.batch_norm = nn.BatchNorm1d(compute_dims)
-        self.layers = nn.ModuleList()
-        for i in range(res_blocks):
-            self.layers.append(ResBlock(compute_dims))
-        self.conv_out = nn.Conv1d(compute_dims, res_out_dims, kernel_size=1)
-
-    def forward(self, x):
-        x = self.conv_in(x)
-        x = self.batch_norm(x)
-        x = F.relu(x)
-        for f in self.layers: x = f(x)
-        x = self.conv_out(x)
-        return x
-
-
-class Stretch2d(nn.Module):
-    def __init__(self, x_scale, y_scale):
-        super().__init__()
-        self.x_scale = x_scale
-        self.y_scale = y_scale
-
-    def forward(self, x):
-        b, c, h, w = x.size()
-        x = x.unsqueeze(-1).unsqueeze(3)
-        x = x.repeat(1, 1, 1, self.y_scale, 1, self.x_scale)
-        return x.view(b, c, h * self.y_scale, w * self.x_scale)
-
-
-class UpsampleNetwork(nn.Module):
-    def __init__(self, feat_dims, upsample_scales, compute_dims,
-                 res_blocks, res_out_dims, pad):
-        super().__init__()
-        total_scale = np.cumproduct(upsample_scales)[-1]
-        self.indent = pad * total_scale
-        self.resnet = MelResNet(res_blocks, feat_dims, compute_dims, res_out_dims, pad)
-        self.resnet_stretch = Stretch2d(total_scale, 1)
-        self.up_layers = nn.ModuleList()
-        for scale in upsample_scales:
-            k_size = (1, scale * 2 + 1)
-            padding = (0, scale)
-            stretch = Stretch2d(scale, 1)
-            conv = nn.Conv2d(1, 1, kernel_size=k_size, padding=padding, bias=False)
-            conv.weight.data.fill_(1. / k_size[1])
-            self.up_layers.append(stretch)
-            self.up_layers.append(conv)
-
-    def forward(self, m):
-        aux = self.resnet(m).unsqueeze(1)
-        aux = self.resnet_stretch(aux)
-        aux = aux.squeeze(1)
-        m = m.unsqueeze(1)
-        for f in self.up_layers: m = f(m)
-        m = m.squeeze(1)[:, :, self.indent:-self.indent]
-        return m.transpose(1, 2), aux.transpose(1, 2)
-
-
-class WaveRNN(nn.Module):
-    def __init__(self, rnn_dims, fc_dims, bits, pad, upsample_factors,
-                 feat_dims, compute_dims, res_out_dims, res_blocks,
-                 hop_length, sample_rate, mode='RAW'):
-        super().__init__()
-        self.mode = mode
-        self.pad = pad
-        if self.mode == 'RAW' :
-            self.n_classes = 2 ** bits
-        elif self.mode == 'MOL' :
-            self.n_classes = 30
-        else :
-            RuntimeError("Unknown model mode value - ", self.mode)
-
-        self.rnn_dims = rnn_dims
-        self.aux_dims = res_out_dims // 4
-        self.hop_length = hop_length
-        self.sample_rate = sample_rate
-
-        self.upsample = UpsampleNetwork(feat_dims, upsample_factors, compute_dims, res_blocks, res_out_dims, pad)
-        self.I = nn.Linear(feat_dims + self.aux_dims + 1, rnn_dims)
-        self.rnn1 = nn.GRU(rnn_dims, rnn_dims, batch_first=True)
-        self.rnn2 = nn.GRU(rnn_dims + self.aux_dims, rnn_dims, batch_first=True)
-        self.fc1 = nn.Linear(rnn_dims + self.aux_dims, fc_dims)
-        self.fc2 = nn.Linear(fc_dims + self.aux_dims, fc_dims)
-        self.fc3 = nn.Linear(fc_dims, self.n_classes)
-
-        self.step = nn.Parameter(torch.zeros(1).long(), requires_grad=False)
-        self.num_params()
-
-    def forward(self, x, mels):
-        self.step += 1
-        bsize = x.size(0)
-        if torch.cuda.is_available():
-            h1 = torch.zeros(1, bsize, self.rnn_dims).cuda()
-            h2 = torch.zeros(1, bsize, self.rnn_dims).cuda()
-        else:
-            h1 = torch.zeros(1, bsize, self.rnn_dims).cpu()
-            h2 = torch.zeros(1, bsize, self.rnn_dims).cpu()
-        mels, aux = self.upsample(mels)
-
-        aux_idx = [self.aux_dims * i for i in range(5)]
-        a1 = aux[:, :, aux_idx[0]:aux_idx[1]]
-        a2 = aux[:, :, aux_idx[1]:aux_idx[2]]
-        a3 = aux[:, :, aux_idx[2]:aux_idx[3]]
-        a4 = aux[:, :, aux_idx[3]:aux_idx[4]]
-
-        x = torch.cat([x.unsqueeze(-1), mels, a1], dim=2)
-        x = self.I(x)
-        res = x
-        x, _ = self.rnn1(x, h1)
-
-        x = x + res
-        res = x
-        x = torch.cat([x, a2], dim=2)
-        x, _ = self.rnn2(x, h2)
-
-        x = x + res
-        x = torch.cat([x, a3], dim=2)
-        x = F.relu(self.fc1(x))
-
-        x = torch.cat([x, a4], dim=2)
-        x = F.relu(self.fc2(x))
-        return self.fc3(x)
-
-    def generate(self, mels, batched, target, overlap, mu_law, progress_callback=None):
-        mu_law = mu_law if self.mode == 'RAW' else False
-        progress_callback = progress_callback or self.gen_display
-
-        self.eval()
-        output = []
-        start = time.time()
-        rnn1 = self.get_gru_cell(self.rnn1)
-        rnn2 = self.get_gru_cell(self.rnn2)
-
-        with torch.no_grad():
-            if torch.cuda.is_available():
-                mels = mels.cuda()
-            else:
-                mels = mels.cpu()
-            wave_len = (mels.size(-1) - 1) * self.hop_length
-            mels = self.pad_tensor(mels.transpose(1, 2), pad=self.pad, side='both')
-            mels, aux = self.upsample(mels.transpose(1, 2))
-
-            if batched:
-                mels = self.fold_with_overlap(mels, target, overlap)
-                aux = self.fold_with_overlap(aux, target, overlap)
-
-            b_size, seq_len, _ = mels.size()
-
-            if torch.cuda.is_available():
-                h1 = torch.zeros(b_size, self.rnn_dims).cuda()
-                h2 = torch.zeros(b_size, self.rnn_dims).cuda()
-                x = torch.zeros(b_size, 1).cuda()
-            else:
-                h1 = torch.zeros(b_size, self.rnn_dims).cpu()
-                h2 = torch.zeros(b_size, self.rnn_dims).cpu()
-                x = torch.zeros(b_size, 1).cpu()
-
-            d = self.aux_dims
-            aux_split = [aux[:, :, d * i:d * (i + 1)] for i in range(4)]
-
-            for i in range(seq_len):
-
-                m_t = mels[:, i, :]
-
-                a1_t, a2_t, a3_t, a4_t = (a[:, i, :] for a in aux_split)
-
-                x = torch.cat([x, m_t, a1_t], dim=1)
-                x = self.I(x)
-                h1 = rnn1(x, h1)
-
-                x = x + h1
-                inp = torch.cat([x, a2_t], dim=1)
-                h2 = rnn2(inp, h2)
-
-                x = x + h2
-                x = torch.cat([x, a3_t], dim=1)
-                x = F.relu(self.fc1(x))
-
-                x = torch.cat([x, a4_t], dim=1)
-                x = F.relu(self.fc2(x))
-
-                logits = self.fc3(x)
-
-                if self.mode == 'MOL':
-                    sample = sample_from_discretized_mix_logistic(logits.unsqueeze(0).transpose(1, 2))
-                    output.append(sample.view(-1))
-                    if torch.cuda.is_available():
-                        # x = torch.FloatTensor([[sample]]).cuda()
-                        x = sample.transpose(0, 1).cuda()
-                    else:
-                        x = sample.transpose(0, 1)
-
-                elif self.mode == 'RAW' :
-                    posterior = F.softmax(logits, dim=1)
-                    distrib = torch.distributions.Categorical(posterior)
-
-                    sample = 2 * distrib.sample().float() / (self.n_classes - 1.) - 1.
-                    output.append(sample)
-                    x = sample.unsqueeze(-1)
-                else:
-                    raise RuntimeError("Unknown model mode value - ", self.mode)
-
-                if i % 100 == 0:
-                    gen_rate = (i + 1) / (time.time() - start) * b_size / 1000
-                    progress_callback(i, seq_len, b_size, gen_rate)
-
-        output = torch.stack(output).transpose(0, 1)
-        output = output.cpu().numpy()
-        output = output.astype(np.float64)
-        
-        if batched:
-            output = self.xfade_and_unfold(output, target, overlap)
-        else:
-            output = output[0]
-
-        if mu_law:
-            output = decode_mu_law(output, self.n_classes, False)
-        if hp.apply_preemphasis:
-            output = de_emphasis(output)
-
-        # Fade-out at the end to avoid signal cutting out suddenly
-        fade_out = np.linspace(1, 0, 20 * self.hop_length)
-        output = output[:wave_len]
-        output[-20 * self.hop_length:] *= fade_out
-        
-        self.train()
-
-        return output
-
-
-    def gen_display(self, i, seq_len, b_size, gen_rate):
-        pbar = progbar(i, seq_len)
-        msg = f'| {pbar} {i*b_size}/{seq_len*b_size} | Batch Size: {b_size} | Gen Rate: {gen_rate:.1f}kHz | '
-        stream(msg)
-
-    def get_gru_cell(self, gru):
-        gru_cell = nn.GRUCell(gru.input_size, gru.hidden_size)
-        gru_cell.weight_hh.data = gru.weight_hh_l0.data
-        gru_cell.weight_ih.data = gru.weight_ih_l0.data
-        gru_cell.bias_hh.data = gru.bias_hh_l0.data
-        gru_cell.bias_ih.data = gru.bias_ih_l0.data
-        return gru_cell
-
-    def pad_tensor(self, x, pad, side='both'):
-        # NB - this is just a quick method i need right now
-        # i.e., it won't generalise to other shapes/dims
-        b, t, c = x.size()
-        total = t + 2 * pad if side == 'both' else t + pad
-        if torch.cuda.is_available():
-            padded = torch.zeros(b, total, c).cuda()
-        else:
-            padded = torch.zeros(b, total, c).cpu()
-        if side == 'before' or side == 'both':
-            padded[:, pad:pad + t, :] = x
-        elif side == 'after':
-            padded[:, :t, :] = x
-        return padded
-
-    def fold_with_overlap(self, x, target, overlap):
-
-        ''' Fold the tensor with overlap for quick batched inference.
-            Overlap will be used for crossfading in xfade_and_unfold()
-
-        Args:
-            x (tensor)    : Upsampled conditioning features.
-                            shape=(1, timesteps, features)
-            target (int)  : Target timesteps for each index of batch
-            overlap (int) : Timesteps for both xfade and rnn warmup
-
-        Return:
-            (tensor) : shape=(num_folds, target + 2 * overlap, features)
-
-        Details:
-            x = [[h1, h2, ... hn]]
-
-            Where each h is a vector of conditioning features
-
-            Eg: target=2, overlap=1 with x.size(1)=10
-
-            folded = [[h1, h2, h3, h4],
-                      [h4, h5, h6, h7],
-                      [h7, h8, h9, h10]]
-        '''
-
-        _, total_len, features = x.size()
-
-        # Calculate variables needed
-        num_folds = (total_len - overlap) // (target + overlap)
-        extended_len = num_folds * (overlap + target) + overlap
-        remaining = total_len - extended_len
-
-        # Pad if some time steps poking out
-        if remaining != 0:
-            num_folds += 1
-            padding = target + 2 * overlap - remaining
-            x = self.pad_tensor(x, padding, side='after')
-
-        if torch.cuda.is_available():
-            folded = torch.zeros(num_folds, target + 2 * overlap, features).cuda()
-        else:
-            folded = torch.zeros(num_folds, target + 2 * overlap, features).cpu()
-
-        # Get the values for the folded tensor
-        for i in range(num_folds):
-            start = i * (target + overlap)
-            end = start + target + 2 * overlap
-            folded[i] = x[:, start:end, :]
-
-        return folded
-
-    def xfade_and_unfold(self, y, target, overlap):
-
-        ''' Applies a crossfade and unfolds into a 1d array.
-
-        Args:
-            y (ndarry)    : Batched sequences of audio samples
-                            shape=(num_folds, target + 2 * overlap)
-                            dtype=np.float64
-            overlap (int) : Timesteps for both xfade and rnn warmup
-
-        Return:
-            (ndarry) : audio samples in a 1d array
-                       shape=(total_len)
-                       dtype=np.float64
-
-        Details:
-            y = [[seq1],
-                 [seq2],
-                 [seq3]]
-
-            Apply a gain envelope at both ends of the sequences
-
-            y = [[seq1_in, seq1_target, seq1_out],
-                 [seq2_in, seq2_target, seq2_out],
-                 [seq3_in, seq3_target, seq3_out]]
-
-            Stagger and add up the groups of samples:
-
-            [seq1_in, seq1_target, (seq1_out + seq2_in), seq2_target, ...]
-
-        '''
-
-        num_folds, length = y.shape
-        target = length - 2 * overlap
-        total_len = num_folds * (target + overlap) + overlap
-
-        # Need some silence for the rnn warmup
-        silence_len = overlap // 2
-        fade_len = overlap - silence_len
-        silence = np.zeros((silence_len), dtype=np.float64)
-
-        # Equal power crossfade
-        t = np.linspace(-1, 1, fade_len, dtype=np.float64)
-        fade_in = np.sqrt(0.5 * (1 + t))
-        fade_out = np.sqrt(0.5 * (1 - t))
-
-        # Concat the silence to the fades
-        fade_in = np.concatenate([silence, fade_in])
-        fade_out = np.concatenate([fade_out, silence])
-
-        # Apply the gain to the overlap samples
-        y[:, :overlap] *= fade_in
-        y[:, -overlap:] *= fade_out
-
-        unfolded = np.zeros((total_len), dtype=np.float64)
-
-        # Loop to add up all the samples
-        for i in range(num_folds):
-            start = i * (target + overlap)
-            end = start + target + 2 * overlap
-            unfolded[start:end] += y[i]
-
-        return unfolded
-
-    def get_step(self) :
-        return self.step.data.item()
-
-    def checkpoint(self, model_dir, optimizer) :
-        k_steps = self.get_step() // 1000
-        self.save(model_dir.joinpath("checkpoint_%dk_steps.pt" % k_steps), optimizer)
-
-    def log(self, path, msg) :
-        with open(path, 'a') as f:
-            print(msg, file=f)
-
-    def load(self, path, optimizer) :
-        checkpoint = torch.load(path)
-        if "optimizer_state" in checkpoint:
-            self.load_state_dict(checkpoint["model_state"])
-            optimizer.load_state_dict(checkpoint["optimizer_state"])
-        else:
-            # Backwards compatibility
-            self.load_state_dict(checkpoint)
-
-    def save(self, path, optimizer) :
-        torch.save({
-            "model_state": self.state_dict(),
-            "optimizer_state": optimizer.state_dict(),
-        }, path)
-
-    def num_params(self, print_out=True):
-        parameters = filter(lambda p: p.requires_grad, self.parameters())
-        parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
-        if print_out :
-            print('Trainable Parameters: %.3fM' % parameters)
diff --git a/spaces/mattmdjaga/segment_anything_base/app.py b/spaces/mattmdjaga/segment_anything_base/app.py
deleted file mode 100644
index 2a36476b3ac752fddfe0031f42471d5a4a9eee3c..0000000000000000000000000000000000000000
--- a/spaces/mattmdjaga/segment_anything_base/app.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import gradio as gr
-import numpy as np
-import torch
-from PIL import Image, ImageDraw
-import requests
-from transformers import SamModel, SamProcessor
-import cv2
-from typing import List
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# Load model and processor
-model = SamModel.from_pretrained("facebook/sam-vit-base").to(device)
-processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
-
-cache_data = None
-
-def mask_2_dots(mask: np.ndarray) -> List[List[int]]:
-    gray = cv2.cvtColor(mask, cv2.COLOR_RGB2GRAY)
-    _, thresh = cv2.threshold(gray, 127, 255, 0)
-    kernel = np.ones((5,5),np.uint8)
-    closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
-    contours, _ = cv2.findContours(closed, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-    points = []
-    for contour in contours:
-        moments = cv2.moments(contour)
-        cx = int(moments['m10']/moments['m00'])
-        cy = int(moments['m01']/moments['m00'])
-        points.append([cx, cy])
-    return [points]
-
-@torch.no_grad()
-def foward_pass(image_input: np.ndarray, points: List[List[int]]) -> np.ndarray:
-    global cache_data
-    image_input = Image.fromarray(image_input)
-    inputs = processor(image_input, input_points=points, return_tensors="pt").to(device)
-    if not cache_data or not torch.equal(inputs['pixel_values'],cache_data[0]):
-        embedding = model.get_image_embeddings(inputs["pixel_values"])
-        pixels = inputs["pixel_values"]
-        cache_data = [pixels, embedding]
-    del inputs["pixel_values"]
-
-    outputs = model.forward(image_embeddings=cache_data[1], **inputs)
-    masks = processor.image_processor.post_process_masks(
-        outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
-    )
-    masks = masks[0].squeeze(0).numpy().transpose(1, 2, 0)
-
-    return masks
-
-def main_func(inputs) -> List[Image.Image]:
-    dots = inputs['mask']
-    points = mask_2_dots(dots)
-    image_input = inputs['image']
-    masks = foward_pass(image_input, points)
-
-    image_input = Image.fromarray(image_input)
-    draw = ImageDraw.Draw(image_input)
-    for point in points[0]:
-        draw.ellipse((point[0] - 10, point[1] - 10, point[0] + 10, point[1] + 10), fill="red")
-
-    pred_masks = [image_input]
-    for i in range(masks.shape[2]):
-        pred_masks.append(Image.fromarray((masks[:,:,i] * 255).astype(np.uint8)))
-
-    return pred_masks
-
-def reset_data():
-    global cache_data
-    cache_data = None
-
-with gr.Blocks() as demo:
-    gr.Markdown("# How to use")
-    gr.Markdown("To start, input an image, then use the brush to create dots on the object which you want to segment, don't worry if your dots aren't perfect as the code will find the middle of each drawn item. Then press the segment button to create masks for the object that the dots are on.")
-    gr.Markdown("# Demo to run Segment Anything base model")
-    gr.Markdown("""This app uses the [Segment Anything](https://huggingface.co/facebook/sam-vit-base) model from Meta to get a mask from a points in an image.
-    """)
-    with gr.Tab("Flip Image"):
-        with gr.Row():
-            image_input = gr.Image(tool='sketch')
-            image_output = gr.Gallery()
-        
-        image_button = gr.Button("Segment Image")
-
-    image_button.click(main_func, inputs=image_input, outputs=image_output)
-    image_input.upload(reset_data)
-
-demo.launch()
diff --git a/spaces/maxmax20160403/vits_chinese/utils.py b/spaces/maxmax20160403/vits_chinese/utils.py
deleted file mode 100644
index f193a3e225b368fe7324852994676ad7236c970e..0000000000000000000000000000000000000000
--- a/spaces/maxmax20160403/vits_chinese/utils.py
+++ /dev/null
@@ -1,319 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
-    iteration = checkpoint_dict["iteration"]
-    learning_rate = checkpoint_dict["learning_rate"]
-    if optimizer is not None:
-        optimizer.load_state_dict(checkpoint_dict["optimizer"])
-    saved_state_dict = checkpoint_dict["model"]
-    if hasattr(model, "module"):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():
-        try:
-            new_state_dict[k] = saved_state_dict[k]
-        except:
-            logger.info("%s is not in the checkpoint" % k)
-            new_state_dict[k] = v
-    if hasattr(model, "module"):
-        model.module.load_state_dict(new_state_dict)
-    else:
-        model.load_state_dict(new_state_dict)
-    logger.info(
-        "Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration)
-    )
-    return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
-    logger.info(
-        "Saving model and optimizer state at iteration {} to {}".format(
-            iteration, checkpoint_path
-        )
-    )
-    if hasattr(model, "module"):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    torch.save(
-        {
-            "model": state_dict,
-            "iteration": iteration,
-            "optimizer": optimizer.state_dict(),
-            "learning_rate": learning_rate,
-        },
-        checkpoint_path,
-    )
-
-
-def load_model(checkpoint_path, model):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
-    saved_state_dict = checkpoint_dict["model"]
-    if hasattr(model, "module"):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():
-        try:
-            new_state_dict[k] = saved_state_dict[k]
-        except:
-            logger.info("%s is not in the checkpoint" % k)
-            new_state_dict[k] = v
-    if hasattr(model, "module"):
-        model.module.load_state_dict(new_state_dict)
-    else:
-        model.load_state_dict(new_state_dict)
-    return model
-
-
-def save_model(model, checkpoint_path):
-    if hasattr(model, 'module'):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    torch.save({'model': state_dict}, checkpoint_path)
-
-
-def summarize(
-    writer,
-    global_step,
-    scalars={},
-    histograms={},
-    images={},
-    audios={},
-    audio_sampling_rate=22050,
-):
-    for k, v in scalars.items():
-        writer.add_scalar(k, v, global_step)
-    for k, v in histograms.items():
-        writer.add_histogram(k, v, global_step)
-    for k, v in images.items():
-        writer.add_image(k, v, global_step, dataformats="HWC")
-    for k, v in audios.items():
-        writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
-    f_list = glob.glob(os.path.join(dir_path, regex))
-    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
-    x = f_list[-1]
-    print(x)
-    return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger("matplotlib")
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
-    plt.colorbar(im, ax=ax)
-    plt.xlabel("Frames")
-    plt.ylabel("Channels")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger("matplotlib")
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    im = ax.imshow(
-        alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
-    )
-    fig.colorbar(im, ax=ax)
-    xlabel = "Decoder timestep"
-    if info is not None:
-        xlabel += "\n\n" + info
-    plt.xlabel(xlabel)
-    plt.ylabel("Encoder timestep")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def load_wav_to_torch(full_path):
-    sampling_rate, data = read(full_path)
-    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-    with open(filename, encoding="utf-8") as f:
-        filepaths_and_text = []
-        for line in f:
-            path_text = line.strip().split(split)
-            filepaths_and_text.append(path_text)
-    return filepaths_and_text
-
-
-def get_hparams(init=True):
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-c",
-        "--config",
-        type=str,
-        default="./configs/bert_vits.json",
-        help="JSON file for configuration",
-    )
-    parser.add_argument("-m", "--model", type=str, required=True, help="Model name")
-
-    args = parser.parse_args()
-    model_dir = os.path.join("./logs", args.model)
-
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-
-    config_path = args.config
-    config_save_path = os.path.join(model_dir, "config.json")
-    if init:
-        with open(config_path, "r") as f:
-            data = f.read()
-        with open(config_save_path, "w") as f:
-            f.write(data)
-    else:
-        with open(config_save_path, "r") as f:
-            data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_dir(model_dir):
-    config_save_path = os.path.join(model_dir, "config.json")
-    with open(config_save_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_file(config_path):
-    with open(config_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def check_git_hash(model_dir):
-    source_dir = os.path.dirname(os.path.realpath(__file__))
-    if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warn(
-            "{} is not a git repository, therefore hash value comparison will be ignored.".format(
-                source_dir
-            )
-        )
-        return
-
-    cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-    path = os.path.join(model_dir, "githash")
-    if os.path.exists(path):
-        saved_hash = open(path).read()
-        if saved_hash != cur_hash:
-            logger.warn(
-                "git hash values are different. {}(saved) != {}(current)".format(
-                    saved_hash[:8], cur_hash[:8]
-                )
-            )
-    else:
-        open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-    global logger
-    logger = logging.getLogger(os.path.basename(model_dir))
-    logger.setLevel(logging.DEBUG)
-
-    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    h = logging.FileHandler(os.path.join(model_dir, filename))
-    h.setLevel(logging.DEBUG)
-    h.setFormatter(formatter)
-    logger.addHandler(h)
-    return logger
-
-
-class HParams:
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if type(v) == dict:
-                v = HParams(**v)
-            self[k] = v
-
-    def keys(self):
-        return self.__dict__.keys()
-
-    def items(self):
-        return self.__dict__.items()
-
-    def values(self):
-        return self.__dict__.values()
-
-    def __len__(self):
-        return len(self.__dict__)
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-    def __setitem__(self, key, value):
-        return setattr(self, key, value)
-
-    def __contains__(self, key):
-        return key in self.__dict__
-
-    def __repr__(self):
-        return self.__dict__.__repr__()
diff --git a/spaces/mehdidc/text_to_image_ddgan/EMA.py b/spaces/mehdidc/text_to_image_ddgan/EMA.py
deleted file mode 100644
index e99bf0b068ae1b68da5d24b46a237706c987d3fb..0000000000000000000000000000000000000000
--- a/spaces/mehdidc/text_to_image_ddgan/EMA.py
+++ /dev/null
@@ -1,106 +0,0 @@
-# ---------------------------------------------------------------
-# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the NVIDIA Source Code License
-# for Denoising Diffusion GAN. To view a copy of this license, see the LICENSE file.
-# ---------------------------------------------------------------
-
-'''
-Codes adapted from https://github.com/NVlabs/LSGM/blob/main/util/ema.py
-'''
-import warnings
-
-import torch
-from torch.optim import Optimizer
-
-
-class EMA(Optimizer):
-    def __init__(self, opt, ema_decay, memory_efficient=False):
-        self.ema_decay = ema_decay
-        self.apply_ema = self.ema_decay > 0.
-        self.optimizer = opt
-        self.state = opt.state
-        self.param_groups = opt.param_groups
-        self.defaults = {}
-        self.memory_efficient = memory_efficient
-
-    def step(self, *args, **kwargs):
-        # for group in self.optimizer.param_groups:
-            # group.setdefault('amsgrad', False)
-            # group.setdefault('maximize', False)
-            # group.setdefault('foreach', None)
-            # group.setdefault('capturable', False)
-            # group.setdefault('differentiable', False)
-            # group.setdefault('fused', False)
-        retval = self.optimizer.step(*args, **kwargs)
-
-        # stop here if we are not applying EMA
-        if not self.apply_ema:
-            return retval
-
-        ema, params = {}, {}
-        for group in self.optimizer.param_groups:
-            for i, p in enumerate(group['params']):
-                if p.grad is None:
-                    continue
-                state = self.optimizer.state[p]
-
-                # State initialization
-                if 'ema' not in state:
-                    state['ema'] = p.data.clone()
-                if p.shape not in params:
-                    params[p.shape] = {'idx': 0, 'data': []}
-                    ema[p.shape] = []
-
-                params[p.shape]['data'].append(p.data)
-                ema[p.shape].append(state['ema'])
-            
-            # def stack(d, dim=0):
-                # return torch.stack([di.cpu() for di in d], dim=dim).cuda()
-
-            for i in params:
-                if self.memory_efficient:
-                    for j in range(len(params[i]['data'])):
-                        ema[i][j].mul_(self.ema_decay).add_(params[i]['data'][j], alpha=1. - self.ema_decay)
-                    ema[i] = torch.stack(ema[i], dim=0)
-                else:
-                    params[i]['data'] = torch.stack(params[i]['data'], dim=0)
-                    ema[i] = torch.stack(ema[i], dim=0)
-                    ema[i].mul_(self.ema_decay).add_(params[i]['data'], alpha=1. - self.ema_decay)
-
-            for p in group['params']:
-                if p.grad is None:
-                    continue
-                idx = params[p.shape]['idx']
-                self.optimizer.state[p]['ema'] = ema[p.shape][idx, :]
-                params[p.shape]['idx'] += 1
-
-        return retval
-
-    def load_state_dict(self, state_dict):
-        super(EMA, self).load_state_dict(state_dict)
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-        self.optimizer.state = self.state
-        self.optimizer.param_groups = self.param_groups
-
-    def swap_parameters_with_ema(self, store_params_in_ema):
-        """ This function swaps parameters with their ema values. It records original parameters in the ema
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-
-        # stop here if we are not applying EMA
-        if not self.apply_ema:
-            warnings.warn('swap_parameters_with_ema was called when there is no EMA weights.')
-            return
-
-        for group in self.optimizer.param_groups:
-            for i, p in enumerate(group['params']):
-                if not p.requires_grad:
-                    continue
-                ema = self.optimizer.state[p]['ema']
-                if store_params_in_ema:
-                    tmp = p.data.detach()
-                    p.data = ema.detach()
-                    self.optimizer.state[p]['ema'] = tmp
-                else:
-                    p.data = ema.detach()
diff --git a/spaces/merve/anonymization/source/third_party/d3_.js b/spaces/merve/anonymization/source/third_party/d3_.js
deleted file mode 100644
index 9c4b6815ec3cdc0e9f8a072b2d05be7ad48fa703..0000000000000000000000000000000000000000
--- a/spaces/merve/anonymization/source/third_party/d3_.js
+++ /dev/null
@@ -1,143 +0,0 @@
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- */
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diff --git a/spaces/merve/hidden-bias/source/third_party/alea.js b/spaces/merve/hidden-bias/source/third_party/alea.js
deleted file mode 100644
index 9effe485ca14df5d6923e20adefaa794b939ee26..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/source/third_party/alea.js
+++ /dev/null
@@ -1,3 +0,0 @@
-// https://github.com/davidbau/seedrandom Copyright 2019 David Bau
-
-!function(n,t,e){function u(n){var t=this,e=function(){var s=4022871197;return function(n){n=String(n);for(var t=0;t<n.length;t++){var e=.02519603282416938*(s+=n.charCodeAt(t));e-=s=e>>>0,s=(e*=s)>>>0,s+=4294967296*(e-=s)}return 2.3283064365386963e-10*(s>>>0)}}();t.next=function(){var n=2091639*t.s0+2.3283064365386963e-10*t.c;return t.s0=t.s1,t.s1=t.s2,t.s2=n-(t.c=0|n)},t.c=1,t.s0=e(" "),t.s1=e(" "),t.s2=e(" "),t.s0-=e(n),t.s0<0&&(t.s0+=1),t.s1-=e(n),t.s1<0&&(t.s1+=1),t.s2-=e(n),t.s2<0&&(t.s2+=1),e=null}function o(n,t){return t.c=n.c,t.s0=n.s0,t.s1=n.s1,t.s2=n.s2,t}function s(n,t){var e=new u(n),s=t&&t.state,r=e.next;return r.int32=function(){return 4294967296*e.next()|0},r.double=function(){return r()+11102230246251565e-32*(2097152*r()|0)},r.quick=r,s&&("object"==typeof s&&o(s,e),r.state=function(){return o(e,{})}),r}t&&t.exports?t.exports=s:e&&e.amd?e(function(){return s}):this.alea=s}(0,"object"==typeof module&&module,"function"==typeof define&&define);
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diff --git a/spaces/metricspace/OcTra/df_local/config.py b/spaces/metricspace/OcTra/df_local/config.py
deleted file mode 100644
index c785140d559532ac3afa69e139c4ac063a0517ef..0000000000000000000000000000000000000000
--- a/spaces/metricspace/OcTra/df_local/config.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import os
-import string
-from configparser import ConfigParser
-from shlex import shlex
-from typing import Any, List, Optional, Tuple, Type, TypeVar, Union
-
-from loguru import logger
-
-T = TypeVar("T")
-
-
-class DfParams:
-    def __init__(self):
-        # Sampling rate used for training
-        self.sr: int = config("SR", cast=int, default=48_000, section="DF")
-        # FFT size in samples
-        self.fft_size: int = config("FFT_SIZE", cast=int, default=960, section="DF")
-        # STFT Hop size in samples
-        self.hop_size: int = config("HOP_SIZE", cast=int, default=480, section="DF")
-        # Number of ERB bands
-        self.nb_erb: int = config("NB_ERB", cast=int, default=32, section="DF")
-        # Number of deep filtering bins; DF is applied from 0th to nb_df-th frequency bins
-        self.nb_df: int = config("NB_DF", cast=int, default=96, section="DF")
-        # Normalization decay factor; used for complex and erb features
-        self.norm_tau: float = config("NORM_TAU", 1, float, section="DF")
-        # Local SNR minimum value, ground truth will be truncated
-        self.lsnr_max: int = config("LSNR_MAX", 35, int, section="DF")
-        # Local SNR maximum value, ground truth will be truncated
-        self.lsnr_min: int = config("LSNR_MIN", -15, int, section="DF")
-        # Minimum number of frequency bins per ERB band
-        self.min_nb_freqs = config("MIN_NB_ERB_FREQS", 2, int, section="DF")
-        # Deep Filtering order
-        self.df_order: int = config("DF_ORDER", cast=int, default=5, section="DF")
-        # Deep Filtering look-ahead
-        self.df_lookahead: int = config("DF_LOOKAHEAD", cast=int, default=0, section="DF")
-        # Pad mode. By default, padding will be handled on the input side:
-        # - `input`, which pads the input features passed to the model
-        # - `output`, which pads the output spectrogram corresponding to `df_lookahead`
-        self.pad_mode: str = config("PAD_MODE", default="input_specf", section="DF")
-
-
-class Config:
-    """Adopted from python-decouple"""
-
-    DEFAULT_SECTION = "settings"
-
-    def __init__(self):
-        self.parser: ConfigParser = None  # type: ignore
-        self.path = ""
-        self.modified = False
-        self.allow_defaults = True
-
-    def load(
-        self, path: Optional[str], config_must_exist=False, allow_defaults=True, allow_reload=False
-    ):
-        self.allow_defaults = allow_defaults
-        if self.parser is not None and not allow_reload:
-            raise ValueError("Config already loaded")
-        self.parser = ConfigParser()
-        self.path = path
-        if path is not None and os.path.isfile(path):
-            with open(path) as f:
-                self.parser.read_file(f)
-        else:
-            if config_must_exist:
-                raise ValueError(f"No config file found at '{path}'.")
-        if not self.parser.has_section(self.DEFAULT_SECTION):
-            self.parser.add_section(self.DEFAULT_SECTION)
-        self._fix_clc()
-        self._fix_df()
-
-    def use_defaults(self):
-        self.load(path=None, config_must_exist=False)
-
-    def save(self, path: str):
-        if not self.modified:
-            logger.debug("Config not modified. No need to overwrite on disk.")
-            return
-        if self.parser is None:
-            self.parser = ConfigParser()
-        for section in self.parser.sections():
-            if len(self.parser[section]) == 0:
-                self.parser.remove_section(section)
-        with open(path, mode="w") as f:
-            self.parser.write(f)
-
-    def tostr(self, value, cast):
-        if isinstance(cast, Csv) and isinstance(value, (tuple, list)):
-            return "".join(str(v) + cast.delimiter for v in value)[:-1]
-        return str(value)
-
-    def set(self, option: str, value: T, cast: Type[T], section: Optional[str] = None) -> T:
-        section = self.DEFAULT_SECTION if section is None else section
-        section = section.lower()
-        if not self.parser.has_section(section):
-            self.parser.add_section(section)
-        if self.parser.has_option(section, option):
-            if value == self.cast(self.parser.get(section, option), cast):
-                return value
-        self.modified = True
-        self.parser.set(section, option, self.tostr(value, cast))
-        return value
-
-    def __call__(
-        self,
-        option: str,
-        default: Any = None,
-        cast: Type[T] = str,
-        save: bool = True,
-        section: Optional[str] = None,
-    ) -> T:
-        # Get value either from an ENV or from the .ini file
-        section = self.DEFAULT_SECTION if section is None else section
-        value = None
-        if self.parser is None:
-            raise ValueError("No configuration loaded")
-        if not self.parser.has_section(section.lower()):
-            self.parser.add_section(section.lower())
-        if option in os.environ:
-            value = os.environ[option]
-            if save:
-                self.parser.set(section, option, self.tostr(value, cast))
-        elif self.parser.has_option(section, option):
-            value = self.read_from_section(section, option, default, cast=cast, save=save)
-        elif self.parser.has_option(section.lower(), option):
-            value = self.read_from_section(section.lower(), option, default, cast=cast, save=save)
-        elif self.parser.has_option(self.DEFAULT_SECTION, option):
-            logger.warning(
-                f"Couldn't find option {option} in section {section}. "
-                "Falling back to default settings section."
-            )
-            value = self.read_from_section(self.DEFAULT_SECTION, option, cast=cast, save=save)
-        elif default is None:
-            raise ValueError("Value {} not found.".format(option))
-        elif not self.allow_defaults and save:
-            raise ValueError(f"Value '{option}' not found in config (defaults not allowed).")
-        else:
-            value = default
-            if save:
-                self.set(option, value, cast, section)
-        return self.cast(value, cast)
-
-    def cast(self, value, cast):
-        # Do the casting to get the correct type
-        if cast is bool:
-            value = str(value).lower()
-            if value in {"true", "yes", "y", "on", "1"}:
-                return True  # type: ignore
-            elif value in {"false", "no", "n", "off", "0"}:
-                return False  # type: ignore
-            raise ValueError("Parse error")
-        return cast(value)
-
-    def get(self, option: str, cast: Type[T] = str, section: Optional[str] = None) -> T:
-        section = self.DEFAULT_SECTION if section is None else section
-        if not self.parser.has_section(section):
-            raise KeyError(section)
-        if not self.parser.has_option(section, option):
-            raise KeyError(option)
-        return self.cast(self.parser.get(section, option), cast)
-
-    def read_from_section(
-        self, section: str, option: str, default: Any = None, cast: Type = str, save: bool = True
-    ) -> str:
-        value = self.parser.get(section, option)
-        if not save:
-            # Set to default or remove to not read it at trainig start again
-            if default is None:
-                self.parser.remove_option(section, option)
-            elif not self.allow_defaults:
-                raise ValueError(f"Value '{option}' not found in config (defaults not allowed).")
-            else:
-                self.parser.set(section, option, self.tostr(default, cast))
-        elif section.lower() != section:
-            self.parser.set(section.lower(), option, self.tostr(value, cast))
-            self.parser.remove_option(section, option)
-            self.modified = True
-        return value
-
-    def overwrite(self, section: str, option: str, value: Any):
-        if not self.parser.has_section(section):
-            return ValueError(f"Section not found: '{section}'")
-        if not self.parser.has_option(section, option):
-            return ValueError(f"Option not found '{option}' in section '{section}'")
-        self.modified = True
-        cast = type(value)
-        return self.parser.set(section, option, self.tostr(value, cast))
-
-    def _fix_df(self):
-        """Renaming of some groups/options for compatibility with old models."""
-        if self.parser.has_section("deepfilternet") and self.parser.has_section("df"):
-            sec_deepfilternet = self.parser["deepfilternet"]
-            sec_df = self.parser["df"]
-            if "df_order" in sec_deepfilternet:
-                sec_df["df_order"] = sec_deepfilternet["df_order"]
-                del sec_deepfilternet["df_order"]
-            if "df_lookahead" in sec_deepfilternet:
-                sec_df["df_lookahead"] = sec_deepfilternet["df_lookahead"]
-                del sec_deepfilternet["df_lookahead"]
-
-    def _fix_clc(self):
-        """Renaming of some groups/options for compatibility with old models."""
-        if (
-            not self.parser.has_section("deepfilternet")
-            and self.parser.has_section("train")
-            and self.parser.get("train", "model") == "convgru5"
-        ):
-            self.overwrite("train", "model", "deepfilternet")
-            self.parser.add_section("deepfilternet")
-            self.parser["deepfilternet"] = self.parser["convgru"]
-            del self.parser["convgru"]
-        if not self.parser.has_section("df") and self.parser.has_section("clc"):
-            self.parser["df"] = self.parser["clc"]
-            del self.parser["clc"]
-        for section in self.parser.sections():
-            for k, v in self.parser[section].items():
-                if "clc" in k.lower():
-                    self.parser.set(section, k.lower().replace("clc", "df"), v)
-                    del self.parser[section][k]
-
-    def __repr__(self):
-        msg = ""
-        for section in self.parser.sections():
-            msg += f"{section}:\n"
-            for k, v in self.parser[section].items():
-                msg += f"  {k}: {v}\n"
-        return msg
-
-
-config = Config()
-
-
-class Csv(object):
-    """
-    Produces a csv parser that return a list of transformed elements. From python-decouple.
-    """
-
-    def __init__(
-        self, cast: Type[T] = str, delimiter=",", strip=string.whitespace, post_process=list
-    ):
-        """
-        Parameters:
-        cast -- callable that transforms the item just before it's added to the list.
-        delimiter -- string of delimiters chars passed to shlex.
-        strip -- string of non-relevant characters to be passed to str.strip after the split.
-        post_process -- callable to post process all casted values. Default is `list`.
-        """
-        self.cast: Type[T] = cast
-        self.delimiter = delimiter
-        self.strip = strip
-        self.post_process = post_process
-
-    def __call__(self, value: Union[str, Tuple[T], List[T]]) -> List[T]:
-        """The actual transformation"""
-        if isinstance(value, (tuple, list)):
-            # if default value is a list
-            value = "".join(str(v) + self.delimiter for v in value)[:-1]
-
-        def transform(s):
-            return self.cast(s.strip(self.strip))
-
-        splitter = shlex(value, posix=True)
-        splitter.whitespace = self.delimiter
-        splitter.whitespace_split = True
-
-        return self.post_process(transform(s) for s in splitter)
diff --git a/spaces/mfrashad/CharacterGAN/netdissect/fullablate.py b/spaces/mfrashad/CharacterGAN/netdissect/fullablate.py
deleted file mode 100644
index f92d2c514c0b92b3f33653c5b53198c9fd09cb80..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/CharacterGAN/netdissect/fullablate.py
+++ /dev/null
@@ -1,235 +0,0 @@
-import torch, sys, os, argparse, textwrap, numbers, numpy, json, PIL
-from torchvision import transforms
-from torch.utils.data import TensorDataset
-from netdissect.progress import default_progress, post_progress, desc_progress
-from netdissect.progress import verbose_progress, print_progress
-from netdissect.nethook import edit_layers
-from netdissect.zdataset import standard_z_sample
-from netdissect.autoeval import autoimport_eval
-from netdissect.easydict import EasyDict
-from netdissect.modelconfig import create_instrumented_model
-
-help_epilog = '''\
-Example:
-
-python -m netdissect.evalablate \
-      --segmenter "netdissect.GanImageSegmenter(segvocab='lowres', segsizes=[160,288], segdiv='quad')" \
-      --model "proggan.from_pth_file('models/lsun_models/${SCENE}_lsun.pth')" \
-      --outdir dissect/dissectdir \
-      --classname tree \
-      --layer layer4 \
-      --size 1000
-
-Output layout:
-dissectdir/layer5/ablation/mirror-iqr.json
-{ class: "mirror",
-  classnum: 43,
-  pixel_total: 41342300,
-  class_pixels: 1234531,
-  layer: "layer5",
-  ranking: "mirror-iqr",
-  ablation_units: [341, 23, 12, 142, 83, ...]
-  ablation_pixels: [143242, 132344, 429931, ...]
-}
-
-'''
-
-def main():
-    # Training settings
-    def strpair(arg):
-        p = tuple(arg.split(':'))
-        if len(p) == 1:
-            p = p + p
-        return p
-
-    parser = argparse.ArgumentParser(description='Ablation eval',
-            epilog=textwrap.dedent(help_epilog),
-            formatter_class=argparse.RawDescriptionHelpFormatter)
-    parser.add_argument('--model', type=str, default=None,
-                        help='constructor for the model to test')
-    parser.add_argument('--pthfile', type=str, default=None,
-                        help='filename of .pth file for the model')
-    parser.add_argument('--outdir', type=str, default='dissect', required=True,
-                        help='directory for dissection output')
-    parser.add_argument('--layer', type=strpair,
-                        help='space-separated list of layer names to edit' + 
-                        ', in the form layername[:reportedname]')
-    parser.add_argument('--classname', type=str,
-                        help='class name to ablate')
-    parser.add_argument('--metric', type=str, default='iou',
-                        help='ordering metric for selecting units')
-    parser.add_argument('--unitcount', type=int, default=30,
-                        help='number of units to ablate')
-    parser.add_argument('--segmenter', type=str,
-                        help='directory containing segmentation dataset')
-    parser.add_argument('--netname', type=str, default=None,
-                        help='name for network in generated reports')
-    parser.add_argument('--batch_size', type=int, default=25,
-                        help='batch size for forward pass')
-    parser.add_argument('--mixed_units', action='store_true', default=False,
-                        help='true to keep alpha for non-zeroed units')
-    parser.add_argument('--size', type=int, default=200,
-                        help='number of images to test')
-    parser.add_argument('--no-cuda', action='store_true', default=False,
-                        help='disables CUDA usage')
-    parser.add_argument('--quiet', action='store_true', default=False,
-                        help='silences console output')
-    if len(sys.argv) == 1:
-        parser.print_usage(sys.stderr)
-        sys.exit(1)
-    args = parser.parse_args()
-
-    # Set up console output
-    verbose_progress(not args.quiet)
-
-    # Speed up pytorch
-    torch.backends.cudnn.benchmark = True
-
-    # Set up CUDA
-    args.cuda = not args.no_cuda and torch.cuda.is_available()
-    if args.cuda:
-        torch.backends.cudnn.benchmark = True
-
-    # Take defaults for model constructor etc from dissect.json settings.
-    with open(os.path.join(args.outdir, 'dissect.json')) as f:
-        dissection = EasyDict(json.load(f))
-    if args.model is None:
-        args.model = dissection.settings.model
-    if args.pthfile is None:
-        args.pthfile = dissection.settings.pthfile
-    if args.segmenter is None:
-        args.segmenter = dissection.settings.segmenter
-    if args.layer is None:
-        args.layer = dissection.settings.layers[0]
-    args.layers = [args.layer]
-
-    # Also load specific analysis
-    layername = args.layer[1]
-    if args.metric == 'iou':
-        summary = dissection
-    else:
-        with open(os.path.join(args.outdir, layername, args.metric,
-                args.classname, 'summary.json')) as f:
-            summary = EasyDict(json.load(f))
-
-    # Instantiate generator
-    model = create_instrumented_model(args, gen=True, edit=True)
-    if model is None:
-        print('No model specified')
-        sys.exit(1)
-
-    # Instantiate model
-    device = next(model.parameters()).device
-    input_shape = model.input_shape
-
-    # 4d input if convolutional, 2d input if first layer is linear.
-    raw_sample = standard_z_sample(args.size, input_shape[1], seed=3).view(
-            (args.size,) + input_shape[1:])
-    dataset = TensorDataset(raw_sample)
-
-    # Create the segmenter
-    segmenter = autoimport_eval(args.segmenter)
-
-    # Now do the actual work.
-    labelnames, catnames = (
-                segmenter.get_label_and_category_names(dataset))
-    label_category = [catnames.index(c) if c in catnames else 0
-            for l, c in labelnames]
-    labelnum_from_name = {n[0]: i for i, n in enumerate(labelnames)}
-
-    segloader = torch.utils.data.DataLoader(dataset,
-                batch_size=args.batch_size, num_workers=10,
-                pin_memory=(device.type == 'cuda'))
-
-    # Index the dissection layers by layer name.
-
-    # First, collect a baseline
-    for l in model.ablation:
-        model.ablation[l] = None
-
-    # For each sort-order, do an ablation
-    progress = default_progress()
-    classname = args.classname
-    classnum = labelnum_from_name[classname]
-
-    # Get iou ranking from dissect.json
-    iou_rankname = '%s-%s' % (classname, 'iou')
-    dissect_layer = {lrec.layer: lrec for lrec in dissection.layers}
-    iou_ranking = next(r for r in dissect_layer[layername].rankings
-                if r.name == iou_rankname)
-
-    # Get trained ranking from summary.json
-    rankname = '%s-%s' % (classname, args.metric)
-    summary_layer = {lrec.layer: lrec for lrec in summary.layers}
-    ranking = next(r for r in summary_layer[layername].rankings
-                if r.name == rankname)
-
-    # Get ordering, first by ranking, then break ties by iou.
-    ordering = [t[2] for t in sorted([(s1, s2, i)
-        for i, (s1, s2) in enumerate(zip(ranking.score, iou_ranking.score))])]
-    values = (-numpy.array(ranking.score))[ordering]
-    if not args.mixed_units:
-        values[...] = 1
-
-    ablationdir = os.path.join(args.outdir, layername, 'fullablation')
-    measurements = measure_full_ablation(segmenter, segloader,
-            model, classnum, layername,
-            ordering[:args.unitcount], values[:args.unitcount])
-    measurements = measurements.cpu().numpy().tolist()
-    os.makedirs(ablationdir, exist_ok=True)
-    with open(os.path.join(ablationdir, '%s.json'%rankname), 'w') as f:
-        json.dump(dict(
-            classname=classname,
-            classnum=classnum,
-            baseline=measurements[0],
-            layer=layername,
-            metric=args.metric,
-            ablation_units=ordering,
-            ablation_values=values.tolist(),
-            ablation_effects=measurements[1:]), f)
-
-def measure_full_ablation(segmenter, loader, model, classnum, layer,
-        ordering, values):
-    '''
-    Quick and easy counting of segmented pixels reduced by ablating units.
-    '''
-    progress = default_progress()
-    device = next(model.parameters()).device
-    feature_units = model.feature_shape[layer][1]
-    feature_shape = model.feature_shape[layer][2:]
-    repeats = len(ordering)
-    total_scores = torch.zeros(repeats + 1)
-    print(ordering)
-    print(values.tolist())
-    with torch.no_grad():
-        for l in model.ablation:
-            model.ablation[l] = None
-        for i, [ibz] in enumerate(progress(loader)):
-            ibz = ibz.cuda()
-            for num_units in progress(range(len(ordering) + 1)):
-                ablation = torch.zeros(feature_units, device=device)
-                ablation[ordering[:num_units]] = torch.tensor(
-                        values[:num_units]).to(ablation.device, ablation.dtype)
-                model.ablation[layer] = ablation
-                tensor_images = model(ibz)
-                seg = segmenter.segment_batch(tensor_images, downsample=2)
-                mask = (seg == classnum).max(1)[0]
-                total_scores[num_units] += mask.sum().float().cpu()
-    return total_scores
-
-def count_segments(segmenter, loader, model):
-    total_bincount = 0
-    data_size = 0
-    progress = default_progress()
-    for i, batch in enumerate(progress(loader)):
-        tensor_images = model(z_batch.to(device))
-        seg = segmenter.segment_batch(tensor_images, downsample=2)
-        bc = (seg + index[:, None, None, None] * self.num_classes).view(-1
-                ).bincount(minlength=z_batch.shape[0] * self.num_classes)
-        data_size += seg.shape[0] * seg.shape[2] * seg.shape[3]
-        total_bincount += batch_label_counts.float().sum(0)
-    normalized_bincount = total_bincount / data_size
-    return normalized_bincount
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/mfrashad/CharacterGAN/netdissect/zdataset.py b/spaces/mfrashad/CharacterGAN/netdissect/zdataset.py
deleted file mode 100644
index eb085d83d676fa1e4b1f1b053dc6f1ba2ff35381..0000000000000000000000000000000000000000
--- a/spaces/mfrashad/CharacterGAN/netdissect/zdataset.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import os, torch, numpy
-from torch.utils.data import TensorDataset
-
-def z_dataset_for_model(model, size=100, seed=1):
-    return TensorDataset(z_sample_for_model(model, size, seed))
-
-def z_sample_for_model(model, size=100, seed=1):
-    # If the model is marked with an input shape, use it.
-    if hasattr(model, 'input_shape'):
-        sample = standard_z_sample(size, model.input_shape[1], seed=seed).view(
-                (size,) + model.input_shape[1:])
-        return sample
-    # Examine first conv in model to determine input feature size.
-    first_layer = [c for c in model.modules()
-            if isinstance(c, (torch.nn.Conv2d, torch.nn.ConvTranspose2d,
-                torch.nn.Linear))][0]
-    # 4d input if convolutional, 2d input if first layer is linear.
-    if isinstance(first_layer, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
-        sample = standard_z_sample(
-                size, first_layer.in_channels, seed=seed)[:,:,None,None]
-    else:
-        sample = standard_z_sample(
-                size, first_layer.in_features, seed=seed)
-    return sample
-
-def standard_z_sample(size, depth, seed=1, device=None):
-	'''
-	Generate a standard set of random Z as a (size, z_dimension) tensor.
-	With the same random seed, it always returns the same z (e.g.,
-	the first one is always the same regardless of the size.)
-	'''
-	# Use numpy RandomState since it can be done deterministically
-	# without affecting global state
-	rng = numpy.random.RandomState(seed)
-	result = torch.from_numpy(
-			rng.standard_normal(size * depth)
-			.reshape(size, depth)).float()
-	if device is not None:
-		result = result.to(device)
-	return result
-
diff --git a/spaces/mkshing/rinna-japanese-gpt-neox-3.6b-instruction-x/app.py b/spaces/mkshing/rinna-japanese-gpt-neox-3.6b-instruction-x/app.py
deleted file mode 100644
index 647220175330a4208639a27ef9e8fc08d3b801a4..0000000000000000000000000000000000000000
--- a/spaces/mkshing/rinna-japanese-gpt-neox-3.6b-instruction-x/app.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import os
-import itertools
-
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import gradio as gr
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-print(f"device: {device}")
-
-tokenizer = AutoTokenizer.from_pretrained(
-    "rinna/japanese-gpt-neox-3.6b-instruction-sft", use_fast=False
-)
-model = AutoModelForCausalLM.from_pretrained(
-    "rinna/japanese-gpt-neox-3.6b-instruction-sft",
-    device_map="auto",
-    torch_dtype=torch.float16,
-)
-model = model.to(device)
-
-
-@torch.no_grad()
-def inference_func(prompt, max_new_tokens=128, temperature=0.7):
-    token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
-    output_ids = model.generate(
-        token_ids.to(model.device),
-        do_sample=True,
-        max_new_tokens=max_new_tokens,
-        temperature=temperature,
-        pad_token_id=tokenizer.pad_token_id,
-        bos_token_id=tokenizer.bos_token_id,
-        eos_token_id=tokenizer.eos_token_id,
-    )
-    output = tokenizer.decode(
-        output_ids.tolist()[0][token_ids.size(1) :], skip_special_tokens=True
-    )
-    output = output.replace("<NL>", "\n")
-    return output
-
-
-def make_prompt(message, chat_history, max_context_size: int = 10):
-    contexts = chat_history + [[message, ""]]
-    contexts = list(itertools.chain.from_iterable(contexts))
-    if max_context_size > 0:
-        context_size = max_context_size - 1
-    else:
-        context_size = 100000
-    contexts = contexts[-context_size:]
-    prompt = []
-    for idx, context in enumerate(reversed(contexts)):
-        if idx % 2 == 0:
-            prompt = [f"システム: {context}"] + prompt
-        else:
-            prompt = [f"ユーザー: {context}"] + prompt
-    prompt = "<NL>".join(prompt)
-    return prompt
-
-
-def interact_func(message, chat_history, max_context_size, max_new_tokens, temperature):
-    prompt = make_prompt(message, chat_history, max_context_size)
-    print(f"prompt: {prompt}")
-    generated = inference_func(prompt, max_new_tokens, temperature)
-    print(f"generated: {generated}")
-    chat_history.append((message, generated))
-    return "", chat_history
-
-
-ORIGINAL_SPACE_ID = "mkshing/rinna-japanese-gpt-neox-3.6b-instruction-sft"
-SPACE_ID = os.getenv("SPACE_ID", ORIGINAL_SPACE_ID)
-SHARED_UI_WARNING = f"""# Attention - This Space doesn't work in this shared UI. You can duplicate and use it with a paid private T4 GPU.
-<center><a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></center>
-"""
-
-if os.getenv("SYSTEM") == "spaces" and SPACE_ID != ORIGINAL_SPACE_ID:
-    SETTINGS = (
-        f'<a href="https://huggingface.co/spaces/{SPACE_ID}/settings">Settings</a>'
-    )
-else:
-    SETTINGS = "Settings"
-CUDA_NOT_AVAILABLE_WARNING = f"""# Attention - Running on CPU.
-<center>
-You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces.
-"T4 small" is sufficient to run this demo.
-</center>
-"""
-
-
-def show_warning(warning_text: str) -> gr.Blocks:
-    with gr.Blocks() as demo:
-        with gr.Box():
-            gr.Markdown(warning_text)
-    return demo
-
-with gr.Blocks() as demo:
-    if os.getenv('IS_SHARED_UI'):
-        show_warning(SHARED_UI_WARNING)
-    if not torch.cuda.is_available():
-        show_warning(CUDA_NOT_AVAILABLE_WARNING)
-    gr.Markdown("""# Chat with `rinna/japanese-gpt-neox-3.6b-instruction-sft`
-    <a href=\"https://colab.research.google.com/github/mkshing/notebooks/blob/main/rinna_japanese_gpt_neox_3_6b_instruction_sft.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>
-
-    This demo is a chat UI for [rinna/japanese-gpt-neox-3.6b-instruction-sft](https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-sft). 
-    """)
-    with gr.Accordion("Configs", open=False):
-        # max_context_size = the number of turns * 2
-        max_context_size = gr.Number(value=10, label="max_context_size", precision=0)
-        max_new_tokens = gr.Number(value=128, label="max_new_tokens", precision=0)
-        temperature = gr.Slider(0.0, 2.0, value=0.7, step=0.1, label="temperature")
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox()
-    clear = gr.Button("Clear")
-    msg.submit(
-        interact_func,
-        [msg, chatbot, max_context_size, max_new_tokens, temperature],
-        [msg, chatbot],
-    )
-    clear.click(lambda: None, None, chatbot, queue=False)
-
-if __name__ == "__main__":
-    demo.launch(debug=True)
diff --git a/spaces/monra/freegpt-webui/g4f/Provider/Providers/Theb.py b/spaces/monra/freegpt-webui/g4f/Provider/Providers/Theb.py
deleted file mode 100644
index aa43ebc55d74ffaa722fe008424fce97c622a323..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui/g4f/Provider/Providers/Theb.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import os
-import json
-import time
-import subprocess
-
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://theb.ai'
-model = ['gpt-3.5-turbo']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-
-    path = os.path.dirname(os.path.realpath(__file__))
-    config = json.dumps({
-        'messages': messages,
-        'model': model}, separators=(',', ':'))
-    
-    cmd = ['python3', f'{path}/helpers/theb.py', config]
-
-    p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
-
-    for line in iter(p.stdout.readline, b''):
-        yield line.decode('utf-8')
-        
-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/mshukor/UnIVAL/fairseq/examples/roberta/wsc/wsc_utils.py b/spaces/mshukor/UnIVAL/fairseq/examples/roberta/wsc/wsc_utils.py
deleted file mode 100644
index da6ba74383a2490e1108609f315f44ad4b3bf002..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/roberta/wsc/wsc_utils.py
+++ /dev/null
@@ -1,241 +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 json
-from functools import lru_cache
-
-
-def convert_sentence_to_json(sentence):
-    if "_" in sentence:
-        prefix, rest = sentence.split("_", 1)
-        query, rest = rest.split("_", 1)
-        query_index = len(prefix.rstrip().split(" "))
-    else:
-        query, query_index = None, None
-
-    prefix, rest = sentence.split("[", 1)
-    pronoun, rest = rest.split("]", 1)
-    pronoun_index = len(prefix.rstrip().split(" "))
-
-    sentence = sentence.replace("_", "").replace("[", "").replace("]", "")
-
-    return {
-        "idx": 0,
-        "text": sentence,
-        "target": {
-            "span1_index": query_index,
-            "span1_text": query,
-            "span2_index": pronoun_index,
-            "span2_text": pronoun,
-        },
-    }
-
-
-def extended_noun_chunks(sentence):
-    noun_chunks = {(np.start, np.end) for np in sentence.noun_chunks}
-    np_start, cur_np = 0, "NONE"
-    for i, token in enumerate(sentence):
-        np_type = token.pos_ if token.pos_ in {"NOUN", "PROPN"} else "NONE"
-        if np_type != cur_np:
-            if cur_np != "NONE":
-                noun_chunks.add((np_start, i))
-            if np_type != "NONE":
-                np_start = i
-            cur_np = np_type
-    if cur_np != "NONE":
-        noun_chunks.add((np_start, len(sentence)))
-    return [sentence[s:e] for (s, e) in sorted(noun_chunks)]
-
-
-def find_token(sentence, start_pos):
-    found_tok = None
-    for tok in sentence:
-        if tok.idx == start_pos:
-            found_tok = tok
-            break
-    return found_tok
-
-
-def find_span(sentence, search_text, start=0):
-    search_text = search_text.lower()
-    for tok in sentence[start:]:
-        remainder = sentence[tok.i :].text.lower()
-        if remainder.startswith(search_text):
-            len_to_consume = len(search_text)
-            start_idx = tok.idx
-            for next_tok in sentence[tok.i :]:
-                end_idx = next_tok.idx + len(next_tok.text)
-                if end_idx - start_idx == len_to_consume:
-                    span = sentence[tok.i : next_tok.i + 1]
-                    return span
-    return None
-
-
-@lru_cache(maxsize=1)
-def get_detokenizer():
-    from sacremoses import MosesDetokenizer
-
-    detok = MosesDetokenizer(lang="en")
-    return detok
-
-
-@lru_cache(maxsize=1)
-def get_spacy_nlp():
-    import en_core_web_lg
-
-    nlp = en_core_web_lg.load()
-    return nlp
-
-
-def jsonl_iterator(input_fname, positive_only=False, ngram_order=3, eval=False):
-    detok = get_detokenizer()
-    nlp = get_spacy_nlp()
-
-    with open(input_fname) as fin:
-        for line in fin:
-            sample = json.loads(line.strip())
-
-            if positive_only and "label" in sample and not sample["label"]:
-                # only consider examples where the query is correct
-                continue
-
-            target = sample["target"]
-
-            # clean up the query
-            query = target["span1_text"]
-            if query is not None:
-                if "\n" in query:
-                    continue
-                if query.endswith(".") or query.endswith(","):
-                    query = query[:-1]
-
-            # split tokens
-            tokens = sample["text"].split(" ")
-
-            def strip_pronoun(x):
-                return x.rstrip('.,"')
-
-            # find the pronoun
-            pronoun_idx = target["span2_index"]
-            pronoun = strip_pronoun(target["span2_text"])
-            if strip_pronoun(tokens[pronoun_idx]) != pronoun:
-                # hack: sometimes the index is misaligned
-                if strip_pronoun(tokens[pronoun_idx + 1]) == pronoun:
-                    pronoun_idx += 1
-                else:
-                    raise Exception("Misaligned pronoun!")
-            assert strip_pronoun(tokens[pronoun_idx]) == pronoun
-
-            # split tokens before and after the pronoun
-            before = tokens[:pronoun_idx]
-            after = tokens[pronoun_idx + 1 :]
-
-            # the GPT BPE attaches leading spaces to tokens, so we keep track
-            # of whether we need spaces before or after the pronoun
-            leading_space = " " if pronoun_idx > 0 else ""
-            trailing_space = " " if len(after) > 0 else ""
-
-            # detokenize
-            before = detok.detokenize(before, return_str=True)
-            pronoun = detok.detokenize([pronoun], return_str=True)
-            after = detok.detokenize(after, return_str=True)
-
-            # hack: when the pronoun ends in a period (or comma), move the
-            # punctuation to the "after" part
-            if pronoun.endswith(".") or pronoun.endswith(","):
-                after = pronoun[-1] + trailing_space + after
-                pronoun = pronoun[:-1]
-
-            # hack: when the "after" part begins with a comma or period, remove
-            # the trailing space
-            if after.startswith(".") or after.startswith(","):
-                trailing_space = ""
-
-            # parse sentence with spacy
-            sentence = nlp(before + leading_space + pronoun + trailing_space + after)
-
-            # find pronoun span
-            start = len(before + leading_space)
-            first_pronoun_tok = find_token(sentence, start_pos=start)
-            pronoun_span = find_span(sentence, pronoun, start=first_pronoun_tok.i)
-            assert pronoun_span.text == pronoun
-
-            if eval:
-                # convert to format where pronoun is surrounded by "[]" and
-                # query is surrounded by "_"
-                query_span = find_span(sentence, query)
-                query_with_ws = "_{}_{}".format(
-                    query_span.text,
-                    (" " if query_span.text_with_ws.endswith(" ") else ""),
-                )
-                pronoun_with_ws = "[{}]{}".format(
-                    pronoun_span.text,
-                    (" " if pronoun_span.text_with_ws.endswith(" ") else ""),
-                )
-                if query_span.start < pronoun_span.start:
-                    first = (query_span, query_with_ws)
-                    second = (pronoun_span, pronoun_with_ws)
-                else:
-                    first = (pronoun_span, pronoun_with_ws)
-                    second = (query_span, query_with_ws)
-                sentence = (
-                    sentence[: first[0].start].text_with_ws
-                    + first[1]
-                    + sentence[first[0].end : second[0].start].text_with_ws
-                    + second[1]
-                    + sentence[second[0].end :].text
-                )
-                yield sentence, sample.get("label", None)
-            else:
-                yield sentence, pronoun_span, query, sample.get("label", None)
-
-
-def winogrande_jsonl_iterator(input_fname, eval=False):
-    with open(input_fname) as fin:
-        for line in fin:
-            sample = json.loads(line.strip())
-            sentence, option1, option2 = (
-                sample["sentence"],
-                sample["option1"],
-                sample["option2"],
-            )
-
-            pronoun_span = (sentence.index("_"), sentence.index("_") + 1)
-
-            if eval:
-                query, cand = option1, option2
-            else:
-                query = option1 if sample["answer"] == "1" else option2
-                cand = option2 if sample["answer"] == "1" else option1
-            yield sentence, pronoun_span, query, cand
-
-
-def filter_noun_chunks(
-    chunks, exclude_pronouns=False, exclude_query=None, exact_match=False
-):
-    if exclude_pronouns:
-        chunks = [
-            np
-            for np in chunks
-            if (np.lemma_ != "-PRON-" and not all(tok.pos_ == "PRON" for tok in np))
-        ]
-
-    if exclude_query is not None:
-        excl_txt = [exclude_query.lower()]
-        filtered_chunks = []
-        for chunk in chunks:
-            lower_chunk = chunk.text.lower()
-            found = False
-            for excl in excl_txt:
-                if (
-                    not exact_match and (lower_chunk in excl or excl in lower_chunk)
-                ) or lower_chunk == excl:
-                    found = True
-                    break
-            if not found:
-                filtered_chunks.append(chunk)
-        chunks = filtered_chunks
-
-    return chunks
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/data/random_input_dataset.py b/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/data/random_input_dataset.py
deleted file mode 100644
index 886505616cc7f7a515ecebf34fae5c2bc541de03..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/wav2vec/unsupervised/data/random_input_dataset.py
+++ /dev/null
@@ -1,62 +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 random
-from typing import List
-
-from fairseq.data import BaseWrapperDataset, data_utils
-
-
-class RandomInputDataset(BaseWrapperDataset):
-    def __init__(
-        self,
-        dataset,
-        random_input_dataset,
-        input_key_path: List[str],
-        add_to_input,
-        pad_idx,
-    ):
-        super().__init__(dataset)
-        self.random_input_dataset = random_input_dataset
-        if isinstance(input_key_path, str):
-            input_key_path = [input_key_path]
-        assert len(input_key_path) > 0
-        self.input_key_path = input_key_path
-        self.add_to_input = add_to_input
-        self.pad_idx = pad_idx
-
-    def get_target(self, item):
-        target_loc = item
-        for p in self.input_key_path[:-1]:
-            target_loc = target_loc[p]
-        return self.input_key_path[-1], target_loc
-
-    def get_target_value(self, item):
-        k, target_loc = self.get_target(item)
-        return target_loc[k]
-
-    def __getitem__(self, index):
-        item = self.dataset[index]
-        k, target_loc = self.get_target(item)
-        target_loc[k] = random.choice(self.random_input_dataset)
-        return item
-
-    def collater(self, samples):
-        collated = self.dataset.collater(samples)
-        if len(collated) == 0:
-            return collated
-        indices = set(collated["id"].tolist())
-
-        random_inputs = data_utils.collate_tokens(
-            [self.get_target_value(s) for s in samples if s["id"] in indices],
-            pad_idx=self.pad_idx,
-            left_pad=False,
-        )
-        k, target_loc = self.get_target(
-            collated if not self.add_to_input else collated["net_input"]
-        )
-        target_loc[k] = random_inputs
-
-        return collated
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/criterions/tacotron2_loss.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/criterions/tacotron2_loss.py
deleted file mode 100644
index 8c7b655c8c52f8fa478b4568850ec8f741dab78e..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/criterions/tacotron2_loss.py
+++ /dev/null
@@ -1,210 +0,0 @@
-# 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 logging
-from typing import Any, Dict, List
-from functools import lru_cache
-from dataclasses import dataclass, field
-
-import torch
-from omegaconf import II
-
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from fairseq.data.data_utils import lengths_to_mask
-import torch.nn.functional as F
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class Tacotron2CriterionConfig(FairseqDataclass):
-    bce_pos_weight: float = field(
-        default=1.0,
-        metadata={"help": "weight of positive examples for BCE loss"},
-    )
-    n_frames_per_step: int = field(
-        default=0,
-        metadata={"help": "Number of frames per decoding step"},
-    )
-    use_guided_attention_loss: bool = field(
-        default=False,
-        metadata={"help": "use guided attention loss"},
-    )
-    guided_attention_loss_sigma: float = field(
-        default=0.4,
-        metadata={"help": "weight of positive examples for BCE loss"},
-    )
-    ctc_weight: float = field(
-        default=0.0, metadata={"help": "weight for CTC loss"}
-    )
-    sentence_avg: bool = II("optimization.sentence_avg")
-
-
-class GuidedAttentionLoss(torch.nn.Module):
-    """
-    Efficiently Trainable Text-to-Speech System Based on Deep Convolutional
-    Networks with Guided Attention (https://arxiv.org/abs/1710.08969)
-    """
-
-    def __init__(self, sigma):
-        super().__init__()
-        self.sigma = sigma
-
-    @staticmethod
-    @lru_cache(maxsize=8)
-    def _get_weight(s_len, t_len, sigma):
-        grid_x, grid_y = torch.meshgrid(torch.arange(t_len), torch.arange(s_len))
-        grid_x = grid_x.to(s_len.device)
-        grid_y = grid_y.to(s_len.device)
-        w = (grid_y.float() / s_len - grid_x.float() / t_len) ** 2
-        return 1.0 - torch.exp(-w / (2 * (sigma ** 2)))
-
-    def _get_weights(self, src_lens, tgt_lens):
-        bsz, max_s_len, max_t_len = len(src_lens), max(src_lens), max(tgt_lens)
-        weights = torch.zeros((bsz, max_t_len, max_s_len))
-        for i, (s_len, t_len) in enumerate(zip(src_lens, tgt_lens)):
-            weights[i, :t_len, :s_len] = self._get_weight(s_len, t_len,
-                                                          self.sigma)
-        return weights
-
-    @staticmethod
-    def _get_masks(src_lens, tgt_lens):
-        in_masks = lengths_to_mask(src_lens)
-        out_masks = lengths_to_mask(tgt_lens)
-        return out_masks.unsqueeze(2) & in_masks.unsqueeze(1)
-
-    def forward(self, attn, src_lens, tgt_lens, reduction="mean"):
-        weights = self._get_weights(src_lens, tgt_lens).to(attn.device)
-        masks = self._get_masks(src_lens, tgt_lens).to(attn.device)
-        loss = (weights * attn.transpose(1, 2)).masked_select(masks)
-        loss = torch.sum(loss) if reduction == "sum" else torch.mean(loss)
-        return loss
-
-
-@register_criterion("tacotron2", dataclass=Tacotron2CriterionConfig)
-class Tacotron2Criterion(FairseqCriterion):
-    def __init__(self, task, sentence_avg, n_frames_per_step,
-                 use_guided_attention_loss, guided_attention_loss_sigma,
-                 bce_pos_weight, ctc_weight):
-        super().__init__(task)
-        self.sentence_avg = sentence_avg
-        self.n_frames_per_step = n_frames_per_step
-        self.bce_pos_weight = bce_pos_weight
-
-        self.guided_attn = None
-        if use_guided_attention_loss:
-            self.guided_attn = GuidedAttentionLoss(guided_attention_loss_sigma)
-        self.ctc_weight = ctc_weight
-
-    def forward(self, model, sample, reduction="mean"):
-        bsz, max_len, _ = sample["target"].size()
-        feat_tgt = sample["target"]
-        feat_len = sample["target_lengths"].view(bsz, 1).expand(-1, max_len)
-        eos_tgt = torch.arange(max_len).to(sample["target"].device)
-        eos_tgt = eos_tgt.view(1, max_len).expand(bsz, -1)
-        eos_tgt = (eos_tgt == (feat_len - 1)).float()
-        src_tokens = sample["net_input"]["src_tokens"]
-        src_lens = sample["net_input"]["src_lengths"]
-        tgt_lens = sample["target_lengths"]
-
-        feat_out, eos_out, extra = model(
-            src_tokens=src_tokens,
-            src_lengths=src_lens,
-            prev_output_tokens=sample["net_input"]["prev_output_tokens"],
-            incremental_state=None,
-            target_lengths=tgt_lens,
-            speaker=sample["speaker"]
-        )
-
-        l1_loss, mse_loss, eos_loss = self.compute_loss(
-            extra["feature_out"], feat_out, eos_out, feat_tgt, eos_tgt,
-            tgt_lens, reduction,
-        )
-        attn_loss = torch.tensor(0.).type_as(l1_loss)
-        if self.guided_attn is not None:
-            attn_loss = self.guided_attn(extra['attn'], src_lens, tgt_lens, reduction)
-        ctc_loss = torch.tensor(0.).type_as(l1_loss)
-        if self.ctc_weight > 0.:
-            net_output = (feat_out, eos_out, extra)
-            lprobs = model.get_normalized_probs(net_output, log_probs=True)
-            lprobs = lprobs.transpose(0, 1)  # T x B x C
-            src_mask = lengths_to_mask(src_lens)
-            src_tokens_flat = src_tokens.masked_select(src_mask)
-            ctc_loss = F.ctc_loss(
-                lprobs, src_tokens_flat, tgt_lens, src_lens,
-                reduction=reduction, zero_infinity=True
-            ) * self.ctc_weight
-        loss = l1_loss + mse_loss + eos_loss + attn_loss + ctc_loss
-
-        sample_size = sample["nsentences"] if self.sentence_avg \
-            else sample["ntokens"]
-        logging_output = {
-            "loss": utils.item(loss.data),
-            "ntokens": sample["ntokens"],
-            "nsentences": sample["nsentences"],
-            "sample_size": sample_size,
-            "l1_loss": utils.item(l1_loss.data),
-            "mse_loss": utils.item(mse_loss.data),
-            "eos_loss": utils.item(eos_loss.data),
-            "attn_loss": utils.item(attn_loss.data),
-            "ctc_loss": utils.item(ctc_loss.data),
-        }
-        return loss, sample_size, logging_output
-
-    def compute_loss(self, feat_out, feat_out_post, eos_out, feat_tgt,
-                     eos_tgt, tgt_lens, reduction="mean"):
-        mask = lengths_to_mask(tgt_lens)
-        _eos_out = eos_out[mask].squeeze()
-        _eos_tgt = eos_tgt[mask]
-        _feat_tgt = feat_tgt[mask]
-        _feat_out = feat_out[mask]
-        _feat_out_post = feat_out_post[mask]
-
-        l1_loss = (
-            F.l1_loss(_feat_out, _feat_tgt, reduction=reduction) +
-            F.l1_loss(_feat_out_post, _feat_tgt, reduction=reduction)
-        )
-        mse_loss = (
-            F.mse_loss(_feat_out, _feat_tgt, reduction=reduction) +
-            F.mse_loss(_feat_out_post, _feat_tgt, reduction=reduction)
-        )
-        eos_loss = F.binary_cross_entropy_with_logits(
-            _eos_out, _eos_tgt, pos_weight=torch.tensor(self.bce_pos_weight),
-            reduction=reduction
-        )
-        return l1_loss, mse_loss, eos_loss
-
-    @classmethod
-    def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None:
-        ns = [log.get("sample_size", 0) for log in logging_outputs]
-        ntot = sum(ns)
-        ws = [n / (ntot + 1e-8) for n in ns]
-        for key in ["loss", "l1_loss", "mse_loss", "eos_loss", "attn_loss", "ctc_loss"]:
-            vals = [log.get(key, 0) for log in logging_outputs]
-            val = sum(val * w for val, w in zip(vals, ws))
-            metrics.log_scalar(key, val, ntot, round=3)
-        metrics.log_scalar("sample_size", ntot, len(logging_outputs))
-
-        # inference metrics
-        if "targ_frames" not in logging_outputs[0]:
-            return
-        n = sum(log.get("targ_frames", 0) for log in logging_outputs)
-        for key, new_key in [
-                ("mcd_loss", "mcd_loss"),
-                ("pred_frames", "pred_ratio"),
-                ("nins", "ins_rate"),
-                ("ndel", "del_rate"),
-        ]:
-            val = sum(log.get(key, 0) for log in logging_outputs)
-            metrics.log_scalar(new_key, val / n, n, round=3)
-
-    @staticmethod
-    def logging_outputs_can_be_summed() -> bool:
-        return False
diff --git a/spaces/mshukor/UnIVAL/run_scripts/refcoco/scst/unival_refcocoplus_acc0_5mediumlarge_lreinf5.sh b/spaces/mshukor/UnIVAL/run_scripts/refcoco/scst/unival_refcocoplus_acc0_5mediumlarge_lreinf5.sh
deleted file mode 100644
index a101c6b75ba360111903bd6e6fde5dbfd0294ac4..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/run_scripts/refcoco/scst/unival_refcocoplus_acc0_5mediumlarge_lreinf5.sh
+++ /dev/null
@@ -1,170 +0,0 @@
-#!/usr/bin/env
-
-# The port for communication. Note that if you want to run multiple tasks on the same machine,
-# you need to specify different port numbers.
-# Number of GPUs per GPU worker
-export GPUS_PER_NODE=8
-# Number of GPU workers, for single-worker training, please set to 1
-export NUM_NODES=$SLURM_NNODES
-# The ip address of the rank-0 worker, for single-worker training, please set to localhost
-master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
-export MASTER_ADDR=$master_addr
-
-# The port for communication
-export MASTER_PORT=12350
-# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0
-export RANK=$SLURM_NODEID
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "RANK :$RANK"
-echo "NUM_NODES :$NUM_NODES"
-echo "GPUS_PER_NODE :$GPUS_PER_NODE"
-
-export MIOPEN_USER_DB_PATH=/lus/home/NAT/gda2204/mshukor/.config/miopen_${MASTER_ADDR}_${SLURM_PROCID}/
-
-echo "MIOPEN_USER_DB_PATH :$MIOPEN_USER_DB_PATH"
-
-
-exp_name=unival_refcocoplus_acc0_5mediumlarge_lreinf5
-
-
-
-ofa_dir=/lus/home/NAT/gda2204/mshukor/code/unival
-base_data_dir=/lus/scratch/NAT/gda2204/SHARED/data
-base_log_dir=/work/NAT/gda2204/mshukor/logs
-
-
-save_dir=${base_log_dir}/ofa/checkpoints/refcocoplus/${exp_name}
-log_dir=${save_dir}
-
-
-mkdir -p $log_dir $save_dir
-
-bpe_dir=${ofa_dir}/utils/BPE
-user_dir=${ofa_dir}/ofa_module
-
-image_dir=${base_data_dir}
-
-data_dir=${base_data_dir}/ofa/refcocoplus_data
-data=${data_dir}/refcocoplus_train.tsv,${data_dir}/refcocoplus_val.tsv
-
-
-restore_file=${base_log_dir}/ofa/checkpoints/refcocoplus/unival_refcocoplus/10_3e-5_512/checkpoint_best.pt
-
-selected_cols=0,4,2,3
-
-task=refcoco
-arch=unival_base
-pretrained_model=  
-
-criterion=refcoco_scst_reward_criterion
-# label_smoothing=0.1
-lr=3e-5
-max_epoch=10
-warmup_ratio=0.06
-batch_size=8
-update_freq=4
-resnet_drop_path_rate=0.0
-encoder_drop_path_rate=0.1
-decoder_drop_path_rate=0.1
-dropout=0.1
-attention_dropout=0.0
-max_src_length=80
-max_tgt_length=20
-num_bins=1000
-patch_image_size=512
-
-
-image_encoder_name=timm_resnet #vit_base_patch16_224
-resnet_type=resnet101
-
-save_interval=1
-validate_interval_updates=2000
-save_interval_updates=0
-
-sample_patch_num='--sample-patch-num=784' # ''
-
-
-echo "max_epoch "${max_epoch}
-echo "lr "${lr}
-echo "patch_image_size "${patch_image_size}
-
-log_file=${log_dir}/${max_epoch}"_"${lr}"_"${patch_image_size}".log"
-save_path=${save_dir}/${max_epoch}"_"${lr}"_"${patch_image_size}
-mkdir -p $save_path
-
-
-
-acc_thresh=0.5
-metric=acc
-
-max_area_size=30000
-
-lambda_reinforce=5.0
-medium_area='--medium-area'
-
-python3 -m torch.distributed.launch \
-    --nnodes=${NUM_NODES} \
-    --nproc_per_node=${GPUS_PER_NODE} \
-    --master_port=${MASTER_PORT} \
-    --node_rank=${RANK} \
-    --master_addr=${MASTER_ADDR} \
-    --use_env ${ofa_dir}/train.py \
-    $data \
-    --selected-cols=${selected_cols} \
-    --bpe-dir=${bpe_dir} \
-    --user-dir=${user_dir} \
-    --restore-file=${restore_file} \
-    --reset-optimizer --reset-dataloader --reset-meters \
-    --save-dir=${save_path} \
-    --task=${task} \
-    --arch=${arch} \
-    --criterion=${criterion} \
-    --batch-size=${batch_size} \
-    --update-freq=${update_freq} \
-    --encoder-normalize-before \
-    --decoder-normalize-before \
-    --share-decoder-input-output-embed \
-    --share-all-embeddings \
-    --layernorm-embedding \
-    --patch-layernorm-embedding \
-    --code-layernorm-embedding \
-    --resnet-drop-path-rate=${resnet_drop_path_rate} \
-    --encoder-drop-path-rate=${encoder_drop_path_rate} \
-    --decoder-drop-path-rate=${decoder_drop_path_rate} \
-    --dropout=${dropout} \
-    --attention-dropout=${attention_dropout} \
-    --weight-decay=0.01 --optimizer=adam --adam-betas="(0.9,0.999)" --adam-eps=1e-08 --clip-norm=1.0 \
-    --lr-scheduler=polynomial_decay --lr=${lr} \
-    --max-epoch=${max_epoch} --warmup-ratio=${warmup_ratio} \
-    --log-format=simple --log-interval=10 \
-    --fixed-validation-seed=7 \
-    --no-epoch-checkpoints --keep-best-checkpoints=1 \
-    --save-interval=${save_interval} --validate-interval=1 \
-    --save-interval-updates=${save_interval_updates} --validate-interval-updates=${validate_interval_updates} \
-    --eval-acc \
-    --eval-args='{"beam":5,"min_len":4,"max_len_a":0,"max_len_b":4}' \
-    --best-checkpoint-metric=score --maximize-best-checkpoint-metric \
-    --max-src-length=${max_src_length} \
-    --max-tgt-length=${max_tgt_length} \
-    --find-unused-parameters \
-    --add-type-embedding \
-    --scale-attn \
-    --scale-fc \
-    --scale-heads \
-    --disable-entangle \
-    --num-bins=${num_bins} \
-    --patch-image-size=${patch_image_size} \
-    --fp16 \
-    --fp16-scale-window=512 \
-    --num-workers=0 \
-    --image-dir=${image_dir} \
-    ${sample_patch_num} \
-    --image-encoder-name=${image_encoder_name} \
-    --scst \
-    --scst-args='{"beam":5,"min_len":4,"max_len_a":0,"max_len_b":4}' \
-    --acc-thresh=${acc_thresh} \
-    --metric=${metric} \
-    --max-area-size=${max_area_size} \
-    --lambda-reinforce=${lambda_reinforce} \
-    ${medium_area}
diff --git a/spaces/msmilauer/AutoGPT-duplicated2/autogpt/speech/gtts.py b/spaces/msmilauer/AutoGPT-duplicated2/autogpt/speech/gtts.py
deleted file mode 100644
index 1c3e9cae0567428582891b11eca42f82a64f5c8e..0000000000000000000000000000000000000000
--- a/spaces/msmilauer/AutoGPT-duplicated2/autogpt/speech/gtts.py
+++ /dev/null
@@ -1,22 +0,0 @@
-""" GTTS Voice. """
-import os
-
-import gtts
-from playsound import playsound
-
-from autogpt.speech.base import VoiceBase
-
-
-class GTTSVoice(VoiceBase):
-    """GTTS Voice."""
-
-    def _setup(self) -> None:
-        pass
-
-    def _speech(self, text: str, _: int = 0) -> bool:
-        """Play the given text."""
-        tts = gtts.gTTS(text)
-        tts.save("speech.mp3")
-        playsound("speech.mp3", True)
-        os.remove("speech.mp3")
-        return True
diff --git a/spaces/muellerzr/accelerate-presentation/Accelerate_files/libs/quarto-html/quarto-syntax-highlighting-dark.css b/spaces/muellerzr/accelerate-presentation/Accelerate_files/libs/quarto-html/quarto-syntax-highlighting-dark.css
deleted file mode 100644
index 941c9dfa9bc083a2a13f7546b0d0c727b9df1f68..0000000000000000000000000000000000000000
--- a/spaces/muellerzr/accelerate-presentation/Accelerate_files/libs/quarto-html/quarto-syntax-highlighting-dark.css
+++ /dev/null
@@ -1,187 +0,0 @@
-/* quarto syntax highlight colors */
-:root {
-  --quarto-hl-al-color: #f07178;
-  --quarto-hl-an-color: #d4d0ab;
-  --quarto-hl-at-color: #00e0e0;
-  --quarto-hl-bn-color: #d4d0ab;
-  --quarto-hl-bu-color: #abe338;
-  --quarto-hl-ch-color: #abe338;
-  --quarto-hl-co-color: #f8f8f2;
-  --quarto-hl-cv-color: #ffd700;
-  --quarto-hl-cn-color: #ffd700;
-  --quarto-hl-cf-color: #ffa07a;
-  --quarto-hl-dt-color: #ffa07a;
-  --quarto-hl-dv-color: #d4d0ab;
-  --quarto-hl-do-color: #f8f8f2;
-  --quarto-hl-er-color: #f07178;
-  --quarto-hl-ex-color: #00e0e0;
-  --quarto-hl-fl-color: #d4d0ab;
-  --quarto-hl-fu-color: #ffa07a;
-  --quarto-hl-im-color: #abe338;
-  --quarto-hl-in-color: #d4d0ab;
-  --quarto-hl-kw-color: #ffa07a;
-  --quarto-hl-op-color: #ffa07a;
-  --quarto-hl-ot-color: #00e0e0;
-  --quarto-hl-pp-color: #dcc6e0;
-  --quarto-hl-re-color: #00e0e0;
-  --quarto-hl-sc-color: #abe338;
-  --quarto-hl-ss-color: #abe338;
-  --quarto-hl-st-color: #abe338;
-  --quarto-hl-va-color: #00e0e0;
-  --quarto-hl-vs-color: #abe338;
-  --quarto-hl-wa-color: #dcc6e0;
-}
-
-/* other quarto variables */
-:root {
-  --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
-}
-
-code span.al {
-  font-weight: bold;
-  color: #f07178;
-}
-
-code span.an {
-  color: #d4d0ab;
-}
-
-code span.at {
-  color: #00e0e0;
-}
-
-code span.bn {
-  color: #d4d0ab;
-}
-
-code span.bu {
-  color: #abe338;
-}
-
-code span.ch {
-  color: #abe338;
-}
-
-code span.co {
-  font-style: italic;
-  color: #f8f8f2;
-}
-
-code span.cv {
-  color: #ffd700;
-}
-
-code span.cn {
-  color: #ffd700;
-}
-
-code span.cf {
-  font-weight: bold;
-  color: #ffa07a;
-}
-
-code span.dt {
-  color: #ffa07a;
-}
-
-code span.dv {
-  color: #d4d0ab;
-}
-
-code span.do {
-  color: #f8f8f2;
-}
-
-code span.er {
-  color: #f07178;
-  text-decoration: underline;
-}
-
-code span.ex {
-  font-weight: bold;
-  color: #00e0e0;
-}
-
-code span.fl {
-  color: #d4d0ab;
-}
-
-code span.fu {
-  color: #ffa07a;
-}
-
-code span.im {
-  color: #abe338;
-}
-
-code span.in {
-  color: #d4d0ab;
-}
-
-code span.kw {
-  font-weight: bold;
-  color: #ffa07a;
-}
-
-pre > code.sourceCode > span {
-  color: #f8f8f2;
-}
-
-code span {
-  color: #f8f8f2;
-}
-
-code.sourceCode > span {
-  color: #f8f8f2;
-}
-
-div.sourceCode,
-div.sourceCode pre.sourceCode {
-  color: #f8f8f2;
-}
-
-code span.op {
-  color: #ffa07a;
-}
-
-code span.ot {
-  color: #00e0e0;
-}
-
-code span.pp {
-  color: #dcc6e0;
-}
-
-code span.re {
-  color: #00e0e0;
-}
-
-code span.sc {
-  color: #abe338;
-}
-
-code span.ss {
-  color: #abe338;
-}
-
-code span.st {
-  color: #abe338;
-}
-
-code span.va {
-  color: #00e0e0;
-}
-
-code span.vs {
-  color: #abe338;
-}
-
-code span.wa {
-  color: #dcc6e0;
-}
-
-.prevent-inlining {
-  content: "</";
-}
-
-/*# sourceMappingURL=935a306eefa94366c21e1a970dddb765.css.map */
diff --git a/spaces/myrad01/Inpaint-Anything/third_party/lama/models/ade20k/base.py b/spaces/myrad01/Inpaint-Anything/third_party/lama/models/ade20k/base.py
deleted file mode 100644
index 8cdbe2d3e7dbadf4ed5e5a7cf2d248761ef25d9c..0000000000000000000000000000000000000000
--- a/spaces/myrad01/Inpaint-Anything/third_party/lama/models/ade20k/base.py
+++ /dev/null
@@ -1,627 +0,0 @@
-"""Modified from https://github.com/CSAILVision/semantic-segmentation-pytorch"""
-
-import os
-
-import pandas as pd
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from scipy.io import loadmat
-from torch.nn.modules import BatchNorm2d
-
-from . import resnet
-from . import mobilenet
-
-
-NUM_CLASS = 150
-base_path = os.path.dirname(os.path.abspath(__file__))  # current file path
-colors_path = os.path.join(base_path, 'color150.mat')
-classes_path = os.path.join(base_path, 'object150_info.csv')
-
-segm_options = dict(colors=loadmat(colors_path)['colors'],
-                    classes=pd.read_csv(classes_path),)
-
-
-class NormalizeTensor:
-    def __init__(self, mean, std, inplace=False):
-        """Normalize a tensor image with mean and standard deviation.
-        .. note::
-            This transform acts out of place by default, i.e., it does not mutates the input tensor.
-        See :class:`~torchvision.transforms.Normalize` for more details.
-        Args:
-            tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
-            mean (sequence): Sequence of means for each channel.
-            std (sequence): Sequence of standard deviations for each channel.
-            inplace(bool,optional): Bool to make this operation inplace.
-        Returns:
-            Tensor: Normalized Tensor image.
-        """
-
-        self.mean = mean
-        self.std = std
-        self.inplace = inplace
-
-    def __call__(self, tensor):
-        if not self.inplace:
-            tensor = tensor.clone()
-
-        dtype = tensor.dtype
-        mean = torch.as_tensor(self.mean, dtype=dtype, device=tensor.device)
-        std = torch.as_tensor(self.std, dtype=dtype, device=tensor.device)
-        tensor.sub_(mean[None, :, None, None]).div_(std[None, :, None, None])
-        return tensor
-
-
-# Model Builder
-class ModelBuilder:
-    # custom weights initialization
-    @staticmethod
-    def weights_init(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.)
-            m.bias.data.fill_(1e-4)
-
-    @staticmethod
-    def build_encoder(arch='resnet50dilated', fc_dim=512, weights=''):
-        pretrained = True if len(weights) == 0 else False
-        arch = arch.lower()
-        if arch == 'mobilenetv2dilated':
-            orig_mobilenet = mobilenet.__dict__['mobilenetv2'](pretrained=pretrained)
-            net_encoder = MobileNetV2Dilated(orig_mobilenet, dilate_scale=8)
-        elif arch == 'resnet18':
-            orig_resnet = resnet.__dict__['resnet18'](pretrained=pretrained)
-            net_encoder = Resnet(orig_resnet)
-        elif arch == 'resnet18dilated':
-            orig_resnet = resnet.__dict__['resnet18'](pretrained=pretrained)
-            net_encoder = ResnetDilated(orig_resnet, dilate_scale=8)
-        elif arch == 'resnet50dilated':
-            orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained)
-            net_encoder = ResnetDilated(orig_resnet, dilate_scale=8)
-        elif arch == 'resnet50':
-            orig_resnet = resnet.__dict__['resnet50'](pretrained=pretrained)
-            net_encoder = Resnet(orig_resnet)
-        else:
-            raise Exception('Architecture undefined!')
-
-        # encoders are usually pretrained
-        # net_encoder.apply(ModelBuilder.weights_init)
-        if len(weights) > 0:
-            print('Loading weights for net_encoder')
-            net_encoder.load_state_dict(
-                torch.load(weights, map_location=lambda storage, loc: storage), strict=False)
-        return net_encoder
-
-    @staticmethod
-    def build_decoder(arch='ppm_deepsup',
-                      fc_dim=512, num_class=NUM_CLASS,
-                      weights='', use_softmax=False, drop_last_conv=False):
-        arch = arch.lower()
-        if arch == 'ppm_deepsup':
-            net_decoder = PPMDeepsup(
-                num_class=num_class,
-                fc_dim=fc_dim,
-                use_softmax=use_softmax,
-                drop_last_conv=drop_last_conv)
-        elif arch == 'c1_deepsup':
-            net_decoder = C1DeepSup(
-                num_class=num_class,
-                fc_dim=fc_dim,
-                use_softmax=use_softmax,
-                drop_last_conv=drop_last_conv)
-        else:
-            raise Exception('Architecture undefined!')
-
-        net_decoder.apply(ModelBuilder.weights_init)
-        if len(weights) > 0:
-            print('Loading weights for net_decoder')
-            net_decoder.load_state_dict(
-                torch.load(weights, map_location=lambda storage, loc: storage), strict=False)
-        return net_decoder
-
-    @staticmethod
-    def get_decoder(weights_path, arch_encoder, arch_decoder, fc_dim, drop_last_conv, *arts, **kwargs):
-        path = os.path.join(weights_path, 'ade20k', f'ade20k-{arch_encoder}-{arch_decoder}/decoder_epoch_20.pth')
-        return ModelBuilder.build_decoder(arch=arch_decoder, fc_dim=fc_dim, weights=path, use_softmax=True, drop_last_conv=drop_last_conv)
-
-    @staticmethod
-    def get_encoder(weights_path, arch_encoder, arch_decoder, fc_dim, segmentation,
-                    *arts, **kwargs):
-        if segmentation:
-            path = os.path.join(weights_path, 'ade20k', f'ade20k-{arch_encoder}-{arch_decoder}/encoder_epoch_20.pth')
-        else:
-            path = ''
-        return ModelBuilder.build_encoder(arch=arch_encoder, fc_dim=fc_dim, weights=path)
-
-
-def conv3x3_bn_relu(in_planes, out_planes, stride=1):
-    return nn.Sequential(
-        nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False),
-        BatchNorm2d(out_planes),
-        nn.ReLU(inplace=True),
-    )
-
-
-class SegmentationModule(nn.Module):
-    def __init__(self,
-                 weights_path,
-                 num_classes=150,
-                 arch_encoder="resnet50dilated",
-                 drop_last_conv=False,
-                 net_enc=None,  # None for Default encoder
-                 net_dec=None,  # None for Default decoder
-                 encode=None,  # {None, 'binary', 'color', 'sky'}
-                 use_default_normalization=False,
-                 return_feature_maps=False,
-                 return_feature_maps_level=3,  # {0, 1, 2, 3}
-                 return_feature_maps_only=True,
-                 **kwargs,
-                 ):
-        super().__init__()
-        self.weights_path = weights_path
-        self.drop_last_conv = drop_last_conv
-        self.arch_encoder = arch_encoder
-        if self.arch_encoder == "resnet50dilated":
-            self.arch_decoder = "ppm_deepsup"
-            self.fc_dim = 2048
-        elif self.arch_encoder == "mobilenetv2dilated":
-            self.arch_decoder = "c1_deepsup"
-            self.fc_dim = 320
-        else:
-            raise NotImplementedError(f"No such arch_encoder={self.arch_encoder}")
-        model_builder_kwargs = dict(arch_encoder=self.arch_encoder,
-                                    arch_decoder=self.arch_decoder,
-                                    fc_dim=self.fc_dim,
-                                    drop_last_conv=drop_last_conv,
-                                    weights_path=self.weights_path)
-
-        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        self.encoder = ModelBuilder.get_encoder(**model_builder_kwargs) if net_enc is None else net_enc
-        self.decoder = ModelBuilder.get_decoder(**model_builder_kwargs) if net_dec is None else net_dec
-        self.use_default_normalization = use_default_normalization
-        self.default_normalization = NormalizeTensor(mean=[0.485, 0.456, 0.406],
-                                                     std=[0.229, 0.224, 0.225])
-
-        self.encode = encode
-
-        self.return_feature_maps = return_feature_maps
-
-        assert 0 <= return_feature_maps_level <= 3
-        self.return_feature_maps_level = return_feature_maps_level
-
-    def normalize_input(self, tensor):
-        if tensor.min() < 0 or tensor.max() > 1:
-            raise ValueError("Tensor should be 0..1 before using normalize_input")
-        return self.default_normalization(tensor)
-
-    @property
-    def feature_maps_channels(self):
-        return 256 * 2**(self.return_feature_maps_level)  # 256, 512, 1024, 2048
-
-    def forward(self, img_data, segSize=None):
-        if segSize is None:
-            raise NotImplementedError("Please pass segSize param. By default: (300, 300)")
-
-        fmaps = self.encoder(img_data, return_feature_maps=True)
-        pred = self.decoder(fmaps, segSize=segSize)
-
-        if self.return_feature_maps:
-            return pred, fmaps
-        # print("BINARY", img_data.shape, pred.shape)
-        return pred
-
-    def multi_mask_from_multiclass(self, pred, classes):
-        def isin(ar1, ar2):
-            return (ar1[..., None] == ar2).any(-1).float()
-        return isin(pred, torch.LongTensor(classes).to(self.device))
-
-    @staticmethod
-    def multi_mask_from_multiclass_probs(scores, classes):
-        res = None
-        for c in classes:
-            if res is None:
-                res = scores[:, c]
-            else:
-                res += scores[:, c]
-        return res
-
-    def predict(self, tensor, imgSizes=(-1,),  # (300, 375, 450, 525, 600)
-                segSize=None):
-        """Entry-point for segmentation. Use this methods instead of forward
-        Arguments:
-            tensor {torch.Tensor} -- BCHW
-        Keyword Arguments:
-            imgSizes {tuple or list} -- imgSizes for segmentation input.
-                default: (300, 450)
-                original implementation: (300, 375, 450, 525, 600)
-
-        """
-        if segSize is None:
-            segSize = tensor.shape[-2:]
-        segSize = (tensor.shape[2], tensor.shape[3])
-        with torch.no_grad():
-            if self.use_default_normalization:
-                tensor = self.normalize_input(tensor)
-            scores = torch.zeros(1, NUM_CLASS, segSize[0], segSize[1]).to(self.device)
-            features = torch.zeros(1, self.feature_maps_channels, segSize[0], segSize[1]).to(self.device)
-
-            result = []
-            for img_size in imgSizes:
-                if img_size != -1:
-                    img_data = F.interpolate(tensor.clone(), size=img_size)
-                else:
-                    img_data = tensor.clone()
-
-                if self.return_feature_maps:
-                    pred_current, fmaps = self.forward(img_data, segSize=segSize)
-                else:
-                    pred_current = self.forward(img_data, segSize=segSize)
-
-
-                result.append(pred_current)
-                scores = scores + pred_current / len(imgSizes)
-
-                # Disclaimer: We use and aggregate only last fmaps: fmaps[3]
-                if self.return_feature_maps:
-                    features = features + F.interpolate(fmaps[self.return_feature_maps_level], size=segSize) / len(imgSizes)
-
-            _, pred = torch.max(scores, dim=1)
-
-            if self.return_feature_maps:
-                return features
-
-            return pred, result
-
-    def get_edges(self, t):
-        edge = torch.cuda.ByteTensor(t.size()).zero_()
-        edge[:, :, :, 1:] = edge[:, :, :, 1:] | (t[:, :, :, 1:] != t[:, :, :, :-1])
-        edge[:, :, :, :-1] = edge[:, :, :, :-1] | (t[:, :, :, 1:] != t[:, :, :, :-1])
-        edge[:, :, 1:, :] = edge[:, :, 1:, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])
-        edge[:, :, :-1, :] = edge[:, :, :-1, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])
-
-        if True:
-            return edge.half()
-        return edge.float()
-
-
-# pyramid pooling, deep supervision
-class PPMDeepsup(nn.Module):
-    def __init__(self, num_class=NUM_CLASS, fc_dim=4096,
-                 use_softmax=False, pool_scales=(1, 2, 3, 6),
-                 drop_last_conv=False):
-        super().__init__()
-        self.use_softmax = use_softmax
-        self.drop_last_conv = drop_last_conv
-
-        self.ppm = []
-        for scale in pool_scales:
-            self.ppm.append(nn.Sequential(
-                nn.AdaptiveAvgPool2d(scale),
-                nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False),
-                BatchNorm2d(512),
-                nn.ReLU(inplace=True)
-            ))
-        self.ppm = nn.ModuleList(self.ppm)
-        self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1)
-
-        self.conv_last = nn.Sequential(
-            nn.Conv2d(fc_dim + len(pool_scales) * 512, 512,
-                      kernel_size=3, padding=1, bias=False),
-            BatchNorm2d(512),
-            nn.ReLU(inplace=True),
-            nn.Dropout2d(0.1),
-            nn.Conv2d(512, num_class, kernel_size=1)
-        )
-        self.conv_last_deepsup = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
-        self.dropout_deepsup = nn.Dropout2d(0.1)
-
-    def forward(self, conv_out, segSize=None):
-        conv5 = conv_out[-1]
-
-        input_size = conv5.size()
-        ppm_out = [conv5]
-        for pool_scale in self.ppm:
-            ppm_out.append(nn.functional.interpolate(
-                pool_scale(conv5),
-                (input_size[2], input_size[3]),
-                mode='bilinear', align_corners=False))
-        ppm_out = torch.cat(ppm_out, 1)
-
-        if self.drop_last_conv:
-            return ppm_out
-        else:
-            x = self.conv_last(ppm_out)
-
-            if self.use_softmax:  # is True during inference
-                x = nn.functional.interpolate(
-                    x, size=segSize, mode='bilinear', align_corners=False)
-                x = nn.functional.softmax(x, dim=1)
-                return x
-
-            # deep sup
-            conv4 = conv_out[-2]
-            _ = self.cbr_deepsup(conv4)
-            _ = self.dropout_deepsup(_)
-            _ = self.conv_last_deepsup(_)
-
-            x = nn.functional.log_softmax(x, dim=1)
-            _ = nn.functional.log_softmax(_, dim=1)
-
-            return (x, _)
-
-
-class Resnet(nn.Module):
-    def __init__(self, orig_resnet):
-        super(Resnet, self).__init__()
-
-        # take pretrained resnet, except AvgPool and FC
-        self.conv1 = orig_resnet.conv1
-        self.bn1 = orig_resnet.bn1
-        self.relu1 = orig_resnet.relu1
-        self.conv2 = orig_resnet.conv2
-        self.bn2 = orig_resnet.bn2
-        self.relu2 = orig_resnet.relu2
-        self.conv3 = orig_resnet.conv3
-        self.bn3 = orig_resnet.bn3
-        self.relu3 = orig_resnet.relu3
-        self.maxpool = orig_resnet.maxpool
-        self.layer1 = orig_resnet.layer1
-        self.layer2 = orig_resnet.layer2
-        self.layer3 = orig_resnet.layer3
-        self.layer4 = orig_resnet.layer4
-
-    def forward(self, x, return_feature_maps=False):
-        conv_out = []
-
-        x = self.relu1(self.bn1(self.conv1(x)))
-        x = self.relu2(self.bn2(self.conv2(x)))
-        x = self.relu3(self.bn3(self.conv3(x)))
-        x = self.maxpool(x)
-
-        x = self.layer1(x); conv_out.append(x);
-        x = self.layer2(x); conv_out.append(x);
-        x = self.layer3(x); conv_out.append(x);
-        x = self.layer4(x); conv_out.append(x);
-
-        if return_feature_maps:
-            return conv_out
-        return [x]
-
-# Resnet Dilated
-class ResnetDilated(nn.Module):
-    def __init__(self, orig_resnet, dilate_scale=8):
-        super().__init__()
-        from functools import partial
-
-        if dilate_scale == 8:
-            orig_resnet.layer3.apply(
-                partial(self._nostride_dilate, dilate=2))
-            orig_resnet.layer4.apply(
-                partial(self._nostride_dilate, dilate=4))
-        elif dilate_scale == 16:
-            orig_resnet.layer4.apply(
-                partial(self._nostride_dilate, dilate=2))
-
-        # take pretrained resnet, except AvgPool and FC
-        self.conv1 = orig_resnet.conv1
-        self.bn1 = orig_resnet.bn1
-        self.relu1 = orig_resnet.relu1
-        self.conv2 = orig_resnet.conv2
-        self.bn2 = orig_resnet.bn2
-        self.relu2 = orig_resnet.relu2
-        self.conv3 = orig_resnet.conv3
-        self.bn3 = orig_resnet.bn3
-        self.relu3 = orig_resnet.relu3
-        self.maxpool = orig_resnet.maxpool
-        self.layer1 = orig_resnet.layer1
-        self.layer2 = orig_resnet.layer2
-        self.layer3 = orig_resnet.layer3
-        self.layer4 = orig_resnet.layer4
-
-    def _nostride_dilate(self, m, dilate):
-        classname = m.__class__.__name__
-        if classname.find('Conv') != -1:
-            # the convolution with stride
-            if m.stride == (2, 2):
-                m.stride = (1, 1)
-                if m.kernel_size == (3, 3):
-                    m.dilation = (dilate // 2, dilate // 2)
-                    m.padding = (dilate // 2, dilate // 2)
-            # other convoluions
-            else:
-                if m.kernel_size == (3, 3):
-                    m.dilation = (dilate, dilate)
-                    m.padding = (dilate, dilate)
-
-    def forward(self, x, return_feature_maps=False):
-        conv_out = []
-
-        x = self.relu1(self.bn1(self.conv1(x)))
-        x = self.relu2(self.bn2(self.conv2(x)))
-        x = self.relu3(self.bn3(self.conv3(x)))
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        conv_out.append(x)
-        x = self.layer2(x)
-        conv_out.append(x)
-        x = self.layer3(x)
-        conv_out.append(x)
-        x = self.layer4(x)
-        conv_out.append(x)
-
-        if return_feature_maps:
-            return conv_out
-        return [x]
-
-class MobileNetV2Dilated(nn.Module):
-    def __init__(self, orig_net, dilate_scale=8):
-        super(MobileNetV2Dilated, self).__init__()
-        from functools import partial
-
-        # take pretrained mobilenet features
-        self.features = orig_net.features[:-1]
-
-        self.total_idx = len(self.features)
-        self.down_idx = [2, 4, 7, 14]
-
-        if dilate_scale == 8:
-            for i in range(self.down_idx[-2], self.down_idx[-1]):
-                self.features[i].apply(
-                    partial(self._nostride_dilate, dilate=2)
-                )
-            for i in range(self.down_idx[-1], self.total_idx):
-                self.features[i].apply(
-                    partial(self._nostride_dilate, dilate=4)
-                )
-        elif dilate_scale == 16:
-            for i in range(self.down_idx[-1], self.total_idx):
-                self.features[i].apply(
-                    partial(self._nostride_dilate, dilate=2)
-                )
-
-    def _nostride_dilate(self, m, dilate):
-        classname = m.__class__.__name__
-        if classname.find('Conv') != -1:
-            # the convolution with stride
-            if m.stride == (2, 2):
-                m.stride = (1, 1)
-                if m.kernel_size == (3, 3):
-                    m.dilation = (dilate//2, dilate//2)
-                    m.padding = (dilate//2, dilate//2)
-            # other convoluions
-            else:
-                if m.kernel_size == (3, 3):
-                    m.dilation = (dilate, dilate)
-                    m.padding = (dilate, dilate)
-
-    def forward(self, x, return_feature_maps=False):
-        if return_feature_maps:
-            conv_out = []
-            for i in range(self.total_idx):
-                x = self.features[i](x)
-                if i in self.down_idx:
-                    conv_out.append(x)
-            conv_out.append(x)
-            return conv_out
-
-        else:
-            return [self.features(x)]
-
-
-# last conv, deep supervision
-class C1DeepSup(nn.Module):
-    def __init__(self, num_class=150, fc_dim=2048, use_softmax=False, drop_last_conv=False):
-        super(C1DeepSup, self).__init__()
-        self.use_softmax = use_softmax
-        self.drop_last_conv = drop_last_conv
-
-        self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1)
-        self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1)
-
-        # last conv
-        self.conv_last = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
-        self.conv_last_deepsup = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
-
-    def forward(self, conv_out, segSize=None):
-        conv5 = conv_out[-1]
-
-        x = self.cbr(conv5)
-
-        if self.drop_last_conv:
-            return x
-        else:
-            x = self.conv_last(x)
-
-            if self.use_softmax:  # is True during inference
-                x = nn.functional.interpolate(
-                    x, size=segSize, mode='bilinear', align_corners=False)
-                x = nn.functional.softmax(x, dim=1)
-                return x
-
-            # deep sup
-            conv4 = conv_out[-2]
-            _ = self.cbr_deepsup(conv4)
-            _ = self.conv_last_deepsup(_)
-
-            x = nn.functional.log_softmax(x, dim=1)
-            _ = nn.functional.log_softmax(_, dim=1)
-
-            return (x, _)
-
-
-# last conv
-class C1(nn.Module):
-    def __init__(self, num_class=150, fc_dim=2048, use_softmax=False):
-        super(C1, self).__init__()
-        self.use_softmax = use_softmax
-
-        self.cbr = conv3x3_bn_relu(fc_dim, fc_dim // 4, 1)
-
-        # last conv
-        self.conv_last = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0)
-
-    def forward(self, conv_out, segSize=None):
-        conv5 = conv_out[-1]
-        x = self.cbr(conv5)
-        x = self.conv_last(x)
-
-        if self.use_softmax: # is True during inference
-            x = nn.functional.interpolate(
-                x, size=segSize, mode='bilinear', align_corners=False)
-            x = nn.functional.softmax(x, dim=1)
-        else:
-            x = nn.functional.log_softmax(x, dim=1)
-
-        return x
-
-
-# pyramid pooling
-class PPM(nn.Module):
-    def __init__(self, num_class=150, fc_dim=4096,
-                 use_softmax=False, pool_scales=(1, 2, 3, 6)):
-        super(PPM, self).__init__()
-        self.use_softmax = use_softmax
-
-        self.ppm = []
-        for scale in pool_scales:
-            self.ppm.append(nn.Sequential(
-                nn.AdaptiveAvgPool2d(scale),
-                nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False),
-                BatchNorm2d(512),
-                nn.ReLU(inplace=True)
-            ))
-        self.ppm = nn.ModuleList(self.ppm)
-
-        self.conv_last = nn.Sequential(
-            nn.Conv2d(fc_dim+len(pool_scales)*512, 512,
-                      kernel_size=3, padding=1, bias=False),
-            BatchNorm2d(512),
-            nn.ReLU(inplace=True),
-            nn.Dropout2d(0.1),
-            nn.Conv2d(512, num_class, kernel_size=1)
-        )
-
-    def forward(self, conv_out, segSize=None):
-        conv5 = conv_out[-1]
-
-        input_size = conv5.size()
-        ppm_out = [conv5]
-        for pool_scale in self.ppm:
-            ppm_out.append(nn.functional.interpolate(
-                pool_scale(conv5),
-                (input_size[2], input_size[3]),
-                mode='bilinear', align_corners=False))
-        ppm_out = torch.cat(ppm_out, 1)
-
-        x = self.conv_last(ppm_out)
-
-        if self.use_softmax:  # is True during inference
-            x = nn.functional.interpolate(
-                x, size=segSize, mode='bilinear', align_corners=False)
-            x = nn.functional.softmax(x, dim=1)
-        else:
-            x = nn.functional.log_softmax(x, dim=1)
-        return x
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/evaluation/testing.py b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/evaluation/testing.py
deleted file mode 100644
index 9e5ae625bb0593fc20739dd3ea549157e4df4f3d..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/evaluation/testing.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import numpy as np
-import pprint
-import sys
-from collections.abc import Mapping
-
-
-def print_csv_format(results):
-    """
-    Print main metrics in a format similar to Detectron,
-    so that they are easy to copypaste into a spreadsheet.
-
-    Args:
-        results (OrderedDict[dict]): task_name -> {metric -> score}
-            unordered dict can also be printed, but in arbitrary order
-    """
-    assert isinstance(results, Mapping) or not len(results), results
-    logger = logging.getLogger(__name__)
-    for task, res in results.items():
-        if isinstance(res, Mapping):
-            # Don't print "AP-category" metrics since they are usually not tracked.
-            important_res = [(k, v) for k, v in res.items() if "-" not in k]
-            logger.info("copypaste: Task: {}".format(task))
-            logger.info("copypaste: " + ",".join([k[0] for k in important_res]))
-            logger.info("copypaste: " + ",".join(["{0:.4f}".format(k[1]) for k in important_res]))
-        else:
-            logger.info(f"copypaste: {task}={res}")
-
-
-def verify_results(cfg, results):
-    """
-    Args:
-        results (OrderedDict[dict]): task_name -> {metric -> score}
-
-    Returns:
-        bool: whether the verification succeeds or not
-    """
-    expected_results = cfg.TEST.EXPECTED_RESULTS
-    if not len(expected_results):
-        return True
-
-    ok = True
-    for task, metric, expected, tolerance in expected_results:
-        actual = results[task].get(metric, None)
-        if actual is None:
-            ok = False
-            continue
-        if not np.isfinite(actual):
-            ok = False
-            continue
-        diff = abs(actual - expected)
-        if diff > tolerance:
-            ok = False
-
-    logger = logging.getLogger(__name__)
-    if not ok:
-        logger.error("Result verification failed!")
-        logger.error("Expected Results: " + str(expected_results))
-        logger.error("Actual Results: " + pprint.pformat(results))
-
-        sys.exit(1)
-    else:
-        logger.info("Results verification passed.")
-    return ok
-
-
-def flatten_results_dict(results):
-    """
-    Expand a hierarchical dict of scalars into a flat dict of scalars.
-    If results[k1][k2][k3] = v, the returned dict will have the entry
-    {"k1/k2/k3": v}.
-
-    Args:
-        results (dict):
-    """
-    r = {}
-    for k, v in results.items():
-        if isinstance(v, Mapping):
-            v = flatten_results_dict(v)
-            for kk, vv in v.items():
-                r[k + "/" + kk] = vv
-        else:
-            r[k] = v
-    return r
diff --git a/spaces/nikitalokhmachev-ai/line-art-colorization/app.py b/spaces/nikitalokhmachev-ai/line-art-colorization/app.py
deleted file mode 100644
index 47f912db899497df7d96210470c0999728be5415..0000000000000000000000000000000000000000
--- a/spaces/nikitalokhmachev-ai/line-art-colorization/app.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import gradio as gr
-from xdog import to_sketch
-from model import Generator, ResNeXtBottleneck
-import torch
-from data_utils import *
-import glob
-gen = torch.load('model/model.pth')
-
-def convert_to_lineart(img, sigma, k, gamma, epsilon, phi, area_min):
-    phi = 10 * phi
-    out = to_sketch(img, sigma=sigma, k=k, gamma=gamma, epsilon=epsilon, phi=phi, area_min=area_min)
-    return out
-
-def inference(sk):
-    return predict_img(gen, sk, hnt = None)
-
-title = "To Line Art"
-description = "Line art colorization showcase. "
-article = "Github Repo"
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            image = gr.Image(type="pil", value='examples/Genshin-Impact-anime.jpg')
-            to_lineart_button = gr.Button("To Lineart")
-
-            gr.Examples(
-                examples=glob.glob('examples/*.jpg'),
-                inputs=image,
-                outputs=image,
-                fn=None,
-                cache_examples=False,
-            )
-
-        with gr.Column():
-            sigma = gr.Slider(0.1, 0.5, value=0.3, step=0.1, label='σ')
-            k = gr.Slider(1.0, 8.0, value=4.5, step=0.5, label='k')
-            gamma = gr.Slider(0.05, 1.0, value=0.95, step=0.05, label='γ')
-            epsilon = gr.Slider(-2, 2, value=-1, step=0.5, label='ε')
-            phi = gr.Slider(10, 20, label = 'φ', value=15)
-            min_area = gr.Slider(1, 5, value=2, step=1, label='Minimal Area')
-        
-        with gr.Column():
-            lineart = gr.Image(type="pil", image_mode='L')
-            inpaint_button = gr.Button("Inpaint")
-
-    to_lineart_button.click(convert_to_lineart, inputs=[image, sigma, k, gamma, epsilon, phi, min_area], outputs=lineart)
-    inpaint_button.click(inference, inputs=lineart, outputs=lineart)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/obsei/obsei-demo/utils.py b/spaces/obsei/obsei-demo/utils.py
deleted file mode 100644
index 9dc10c38f86f86bebd6bb9e7fdd83af917bc6ed1..0000000000000000000000000000000000000000
--- a/spaces/obsei/obsei-demo/utils.py
+++ /dev/null
@@ -1,216 +0,0 @@
-import base64
-import logging
-import pathlib
-import re
-import sys
-import uuid
-
-import streamlit as st
-import yaml
-
-from obsei.configuration import ObseiConfiguration
-
-logger = logging.getLogger(__name__)
-logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-
-
-def img_to_bytes(img_path):
-    img_bytes = pathlib.Path(img_path).read_bytes()
-    encoded = base64.b64encode(img_bytes).decode()
-    return encoded
-
-
-# Copied from https://github.com/jrieke/traingenerator/blob/main/app/utils.py
-def download_button(
-    object_to_download, download_filename, button_text  # , pickle_it=False
-):
-    try:
-        # some strings <-> bytes conversions necessary here
-        b64 = base64.b64encode(object_to_download.encode()).decode()
-    except AttributeError as e:
-        b64 = base64.b64encode(object_to_download).decode()
-
-    button_uuid = str(uuid.uuid4()).replace("-", "")
-    button_id = re.sub("\d+", "", button_uuid)
-
-    custom_css = f"""
-        <style>
-            #{button_id} {{
-                display: inline-flex;
-                align-items: center;
-                justify-content: center;
-                background-color: rgb(255, 255, 255);
-                color: rgb(38, 39, 48);
-                padding: .25rem .75rem;
-                position: relative;
-                text-decoration: none;
-                border-radius: 4px;
-                border-width: 1px;
-                border-style: solid;
-                border-color: rgb(230, 234, 241);
-                border-image: initial;
-            }}
-            #{button_id}:hover {{
-                border-color: rgb(246, 51, 102);
-                color: rgb(246, 51, 102);
-            }}
-            #{button_id}:active {{
-                box-shadow: none;
-                background-color: rgb(246, 51, 102);
-                color: white;
-                }}
-        </style> """
-
-    dl_link = (
-        custom_css
-        + f'<a download="{download_filename}" id="{button_id}" href="data:file/txt;base64,{b64}">{button_text}</a><br><br>'
-    )
-    # dl_link = f'<a download="{download_filename}" id="{button_id}" href="data:file/txt;base64,{b64}"><input type="button" kind="primary" value="{button_text}"></a><br></br>'
-
-    st.markdown(dl_link, unsafe_allow_html=True)
-
-
-def get_obsei_config(current_path, file_name):
-    return ObseiConfiguration(
-        config_path=current_path,
-        config_filename=file_name,
-    ).configuration
-
-
-@st.cache
-def get_icon_name(name, icon, icon_size=40, font_size=1):
-    if not name:
-        return f'<img style="vertical-align:middle;margin:5px 5px" src="{icon}" width="{icon_size}" height="{icon_size}">'
-    return (
-        f'<p style="font-size:{font_size}px">'
-        f'<img style="vertical-align:middle;margin:1px 5px" src="{icon}" width="{icon_size}" height="{icon_size}">'
-        f"{name}</p>"
-    )
-
-
-def render_config(config, component, help_str=None, parent_key=None):
-    if config is None:
-        return
-
-    prefix = "" if parent_key is None else f"{parent_key}."
-    if help_str is not None:
-        with component.expander("Info", False):
-            help_area = "\n".join(help_str)
-            st.code(f"{help_area}")
-    for k, v in config.items():
-        if k == "_target_":
-            continue
-
-        if isinstance(v, dict):
-            render_config(v, component, None, k)
-        elif isinstance(v, list):
-            if len(v) == 0:
-                continue
-            is_object = isinstance(v[0], dict)
-            if is_object:
-                for idx, sub_element in enumerate(v):
-                    render_config(sub_element, component, None, f"{k}[{idx}]")
-            else:
-                text_data = component.text_area(
-                    f"{prefix}{k}", ", ".join(v), help="Comma separated list"
-                )
-                text_list = text_data.split(",")
-                config[k] = [text.strip() for text in text_list]
-        elif isinstance(v, bool):
-            options = [True, False]
-            selected_option = component.radio(f"{prefix}{k}", options, options.index(v))
-            config[k] = bool(selected_option)
-        else:
-            tokens = k.split("_")
-            is_secret = tokens[-1] in ["key", "password", "token", "secret"]
-            hint = (
-                "Enter value"
-                if "lookup" not in tokens
-                else "Format: `<number><d|h|m>` d=day, h=hour & m=minute"
-            )
-            config[k] = component.text_input(
-                f"{prefix}{k}",
-                v,
-                type="password" if is_secret else "default",
-                help=hint,
-            )
-
-
-def generate_python(generate_config):
-    return f"""
-from obsei.configuration import ObseiConfiguration
-
-# This is Obsei workflow path and filename
-config_path = "./"
-config_filename = "workflow.yml"
-
-# Extract config via yaml file using `config_path` and `config_filename`
-obsei_configuration = ObseiConfiguration(config_path=config_path, config_filename=config_filename)
-
-# Initialize objects using configuration
-source_config = obsei_configuration.initialize_instance("source_config")
-source = obsei_configuration.initialize_instance("source")
-analyzer = obsei_configuration.initialize_instance("analyzer")
-analyzer_config = obsei_configuration.initialize_instance("analyzer_config")
-sink_config = obsei_configuration.initialize_instance("sink_config")
-sink = obsei_configuration.initialize_instance("sink")
-
-# This will fetch information from configured source ie twitter, app store etc
-source_response_list = source.lookup(source_config)
-
-# This will execute analyzer (Sentiment, classification etc) on source data with provided analyzer_config
-# Analyzer will it's output to `segmented_data` inside `analyzer_response`
-analyzer_response_list = analyzer.analyze_input(
-    source_response_list=source_response_list,
-    analyzer_config=analyzer_config
-)
-
-# This will send analyzed output to configure sink ie Slack, Zendesk etc
-sink_response_list = sink.send_data(analyzer_response_list, sink_config)
-"""
-
-
-def generate_yaml(generate_config):
-    return yaml.dump(generate_config)
-
-
-def execute_workflow(generate_config, component=None, log_components=None):
-    progress_show = None
-    if component:
-        progress_show = component.empty()
-        progress_show.code("🏄🏄🏄 Processing 🐢🐢🐢")
-    try:
-        obsei_configuration = ObseiConfiguration(configuration=generate_config)
-
-        source_config = obsei_configuration.initialize_instance("source_config")
-        source = obsei_configuration.initialize_instance("source")
-
-        analyzer = obsei_configuration.initialize_instance("analyzer")
-        analyzer_config = obsei_configuration.initialize_instance("analyzer_config")
-
-        sink_config = obsei_configuration.initialize_instance("sink_config")
-        sink = obsei_configuration.initialize_instance("sink")
-
-        source_response_list = source.lookup(source_config)
-        log_components["source"].write([vars(response) for response in source_response_list])
-
-        analyzer_response_list = analyzer.analyze_input(
-            source_response_list=source_response_list, analyzer_config=analyzer_config
-        )
-        log_components["analyzer"].write([vars(response) for response in analyzer_response_list])
-
-        sink_response_list = sink.send_data(analyzer_response_list, sink_config)
-        if sink.TYPE == 'Pandas':
-            log_components["sink"].write(sink_response_list)
-        elif sink_response_list is not None:
-            log_components["sink"].write([vars(response) for response in sink_response_list])
-        else:
-            log_components["sink"].write("No Data")
-
-        if progress_show:
-            progress_show.code("🎉🎉🎉 Processing Complete!! 🍾🍾🍾")
-    except Exception as ex:
-        if progress_show:
-            progress_show.code(f"❗❗❗ Processing Failed!! 😞😞😞 \n 👉 ({str(ex)})")
-
-        raise ex
diff --git a/spaces/oguzakif/video-object-remover/FGT_codes/FGT/models/temporal_patch_gan.py b/spaces/oguzakif/video-object-remover/FGT_codes/FGT/models/temporal_patch_gan.py
deleted file mode 100644
index 726cf27244768857b7472412516fd4a9fb16eba6..0000000000000000000000000000000000000000
--- a/spaces/oguzakif/video-object-remover/FGT_codes/FGT/models/temporal_patch_gan.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# temporal patch GAN to maintain the temporal consecutive of the flows
-import torch
-import torch.nn as nn
-from .BaseNetwork import BaseNetwork
-
-
-class Discriminator(BaseNetwork):
-    def __init__(self, in_channels, conv_type, dist_cnum, use_sigmoid=False, use_spectral_norm=True, init_weights=True):
-        """
-
-        Args:
-            in_channels: The input channels of the discriminator
-            use_sigmoid: Whether to use sigmoid for the base network (true for the nsgan)
-            use_spectral_norm: The usage of the spectral norm: always be true for the stability of GAN
-            init_weights: always be True
-        """
-        super(Discriminator, self).__init__(conv_type)
-        self.use_sigmoid = use_sigmoid
-        nf = dist_cnum
-
-        self.conv = nn.Sequential(
-            spectral_norm(
-                nn.Conv3d(in_channels=in_channels, out_channels=nf * 1, kernel_size=(3, 5, 5), stride=(1, 2, 2),
-                          padding=(1, 2, 2),
-                          bias=not use_spectral_norm), use_spectral_norm),
-            nn.LeakyReLU(0.2, inplace=True),
-            spectral_norm(
-                nn.Conv3d(in_channels=nf * 1, out_channels=nf * 2, kernel_size=(3, 5, 5), stride=(1, 2, 2),
-                          padding=(1, 2, 2),
-                          bias=not use_spectral_norm), use_spectral_norm),
-            nn.LeakyReLU(0.2, inplace=True),
-            spectral_norm(
-                nn.Conv3d(in_channels=nf * 2, out_channels=nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2),
-                          padding=(1, 2, 2),
-                          bias=not use_spectral_norm), use_spectral_norm),
-            nn.LeakyReLU(0.2, inplace=True),
-            spectral_norm(
-                nn.Conv3d(in_channels=nf * 4, out_channels=nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2),
-                          padding=(1, 2, 2),
-                          bias=not use_spectral_norm), use_spectral_norm),
-            nn.LeakyReLU(0.2, inplace=True),
-            spectral_norm(
-                nn.Conv3d(in_channels=nf * 4, out_channels=nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2),
-                          padding=(1, 2, 2),
-                          bias=not use_spectral_norm), use_spectral_norm),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Conv3d(in_channels=nf * 4, out_channels=nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2),
-                      padding=(1, 2, 2))
-        )
-
-        if init_weights:
-            self.init_weights()
-
-    def forward(self, xs, t):
-        """
-
-        Args:
-            xs: Input feature, with shape of [bt, c, h, w]
-
-        Returns: The discriminative map from the GAN
-
-        """
-        bt, c, h, w = xs.shape
-        b = bt // t
-        xs = xs.view(b, t, c, h, w).permute(0, 2, 1, 3, 4).contiguous()
-        feat = self.conv(xs)
-        if self.use_sigmoid:
-            feat = torch.sigmoid(feat)
-        out = torch.transpose(feat, 1, 2)  # [b, t, c, h, w]
-        return out
-
-
-def spectral_norm(module, mode=True):
-    if mode:
-        return nn.utils.spectral_norm(module)
-    return module
diff --git a/spaces/olanigan/glaiveai-glaive-coder-7b/app.py b/spaces/olanigan/glaiveai-glaive-coder-7b/app.py
deleted file mode 100644
index ea6382c97fc93cc870f037c0b7be76a546afce7b..0000000000000000000000000000000000000000
--- a/spaces/olanigan/glaiveai-glaive-coder-7b/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/glaiveai/glaive-coder-7b").launch()
\ No newline at end of file
diff --git a/spaces/omlab/vlchecklist_demo/models/vilt/datasets/coco_caption_karpathy_dataset.py b/spaces/omlab/vlchecklist_demo/models/vilt/datasets/coco_caption_karpathy_dataset.py
deleted file mode 100644
index d378ad7231f8b1adb30c23079e848bca0cdcf68c..0000000000000000000000000000000000000000
--- a/spaces/omlab/vlchecklist_demo/models/vilt/datasets/coco_caption_karpathy_dataset.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from .base_dataset import BaseDataset
-
-
-class CocoCaptionKarpathyDataset(BaseDataset):
-    def __init__(self, *args, split="", **kwargs):
-        assert split in ["train", "val", "test"]
-        self.split = split
-
-        if split == "train":
-            names = ["coco_caption_karpathy_train", "coco_caption_karpathy_restval"]
-        elif split == "val":
-            # names = ["coco_caption_karpathy_val"]
-            names = ["coco_caption_karpathy_test"]
-        elif split == "test":
-            names = ["coco_caption_karpathy_test"]
-
-        super().__init__(*args, **kwargs, names=names, text_column_name="caption")
-
-    def __getitem__(self, index):
-        suite = self.get_suite(index)
-
-        if "test" in self.split:
-            _index, _question_index = self.index_mapper[index]
-            iid = self.table["image_id"][_index].as_py()
-            iid = int(iid.split(".")[0].split("_")[-1])
-            suite.update({"iid": iid})
-
-        return suite
diff --git a/spaces/openaccess-ai-collective/jackalope-7b/app.py b/spaces/openaccess-ai-collective/jackalope-7b/app.py
deleted file mode 100644
index 116a42e34d7a6414459d39722e9a5668178f8ecd..0000000000000000000000000000000000000000
--- a/spaces/openaccess-ai-collective/jackalope-7b/app.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import os
-import re
-import gradio as gr
-import openai
-
-openai.api_base = os.environ.get("OPENAI_API_BASE")
-openai.api_key = os.environ.get("OPENAI_API_KEY")
-
-BASE_SYSTEM_MESSAGE = """I carefully provide accurate, factual, thoughtful, nuanced answers and am brilliant at reasoning. 
-I am an assistant who thinks through their answers step-by-step to be sure I always get the right answer. 
-I think more clearly if I write out my thought process in a scratchpad manner first; therefore, I always explain background context, assumptions, and step-by-step thinking BEFORE trying to answer or solve anything."""
-
-
-def make_prediction(prompt, max_tokens=None, temperature=None, top_p=None, top_k=None, repetition_penalty=None):
-    completion = openai.Completion.create(model="openaccess-ai-collective/jackalope-7b", prompt=prompt, max_tokens=max_tokens, temperature=temperature, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty, stream=True, stop=["</s>", "<|im_end|>"])
-    for chunk in completion:
-        yield chunk["choices"][0]["text"]
-
-
-def clear_chat(chat_history_state, chat_message):
-    chat_history_state = []
-    chat_message = ''
-    return chat_history_state, chat_message
-
-
-def user(message, history):
-    history = history or []
-    # Append the user's message to the conversation history
-    history.append([message, ""])
-    return "", history
-
-
-def pop_last(history):
-    turn = history.pop()
-    # append the user's last message to the conversation history
-    history.append([turn[0], ""])
-
-    return history
-
-
-def chat(history, system_message, max_tokens, temperature, top_p, top_k, repetition_penalty):
-    history = history or []
-
-    sys_prompt = system_message.strip() or BASE_SYSTEM_MESSAGE
-    messages = "<|im_start|> "+"system\n" + sys_prompt + "<|im_end|>\n" + \
-               "\n".join(["\n".join(["<|im_start|> "+"user\n"+item[0]+"<|im_end|>", "<|im_start|> assistant\n"+item[1]+"<|im_end|>"])
-                          for item in history])
-
-    # strip the last `<|im_end|>` from the messages
-    messages = messages.rstrip("<|im_end|>")
-    # remove last space from assistant, some models output a ZWSP if you leave a space
-    messages = messages.rstrip()
-
-    # If temperature is set to 0, force Top P to 1 and Top K to -1
-    if temperature == 0:
-        top_p = 1
-        top_k = -1
-
-    prediction = make_prediction(
-        messages,
-        max_tokens=max_tokens,
-        temperature=temperature,
-        top_p=top_p,
-        top_k=top_k,
-        repetition_penalty=repetition_penalty,
-    )
-    for tokens in prediction:
-        tokens = re.findall(r'(.*?)(\s|$)', tokens)
-        for subtoken in tokens:
-            subtoken = "".join(subtoken)
-            answer = subtoken
-            history[-1][1] += answer
-            # stream the response
-            yield history, history, ""
-
-
-start_message = BASE_SYSTEM_MESSAGE
-
-CSS ="""
-.contain { display: flex; flex-direction: column; }
-.gradio-container { height: 100vh !important; }
-#component-0 { height: 100%; }
-#chatbot { flex-grow: 1; overflow: auto; resize: vertical; }
-"""
-
-#with gr.Blocks() as demo:
-with gr.Blocks(css=CSS) as demo:
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(f"""
-                    [<img src="https://huggingface.co/spaces/openaccess-ai-collective/jackalope-7b/resolve/main/banner.png" width="728" height="120" alt="Jackalope 7B by OpenAccess AI Collective" />](https://huggingface.co/openaccess-ai-collective/jackalope-7b)
-                    
-                    [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
-                    
-                    ### [Download Model Weights](https://huggingface.co/openaccess-ai-collective/jackalope-7b)
-                    """)
-    with gr.Row():
-        gr.Markdown("# 🐰🦌 Jackalope 7B Playground Space! 🐰🦌")
-    with gr.Row():
-        system_msg = gr.Textbox(
-            start_message, label="System Message", interactive=True, visible=True, placeholder="System prompt. Provide instructions which you want the model to remember.", lines=5)
-    with gr.Row():
-        chatbot = gr.Chatbot(elem_id="chatbot")
-    with gr.Row():
-        message = gr.Textbox(
-            label="What do you want to chat about?",
-            placeholder="Ask me anything.",
-            lines=3,
-        )
-    with gr.Row():
-        submit = gr.Button(value="Send message", variant="primary").style(full_width=True)
-        clear = gr.Button(value="New topic", variant="secondary").style(full_width=False)
-        stop = gr.Button(value="Stop", variant="secondary").style(full_width=False)
-        regenerate = gr.Button(value="Regenerate", variant="secondary").style(full_width=False)
-    with gr.Accordion("Show Model Parameters", open=False):
-        with gr.Row():
-            with gr.Column():
-                max_tokens = gr.Slider(20, 2500, label="Max Tokens", step=20, value=500)
-                temperature = gr.Slider(0.0, 2.0, label="Temperature", step=0.1, value=0.4)
-                top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.95)
-                top_k = gr.Slider(1, 100, label="Top K", step=1, value=40)
-                repetition_penalty = gr.Slider(1.0, 2.0, label="Repetition Penalty", step=0.1, value=1.1)
-
-    chat_history_state = gr.State()
-    clear.click(clear_chat, inputs=[chat_history_state, message], outputs=[chat_history_state, message], queue=False)
-    clear.click(lambda: None, None, chatbot, queue=False)
-
-    submit_click_event = submit.click(
-        fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=True
-    ).then(
-        fn=chat, inputs=[chat_history_state, system_msg, max_tokens, temperature, top_p, top_k, repetition_penalty], outputs=[chatbot, chat_history_state, message], queue=True
-    )
-    regenerate_click_event = regenerate.click(
-        fn=pop_last, inputs=[chat_history_state], outputs=[chat_history_state], queue=True
-    ).then(
-        fn=chat, inputs=[chat_history_state, system_msg, max_tokens, temperature, top_p, top_k, repetition_penalty], outputs=[chatbot, chat_history_state, message], queue=True
-    )
-    stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_click_event, regenerate_click_event], queue=False)
-
-demo.queue(max_size=128, concurrency_count=48).launch(debug=True, server_name="0.0.0.0", server_port=7860)
\ No newline at end of file
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docker/diffusers-onnxruntime-cpu/Dockerfile b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docker/diffusers-onnxruntime-cpu/Dockerfile
deleted file mode 100644
index 75f45be87a033e9476c4038218c9c2fd2f1255a5..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docker/diffusers-onnxruntime-cpu/Dockerfile
+++ /dev/null
@@ -1,44 +0,0 @@
-FROM ubuntu:20.04
-LABEL maintainer="Hugging Face"
-LABEL repository="diffusers"
-
-ENV DEBIAN_FRONTEND=noninteractive
-
-RUN apt update && \
-    apt install -y bash \
-                   build-essential \
-                   git \
-                   git-lfs \
-                   curl \
-                   ca-certificates \
-                   libsndfile1-dev \
-                   python3.8 \
-                   python3-pip \
-                   python3.8-venv && \
-    rm -rf /var/lib/apt/lists
-
-# make sure to use venv
-RUN python3 -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-
-# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
-RUN python3 -m pip install --no-cache-dir --upgrade pip && \
-    python3 -m pip install --no-cache-dir \
-        torch \
-        torchvision \
-        torchaudio \
-        onnxruntime \
-        --extra-index-url https://download.pytorch.org/whl/cpu && \
-    python3 -m pip install --no-cache-dir \
-        accelerate \
-        datasets \
-        hf-doc-builder \
-        huggingface-hub \
-        Jinja2 \
-        librosa \
-        numpy \
-        scipy \
-        tensorboard \
-        transformers
-
-CMD ["/bin/bash"]
\ No newline at end of file
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/conceptual/philosophy.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/conceptual/philosophy.md
deleted file mode 100644
index aed09169bfce0f583fba5d6f33c56fac34138242..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/conceptual/philosophy.md
+++ /dev/null
@@ -1,110 +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.
--->
-
-# Philosophy
-
-🧨 Diffusers provides **state-of-the-art** pretrained diffusion models across multiple modalities.
-Its purpose is to serve as a **modular toolbox** for both inference and training.
-
-We aim at building a library that stands the test of time and therefore take API design very seriously.
-
-In a nutshell, Diffusers is built to be a natural extension of PyTorch. Therefore, most of our design choices are based on [PyTorch's Design Principles](https://pytorch.org/docs/stable/community/design.html#pytorch-design-philosophy). Let's go over the most important ones:
-
-## Usability over Performance
-
-- While Diffusers has many built-in performance-enhancing features (see [Memory and Speed](https://huggingface.co/docs/diffusers/optimization/fp16)), models are always loaded with the highest precision and lowest optimization. Therefore, by default diffusion pipelines are always instantiated on CPU with float32 precision if not otherwise defined by the user. This ensures usability across different platforms and accelerators and means that no complex installations are required to run the library.
-- Diffusers aim at being a **light-weight** package and therefore has very few required dependencies, but many soft dependencies that can improve performance (such as `accelerate`, `safetensors`, `onnx`, etc...). We strive to keep the library as lightweight as possible so that it can be added without much concern as a dependency on other packages.
-- Diffusers prefers simple, self-explainable code over condensed, magic code. This means that short-hand code syntaxes such as lambda functions, and advanced PyTorch operators are often not desired.
-
-## Simple over easy
-
-As PyTorch states, **explicit is better than implicit** and **simple is better than complex**. This design philosophy is reflected in multiple parts of the library: 
-- We follow PyTorch's API with methods like [`DiffusionPipeline.to`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.to) to let the user handle device management.
-- Raising concise error messages is preferred to silently correct erroneous input. Diffusers aims at teaching the user, rather than making the library as easy to use as possible.
-- Complex model vs. scheduler logic is exposed instead of magically handled inside. Schedulers/Samplers are separated from diffusion models with minimal dependencies on each other. This forces the user to write the unrolled denoising loop. However, the separation allows for easier debugging and gives the user more control over adapting the denoising process or switching out diffusion models or schedulers.
-- Separately trained components of the diffusion pipeline, *e.g.* the text encoder, the unet, and the variational autoencoder, each have their own model class. This forces the user to handle the interaction between the different model components, and the serialization format separates the model components into different files. However, this allows for easier debugging and customization. Dreambooth or textual inversion training 
-is very simple thanks to diffusers' ability to separate single components of the diffusion pipeline.
-
-## Tweakable, contributor-friendly over abstraction
-
-For large parts of the library, Diffusers adopts an important design principle of the [Transformers library](https://github.com/huggingface/transformers), which is to prefer copy-pasted code over hasty abstractions. This design principle is very opinionated and stands in stark contrast to popular design principles such as [Don't repeat yourself (DRY)](https://en.wikipedia.org/wiki/Don%27t_repeat_yourself). 
-In short, just like Transformers does for modeling files, diffusers prefers to keep an extremely low level of abstraction and very self-contained code for pipelines and schedulers.
-Functions, long code blocks, and even classes can be copied across multiple files which at first can look like a bad, sloppy design choice that makes the library unmaintainable. 
-**However**, this design has proven to be extremely successful for Transformers and makes a lot of sense for community-driven, open-source machine learning libraries because:
-- Machine Learning is an extremely fast-moving field in which paradigms, model architectures, and algorithms are changing rapidly, which therefore makes it very difficult to define long-lasting code abstractions.
-- Machine Learning practitioners like to be able to quickly tweak existing code for ideation and research and therefore prefer self-contained code over one that contains many abstractions.
-- Open-source libraries rely on community contributions and therefore must build a library that is easy to contribute to. The more abstract the code, the more dependencies, the harder to read, and the harder to contribute to. Contributors simply stop contributing to very abstract libraries out of fear of breaking vital functionality. If contributing to a library cannot break other fundamental code, not only is it more inviting for potential new contributors, but it is also easier to review and contribute to multiple parts in parallel.
-
-At Hugging Face, we call this design the **single-file policy** which means that almost all of the code of a certain class should be written in a single, self-contained file. To read more about the philosophy, you can have a look
-at [this blog post](https://huggingface.co/blog/transformers-design-philosophy).
-
-In diffusers, we follow this philosophy for both pipelines and schedulers, but only partly for diffusion models. The reason we don't follow this design fully for diffusion models is because almost all diffusion pipelines, such 
-as [DDPM](https://huggingface.co/docs/diffusers/v0.12.0/en/api/pipelines/ddpm), [Stable Diffusion](https://huggingface.co/docs/diffusers/v0.12.0/en/api/pipelines/stable_diffusion/overview#stable-diffusion-pipelines), [UnCLIP (Dalle-2)](https://huggingface.co/docs/diffusers/v0.12.0/en/api/pipelines/unclip#overview) and [Imagen](https://imagen.research.google/) all rely on the same diffusion model, the [UNet](https://huggingface.co/docs/diffusers/api/models#diffusers.UNet2DConditionModel).
-
-Great, now you should have generally understood why 🧨 Diffusers is designed the way it is 🤗. 
-We try to apply these design principles consistently across the library. Nevertheless, there are some minor exceptions to the philosophy or some unlucky design choices. If you have feedback regarding the design, we would ❤️  to hear it [directly on GitHub](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feedback.md&title=).
-
-## Design Philosophy in Details
-
-Now, let's look a bit into the nitty-gritty details of the design philosophy. Diffusers essentially consist of three major classes, [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines), [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models), and [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers).
-Let's walk through more in-detail design decisions for each class.
-
-### Pipelines
-
-Pipelines are designed to be easy to use (therefore do not follow [*Simple over easy*](#simple-over-easy) 100%), are not feature complete, and should loosely be seen as examples of how to use [models](#models) and [schedulers](#schedulers) for inference.
-
-The following design principles are followed:
-- Pipelines follow the single-file policy. All pipelines can be found in individual directories under src/diffusers/pipelines. One pipeline folder corresponds to one diffusion paper/project/release. Multiple pipeline files can be gathered in one pipeline folder, as it’s done for [`src/diffusers/pipelines/stable-diffusion`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/stable_diffusion). If pipelines share similar functionality, one can make use of the [#Copied from mechanism](https://github.com/huggingface/diffusers/blob/125d783076e5bd9785beb05367a2d2566843a271/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L251).
-- Pipelines all inherit from [`DiffusionPipeline`].
-- Every pipeline consists of different model and scheduler components, that are documented in the [`model_index.json` file](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/model_index.json), are accessible under the same name as attributes of the pipeline and can be shared between pipelines with [`DiffusionPipeline.components`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.components) function.
-- Every pipeline should be loadable via the [`DiffusionPipeline.from_pretrained`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained) function.
-- Pipelines should be used **only** for inference.
-- Pipelines should be very readable, self-explanatory, and easy to tweak.
-- Pipelines should be designed to build on top of each other and be easy to integrate into higher-level APIs.
-- Pipelines are **not** intended to be feature-complete user interfaces. For future complete user interfaces one should rather have a look at [InvokeAI](https://github.com/invoke-ai/InvokeAI), [Diffuzers](https://github.com/abhishekkrthakur/diffuzers), and [lama-cleaner](https://github.com/Sanster/lama-cleaner).
-- Every pipeline should have one and only one way to run it via a `__call__` method. The naming of the `__call__` arguments should be shared across all pipelines.
-- Pipelines should be named after the task they are intended to solve.
-- In almost all cases, novel diffusion pipelines shall be implemented in a new pipeline folder/file.
-
-### Models
-
-Models are designed as configurable toolboxes that are natural extensions of [PyTorch's Module class](https://pytorch.org/docs/stable/generated/torch.nn.Module.html). They only partly follow the **single-file policy**.
-
-The following design principles are followed:
-- Models correspond to **a type of model architecture**. *E.g.* the [`UNet2DConditionModel`] class is used for all UNet variations that expect 2D image inputs and are conditioned on some context.
-- All models can be found in [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and every model architecture shall be defined in its file, e.g. [`unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py), [`transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformer_2d.py), etc...
-- Models **do not** follow the single-file policy and should make use of smaller model building blocks, such as [`attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py), [`resnet.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py), [`embeddings.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/embeddings.py), etc... **Note**: This is in stark contrast to Transformers' modeling files and shows that models do not really follow the single-file policy.
-- Models intend to expose complexity, just like PyTorch's module does, and give clear error messages.
-- Models all inherit from `ModelMixin` and `ConfigMixin`.
-- Models can be optimized for performance when it doesn’t demand major code changes, keeps backward compatibility, and gives significant memory or compute gain.
-- Models should by default have the highest precision and lowest performance setting.
-- To integrate new model checkpoints whose general architecture can be classified as an architecture that already exists in Diffusers, the existing model architecture shall be adapted to make it work with the new checkpoint. One should only create a new file if the model architecture is fundamentally different.
-- Models should be designed to be easily extendable to future changes. This can be achieved by limiting public function arguments, configuration arguments, and "foreseeing" future changes, *e.g.* it is usually better to add `string` "...type" arguments that can easily be extended to new future types instead of boolean `is_..._type` arguments. Only the minimum amount of changes shall be made to existing architectures to make a new model checkpoint work.
-- The model design is a difficult trade-off between keeping code readable and concise and supporting many model checkpoints. For most parts of the modeling code, classes shall be adapted for new model checkpoints, while there are some exceptions where it is preferred to add new classes to make sure the code is kept concise and 
-readable longterm, such as [UNet blocks](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py) and [Attention processors](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-
-### Schedulers
-
-Schedulers are responsible to guide the denoising process for inference as well as to define a noise schedule for training. They are designed as individual classes with loadable configuration files and strongly follow the **single-file policy**.
-
-The following design principles are followed:
-- All schedulers are found in [`src/diffusers/schedulers`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers). 
-- Schedulers are **not** allowed to import from large utils files and shall be kept very self-contained. 
-- One scheduler python file corresponds to one scheduler algorithm (as might be defined in a paper). 
-- If schedulers share similar functionalities, we can make use of the `#Copied from` mechanism.
-- Schedulers all inherit from `SchedulerMixin` and `ConfigMixin`.
-- Schedulers can be easily swapped out with the [`ConfigMixin.from_config`](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) method as explained in detail [here](./using-diffusers/schedulers.md).
-- Every scheduler has to have a `set_num_inference_steps`, and a `step` function. `set_num_inference_steps(...)` has to be called before every denoising process, *i.e.* before `step(...)` is called.
-- Every scheduler exposes the timesteps to be "looped over" via a `timesteps` attribute, which is an array of timesteps the model will be called upon.
-- The `step(...)` function takes a predicted model output and the "current" sample (x_t) and returns the "previous", slightly more denoised sample (x_t-1).
-- Given the complexity of diffusion schedulers, the `step` function does not expose all the complexity and can be a bit of a "black box".
-- In almost all cases, novel schedulers shall be implemented in a new scheduling file.
diff --git a/spaces/pen-one/bingo-pen-one/README.md b/spaces/pen-one/bingo-pen-one/README.md
deleted file mode 100644
index 5d6936218874c647b5d22e13ad4be7edb8936f92..0000000000000000000000000000000000000000
--- a/spaces/pen-one/bingo-pen-one/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
----
-title: bingo
-emoji: 😊
-colorFrom: red
-colorTo: red
-sdk: docker
-license: mit
-duplicated_from: hf4all/bingo
----
-
-<div align="center">
-
-# Bingo 
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-![Github stars](https://badgen.net/github/stars/weaigc/bingo?icon=github&label=stars)
-![Gthub issues](https://img.shields.io/github/issues/weaigc/bingo)
-[![docker build](https://github.com/weaigc/bingo/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![docker hub](https://badgen.net/docker/size/weaigc/bingo?icon=docker&label=image%20size)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![MIT License](https://img.shields.io/badge/license-MIT-97c50f)](https://github.com/weaigc/bingo/blob/main/license)
-
-问题反馈请前往 https://github.com/weaigc/bingo/issues
-</div>
-
-
diff --git a/spaces/pikto/Elite-freegpt-webui/g4f/models.py b/spaces/pikto/Elite-freegpt-webui/g4f/models.py
deleted file mode 100644
index 0f52fff286ee3bba4aa20a6e1aef09edd747e20f..0000000000000000000000000000000000000000
--- a/spaces/pikto/Elite-freegpt-webui/g4f/models.py
+++ /dev/null
@@ -1,231 +0,0 @@
-from g4f import Provider
-import random
-
-class Model:
-    class model:
-        name: str
-        base_provider: str
-        best_provider: str
-
-    class gpt_35_turbo:
-        name: str = 'gpt-3.5-turbo'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.DeepAi
-
-    class gpt_35_turbo_0613:
-        name: str = 'gpt-3.5-turbo-0613'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Gravityengine
-
-    class gpt_35_turbo_16k_0613:
-        name: str = 'gpt-3.5-turbo-16k-0613'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Gravityengine
-
-    class gpt_35_turbo_16k:
-        name: str = 'gpt-3.5-turbo-16k'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Gravityengine
-
-    class gpt_4_dev:
-        name: str = 'gpt-4-for-dev'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Phind
-
-    class gpt_4:
-        name: str = 'gpt-4'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.ChatgptAi
-    
-    class gpt_4_0613:
-        name: str = 'gpt-4-0613'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Lockchat
-        best_providers: list = [Provider.Bing, Provider.Lockchat]
-
-    class claude_instant_v1_100k:
-        name: str = 'claude-instant-v1-100k'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class claude_instant_v1:
-        name: str = 'claude-instant-v1'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class claude_v1_100k:
-        name: str = 'claude-v1-100k'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class claude_v1:
-        name: str = 'claude-v1'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class alpaca_7b:
-        name: str = 'alpaca-7b'
-        base_provider: str = 'replicate'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class stablelm_tuned_alpha_7b:
-        name: str = 'stablelm-tuned-alpha-7b'
-        base_provider: str = 'replicate'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class bloom:
-        name: str = 'bloom'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class bloomz:
-        name: str = 'bloomz'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class flan_t5_xxl:
-        name: str = 'flan-t5-xxl'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class flan_ul2:
-        name: str = 'flan-ul2'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class gpt_neox_20b:
-        name: str = 'gpt-neox-20b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class oasst_sft_4_pythia_12b_epoch_35:
-        name: str = 'oasst-sft-4-pythia-12b-epoch-3.5'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class santacoder:
-        name: str = 'santacoder'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class command_medium_nightly:
-        name: str = 'command-medium-nightly'
-        base_provider: str = 'cohere'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class command_xlarge_nightly:
-        name: str = 'command-xlarge-nightly'
-        base_provider: str = 'cohere'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class code_cushman_001:
-        name: str = 'code-cushman-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class code_davinci_002:
-        name: str = 'code-davinci-002'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_ada_001:
-        name: str = 'text-ada-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_babbage_001:
-        name: str = 'text-babbage-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_curie_001:
-        name: str = 'text-curie-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_davinci_002:
-        name: str = 'text-davinci-002'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_davinci_003:
-        name: str = 'text-davinci-003'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-        
-    class palm:
-        name: str = 'palm2'
-        base_provider: str = 'google'
-        best_provider: Provider.Provider = Provider.Bard
-        
-            
-    """    'falcon-40b': Model.falcon_40b,
-    'falcon-7b': Model.falcon_7b,
-    'llama-13b': Model.llama_13b,"""
-    
-    class falcon_40b:
-        name: str = 'falcon-40b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.H2o
-    
-    class falcon_7b:
-        name: str = 'falcon-7b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.H2o
-        
-    class llama_13b:
-        name: str = 'llama-13b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.H2o
-    
-class ModelUtils:
-    convert: dict = {
-        'gpt-3.5-turbo': Model.gpt_35_turbo,
-        'gpt-3.5-turbo-0613': Model.gpt_35_turbo_0613,
-        'gpt-4': Model.gpt_4,
-        'gpt-4-0613': Model.gpt_4_0613,
-        'gpt-4-for-dev': Model.gpt_4_dev,
-        'gpt-3.5-turbo-16k': Model.gpt_35_turbo_16k,
-        'gpt-3.5-turbo-16k-0613': Model.gpt_35_turbo_16k_0613,
-        
-        'claude-instant-v1-100k': Model.claude_instant_v1_100k,
-        'claude-v1-100k': Model.claude_v1_100k,
-        'claude-instant-v1': Model.claude_instant_v1,
-        'claude-v1': Model.claude_v1,
-        
-        'alpaca-7b': Model.alpaca_7b,
-        'stablelm-tuned-alpha-7b': Model.stablelm_tuned_alpha_7b,
-        
-        'bloom': Model.bloom,
-        'bloomz': Model.bloomz,
-        
-        'flan-t5-xxl': Model.flan_t5_xxl,
-        'flan-ul2': Model.flan_ul2,
-        
-        'gpt-neox-20b': Model.gpt_neox_20b,
-        'oasst-sft-4-pythia-12b-epoch-3.5': Model.oasst_sft_4_pythia_12b_epoch_35,
-        'santacoder': Model.santacoder,
-        
-        'command-medium-nightly': Model.command_medium_nightly,
-        'command-xlarge-nightly': Model.command_xlarge_nightly,
-        
-        'code-cushman-001': Model.code_cushman_001,
-        'code-davinci-002': Model.code_davinci_002,
-        
-        'text-ada-001': Model.text_ada_001,
-        'text-babbage-001': Model.text_babbage_001,
-        'text-curie-001': Model.text_curie_001,
-        'text-davinci-002': Model.text_davinci_002,
-        'text-davinci-003': Model.text_davinci_003,
-        
-        'palm2': Model.palm,
-        'palm': Model.palm,
-        'google': Model.palm,
-        'google-bard': Model.palm,
-        'google-palm': Model.palm,
-        'bard': Model.palm,
-        
-        'falcon-40b': Model.falcon_40b,
-        'falcon-7b': Model.falcon_7b,
-        'llama-13b': Model.llama_13b,
-    }
diff --git a/spaces/pixiou/bingo/src/components/chat.tsx b/spaces/pixiou/bingo/src/components/chat.tsx
deleted file mode 100644
index a37ab1cc96ca2e6bfd9acbe313a8d946bfd5c3d4..0000000000000000000000000000000000000000
--- a/spaces/pixiou/bingo/src/components/chat.tsx
+++ /dev/null
@@ -1,93 +0,0 @@
-'use client'
-
-import { useCallback, useEffect, useMemo, useState } from 'react'
-import { useAtom } from 'jotai'
-import Image from 'next/image'
-import { cn } from '@/lib/utils'
-import { ChatList } from '@/components/chat-list'
-import { ChatPanel } from '@/components/chat-panel'
-import { WelcomeScreen } from '@/components/welcome-screen'
-import { ChatScrollAnchor } from '@/components/chat-scroll-anchor'
-import { ToneSelector } from './tone-selector'
-import { ChatHeader } from './chat-header'
-import { ChatSuggestions } from './chat-suggestions'
-import { bingConversationStyleAtom } from '@/state'
-import { ButtonScrollToBottom } from '@/components/button-scroll-to-bottom'
-import StopIcon from '@/assets/images/stop.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { ChatNotification } from './chat-notification'
-import { Settings } from './settings'
-import { ChatHistory } from './chat-history'
-
-export type ChatProps = React.ComponentProps<'div'> & { initialMessages?: ChatMessageModel[] }
-
-export default function Chat({ className }: ChatProps) {
-
-  const [bingStyle, setBingStyle] = useAtom(bingConversationStyleAtom)
-  const {
-    messages,
-    sendMessage,
-    resetConversation,
-    stopGenerating,
-    setInput,
-    bot,
-    input,
-    generating,
-    isSpeaking,
-    uploadImage,
-    attachmentList,
-    setAttachmentList,
-  } = useBing()
-
-  useEffect(() => {
-    window.scrollTo({
-      top: document.body.offsetHeight,
-      behavior: 'smooth'
-    })
-  }, [])
-
-  return (
-    <div className="flex flex-1 flex-col">
-      <Settings />
-      <div className={cn('flex-1 pb-16', className)}>
-        <ChatHeader />
-        <WelcomeScreen setInput={setInput} />
-        <ToneSelector type={bingStyle} onChange={setBingStyle} />
-        {messages.length ? (
-          <>
-            <ChatList messages={messages} />
-            <ChatScrollAnchor trackVisibility={generating} />
-            <ChatNotification message={messages.at(-1)} bot={bot} />
-            <ChatSuggestions setInput={setInput} suggestions={messages.at(-1)?.suggestedResponses} />
-
-            {generating ? (
-              <div className="flex h-10 items-center justify-center my-4">
-                <button
-                  onClick={stopGenerating}
-                  className="typing-control-item stop"
-                >
-                  <Image alt="stop" src={StopIcon} width={24} className="mr-1" />
-                  <span>停止响应</span>
-                </button>
-              </div>
-            ) : null}
-          </>
-        ) : null}
-      </div>
-      <ChatPanel
-        className="pt-24 z-10"
-        isSpeaking={isSpeaking}
-        generating={generating}
-        sendMessage={sendMessage}
-        input={input}
-        setInput={setInput}
-        resetConversation={resetConversation}
-        uploadImage={uploadImage}
-        attachmentList={attachmentList}
-        setAttachmentList={setAttachmentList}
-      />
-      <ButtonScrollToBottom />
-    </div>
-  )
-}
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/vcs/__init__.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/vcs/__init__.py
deleted file mode 100644
index b6beddbe6d24d2949dc89ed07abfebd59d8b63b9..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/vcs/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Expose a limited set of classes and functions so callers outside of
-# the vcs package don't need to import deeper than `pip._internal.vcs`.
-# (The test directory may still need to import from a vcs sub-package.)
-# Import all vcs modules to register each VCS in the VcsSupport object.
-import pip._internal.vcs.bazaar
-import pip._internal.vcs.git
-import pip._internal.vcs.mercurial
-import pip._internal.vcs.subversion  # noqa: F401
-from pip._internal.vcs.versioncontrol import (  # noqa: F401
-    RemoteNotFoundError,
-    RemoteNotValidError,
-    is_url,
-    make_vcs_requirement_url,
-    vcs,
-)
diff --git a/spaces/power2/JoJoGan-powerhow2/e4e/criteria/lpips/__init__.py b/spaces/power2/JoJoGan-powerhow2/e4e/criteria/lpips/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/filelock/version.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/filelock/version.py
deleted file mode 100644
index cc9fc1550b3b64cc4ff85291e33b6cb0a745af97..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/filelock/version.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# file generated by setuptools_scm
-# don't change, don't track in version control
-TYPE_CHECKING = False
-if TYPE_CHECKING:
-    from typing import Tuple, Union
-    VERSION_TUPLE = Tuple[Union[int, str], ...]
-else:
-    VERSION_TUPLE = object
-
-version: str
-__version__: str
-__version_tuple__: VERSION_TUPLE
-version_tuple: VERSION_TUPLE
-
-__version__ = version = '3.13.1'
-__version_tuple__ = version_tuple = (3, 13, 1)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/implementations/jupyter.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/implementations/jupyter.py
deleted file mode 100644
index 2839f4c1feea56dddd54bdc00f0b884c8461d29e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fsspec/implementations/jupyter.py
+++ /dev/null
@@ -1,124 +0,0 @@
-import base64
-import io
-import re
-
-import requests
-
-import fsspec
-
-
-class JupyterFileSystem(fsspec.AbstractFileSystem):
-    """View of the files as seen by a Jupyter server (notebook or lab)"""
-
-    protocol = ("jupyter", "jlab")
-
-    def __init__(self, url, tok=None, **kwargs):
-        """
-
-        Parameters
-        ----------
-        url : str
-            Base URL of the server, like "http://127.0.0.1:8888". May include
-            token in the string, which is given by the process when starting up
-        tok : str
-            If the token is obtained separately, can be given here
-        kwargs
-        """
-        if "?" in url:
-            if tok is None:
-                try:
-                    tok = re.findall("token=([a-z0-9]+)", url)[0]
-                except IndexError as e:
-                    raise ValueError("Could not determine token") from e
-            url = url.split("?", 1)[0]
-        self.url = url.rstrip("/") + "/api/contents"
-        self.session = requests.Session()
-        if tok:
-            self.session.headers["Authorization"] = f"token {tok}"
-
-        super().__init__(**kwargs)
-
-    def ls(self, path, detail=True, **kwargs):
-        path = self._strip_protocol(path)
-        r = self.session.get(f"{self.url}/{path}")
-        if r.status_code == 404:
-            return FileNotFoundError(path)
-        r.raise_for_status()
-        out = r.json()
-
-        if out["type"] == "directory":
-            out = out["content"]
-        else:
-            out = [out]
-        for o in out:
-            o["name"] = o.pop("path")
-            o.pop("content")
-            if o["type"] == "notebook":
-                o["type"] = "file"
-        if detail:
-            return out
-        return [o["name"] for o in out]
-
-    def cat_file(self, path, start=None, end=None, **kwargs):
-        path = self._strip_protocol(path)
-        r = self.session.get(f"{self.url}/{path}")
-        if r.status_code == 404:
-            return FileNotFoundError(path)
-        r.raise_for_status()
-        out = r.json()
-        if out["format"] == "text":
-            # data should be binary
-            b = out["content"].encode()
-        else:
-            b = base64.b64decode(out["content"])
-        return b[start:end]
-
-    def pipe_file(self, path, value, **_):
-        path = self._strip_protocol(path)
-        json = {
-            "name": path.rsplit("/", 1)[-1],
-            "path": path,
-            "size": len(value),
-            "content": base64.b64encode(value).decode(),
-            "format": "base64",
-            "type": "file",
-        }
-        self.session.put(f"{self.url}/{path}", json=json)
-
-    def mkdir(self, path, create_parents=True, **kwargs):
-        path = self._strip_protocol(path)
-        if create_parents and "/" in path:
-            self.mkdir(path.rsplit("/", 1)[0], True)
-        json = {
-            "name": path.rsplit("/", 1)[-1],
-            "path": path,
-            "size": None,
-            "content": None,
-            "type": "directory",
-        }
-        self.session.put(f"{self.url}/{path}", json=json)
-
-    def _rm(self, path):
-        path = self._strip_protocol(path)
-        self.session.delete(f"{self.url}/{path}")
-
-    def _open(self, path, mode="rb", **kwargs):
-        path = self._strip_protocol(path)
-        if mode == "rb":
-            data = self.cat_file(path)
-            return io.BytesIO(data)
-        else:
-            return SimpleFileWriter(self, path, mode="wb")
-
-
-class SimpleFileWriter(fsspec.spec.AbstractBufferedFile):
-    def _upload_chunk(self, final=False):
-        """Never uploads a chunk until file is done
-
-        Not suitable for large files
-        """
-        if final is False:
-            return False
-        self.buffer.seek(0)
-        data = self.buffer.read()
-        self.fs.pipe_file(self.path, data)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/_tight_bbox.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/_tight_bbox.py
deleted file mode 100644
index db72bbdff020680dfd833229cac41e9b33e428ee..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/_tight_bbox.py
+++ /dev/null
@@ -1,84 +0,0 @@
-"""
-Helper module for the *bbox_inches* parameter in `.Figure.savefig`.
-"""
-
-from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
-
-
-def adjust_bbox(fig, bbox_inches, fixed_dpi=None):
-    """
-    Temporarily adjust the figure so that only the specified area
-    (bbox_inches) is saved.
-
-    It modifies fig.bbox, fig.bbox_inches,
-    fig.transFigure._boxout, and fig.patch.  While the figure size
-    changes, the scale of the original figure is conserved.  A
-    function which restores the original values are returned.
-    """
-    origBbox = fig.bbox
-    origBboxInches = fig.bbox_inches
-    _boxout = fig.transFigure._boxout
-
-    old_aspect = []
-    locator_list = []
-    sentinel = object()
-    for ax in fig.axes:
-        locator = ax.get_axes_locator()
-        if locator is not None:
-            ax.apply_aspect(locator(ax, None))
-        locator_list.append(locator)
-        current_pos = ax.get_position(original=False).frozen()
-        ax.set_axes_locator(lambda a, r, _pos=current_pos: _pos)
-        # override the method that enforces the aspect ratio on the Axes
-        if 'apply_aspect' in ax.__dict__:
-            old_aspect.append(ax.apply_aspect)
-        else:
-            old_aspect.append(sentinel)
-        ax.apply_aspect = lambda pos=None: None
-
-    def restore_bbox():
-        for ax, loc, aspect in zip(fig.axes, locator_list, old_aspect):
-            ax.set_axes_locator(loc)
-            if aspect is sentinel:
-                # delete our no-op function which un-hides the original method
-                del ax.apply_aspect
-            else:
-                ax.apply_aspect = aspect
-
-        fig.bbox = origBbox
-        fig.bbox_inches = origBboxInches
-        fig.transFigure._boxout = _boxout
-        fig.transFigure.invalidate()
-        fig.patch.set_bounds(0, 0, 1, 1)
-
-    if fixed_dpi is None:
-        fixed_dpi = fig.dpi
-    tr = Affine2D().scale(fixed_dpi)
-    dpi_scale = fixed_dpi / fig.dpi
-
-    fig.bbox_inches = Bbox.from_bounds(0, 0, *bbox_inches.size)
-    x0, y0 = tr.transform(bbox_inches.p0)
-    w1, h1 = fig.bbox.size * dpi_scale
-    fig.transFigure._boxout = Bbox.from_bounds(-x0, -y0, w1, h1)
-    fig.transFigure.invalidate()
-
-    fig.bbox = TransformedBbox(fig.bbox_inches, tr)
-
-    fig.patch.set_bounds(x0 / w1, y0 / h1,
-                         fig.bbox.width / w1, fig.bbox.height / h1)
-
-    return restore_bbox
-
-
-def process_figure_for_rasterizing(fig, bbox_inches_restore, fixed_dpi=None):
-    """
-    A function that needs to be called when figure dpi changes during the
-    drawing (e.g., rasterizing).  It recovers the bbox and re-adjust it with
-    the new dpi.
-    """
-
-    bbox_inches, restore_bbox = bbox_inches_restore
-    restore_bbox()
-    r = adjust_bbox(fig, bbox_inches, fixed_dpi)
-
-    return bbox_inches, r
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexing/test_indexers.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexing/test_indexers.py
deleted file mode 100644
index ddc5c039160d5ada6c6dccb62514590a4ce9f620..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexing/test_indexers.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# Tests aimed at pandas.core.indexers
-import numpy as np
-import pytest
-
-from pandas.core.indexers import (
-    is_scalar_indexer,
-    length_of_indexer,
-    validate_indices,
-)
-
-
-def test_length_of_indexer():
-    arr = np.zeros(4, dtype=bool)
-    arr[0] = 1
-    result = length_of_indexer(arr)
-    assert result == 1
-
-
-def test_is_scalar_indexer():
-    indexer = (0, 1)
-    assert is_scalar_indexer(indexer, 2)
-    assert not is_scalar_indexer(indexer[0], 2)
-
-    indexer = (np.array([2]), 1)
-    assert not is_scalar_indexer(indexer, 2)
-
-    indexer = (np.array([2]), np.array([3]))
-    assert not is_scalar_indexer(indexer, 2)
-
-    indexer = (np.array([2]), np.array([3, 4]))
-    assert not is_scalar_indexer(indexer, 2)
-
-    assert not is_scalar_indexer(slice(None), 1)
-
-    indexer = 0
-    assert is_scalar_indexer(indexer, 1)
-
-    indexer = (0,)
-    assert is_scalar_indexer(indexer, 1)
-
-
-class TestValidateIndices:
-    def test_validate_indices_ok(self):
-        indices = np.asarray([0, 1])
-        validate_indices(indices, 2)
-        validate_indices(indices[:0], 0)
-        validate_indices(np.array([-1, -1]), 0)
-
-    def test_validate_indices_low(self):
-        indices = np.asarray([0, -2])
-        with pytest.raises(ValueError, match="'indices' contains"):
-            validate_indices(indices, 2)
-
-    def test_validate_indices_high(self):
-        indices = np.asarray([0, 1, 2])
-        with pytest.raises(IndexError, match="indices are out"):
-            validate_indices(indices, 2)
-
-    def test_validate_indices_empty(self):
-        with pytest.raises(IndexError, match="indices are out"):
-            validate_indices(np.array([0, 1]), 0)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/pygments/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/pygments/__init__.py
deleted file mode 100644
index 22c50b356adf906bd6a579749d0c120f9cac8381..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/pygments/__init__.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""
-    Pygments
-    ~~~~~~~~
-
-    Pygments is a syntax highlighting package written in Python.
-
-    It is a generic syntax highlighter for general use in all kinds of software
-    such as forum systems, wikis or other applications that need to prettify
-    source code. Highlights are:
-
-    * a wide range of common languages and markup formats is supported
-    * special attention is paid to details, increasing quality by a fair amount
-    * support for new languages and formats are added easily
-    * a number of output formats, presently HTML, LaTeX, RTF, SVG, all image
-      formats that PIL supports, and ANSI sequences
-    * it is usable as a command-line tool and as a library
-    * ... and it highlights even Brainfuck!
-
-    The `Pygments master branch`_ is installable with ``easy_install Pygments==dev``.
-
-    .. _Pygments master branch:
-       https://github.com/pygments/pygments/archive/master.zip#egg=Pygments-dev
-
-    :copyright: Copyright 2006-2021 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-from io import StringIO, BytesIO
-
-__version__ = '2.11.2'
-__docformat__ = 'restructuredtext'
-
-__all__ = ['lex', 'format', 'highlight']
-
-
-def lex(code, lexer):
-    """
-    Lex ``code`` with ``lexer`` and return an iterable of tokens.
-    """
-    try:
-        return lexer.get_tokens(code)
-    except TypeError as err:
-        if (isinstance(err.args[0], str) and
-            ('unbound method get_tokens' in err.args[0] or
-             'missing 1 required positional argument' in err.args[0])):
-            raise TypeError('lex() argument must be a lexer instance, '
-                            'not a class')
-        raise
-
-
-def format(tokens, formatter, outfile=None):  # pylint: disable=redefined-builtin
-    """
-    Format a tokenlist ``tokens`` with the formatter ``formatter``.
-
-    If ``outfile`` is given and a valid file object (an object
-    with a ``write`` method), the result will be written to it, otherwise
-    it is returned as a string.
-    """
-    try:
-        if not outfile:
-            realoutfile = getattr(formatter, 'encoding', None) and BytesIO() or StringIO()
-            formatter.format(tokens, realoutfile)
-            return realoutfile.getvalue()
-        else:
-            formatter.format(tokens, outfile)
-    except TypeError as err:
-        if (isinstance(err.args[0], str) and
-            ('unbound method format' in err.args[0] or
-             'missing 1 required positional argument' in err.args[0])):
-            raise TypeError('format() argument must be a formatter instance, '
-                            'not a class')
-        raise
-
-
-def highlight(code, lexer, formatter, outfile=None):
-    """
-    Lex ``code`` with ``lexer`` and format it with the formatter ``formatter``.
-
-    If ``outfile`` is given and a valid file object (an object
-    with a ``write`` method), the result will be written to it, otherwise
-    it is returned as a string.
-    """
-    return format(lex(code, lexer), formatter, outfile)
-
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tqdm/_utils.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tqdm/_utils.py
deleted file mode 100644
index 385e849e106d1319fe21045f14eb0aa6552fb153..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tqdm/_utils.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from warnings import warn
-
-from .std import TqdmDeprecationWarning
-from .utils import (  # NOQA, pylint: disable=unused-import
-    CUR_OS, IS_NIX, IS_WIN, RE_ANSI, Comparable, FormatReplace, SimpleTextIOWrapper,
-    _environ_cols_wrapper, _is_ascii, _is_utf, _screen_shape_linux, _screen_shape_tput,
-    _screen_shape_windows, _screen_shape_wrapper, _supports_unicode, _term_move_up, colorama)
-
-warn("This function will be removed in tqdm==5.0.0\n"
-     "Please use `tqdm.utils.*` instead of `tqdm._utils.*`",
-     TqdmDeprecationWarning, stacklevel=2)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/websockets/extensions/base.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/websockets/extensions/base.py
deleted file mode 100644
index 6c481a46cc74cf8eb6a2d2551919f9d42ed304d8..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/websockets/extensions/base.py
+++ /dev/null
@@ -1,133 +0,0 @@
-from __future__ import annotations
-
-from typing import List, Optional, Sequence, Tuple
-
-from .. import frames
-from ..typing import ExtensionName, ExtensionParameter
-
-
-__all__ = ["Extension", "ClientExtensionFactory", "ServerExtensionFactory"]
-
-
-class Extension:
-    """
-    Base class for extensions.
-
-    """
-
-    name: ExtensionName
-    """Extension identifier."""
-
-    def decode(
-        self,
-        frame: frames.Frame,
-        *,
-        max_size: Optional[int] = None,
-    ) -> frames.Frame:
-        """
-        Decode an incoming frame.
-
-        Args:
-            frame (Frame): incoming frame.
-            max_size: maximum payload size in bytes.
-
-        Returns:
-            Frame: Decoded frame.
-
-        Raises:
-            PayloadTooBig: if decoding the payload exceeds ``max_size``.
-
-        """
-        raise NotImplementedError
-
-    def encode(self, frame: frames.Frame) -> frames.Frame:
-        """
-        Encode an outgoing frame.
-
-        Args:
-            frame (Frame): outgoing frame.
-
-        Returns:
-            Frame: Encoded frame.
-
-        """
-        raise NotImplementedError
-
-
-class ClientExtensionFactory:
-    """
-    Base class for client-side extension factories.
-
-    """
-
-    name: ExtensionName
-    """Extension identifier."""
-
-    def get_request_params(self) -> List[ExtensionParameter]:
-        """
-        Build parameters to send to the server for this extension.
-
-        Returns:
-            List[ExtensionParameter]: Parameters to send to the server.
-
-        """
-        raise NotImplementedError
-
-    def process_response_params(
-        self,
-        params: Sequence[ExtensionParameter],
-        accepted_extensions: Sequence[Extension],
-    ) -> Extension:
-        """
-        Process parameters received from the server.
-
-        Args:
-            params (Sequence[ExtensionParameter]): parameters received from
-                the server for this extension.
-            accepted_extensions (Sequence[Extension]): list of previously
-                accepted extensions.
-
-        Returns:
-            Extension: An extension instance.
-
-        Raises:
-            NegotiationError: if parameters aren't acceptable.
-
-        """
-        raise NotImplementedError
-
-
-class ServerExtensionFactory:
-    """
-    Base class for server-side extension factories.
-
-    """
-
-    name: ExtensionName
-    """Extension identifier."""
-
-    def process_request_params(
-        self,
-        params: Sequence[ExtensionParameter],
-        accepted_extensions: Sequence[Extension],
-    ) -> Tuple[List[ExtensionParameter], Extension]:
-        """
-        Process parameters received from the client.
-
-        Args:
-            params (Sequence[ExtensionParameter]): parameters received from
-                the client for this extension.
-            accepted_extensions (Sequence[Extension]): list of previously
-                accepted extensions.
-
-        Returns:
-            Tuple[List[ExtensionParameter], Extension]: To accept the offer,
-            parameters to send to the client for this extension and an
-            extension instance.
-
-        Raises:
-            NegotiationError: to reject the offer, if parameters received from
-                the client aren't acceptable.
-
-        """
-        raise NotImplementedError
diff --git a/spaces/projecte-aina/transcripcio-fonetica-catala/engine.py b/spaces/projecte-aina/transcripcio-fonetica-catala/engine.py
deleted file mode 100644
index b3592f73a59cef370597f05b75eb7d33b39711aa..0000000000000000000000000000000000000000
--- a/spaces/projecte-aina/transcripcio-fonetica-catala/engine.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import io
-import json
-import os
-import wave
-from dataclasses import dataclass
-from pathlib import Path
-from typing import List, Mapping, Optional, Sequence, Union
-
-import numpy as np
-import onnxruntime
-from espeak_phonemizer import Phonemizer
-
-_BOS = "^"
-_EOS = "$"
-_PAD = "_"
-
-
-@dataclass
-class PiperConfig:
-    num_symbols: int
-    num_speakers: int
-    sample_rate: int
-    espeak_voice: str
-    length_scale: float
-    noise_scale: float
-    noise_w: float
-    phoneme_id_map: Mapping[str, Sequence[int]]
-
-
-class Piper:
-    def __init__(
-        self,
-        model_path: Union[str, Path],
-        config_path: Optional[Union[str, Path]] = None,
-        use_cuda: bool = False,
-    ):
-        if config_path is None:
-            config_path = f"{model_path}.json"
-
-        self.config = load_config(config_path)
-        self.phonemizer = Phonemizer(self.config.espeak_voice)
-        self.onnx_options = onnxruntime.SessionOptions()
-        self.onnx_options.intra_op_num_threads = os.cpu_count() - 1
-        self.model = onnxruntime.InferenceSession(
-            str(model_path),
-            sess_options=self.onnx_options,
-            providers=["CPUExecutionProvider"]
-            if not use_cuda
-            else ["CUDAExecutionProvider"],
-        )
-
-    def synthesize(
-        self,
-        text: str,
-        speaker_id: Optional[int] = None,
-        length_scale: Optional[float] = None,
-        noise_scale: Optional[float] = None,
-        noise_w: Optional[float] = None,
-    ) -> bytes:
-        """Synthesize WAV audio from text."""
-        if length_scale is None:
-            length_scale = self.config.length_scale
-
-        if noise_scale is None:
-            noise_scale = self.config.noise_scale
-
-        if noise_w is None:
-            noise_w = self.config.noise_w
-
-        phonemes_str = self.phonemizer.phonemize(text, keep_clause_breakers=True)
-        phonemes = [_BOS] + list(phonemes_str)
-        phoneme_ids: List[int] = []
-
-        for phoneme in phonemes:
-            phoneme_ids.extend(self.config.phoneme_id_map[phoneme])
-            phoneme_ids.extend(self.config.phoneme_id_map[_PAD])
-
-        phoneme_ids.extend(self.config.phoneme_id_map[_EOS])
-
-        phoneme_ids_array = np.expand_dims(np.array(phoneme_ids, dtype=np.int64), 0)
-        phoneme_ids_lengths = np.array([phoneme_ids_array.shape[1]], dtype=np.int64)
-        scales = np.array(
-            [noise_scale, length_scale, noise_w],
-            dtype=np.float32,
-        )
-
-        if (self.config.num_speakers > 1) and (speaker_id is not None):
-            # Default speaker
-            speaker_id = 0
-
-        sid = None
-
-        if speaker_id is not None:
-            sid = np.array([speaker_id], dtype=np.int64)
-
-        # Synthesize through Onnx
-        audio = self.model.run(
-            None,
-            {
-                "input": phoneme_ids_array,
-                "input_lengths": phoneme_ids_lengths,
-                "scales": scales,
-                "sid": sid,
-            },
-        )[0].squeeze((0, 1))
-        audio = audio_float_to_int16(audio.squeeze())
-
-        # Convert to WAV
-        with io.BytesIO() as wav_io:
-            wav_file: wave.Wave_write = wave.open(wav_io, "wb")
-            with wav_file:
-                wav_file.setframerate(self.config.sample_rate)
-                wav_file.setsampwidth(2)
-                wav_file.setnchannels(1)
-                wav_file.writeframes(audio.tobytes())
-
-            return wav_io.getvalue()
-
-
-def load_config(config_path: Union[str, Path]) -> PiperConfig:
-    with open(config_path, "r", encoding="utf-8") as config_file:
-        config_dict = json.load(config_file)
-        inference = config_dict.get("inference", {})
-
-        return PiperConfig(
-            num_symbols=config_dict["num_symbols"],
-            num_speakers=config_dict["num_speakers"],
-            sample_rate=config_dict["audio"]["sample_rate"],
-            espeak_voice=config_dict["espeak"]["voice"],
-            noise_scale=inference.get("noise_scale", 0.667),
-            length_scale=inference.get("length_scale", 1.0),
-            noise_w=inference.get("noise_w", 0.8),
-            phoneme_id_map=config_dict["phoneme_id_map"],
-        )
-
-
-def audio_float_to_int16(
-    audio: np.ndarray, max_wav_value: float = 32767.0
-) -> np.ndarray:
-    """Normalize audio and convert to int16 range"""
-    audio_norm = audio * (max_wav_value / max(0.01, np.max(np.abs(audio))))
-    audio_norm = np.clip(audio_norm, -max_wav_value, max_wav_value)
-    audio_norm = audio_norm.astype("int16")
-    return audio_norm
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Authorization Code Sft2841 120 ((BETTER)).md b/spaces/quidiaMuxgu/Expedit-SAM/Authorization Code Sft2841 120 ((BETTER)).md
deleted file mode 100644
index 93db5919b4dc84e65b61ae136dfc0397ad95e28c..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Authorization Code Sft2841 120 ((BETTER)).md	
+++ /dev/null
@@ -1,30 +0,0 @@
-
-<h1>How to Get Authorization Code SFT2841 120 for Schneider Electric Software</h1>
-<p>If you are looking for a way to get authorization code SFT2841 120 for Schneider Electric software, you are in the right place. In this article, we will show you how to obtain and activate this code to use the SFT2841 software, which is a powerful tool for configuring and monitoring Sepam protection relays.</p>
-<h2>authorization code sft2841 120</h2><br /><p><b><b>Download File</b> &#8230;&#8230;&#8230; <a href="https://geags.com/2uCr5i">https://geags.com/2uCr5i</a></b></p><br /><br />
-<p>Authorization code SFT2841 120 is a 16-digit alphanumeric code that you need to enter in the SFT2841 software to unlock its full functionality. Without this code, you can only use the software in demo mode, which limits some features and does not allow you to save or print your settings.</p>
-<h2>How to Obtain Authorization Code SFT2841 120</h2>
-<p>To obtain authorization code SFT2841 120, you need to purchase a license from Schneider Electric or one of its authorized distributors. You can find the list of distributors on the Schneider Electric website. Once you purchase a license, you will receive an email with your authorization code and a link to download the software.</p>
-<h2>How to Activate Authorization Code SFT2841 120</h2>
-<p>To activate authorization code SFT2841 120, you need to install the SFT2841 software on your computer and follow these steps:</p>
-<p></p>
-<ol>
-<li>Launch the SFT2841 software and click on the "Help" menu.</li>
-<li>Select "About SFT2841" and click on the "Authorization" button.</li>
-<li>Enter your authorization code in the corresponding field and click on "OK".</li>
-<li>A confirmation message will appear, indicating that your code has been validated and your software is ready to use.</li>
-</ol>
-<p>Congratulations! You have successfully activated authorization code SFT2841 120 for Schneider Electric software. You can now enjoy all the features and benefits of the SFT2841 software for configuring and monitoring Sepam protection relays.</p>
-
-<h2>What are the Features and Benefits of SFT2841 Software</h2>
-<p>SFT2841 software is a powerful and user-friendly tool that allows you to configure and monitor Sepam protection relays with ease. Some of the features and benefits of SFT2841 software are:</p>
-<ul>
-<li>It supports all Sepam series 20, 40 and 80 relays, which cover a wide range of applications such as feeder, motor, transformer and generator protection.</li>
-<li>It allows you to create, edit, save and print parameter and protection settings for Sepam relays, as well as upload and download them to and from the relays.</li>
-<li>It provides graphical and numerical displays of Sepam measurements, alarms, events and waveforms, as well as historical data logging and analysis.</li>
-<li>It enables remote control and supervision of Sepam relays via communication networks such as Modbus, Ethernet or IEC 61850.</li>
-<li>It offers online help and documentation for easy reference and troubleshooting.</li>
-</ul>
-<p>With SFT2841 software, you can optimize the performance and reliability of your electrical network by using Sepam protection relays more efficiently and effectively.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/FS2004 FSX - Flight1 - Airport Facilitator X V1.01 Generator.md b/spaces/quidiaMuxgu/Expedit-SAM/FS2004 FSX - Flight1 - Airport Facilitator X V1.01 Generator.md
deleted file mode 100644
index 334d9533c02810189dfe6d639bcba60ec01dfdf7..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/FS2004 FSX - Flight1 - Airport Facilitator X V1.01 Generator.md	
+++ /dev/null
@@ -1,46 +0,0 @@
-<h2>FS2004 FSX - Flight1 - Airport Facilitator X V1.01 Generator</h2><br /><p><b><b>Download File</b> &rArr;&rArr;&rArr; <a href="https://geags.com/2uCreo">https://geags.com/2uCreo</a></b></p><br /><br />
-<br />
-If you are new to X-Plane, here is a quick summary of X-Plane vs. Flight Simulator .
-
-Airports from all over the world are now being presented in multiple sources that are not only varied in size and shape, but also in appearance. The very nature of a 3D engine is that of realism. FS2004 introduces new ways to quickly edit airports. In this article, we will show you several ways to edit airports with FXF.
-
-There are four categories of airports: Basic, Transport, Combat, and Special. We have an example of each:
-
-Basic is the default type.
-
-Transport is the type that allows you to easily control aircraft movement and use of runways. It is a good choice for normal or ordinary airports.
-
-Combat is the type of airport used for air-to-air combat in which airplanes should be avoided.
-
-Special allows you to add a unique feature to an airport such as an anti-aircraft gun or unique equipment that is used in conjunction with the airport.
-
-This article presents a number of ways to edit airports. While some work for all types of airports, others are most useful for specific types.
-
-The primary type of airport is the Basic, which is the most basic airport type available.
-
-The Transport type is intended for use in designing airports that are on the ground.
-
-Combat airports are for aircraft that are involved in an air-to-air combat.
-
-Special airports are for unusual airports, often with their own unique features.
-
-Editing a Basic airport is very simple and is discussed in detail in the section Basic.
-
-Transport and Combat airports are discussed in the next section.
-
-While any of these airports can be edited by the user, we will describe some of the editing features in those type of airports.
-
-The Basic airport is the type of airport that is most commonly used for beginners.
-
-Airports of Transport type are intended for use by pilots to help the pilots control their aircraft while on the ground, often for the purpose of designing an airport.
-
-Combat airports are for aircraft that are engaged in an air-to-air combat.
-
-Special airports are for airports that have unique attributes.
-
-X-Plane and FSX: FXF, the Part File Format
-
-FSX uses the part file format (“.pf”) to save and load flight plans. FXF, the 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Hoja De Respuestas Kostick Pdf 40.md b/spaces/quidiaMuxgu/Expedit-SAM/Hoja De Respuestas Kostick Pdf 40.md
deleted file mode 100644
index 38b6f4d65949afa2d423050c4b3021269c9cb96e..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Hoja De Respuestas Kostick Pdf 40.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>hoja de respuestas kostick pdf 40</h2><br /><p><b><b>DOWNLOAD</b> &#10084; <a href="https://geags.com/2uCrq2">https://geags.com/2uCrq2</a></b></p><br /><br />
-
- d5da3c52bf<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/qwebeck/echo-net-dynamic-segmentations/app.py b/spaces/qwebeck/echo-net-dynamic-segmentations/app.py
deleted file mode 100644
index 0568f652268a5655ed24152793e2a56318c1848e..0000000000000000000000000000000000000000
--- a/spaces/qwebeck/echo-net-dynamic-segmentations/app.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import random
-import gradio as gr
-import requests
-import os
-import json
-
-
-def get_response(code):
-    default_code = 'joke'
-    code = next((c for c in ['joke', 'fact'] if c == code), default_code)
-    response = requests.get(
-        f'https://api.api-ninjas.com/v1/{code}s?limit=1',
-        headers={'X-Api-Key': os.environ.get('API_NINJAS_KEY')})
-    if response.status_code == requests.codes.ok:
-        return json.loads(response.text)[0][code]
-    else:
-        return 'Sorry, but today is not your day :('
-
-
-def chat(message, history):
-    """
-    Returns a joke or a fact depending on you wish. Or ITS wish..... Buhahaha 
-    """
-    history = history or []
-    message = message.lower()
-    response = get_response(message)
-    history.append((message, response))
-    return history, history
-
-
-chatbot = gr.Chatbot().style(color_map=("green", "pink"))
-
-demo = gr.Interface(
-    chat,
-    ["text", "state"],
-    [chatbot, "state"],
-    allow_flagging="never",
-)
-
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/radames/UserControllableLT-Latent-Transformer/interface/pixel2style2pixel/models/encoders/psp_encoders.py b/spaces/radames/UserControllableLT-Latent-Transformer/interface/pixel2style2pixel/models/encoders/psp_encoders.py
deleted file mode 100644
index 5a016637603b7812072512337d7c33e1be6ac0bb..0000000000000000000000000000000000000000
--- a/spaces/radames/UserControllableLT-Latent-Transformer/interface/pixel2style2pixel/models/encoders/psp_encoders.py
+++ /dev/null
@@ -1,186 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-from torch.nn import Linear, Conv2d, BatchNorm2d, PReLU, Sequential, Module
-
-from pixel2style2pixel.models.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE
-from pixel2style2pixel.models.stylegan2.model import EqualLinear
-
-
-class GradualStyleBlock(Module):
-    def __init__(self, in_c, out_c, spatial):
-        super(GradualStyleBlock, self).__init__()
-        self.out_c = out_c
-        self.spatial = spatial
-        num_pools = int(np.log2(spatial))
-        modules = []
-        modules += [Conv2d(in_c, out_c, kernel_size=3, stride=2, padding=1),
-                    nn.LeakyReLU()]
-        for i in range(num_pools - 1):
-            modules += [
-                Conv2d(out_c, out_c, kernel_size=3, stride=2, padding=1),
-                nn.LeakyReLU()
-            ]
-        self.convs = nn.Sequential(*modules)
-        self.linear = EqualLinear(out_c, out_c, lr_mul=1)
-
-    def forward(self, x):
-        x = self.convs(x)
-        x = x.view(-1, self.out_c)
-        x = self.linear(x)
-        return x
-
-
-class GradualStyleEncoder(Module):
-    def __init__(self, num_layers, mode='ir', opts=None):
-        super(GradualStyleEncoder, self).__init__()
-        assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
-        assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
-        blocks = get_blocks(num_layers)
-        if mode == 'ir':
-            unit_module = bottleneck_IR
-        elif mode == 'ir_se':
-            unit_module = bottleneck_IR_SE
-        self.input_layer = Sequential(Conv2d(opts.input_nc, 64, (3, 3), 1, 1, bias=False),
-                                      BatchNorm2d(64),
-                                      PReLU(64))
-        modules = []
-        for block in blocks:
-            for bottleneck in block:
-                modules.append(unit_module(bottleneck.in_channel,
-                                           bottleneck.depth,
-                                           bottleneck.stride))
-        self.body = Sequential(*modules)
-
-        self.styles = nn.ModuleList()
-        self.style_count = opts.n_styles
-        self.coarse_ind = 3
-        self.middle_ind = 7
-        for i in range(self.style_count):
-            if i < self.coarse_ind:
-                style = GradualStyleBlock(512, 512, 16)
-            elif i < self.middle_ind:
-                style = GradualStyleBlock(512, 512, 32)
-            else:
-                style = GradualStyleBlock(512, 512, 64)
-            self.styles.append(style)
-        self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0)
-        self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0)
-
-    def _upsample_add(self, x, y):
-        '''Upsample and add two feature maps.
-        Args:
-          x: (Variable) top feature map to be upsampled.
-          y: (Variable) lateral feature map.
-        Returns:
-          (Variable) added feature map.
-        Note in PyTorch, when input size is odd, the upsampled feature map
-        with `F.upsample(..., scale_factor=2, mode='nearest')`
-        maybe not equal to the lateral feature map size.
-        e.g.
-        original input size: [N,_,15,15] ->
-        conv2d feature map size: [N,_,8,8] ->
-        upsampled feature map size: [N,_,16,16]
-        So we choose bilinear upsample which supports arbitrary output sizes.
-        '''
-        _, _, H, W = y.size()
-        return F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True) + y
-
-    def forward(self, x):
-        x = self.input_layer(x)
-
-        latents = []
-        modulelist = list(self.body._modules.values())
-        for i, l in enumerate(modulelist):
-            x = l(x)
-            if i == 6:
-                c1 = x
-            elif i == 20:
-                c2 = x
-            elif i == 23:
-                c3 = x
-
-        for j in range(self.coarse_ind):
-            latents.append(self.styles[j](c3))
-
-        p2 = self._upsample_add(c3, self.latlayer1(c2))
-        for j in range(self.coarse_ind, self.middle_ind):
-            latents.append(self.styles[j](p2))
-
-        p1 = self._upsample_add(p2, self.latlayer2(c1))
-        for j in range(self.middle_ind, self.style_count):
-            latents.append(self.styles[j](p1))
-
-        out = torch.stack(latents, dim=1)
-        return out
-
-
-class BackboneEncoderUsingLastLayerIntoW(Module):
-    def __init__(self, num_layers, mode='ir', opts=None):
-        super(BackboneEncoderUsingLastLayerIntoW, self).__init__()
-        print('Using BackboneEncoderUsingLastLayerIntoW')
-        assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
-        assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
-        blocks = get_blocks(num_layers)
-        if mode == 'ir':
-            unit_module = bottleneck_IR
-        elif mode == 'ir_se':
-            unit_module = bottleneck_IR_SE
-        self.input_layer = Sequential(Conv2d(opts.input_nc, 64, (3, 3), 1, 1, bias=False),
-                                      BatchNorm2d(64),
-                                      PReLU(64))
-        self.output_pool = torch.nn.AdaptiveAvgPool2d((1, 1))
-        self.linear = EqualLinear(512, 512, lr_mul=1)
-        modules = []
-        for block in blocks:
-            for bottleneck in block:
-                modules.append(unit_module(bottleneck.in_channel,
-                                           bottleneck.depth,
-                                           bottleneck.stride))
-        self.body = Sequential(*modules)
-
-    def forward(self, x):
-        x = self.input_layer(x)
-        x = self.body(x)
-        x = self.output_pool(x)
-        x = x.view(-1, 512)
-        x = self.linear(x)
-        return x
-
-
-class BackboneEncoderUsingLastLayerIntoWPlus(Module):
-    def __init__(self, num_layers, mode='ir', opts=None):
-        super(BackboneEncoderUsingLastLayerIntoWPlus, self).__init__()
-        print('Using BackboneEncoderUsingLastLayerIntoWPlus')
-        assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
-        assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
-        blocks = get_blocks(num_layers)
-        if mode == 'ir':
-            unit_module = bottleneck_IR
-        elif mode == 'ir_se':
-            unit_module = bottleneck_IR_SE
-        self.n_styles = opts.n_styles
-        self.input_layer = Sequential(Conv2d(opts.input_nc, 64, (3, 3), 1, 1, bias=False),
-                                      BatchNorm2d(64),
-                                      PReLU(64))
-        self.output_layer_2 = Sequential(BatchNorm2d(512),
-                                         torch.nn.AdaptiveAvgPool2d((7, 7)),
-                                         Flatten(),
-                                         Linear(512 * 7 * 7, 512))
-        self.linear = EqualLinear(512, 512 * self.n_styles, lr_mul=1)
-        modules = []
-        for block in blocks:
-            for bottleneck in block:
-                modules.append(unit_module(bottleneck.in_channel,
-                                           bottleneck.depth,
-                                           bottleneck.stride))
-        self.body = Sequential(*modules)
-
-    def forward(self, x):
-        x = self.input_layer(x)
-        x = self.body(x)
-        x = self.output_layer_2(x)
-        x = self.linear(x)
-        x = x.view(-1, self.n_styles, 512)
-        return x
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/DFX Audio Enhancer 13.028 Crack VERIFIED.md b/spaces/raedeXanto/academic-chatgpt-beta/DFX Audio Enhancer 13.028 Crack VERIFIED.md
deleted file mode 100644
index 0a40e60be02da99822cd3c64e90e3893fa6ebe60..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/DFX Audio Enhancer 13.028 Crack VERIFIED.md	
+++ /dev/null
@@ -1,124 +0,0 @@
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Ex4 To Mq4 Decompiler 2015 Crack __TOP__.md b/spaces/raedeXanto/academic-chatgpt-beta/Ex4 To Mq4 Decompiler 2015 Crack __TOP__.md
deleted file mode 100644
index 1c7d42b6167161a695a1dce3cf5d3ace05d8fe6b..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Ex4 To Mq4 Decompiler 2015 Crack __TOP__.md	
+++ /dev/null
@@ -1,34 +0,0 @@
-
-<h1>Ex4 to Mq4 Decompiler 2015 Crack: What Is It and How to Use It</h1>
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\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Floricienta Primera Temporada Torrent Descarga los 10 primeros episodios gratis.md b/spaces/raedeXanto/academic-chatgpt-beta/Floricienta Primera Temporada Torrent Descarga los 10 primeros episodios gratis.md
deleted file mode 100644
index 6459f4439d712d130b08112fdbc91e4e3a1eb083..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Floricienta Primera Temporada Torrent Descarga los 10 primeros episodios gratis.md	
+++ /dev/null
@@ -1,188 +0,0 @@
-
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-<li>Find the torrent file that you want to download and click on it. You will see some information about the file, such as the file name, size, seeders, leechers, and comments.</li>
-<li>Click on the download button or the magnet link to start downloading the torrent file. A magnet link is a link that contains all the information needed to download the file without having to download the torrent file first.</li>
-<li>Your torrent client will open and ask you where you want to save the file. Choose a location on your computer or device and click OK.</li>
-<li>Your torrent client will start downloading the file from other users who have it. You can see the progress of your download on your torrent client.</li>
-<li>Once your download is complete, you can open the file and enjoy watching Floricienta.</li>
-</ol>
-<h2>Floricienta primera temporada torrent: A guide for beginners</h2>
-<h3>What is a torrent?</h3>
-<p>A torrent is a method of file sharing that allows you to download large files from many sources at once. Unlike traditional downloads, where you get a file from one server, torrents allow you to get pieces of a file from many users who have that file on their computers or devices.</p>
-<p>This way, you can download files faster and more efficiently, as you don't have to rely on one source or server. You also help other users by sharing the pieces of the file that you have downloaded with them.</p>
-<p>A torrent works by using a peer-to-peer (P2P) network, which is a network of computers or devices that share files without a central server or authority. Each computer or device in a P2P network is called a peer. Each peer acts as both a client and a server, meaning that it can request and provide files to other peers.</p>
-<p>A torrent consists of two main components: a tracker and a swarm. A tracker is a server that keeps track of which peers have which pieces of a file. A swarm is a group of peers that are downloading or uploading the same file. When you join a swarm, you connect to a tracker that tells you which peers have which pieces of the file. Then, you start downloading pieces of the file from those peers while also uploading pieces of the file to other peers who need them.</p>
-<h3>What are the benefits of using torrents?</h3>
-<p>Using torrents has many benefits over traditional downloads. Some of them are:</p>
-<ul>
-<li>You can download large files faster and more efficiently, as you don't have to rely on one source or server.</li>
-<li>You can resume your downloads if they are interrupted by power outages, network problems, or computer crashes.</li>
-<li>You can access files that are not available on regular websites or servers, such as rare or old content.</li>
-<li>You can contribute to the P2P community by sharing files with other users.</li>
-<h3>What are the risks of using torrents?</h3>
-<p>Using torrents also has some risks that you should be aware of before you start downloading. Some of them are:</p>
-<ul>
-<li>You may download malware or viruses along with your torrent file. Some torrent files may contain malicious software that can infect your computer or device and compromise your security. You may also download fake or corrupted files that don't work or damage your system. [6]</li>
-<li>You may face legal issues if you download copyrighted material. Many torrent files contain content that is protected by intellectual property laws, such as movies, music, games, books, and software. Downloading or sharing such content without permission is illegal and can result in fines, lawsuits, or even jail time. You may also receive warning letters from your ISP or copyright holders if they detect your torrent activity. [7]</li>
-<li>You may expose your identity and location to other users. When you join a torrent swarm, you share pieces of the file with other peers who can see your IP address and other information about your connection. This can make you vulnerable to cyberattacks, hacking, spying, or harassment from malicious users who may try to access your personal data or harm your device. [8]</li>
-<li>You may experience slow or unstable internet speeds. Torrenting can consume a lot of bandwidth and resources on your computer or device, especially if you download or upload multiple files at once. This can affect your internet speed and performance, as well as interfere with other online activities such as browsing, streaming, gaming, or working. You may also face network throttling from your ISP if they notice excessive torrent traffic on your connection. [9]</li>
-<h2>Floricienta primera temporada torrent: A guide for advanced users</h2>
-<h3>How to use RSS feeds to download Floricienta episodes automatically?</h3>
-<p>If you are a fan of Floricienta and you don't want to miss any episode, you can use RSS feeds to download them automatically as soon as they are available. RSS feeds are a way of getting updates from websites that offer torrent files. You can subscribe to RSS feeds using your torrent client and set some rules to filter and download the torrents that match your criteria.</p>
-<p>For example, you can use qBittorrent as your torrent client and follow these steps to use RSS feeds to download Floricienta episodes automatically:</p>
-<ol>
-<li>Open qBittorrent and go to View > RSS Reader.</li>
-<li>Click on the New subscription button and enter the URL of the RSS feed that has Floricienta primera temporada torrent. For example, you can use this feed from archive.org that has the first 10 episodes in Spanish with no subtitles: https://archive.org/services/collection-rss.php?collection=Floricienta-Episodios1-10</li>
-<li>Give a name to your subscription and click OK.</li>
-<li>Click on the Options button and go to RSS tab.</li>
-<li>Check the boxes for Enable fetching RSS feeds and Enable auto downloading of RSS torrents.</li>
-<li>Click on the RSS Downloader button and go to Rules tab.</li>
-<li>Click on the Add rule button and give a name to your rule. For example, Floricienta.</li>
-<li>Under Must contain, enter a keyword or phrase that matches the torrents you want to download. For example, Floricienta.</li>
-<li>Under Save to a different directory, choose a location on your computer or device where you want to save the downloaded files.</li>
-<li>Under Episode filter, enter a range of episodes that you want to download. For example, 1-176 for the first season.</li>
-<li>Under Assign category, choose a category for your torrents. For example, TV Shows.</li>
-<li>Under Apply rule to feeds, select the feed that you subscribed to in step 2.</li>
-<li>Click OK to save your rule.</li>
-</ol>
-<p>Now, qBittorrent will automatically download any torrent that matches your rule from the RSS feed. You can also add more rules and feeds if you want to download other seasons or versions of Floricienta.</p>
-<h3>How to use subtitles to watch Floricienta in different languages?</h3>
-<p>If you want to watch Floricienta in a different language than the original audio, you can use subtitles to understand what is going on. Subtitles are text files that display the dialogue and other sounds in a video in a different language. You can find subtitles for Floricienta in various languages on websites like OpenSubtitles.org or Subscene.com.</p>
-<p>To use subtitles to watch Floricienta, you will need a video player that supports subtitles. For example, you can use VLC Media Player, which is a free and open-source video player that can play almost any video format and subtitle file. You can download VLC Media Player from its official website or from trusted sources.</p>
-<p>To use subtitles with VLC Media Player, you can follow these steps:</p>
-<ol>
-<li>Open VLC Media Player and go to Media > Open File.</li>
-<li>Browse and select the video file that you want to play. For example, Floricienta - Episodio 1.avi</li>
-<li>Click on Play to start playing the video.</li>
-<li>Go to Subtitle > Add Subtitle File.</li>
-<li>Browse and select the subtitle file that matches the video file. For example, Floricienta - Episodio 1.srt</li>
-<li>VLC Media Player will load the subtitle file and display it on the video.</li>
-</ol>
-<h3>How to use VPNs to protect your privacy and bypass geo-restrictions?</h3>
-<p>One of the most important and useful tools for torrenting safely and anonymously is a VPN, or a virtual private network. A VPN is a service that creates a secure and encrypted connection between your device and a server operated by the VPN provider. This way, you can hide your real IP address and location from anyone who might be snooping on your torrent activity.</p>
-<p>A VPN also allows you to bypass geo-restrictions and access torrent sites that might be blocked in your country or region. For example, if you live in a country where The Pirate Bay is banned, you can use a VPN to connect to a server in another country where The Pirate Bay is accessible. Then, you can browse and download torrents from The Pirate Bay as if you were in that country.</p>
-<p>To use a VPN for torrenting, you will need to choose a reliable and trustworthy VPN provider that offers the following features:</p>
-<ul>
-<li>A strict no-logs policy that ensures that the VPN provider does not keep any records of your online activity or personal information.</li>
-<li>A kill switch that automatically disconnects your internet connection if the VPN connection drops unexpectedly. This prevents your torrent traffic from being exposed to your ISP or other third parties.</li>
-<li>High-speed servers that support P2P file sharing and offer unlimited bandwidth and data. This ensures that you can download torrents fast and without any interruptions or throttling.</li>
-<li>Strong encryption and security protocols that protect your data from hackers, malware, and leaks. For example, AES-256 encryption, OpenVPN protocol, DNS leak protection, etc.</li>
-<li>A large network of servers in different countries and regions that allow you to access torrent sites that might be blocked or censored in your location.</li>
-</ul>
-<p>One of the best VPNs for torrenting is NordVPN. It has all the features mentioned above and more. It has over 5,000 servers in 60 countries, including dedicated P2P servers that are optimized for torrenting. It has a strict no-logs policy and a kill switch that ensure your privacy and anonymity. It has fast speeds and unlimited bandwidth that allow you to download torrents quickly and smoothly. It has strong encryption and security protocols that protect your data from hackers, malware, and leaks. It also has a 30-day money-back guarantee that lets you try it risk-free.</p>
-<p>To use NordVPN for torrenting, you can follow these steps:</p>
-<ol>
-<li>Sign up for NordVPN on its official website or from trusted sources.</li>
-<li>Download and install the NordVPN app on your device.</li>
-<li>Launch the NordVPN app and log in with your credentials.</li>
-<li>Select a server that supports P2P file sharing. You can either choose one manually or let NordVPN choose one for you automatically.</li>
-<li>Once you are connected to a server, open your torrent client and start downloading torrents as usual.</li>
-</ol>
-<h2>Conclusion</h2>
-<h3>Summary of the main points</h3>
-<p>In this article, we have explained what Floricienta is, why it is popular, and how to download Floricienta primera temporada torrent. We have also provided a guide for beginners and advanced users on how to use torrents safely and anonymously. We have covered topics such as what a torrent is, what are the benefits and risks of using torrents, how to use RSS feeds to download torrents automatically, how to use subtitles to watch torrents in different languages, and how to use VPNs to protect your privacy and bypass geo-restrictions.</p>
-<h3>Call to action</h3>
-<p>If you are a fan of romantic comedies, musicals, and fairy tales, you should definitely watch Floricienta. It is a charming and funny show that will make you laugh, cry, and sing along. You can download Floricienta primera temporada torrent from various websites and enjoy watching it on your device. However, you should always use a VPN to torrent safely and anonymously. A VPN will hide your IP address and encrypt your data from anyone who might be snooping on your torrent activity. It will also allow you to access torrent sites that might be blocked or censored in your location.</p>
-<p>One of the best VPNs for torrenting is NordVPN. It has over 5,000 servers in 60 countries, including dedicated P2P servers that are optimized for torrenting. It has a strict no-logs policy and a kill switch that ensure your privacy and anonymity. It has fast speeds and unlimited bandwidth that allow you to download torrents quickly and smoothly. It has strong encryption and security protocols that protect your data from hackers, malware, and leaks. It also has a 30-day money-back guarantee that lets you try it risk-free.</p>
-<p>If you want to watch Floricienta primera temporada torrent with NordVPN, you can sign up for NordVPN on its official website or from trusted sources. You can also get a special discount if you use our exclusive link below:</p>
-<h3>FAQs</h3>
-<p>Here are some frequently asked questions about Floricienta primera temporada torrent and torrenting in general:</p>
-<ol>
-<li><strong>Is Floricienta available on Netflix or other streaming platforms?</strong></li>
-<p>No, Floricienta is not available on Netflix or other streaming platforms. The only way to watch it online is to download it from torrent sites or other sources.</p>
-<li><strong>Is Floricienta dubbed or subtitled in other languages?</strong></li>
-<p>Yes, Floricienta has been dubbed or subtitled in many languages, such as English, French, Italian, Portuguese, Turkish, Greek, Arabic, Hebrew, and more. You can find the dubbed or subtitled versions on torrent sites or other sources.</p>
-<li><strong>What are some alternatives to NordVPN for torrenting?</strong></li>
-<p>Some alternatives to NordVPN for torrenting are Surfshark, ExpressVPN, CyberGhost, and IPVanish. They all offer similar features and benefits for torrenting, such as no-logs policy, kill switch, P2P servers, fast speeds, unlimited bandwidth, strong encryption, and security protocols. However, they may differ in terms of price, server network, customer support, and user interface.</p>
-<li><strong>What are some tips and tricks to improve your torrenting experience?</strong></li>
-<p>Some tips and tricks to improve your torrenting experience are:</p>
-<ul>
-<li>Choose a torrent file with a high number of seeders and a low number of leechers. This will ensure faster download speeds and better availability.</li>
-<li>Check the comments and ratings of the torrent file before downloading it. This will help you avoid fake or corrupted files that may contain malware or viruses.</li>
-<li>Use a torrent client that has features such as bandwidth management, scheduling, prioritizing, and selective downloading. This will help you optimize your torrent downloads and uploads according to your preferences and needs.</li>
-<li>Seed the torrent file after downloading it. This will help you maintain a good ratio and contribute to the P2P community.</li>
-</ul>
-<li><strong>What are some of the best torrent sites for downloading Floricienta primera temporada torrent?</strong></li>
-<p>Some of the best torrent sites for downloading Floricienta primera temporada torrent are:</p>
-<ul>
-<li><a href="https://archive.org/details/Floricienta-Episodios1-10">archive.org</a>: This site has the first 10 episodes of Floricienta in Spanish with no subtitles.</li>
-<li><a href="https://www.torrentdownloads.me/search/?search=floricienta">torrentdownloads.me</a>: This site has various versions of Floricienta in different languages and qualities.</li>
-<li><a href="https://www.limetorrents.info/search/all/floricienta/">limetorrents.info</a>: This site has various versions of Floricienta in different languages and qualities.</li>
-</ul>
-</p> 0a6ba089eb<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Gestionale 1 Zucchetti Crack !!INSTALL!! 12.md b/spaces/raedeXanto/academic-chatgpt-beta/Gestionale 1 Zucchetti Crack !!INSTALL!! 12.md
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+++ /dev/null
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Gsm Unlocker V3 0 Cracked The Ultimate Tool for Unlocking Any Phone.md b/spaces/raedeXanto/academic-chatgpt-beta/Gsm Unlocker V3 0 Cracked The Ultimate Tool for Unlocking Any Phone.md
deleted file mode 100644
index f165aa6813cfa7c3539e2bd5416592f4d247aaa4..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Gsm Unlocker V3 0 Cracked The Ultimate Tool for Unlocking Any Phone.md	
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-</ol>
- <h2>How to use Gsm Unlocker V3 0 to unlock your phone</h2>
-<p>To use Gsm Unlocker V3 0 to unlock your phone, follow these steps:</p>
-<ol>
-<li>Open eBaybypasser.exe on your PC as an administrator.</li>
-<li>Select "Unlock GSM" from the menu.</li>
-<li>Select your phone brand and model from the list.</li>
-<li>Select your network provider from the list.</li>
-<li>Click on "Start" button and wait for the tool to generate an unlock code for your phone.</li>
-<li>The tool will display the unlock code on the screen. Write it down or copy it somewhere.</li>
-<li>The tool will also prompt you to enter a code on your phone. Enter *#06# on your phone's dial pad and press call button. This will display your IMEI number on the screen. Write it down or copy it somewhere.</li>
-<li>The tool will then ask you to enter another code on your phone. Enter *#7465625*638*# followed by the unlock code that was generated by the tool followed by #. For example: *#7465625*638*12345678#. Press call button after entering this code.</li>
-<li>Your phone will display a message saying "Network Lock Deactivated". This means that your phone is unlocked successfully.</li>
-<li>You can now turn off your phone and insert any SIM card from any network provider on it. Turn on your phone again and enjoy using it with any SIM card.</li>
- <h2>Tips and tricks for using Gsm Unlocker V3 0</h2>
- <p>To use Gsm Unlocker V3 0 safely and effectively, here are some tips and tricks that you should follow:</p>
- <ul>
- <li>Avoid viruses and malware when downloading Gsm Unlocker V3 0. Some websites may offer fake or infected versions of Gsm Unlocker V3 0 that can harm your PC or phone. Make sure you download Gsm Unlocker V3 0 from a trusted and reputable website. You can also scan the file with an antivirus software before running it.</li>
-<li>Troubleshoot common errors and issues with Gsm Unlocker V3 0. Sometimes, you may encounter some errors or issues when using Gsm Unlocker V3 0 to unlock your phone. Here are some common ones and how to fix them:</li>
-<table>
-<tr>
-<th>Error/Issue</th>
-<th>Solution</th>
-</tr>
-<tr>
-<td>The tool does not detect your phone.</td>
-<td>Make sure you have installed iTunes on your PC. Make sure you have enabled USB debugging mode on your phone. Make sure you have connected your phone to the PC using a USB cable. Try using a different USB port or cable.</td>
-</tr>
-<tr>
-<td>The tool does not generate an unlock code for your phone.</td>
-<td>Make sure you have selected the correct phone brand and model from the list. Make sure you have selected the correct network provider from the list. Make sure you have entered the correct IMEI number of your phone. Try restarting the tool and repeating the process.</td>
-</tr>
-<tr>
-<td>The tool generates an invalid unlock code for your phone.</td>
-<td>Make sure you have entered the unlock code correctly on your phone. Make sure you have followed the instructions carefully. Try generating a new unlock code and entering it again.</td>
-</tr>
-<tr>
-<td>The tool fails to unlock your phone.</td>
-<td>Make sure your phone is compatible with Gsm Unlocker V3 0. Some phones may not be supported by the tool. Make sure your phone is not blacklisted or reported as stolen or lost by the network provider. Some phones may not be unlocked if they are blacklisted or reported as stolen or lost. Make sure your phone is not already unlocked. Some phones may not be locked by the network provider in the first place.</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
- <p>In conclusion, Gsm Unlocker V3 0 is a powerful and useful tool that can unlock any phone from any network provider for free. It is easy to use and does not require any technical skills or knowledge. It is fast, safe, permanent, and legal. It can help you save money, avoid roaming charges, increase resale value, have more freedom and flexibility, and access more features and services on your phone.</p>
- <p>If you want to unlock your phone for free, you should try Gsm Unlocker V3 0 today. You can download it from <a href="https://imeibypass.com/download-e-gsm-tool-v3-0/">https://imeibypass.com/download-e-gsm-tool-v3-0/</a>. Just follow the steps in this article and you will be able to unlock your phone in minutes.</p>
- <p>What are you waiting for? Download Gsm Unlocker V3 0 now and enjoy using any SIM card on your phone.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Gsm Unlocker V3 0:</p>
- <ol>
- <li>Is Gsm Unlocker V3 0 legal?</li>
- <p>Yes, Gsm Unlocker V3 0 is legal in most countries. However, some network providers or manufacturers may have terms or conditions that prohibit unlocking your phone without their permission. You should check with them before using Gsm Unlocker V3 0 to avoid any legal issues.</p>
- <li>Will unlocking my phone void my warranty?</li>
- <p>No, unlocking your phone with Gsm Unlocker V3 0 will not void your warranty. However, some network providers or manufacturers may have terms or conditions that void your warranty if you unlock your phone without their permission. You should check with them before using Gsm Unlocker V3 0 to avoid any warranty issues.</p>
- <li>Will unlocking my phone affect its performance?</li>
- <p>No, unlocking your phone with Gsm Unlocker V3 0 will not affect its performance. However, some network providers or manufacturers may have terms or conditions that limit or restrict some features or services on your phone if you unlock it without their permission. You should check with them before using Gsm Unlocker V3 0 to avoid any performance issues.</p>
- <li>Can I update or reset my phone after unlocking it?</li>
- <p>Yes, you can update or reset your phone after unlocking it with Gsm Unlocker V3 0. Your phone will remain unlocked and you can use any SIM card on it.</p>
- <li>Can I lock my phone again after unlocking it?</li>
- <p>No, you cannot lock your phone again after unlocking it with Gsm Unlocker V3 0. Your phone will be permanently unlocked and you cannot revert it back to its original state.</p>
- </ol>
- </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/cluster.d.ts b/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/cluster.d.ts
deleted file mode 100644
index 37dbc5746171fc04bcb8ea6f8b7183fa1b5db448..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/cluster.d.ts
+++ /dev/null
@@ -1,410 +0,0 @@
-/**
- * Clusters of Node.js processes can be used to run multiple instances of Node.js
- * that can distribute workloads among their application threads. When process
- * isolation is not needed, use the `worker_threads` module instead, which
- * allows running multiple application threads within a single Node.js instance.
- *
- * The cluster module allows easy creation of child processes that all share
- * server ports.
- *
- * ```js
- * import cluster from 'cluster';
- * import http from 'http';
- * import { cpus } from 'os';
- * import process from 'process';
- *
- * const numCPUs = cpus().length;
- *
- * if (cluster.isPrimary) {
- *   console.log(`Primary ${process.pid} is running`);
- *
- *   // Fork workers.
- *   for (let i = 0; i < numCPUs; i++) {
- *     cluster.fork();
- *   }
- *
- *   cluster.on('exit', (worker, code, signal) => {
- *     console.log(`worker ${worker.process.pid} died`);
- *   });
- * } else {
- *   // Workers can share any TCP connection
- *   // In this case it is an HTTP server
- *   http.createServer((req, res) => {
- *     res.writeHead(200);
- *     res.end('hello world\n');
- *   }).listen(8000);
- *
- *   console.log(`Worker ${process.pid} started`);
- * }
- * ```
- *
- * Running Node.js will now share port 8000 between the workers:
- *
- * ```console
- * $ node server.js
- * Primary 3596 is running
- * Worker 4324 started
- * Worker 4520 started
- * Worker 6056 started
- * Worker 5644 started
- * ```
- *
- * On Windows, it is not yet possible to set up a named pipe server in a worker.
- * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/cluster.js)
- */
-declare module 'cluster' {
-    import * as child from 'node:child_process';
-    import EventEmitter = require('node:events');
-    import * as net from 'node:net';
-    export interface ClusterSettings {
-        execArgv?: string[] | undefined; // default: process.execArgv
-        exec?: string | undefined;
-        args?: string[] | undefined;
-        silent?: boolean | undefined;
-        stdio?: any[] | undefined;
-        uid?: number | undefined;
-        gid?: number | undefined;
-        inspectPort?: number | (() => number) | undefined;
-    }
-    export interface Address {
-        address: string;
-        port: number;
-        addressType: number | 'udp4' | 'udp6'; // 4, 6, -1, "udp4", "udp6"
-    }
-    /**
-     * A `Worker` object contains all public information and method about a worker.
-     * In the primary it can be obtained using `cluster.workers`. In a worker
-     * it can be obtained using `cluster.worker`.
-     * @since v0.7.0
-     */
-    export class Worker extends EventEmitter {
-        /**
-         * Each new worker is given its own unique id, this id is stored in the`id`.
-         *
-         * While a worker is alive, this is the key that indexes it in`cluster.workers`.
-         * @since v0.8.0
-         */
-        id: number;
-        /**
-         * All workers are created using `child_process.fork()`, the returned object
-         * from this function is stored as `.process`. In a worker, the global `process`is stored.
-         *
-         * See: `Child Process module`.
-         *
-         * Workers will call `process.exit(0)` if the `'disconnect'` event occurs
-         * on `process` and `.exitedAfterDisconnect` is not `true`. This protects against
-         * accidental disconnection.
-         * @since v0.7.0
-         */
-        process: child.ChildProcess;
-        /**
-         * Send a message to a worker or primary, optionally with a handle.
-         *
-         * In the primary, this sends a message to a specific worker. It is identical to `ChildProcess.send()`.
-         *
-         * In a worker, this sends a message to the primary. It is identical to`process.send()`.
-         *
-         * This example will echo back all messages from the primary:
-         *
-         * ```js
-         * if (cluster.isPrimary) {
-         *   const worker = cluster.fork();
-         *   worker.send('hi there');
-         *
-         * } else if (cluster.isWorker) {
-         *   process.on('message', (msg) => {
-         *     process.send(msg);
-         *   });
-         * }
-         * ```
-         * @since v0.7.0
-         * @param options The `options` argument, if present, is an object used to parameterize the sending of certain types of handles. `options` supports the following properties:
-         */
-        send(message: child.Serializable, callback?: (error: Error | null) => void): boolean;
-        send(message: child.Serializable, sendHandle: child.SendHandle, callback?: (error: Error | null) => void): boolean;
-        send(message: child.Serializable, sendHandle: child.SendHandle, options?: child.MessageOptions, callback?: (error: Error | null) => void): boolean;
-        /**
-         * This function will kill the worker. In the primary worker, it does this by
-         * disconnecting the `worker.process`, and once disconnected, killing with`signal`. In the worker, it does it by killing the process with `signal`.
-         *
-         * The `kill()` function kills the worker process without waiting for a graceful
-         * disconnect, it has the same behavior as `worker.process.kill()`.
-         *
-         * This method is aliased as `worker.destroy()` for backwards compatibility.
-         *
-         * In a worker, `process.kill()` exists, but it is not this function;
-         * it is `kill()`.
-         * @since v0.9.12
-         * @param [signal='SIGTERM'] Name of the kill signal to send to the worker process.
-         */
-        kill(signal?: string): void;
-        destroy(signal?: string): void;
-        /**
-         * In a worker, this function will close all servers, wait for the `'close'` event
-         * on those servers, and then disconnect the IPC channel.
-         *
-         * In the primary, an internal message is sent to the worker causing it to call`.disconnect()` on itself.
-         *
-         * Causes `.exitedAfterDisconnect` to be set.
-         *
-         * After a server is closed, it will no longer accept new connections,
-         * but connections may be accepted by any other listening worker. Existing
-         * connections will be allowed to close as usual. When no more connections exist,
-         * see `server.close()`, the IPC channel to the worker will close allowing it
-         * to die gracefully.
-         *
-         * The above applies _only_ to server connections, client connections are not
-         * automatically closed by workers, and disconnect does not wait for them to close
-         * before exiting.
-         *
-         * In a worker, `process.disconnect` exists, but it is not this function;
-         * it is `disconnect()`.
-         *
-         * Because long living server connections may block workers from disconnecting, it
-         * may be useful to send a message, so application specific actions may be taken to
-         * close them. It also may be useful to implement a timeout, killing a worker if
-         * the `'disconnect'` event has not been emitted after some time.
-         *
-         * ```js
-         * if (cluster.isPrimary) {
-         *   const worker = cluster.fork();
-         *   let timeout;
-         *
-         *   worker.on('listening', (address) => {
-         *     worker.send('shutdown');
-         *     worker.disconnect();
-         *     timeout = setTimeout(() => {
-         *       worker.kill();
-         *     }, 2000);
-         *   });
-         *
-         *   worker.on('disconnect', () => {
-         *     clearTimeout(timeout);
-         *   });
-         *
-         * } else if (cluster.isWorker) {
-         *   const net = require('net');
-         *   const server = net.createServer((socket) => {
-         *     // Connections never end
-         *   });
-         *
-         *   server.listen(8000);
-         *
-         *   process.on('message', (msg) => {
-         *     if (msg === 'shutdown') {
-         *       // Initiate graceful close of any connections to server
-         *     }
-         *   });
-         * }
-         * ```
-         * @since v0.7.7
-         * @return A reference to `worker`.
-         */
-        disconnect(): void;
-        /**
-         * This function returns `true` if the worker is connected to its primary via its
-         * IPC channel, `false` otherwise. A worker is connected to its primary after it
-         * has been created. It is disconnected after the `'disconnect'` event is emitted.
-         * @since v0.11.14
-         */
-        isConnected(): boolean;
-        /**
-         * This function returns `true` if the worker's process has terminated (either
-         * because of exiting or being signaled). Otherwise, it returns `false`.
-         *
-         * ```js
-         * import cluster from 'cluster';
-         * import http from 'http';
-         * import { cpus } from 'os';
-         * import process from 'process';
-         *
-         * const numCPUs = cpus().length;
-         *
-         * if (cluster.isPrimary) {
-         *   console.log(`Primary ${process.pid} is running`);
-         *
-         *   // Fork workers.
-         *   for (let i = 0; i < numCPUs; i++) {
-         *     cluster.fork();
-         *   }
-         *
-         *   cluster.on('fork', (worker) => {
-         *     console.log('worker is dead:', worker.isDead());
-         *   });
-         *
-         *   cluster.on('exit', (worker, code, signal) => {
-         *     console.log('worker is dead:', worker.isDead());
-         *   });
-         * } else {
-         *   // Workers can share any TCP connection. In this case, it is an HTTP server.
-         *   http.createServer((req, res) => {
-         *     res.writeHead(200);
-         *     res.end(`Current process\n ${process.pid}`);
-         *     process.kill(process.pid);
-         *   }).listen(8000);
-         * }
-         * ```
-         * @since v0.11.14
-         */
-        isDead(): boolean;
-        /**
-         * This property is `true` if the worker exited due to `.disconnect()`.
-         * If the worker exited any other way, it is `false`. If the
-         * worker has not exited, it is `undefined`.
-         *
-         * The boolean `worker.exitedAfterDisconnect` allows distinguishing between
-         * voluntary and accidental exit, the primary may choose not to respawn a worker
-         * based on this value.
-         *
-         * ```js
-         * cluster.on('exit', (worker, code, signal) => {
-         *   if (worker.exitedAfterDisconnect === true) {
-         *     console.log('Oh, it was just voluntary – no need to worry');
-         *   }
-         * });
-         *
-         * // kill worker
-         * worker.kill();
-         * ```
-         * @since v6.0.0
-         */
-        exitedAfterDisconnect: boolean;
-        /**
-         * events.EventEmitter
-         *   1. disconnect
-         *   2. error
-         *   3. exit
-         *   4. listening
-         *   5. message
-         *   6. online
-         */
-        addListener(event: string, listener: (...args: any[]) => void): this;
-        addListener(event: 'disconnect', listener: () => void): this;
-        addListener(event: 'error', listener: (error: Error) => void): this;
-        addListener(event: 'exit', listener: (code: number, signal: string) => void): this;
-        addListener(event: 'listening', listener: (address: Address) => void): this;
-        addListener(event: 'message', listener: (message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        addListener(event: 'online', listener: () => void): this;
-        emit(event: string | symbol, ...args: any[]): boolean;
-        emit(event: 'disconnect'): boolean;
-        emit(event: 'error', error: Error): boolean;
-        emit(event: 'exit', code: number, signal: string): boolean;
-        emit(event: 'listening', address: Address): boolean;
-        emit(event: 'message', message: any, handle: net.Socket | net.Server): boolean;
-        emit(event: 'online'): boolean;
-        on(event: string, listener: (...args: any[]) => void): this;
-        on(event: 'disconnect', listener: () => void): this;
-        on(event: 'error', listener: (error: Error) => void): this;
-        on(event: 'exit', listener: (code: number, signal: string) => void): this;
-        on(event: 'listening', listener: (address: Address) => void): this;
-        on(event: 'message', listener: (message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        on(event: 'online', listener: () => void): this;
-        once(event: string, listener: (...args: any[]) => void): this;
-        once(event: 'disconnect', listener: () => void): this;
-        once(event: 'error', listener: (error: Error) => void): this;
-        once(event: 'exit', listener: (code: number, signal: string) => void): this;
-        once(event: 'listening', listener: (address: Address) => void): this;
-        once(event: 'message', listener: (message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        once(event: 'online', listener: () => void): this;
-        prependListener(event: string, listener: (...args: any[]) => void): this;
-        prependListener(event: 'disconnect', listener: () => void): this;
-        prependListener(event: 'error', listener: (error: Error) => void): this;
-        prependListener(event: 'exit', listener: (code: number, signal: string) => void): this;
-        prependListener(event: 'listening', listener: (address: Address) => void): this;
-        prependListener(event: 'message', listener: (message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        prependListener(event: 'online', listener: () => void): this;
-        prependOnceListener(event: string, listener: (...args: any[]) => void): this;
-        prependOnceListener(event: 'disconnect', listener: () => void): this;
-        prependOnceListener(event: 'error', listener: (error: Error) => void): this;
-        prependOnceListener(event: 'exit', listener: (code: number, signal: string) => void): this;
-        prependOnceListener(event: 'listening', listener: (address: Address) => void): this;
-        prependOnceListener(event: 'message', listener: (message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        prependOnceListener(event: 'online', listener: () => void): this;
-    }
-    export interface Cluster extends EventEmitter {
-        disconnect(callback?: () => void): void;
-        fork(env?: any): Worker;
-        /** @deprecated since v16.0.0 - use isPrimary. */
-        readonly isMaster: boolean;
-        readonly isPrimary: boolean;
-        readonly isWorker: boolean;
-        schedulingPolicy: number;
-        readonly settings: ClusterSettings;
-        /** @deprecated since v16.0.0 - use setupPrimary. */
-        setupMaster(settings?: ClusterSettings): void;
-        /**
-         * `setupPrimary` is used to change the default 'fork' behavior. Once called, the settings will be present in cluster.settings.
-         */
-        setupPrimary(settings?: ClusterSettings): void;
-        readonly worker?: Worker | undefined;
-        readonly workers?: NodeJS.Dict<Worker> | undefined;
-        readonly SCHED_NONE: number;
-        readonly SCHED_RR: number;
-        /**
-         * events.EventEmitter
-         *   1. disconnect
-         *   2. exit
-         *   3. fork
-         *   4. listening
-         *   5. message
-         *   6. online
-         *   7. setup
-         */
-        addListener(event: string, listener: (...args: any[]) => void): this;
-        addListener(event: 'disconnect', listener: (worker: Worker) => void): this;
-        addListener(event: 'exit', listener: (worker: Worker, code: number, signal: string) => void): this;
-        addListener(event: 'fork', listener: (worker: Worker) => void): this;
-        addListener(event: 'listening', listener: (worker: Worker, address: Address) => void): this;
-        addListener(event: 'message', listener: (worker: Worker, message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        addListener(event: 'online', listener: (worker: Worker) => void): this;
-        addListener(event: 'setup', listener: (settings: ClusterSettings) => void): this;
-        emit(event: string | symbol, ...args: any[]): boolean;
-        emit(event: 'disconnect', worker: Worker): boolean;
-        emit(event: 'exit', worker: Worker, code: number, signal: string): boolean;
-        emit(event: 'fork', worker: Worker): boolean;
-        emit(event: 'listening', worker: Worker, address: Address): boolean;
-        emit(event: 'message', worker: Worker, message: any, handle: net.Socket | net.Server): boolean;
-        emit(event: 'online', worker: Worker): boolean;
-        emit(event: 'setup', settings: ClusterSettings): boolean;
-        on(event: string, listener: (...args: any[]) => void): this;
-        on(event: 'disconnect', listener: (worker: Worker) => void): this;
-        on(event: 'exit', listener: (worker: Worker, code: number, signal: string) => void): this;
-        on(event: 'fork', listener: (worker: Worker) => void): this;
-        on(event: 'listening', listener: (worker: Worker, address: Address) => void): this;
-        on(event: 'message', listener: (worker: Worker, message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        on(event: 'online', listener: (worker: Worker) => void): this;
-        on(event: 'setup', listener: (settings: ClusterSettings) => void): this;
-        once(event: string, listener: (...args: any[]) => void): this;
-        once(event: 'disconnect', listener: (worker: Worker) => void): this;
-        once(event: 'exit', listener: (worker: Worker, code: number, signal: string) => void): this;
-        once(event: 'fork', listener: (worker: Worker) => void): this;
-        once(event: 'listening', listener: (worker: Worker, address: Address) => void): this;
-        once(event: 'message', listener: (worker: Worker, message: any, handle: net.Socket | net.Server) => void): this; // the handle is a net.Socket or net.Server object, or undefined.
-        once(event: 'online', listener: (worker: Worker) => void): this;
-        once(event: 'setup', listener: (settings: ClusterSettings) => void): this;
-        prependListener(event: string, listener: (...args: any[]) => void): this;
-        prependListener(event: 'disconnect', listener: (worker: Worker) => void): this;
-        prependListener(event: 'exit', listener: (worker: Worker, code: number, signal: string) => void): this;
-        prependListener(event: 'fork', listener: (worker: Worker) => void): this;
-        prependListener(event: 'listening', listener: (worker: Worker, address: Address) => void): this;
-        // the handle is a net.Socket or net.Server object, or undefined.
-        prependListener(event: 'message', listener: (worker: Worker, message: any, handle?: net.Socket | net.Server) => void): this;
-        prependListener(event: 'online', listener: (worker: Worker) => void): this;
-        prependListener(event: 'setup', listener: (settings: ClusterSettings) => void): this;
-        prependOnceListener(event: string, listener: (...args: any[]) => void): this;
-        prependOnceListener(event: 'disconnect', listener: (worker: Worker) => void): this;
-        prependOnceListener(event: 'exit', listener: (worker: Worker, code: number, signal: string) => void): this;
-        prependOnceListener(event: 'fork', listener: (worker: Worker) => void): this;
-        prependOnceListener(event: 'listening', listener: (worker: Worker, address: Address) => void): this;
-        // the handle is a net.Socket or net.Server object, or undefined.
-        prependOnceListener(event: 'message', listener: (worker: Worker, message: any, handle: net.Socket | net.Server) => void): this;
-        prependOnceListener(event: 'online', listener: (worker: Worker) => void): this;
-        prependOnceListener(event: 'setup', listener: (settings: ClusterSettings) => void): this;
-    }
-    const cluster: Cluster;
-    export default cluster;
-}
-declare module 'node:cluster' {
-    export * from 'cluster';
-    export { default as default } from 'cluster';
-}
diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/array-flatten/array-flatten.js b/spaces/rayan-saleh/whisper2notion/server/node_modules/array-flatten/array-flatten.js
deleted file mode 100644
index 089117b322f5857b8bb6bccf7a659686aca067c0..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/array-flatten/array-flatten.js
+++ /dev/null
@@ -1,64 +0,0 @@
-'use strict'
-
-/**
- * Expose `arrayFlatten`.
- */
-module.exports = arrayFlatten
-
-/**
- * Recursive flatten function with depth.
- *
- * @param  {Array}  array
- * @param  {Array}  result
- * @param  {Number} depth
- * @return {Array}
- */
-function flattenWithDepth (array, result, depth) {
-  for (var i = 0; i < array.length; i++) {
-    var value = array[i]
-
-    if (depth > 0 && Array.isArray(value)) {
-      flattenWithDepth(value, result, depth - 1)
-    } else {
-      result.push(value)
-    }
-  }
-
-  return result
-}
-
-/**
- * Recursive flatten function. Omitting depth is slightly faster.
- *
- * @param  {Array} array
- * @param  {Array} result
- * @return {Array}
- */
-function flattenForever (array, result) {
-  for (var i = 0; i < array.length; i++) {
-    var value = array[i]
-
-    if (Array.isArray(value)) {
-      flattenForever(value, result)
-    } else {
-      result.push(value)
-    }
-  }
-
-  return result
-}
-
-/**
- * Flatten an array, with the ability to define a depth.
- *
- * @param  {Array}  array
- * @param  {Number} depth
- * @return {Array}
- */
-function arrayFlatten (array, depth) {
-  if (depth == null) {
-    return flattenForever(array, [])
-  }
-
-  return flattenWithDepth(array, [], depth)
-}
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Aligarh Movie In Hindi 720p Torrent.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Aligarh Movie In Hindi 720p Torrent.md
deleted file mode 100644
index e77d1b31f5795625d13f8a7edd14379c46d2a351..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Aligarh Movie In Hindi 720p Torrent.md	
+++ /dev/null
@@ -1,28 +0,0 @@
-<h2>Aligarh Movie In Hindi 720p Torrent</h2><br /><p><b><b>Download File</b> &#10002; &#10002; &#10002; <a href="https://urlgoal.com/2uCJhQ">https://urlgoal.com/2uCJhQ</a></b></p><br /><br />
-<br />
-xtra,lut 15 jan 2013. Watch our movie 'aligarh movie in hindi 720p' for free online.A threat to the Southern Border
-
-The administration is conducting a “military operation”
-
-By Rand Paul
-
-So, it seems that President Trump may be contemplating an increase in troops at the border. After, as The Hill reported, “reports this week that the Pentagon is considering sending more than 5,000 troops to the U.S.-Mexico border,” the president tweeted, “Mexico is doing very little, if not NOTHING, at stopping people from flowing into Mexico through their Southern Border, and then into the U.S. We will be sending, by request, some of those great Military people that got the job done in Syria, etc., and we will be sending them directly to the Southern Border.” And, you may recall, the president has claimed that the Mexican government was not sending their troops to the border.
-
-So, what will happen if the Trump administration increases the number of troops at the border?
-
-My first reaction was that we must pray for the protection of our border and our families. So, I asked my Senate colleagues if they would sign a letter with me that would serve as a reminder to our president that he must consult with Congress before sending our military personnel to the border. Here is that letter:
-
-“To the President:
-
-“We are deeply concerned that the deployment of U.S. military personnel to the U.S. southern border in response to the flood of migrants and asylum seekers illegally entering the U.S. is not a valid deployment of our military resources.
-
-“In order to properly protect our national security and enforce the laws of this land, it is incumbent that we work together to establish clear protocols for deploying our armed forces to areas that do not fall under the discretion of the Commander in Chief.
-
-“Additionally, the use of force in situations where civilians are involved does not hold up to the level of scrutiny as the deployment of our troops in Iraq or Afghanistan.
-
-“In those two foreign conflicts, our troops were primarily engaged in the business of war. The concern for civilian casualties and the prospect of having to occupy sovereign foreign nations was contemplated prior to the military action.
-
-“Despite your rhetoric against the current administration’s actions, your stated intention of ‘protecting the border’ with 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/renatotn7/teste2/gfpgan/archs/gfpganv1_arch.py b/spaces/renatotn7/teste2/gfpgan/archs/gfpganv1_arch.py
deleted file mode 100644
index e092b4f7633dece505e5cd3bac4a482df3746654..0000000000000000000000000000000000000000
--- a/spaces/renatotn7/teste2/gfpgan/archs/gfpganv1_arch.py
+++ /dev/null
@@ -1,439 +0,0 @@
-import math
-import random
-import torch
-from basicsr.archs.stylegan2_arch import (ConvLayer, EqualConv2d, EqualLinear, ResBlock, ScaledLeakyReLU,
-                                          StyleGAN2Generator)
-from basicsr.ops.fused_act import FusedLeakyReLU
-from basicsr.utils.registry import ARCH_REGISTRY
-from torch import nn
-from torch.nn import functional as F
-
-
-class StyleGAN2GeneratorSFT(StyleGAN2Generator):
-    """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).
-
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be
-            applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1).
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(self,
-                 out_size,
-                 num_style_feat=512,
-                 num_mlp=8,
-                 channel_multiplier=2,
-                 resample_kernel=(1, 3, 3, 1),
-                 lr_mlp=0.01,
-                 narrow=1,
-                 sft_half=False):
-        super(StyleGAN2GeneratorSFT, self).__init__(
-            out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            resample_kernel=resample_kernel,
-            lr_mlp=lr_mlp,
-            narrow=narrow)
-        self.sft_half = sft_half
-
-    def forward(self,
-                styles,
-                conditions,
-                input_is_latent=False,
-                noise=None,
-                randomize_noise=True,
-                truncation=1,
-                truncation_latent=None,
-                inject_index=None,
-                return_latents=False):
-        """Forward function for StyleGAN2GeneratorSFT.
-
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            conditions (list[Tensor]): SFT conditions to generators.
-            input_is_latent (bool): Whether input is latent style. Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-            truncation (float): The truncation ratio. Default: 1.
-            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
-            inject_index (int | None): The injection index for mixing noise. Default: None.
-            return_latents (bool): Whether to return style latents. Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
-            styles = style_truncation
-        # get style latents with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2],
-                                                        noise[2::2], self.to_rgbs):
-            out = conv1(out, latent[:, i], noise=noise1)
-
-            # the conditions may have fewer levels
-            if i < len(conditions):
-                # SFT part to combine the conditions
-                if self.sft_half:  # only apply SFT to half of the channels
-                    out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
-                    out_sft = out_sft * conditions[i - 1] + conditions[i]
-                    out = torch.cat([out_same, out_sft], dim=1)
-                else:  # apply SFT to all the channels
-                    out = out * conditions[i - 1] + conditions[i]
-
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
-
-
-class ConvUpLayer(nn.Module):
-    """Convolutional upsampling layer. It uses bilinear upsampler + Conv.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        stride (int): Stride of the convolution. Default: 1
-        padding (int): Zero-padding added to both sides of the input. Default: 0.
-        bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``.
-        bias_init_val (float): Bias initialized value. Default: 0.
-        activate (bool): Whether use activateion. Default: True.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 bias=True,
-                 bias_init_val=0,
-                 activate=True):
-        super(ConvUpLayer, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-        # self.scale is used to scale the convolution weights, which is related to the common initializations.
-        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
-
-        self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size))
-
-        if bias and not activate:
-            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
-        else:
-            self.register_parameter('bias', None)
-
-        # activation
-        if activate:
-            if bias:
-                self.activation = FusedLeakyReLU(out_channels)
-            else:
-                self.activation = ScaledLeakyReLU(0.2)
-        else:
-            self.activation = None
-
-    def forward(self, x):
-        # bilinear upsample
-        out = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
-        # conv
-        out = F.conv2d(
-            out,
-            self.weight * self.scale,
-            bias=self.bias,
-            stride=self.stride,
-            padding=self.padding,
-        )
-        # activation
-        if self.activation is not None:
-            out = self.activation(out)
-        return out
-
-
-class ResUpBlock(nn.Module):
-    """Residual block with upsampling.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-    """
-
-    def __init__(self, in_channels, out_channels):
-        super(ResUpBlock, self).__init__()
-
-        self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)
-        self.conv2 = ConvUpLayer(in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True)
-        self.skip = ConvUpLayer(in_channels, out_channels, 1, bias=False, activate=False)
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = self.conv2(out)
-        skip = self.skip(x)
-        out = (out + skip) / math.sqrt(2)
-        return out
-
-
-@ARCH_REGISTRY.register()
-class GFPGANv1(nn.Module):
-    """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.
-
-    Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.
-
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be
-            applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1).
-        decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.
-        fix_decoder (bool): Whether to fix the decoder. Default: True.
-
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        input_is_latent (bool): Whether input is latent style. Default: False.
-        different_w (bool): Whether to use different latent w for different layers. Default: False.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-            self,
-            out_size,
-            num_style_feat=512,
-            channel_multiplier=1,
-            resample_kernel=(1, 3, 3, 1),
-            decoder_load_path=None,
-            fix_decoder=True,
-            # for stylegan decoder
-            num_mlp=8,
-            lr_mlp=0.01,
-            input_is_latent=False,
-            different_w=False,
-            narrow=1,
-            sft_half=False):
-
-        super(GFPGANv1, self).__init__()
-        self.input_is_latent = input_is_latent
-        self.different_w = different_w
-        self.num_style_feat = num_style_feat
-
-        unet_narrow = narrow * 0.5  # by default, use a half of input channels
-        channels = {
-            '4': int(512 * unet_narrow),
-            '8': int(512 * unet_narrow),
-            '16': int(512 * unet_narrow),
-            '32': int(512 * unet_narrow),
-            '64': int(256 * channel_multiplier * unet_narrow),
-            '128': int(128 * channel_multiplier * unet_narrow),
-            '256': int(64 * channel_multiplier * unet_narrow),
-            '512': int(32 * channel_multiplier * unet_narrow),
-            '1024': int(16 * channel_multiplier * unet_narrow)
-        }
-
-        self.log_size = int(math.log(out_size, 2))
-        first_out_size = 2**(int(math.log(out_size, 2)))
-
-        self.conv_body_first = ConvLayer(3, channels[f'{first_out_size}'], 1, bias=True, activate=True)
-
-        # downsample
-        in_channels = channels[f'{first_out_size}']
-        self.conv_body_down = nn.ModuleList()
-        for i in range(self.log_size, 2, -1):
-            out_channels = channels[f'{2**(i - 1)}']
-            self.conv_body_down.append(ResBlock(in_channels, out_channels, resample_kernel))
-            in_channels = out_channels
-
-        self.final_conv = ConvLayer(in_channels, channels['4'], 3, bias=True, activate=True)
-
-        # upsample
-        in_channels = channels['4']
-        self.conv_body_up = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f'{2**i}']
-            self.conv_body_up.append(ResUpBlock(in_channels, out_channels))
-            in_channels = out_channels
-
-        # to RGB
-        self.toRGB = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            self.toRGB.append(EqualConv2d(channels[f'{2**i}'], 3, 1, stride=1, padding=0, bias=True, bias_init_val=0))
-
-        if different_w:
-            linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat
-        else:
-            linear_out_channel = num_style_feat
-
-        self.final_linear = EqualLinear(
-            channels['4'] * 4 * 4, linear_out_channel, bias=True, bias_init_val=0, lr_mul=1, activation=None)
-
-        # the decoder: stylegan2 generator with SFT modulations
-        self.stylegan_decoder = StyleGAN2GeneratorSFT(
-            out_size=out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            resample_kernel=resample_kernel,
-            lr_mlp=lr_mlp,
-            narrow=narrow,
-            sft_half=sft_half)
-
-        # load pre-trained stylegan2 model if necessary
-        if decoder_load_path:
-            self.stylegan_decoder.load_state_dict(
-                torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema'])
-        # fix decoder without updating params
-        if fix_decoder:
-            for _, param in self.stylegan_decoder.named_parameters():
-                param.requires_grad = False
-
-        # for SFT modulations (scale and shift)
-        self.condition_scale = nn.ModuleList()
-        self.condition_shift = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f'{2**i}']
-            if sft_half:
-                sft_out_channels = out_channels
-            else:
-                sft_out_channels = out_channels * 2
-            self.condition_scale.append(
-                nn.Sequential(
-                    EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0),
-                    ScaledLeakyReLU(0.2),
-                    EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=1)))
-            self.condition_shift.append(
-                nn.Sequential(
-                    EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0),
-                    ScaledLeakyReLU(0.2),
-                    EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0)))
-
-    def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True):
-        """Forward function for GFPGANv1.
-
-        Args:
-            x (Tensor): Input images.
-            return_latents (bool): Whether to return style latents. Default: False.
-            return_rgb (bool): Whether return intermediate rgb images. Default: True.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-        """
-        conditions = []
-        unet_skips = []
-        out_rgbs = []
-
-        # encoder
-        feat = self.conv_body_first(x)
-        for i in range(self.log_size - 2):
-            feat = self.conv_body_down[i](feat)
-            unet_skips.insert(0, feat)
-
-        feat = self.final_conv(feat)
-
-        # style code
-        style_code = self.final_linear(feat.view(feat.size(0), -1))
-        if self.different_w:
-            style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)
-
-        # decode
-        for i in range(self.log_size - 2):
-            # add unet skip
-            feat = feat + unet_skips[i]
-            # ResUpLayer
-            feat = self.conv_body_up[i](feat)
-            # generate scale and shift for SFT layers
-            scale = self.condition_scale[i](feat)
-            conditions.append(scale.clone())
-            shift = self.condition_shift[i](feat)
-            conditions.append(shift.clone())
-            # generate rgb images
-            if return_rgb:
-                out_rgbs.append(self.toRGB[i](feat))
-
-        # decoder
-        image, _ = self.stylegan_decoder([style_code],
-                                         conditions,
-                                         return_latents=return_latents,
-                                         input_is_latent=self.input_is_latent,
-                                         randomize_noise=randomize_noise)
-
-        return image, out_rgbs
-
-
-@ARCH_REGISTRY.register()
-class FacialComponentDiscriminator(nn.Module):
-    """Facial component (eyes, mouth, noise) discriminator used in GFPGAN.
-    """
-
-    def __init__(self):
-        super(FacialComponentDiscriminator, self).__init__()
-        # It now uses a VGG-style architectrue with fixed model size
-        self.conv1 = ConvLayer(3, 64, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
-        self.conv2 = ConvLayer(64, 128, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
-        self.conv3 = ConvLayer(128, 128, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
-        self.conv4 = ConvLayer(128, 256, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
-        self.conv5 = ConvLayer(256, 256, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
-        self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False)
-
-    def forward(self, x, return_feats=False):
-        """Forward function for FacialComponentDiscriminator.
-
-        Args:
-            x (Tensor): Input images.
-            return_feats (bool): Whether to return intermediate features. Default: False.
-        """
-        feat = self.conv1(x)
-        feat = self.conv3(self.conv2(feat))
-        rlt_feats = []
-        if return_feats:
-            rlt_feats.append(feat.clone())
-        feat = self.conv5(self.conv4(feat))
-        if return_feats:
-            rlt_feats.append(feat.clone())
-        out = self.final_conv(feat)
-
-        if return_feats:
-            return out, rlt_feats
-        else:
-            return out, None
diff --git a/spaces/robin0307/MMOCR/configs/_base_/recog_datasets/MJ_train.py b/spaces/robin0307/MMOCR/configs/_base_/recog_datasets/MJ_train.py
deleted file mode 100644
index be42cc47035d02403a036330eb0af7d0058b8675..0000000000000000000000000000000000000000
--- a/spaces/robin0307/MMOCR/configs/_base_/recog_datasets/MJ_train.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# Text Recognition Training set, including:
-# Synthetic Datasets: Syn90k
-
-train_root = 'data/mixture/Syn90k'
-
-train_img_prefix = f'{train_root}/mnt/ramdisk/max/90kDICT32px'
-train_ann_file = f'{train_root}/label.lmdb'
-
-train = dict(
-    type='OCRDataset',
-    img_prefix=train_img_prefix,
-    ann_file=train_ann_file,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='lmdb',
-        parser=dict(type='LineJsonParser', keys=['filename', 'text'])),
-    pipeline=None,
-    test_mode=False)
-
-train_list = [train]
diff --git a/spaces/rorallitri/biomedical-language-models/logs/CyberLink PerfectCam Premium 1.0.1018.0 Free Download for Professional Video Calls.md b/spaces/rorallitri/biomedical-language-models/logs/CyberLink PerfectCam Premium 1.0.1018.0 Free Download for Professional Video Calls.md
deleted file mode 100644
index 1907f6aff6db2d5d13fe40bc0a597b287c47e2e9..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/CyberLink PerfectCam Premium 1.0.1018.0 Free Download for Professional Video Calls.md	
+++ /dev/null
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/El cliente ante todo resumen pdf 13 de los cinco principios fundamentales para brindar un excelente servicio al cliente segn Feargal Quinn.md b/spaces/rorallitri/biomedical-language-models/logs/El cliente ante todo resumen pdf 13 de los cinco principios fundamentales para brindar un excelente servicio al cliente segn Feargal Quinn.md
deleted file mode 100644
index 2443833512bc0b5ea5e4c57f3de7056cf1249f17..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/El cliente ante todo resumen pdf 13 de los cinco principios fundamentales para brindar un excelente servicio al cliente segn Feargal Quinn.md	
+++ /dev/null
@@ -1,12 +0,0 @@
-
-<p>Apply the official Football Manager 2006 v6.0.1 Patch. Mount the FM 2006.MDS CD-Image in DAEMON Tools v3.47 or the latest Alcohol 120%. Use an anti-blacklisting tool, like AntiBlah or Virtual CD Hide to avoid blacklisting of DAEMON Tools or Alcohol 120%. Originally known as FileFront, GameFront has been serving up the very best mods, maps, skins, tools, and utilities since 1998. Since 2010, we've also been bringing you the very best in gaming culture, short-form reporting and video content! Download portuguese ltc fm 2010 pc using General-Search.com File Share Search Engine. 39 rapidshare portuguese ltc fm 2010 links available for free instant download.</p>
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-<p>Portuguese.ltc Fm 2010 Download June 6 2020 portuguese, portuguese water dog, portuguese flag, portuguese people, portuguese to english, portuguese translation, portuguese words, portuguese capital, portuguese sweet bread, portuguese sausage, portuguese league, portuguese translator, portuguese food. 3.1 Copie o arquivo 'portuguese.ltc' para a pasta. Meus documentosSports InteractiveFootball Manager 2009dbE4ALL Update v 1.0 - 9.1.0 - 9.2.0 - FM 2009 6.2 Quando iniciar um novo jogo, selecione o Banco de Dados E4ALL Update v 1.0 - 9.1.0 - 9.2.0 - FM 2009 e tenha um bom jogo.</p>
-<p>Steam powered Sports Interactive have announced that the game will be downloadable using the. This will allow gamers to have their games automatically patched to the latest version of the game. Miles Jacobson, the studio director at Sports Interactive, said 'It gives us the opportunity for all of our games players to get any patches or updates that we make available quickly and easily, as well as a place for us to host any other downloads, such as demos and promotional videos, with super fast broadband speeds for download, and no issues with bandwidth, which we are hit with each time we release a demo'. The Steam version of the game retains the copy protection with its 5 activation limit. Demo There were two 'flavours' of the demo.</p>
-<p>In response, SEGA issued a press release apologising for the problems faced by purchasers. As of May 2011, the game can no longer be activated as the website that deals with product activation of Football Manager 2009 has shut down. Bugs and patches A patch was also released on 14 November 2008, for the PC version, fixing a number of compatibility issues and bugs. A Mac patch was released a few days later. A second patch was released on 19 December 2008.</p>
-<p>The third patch was released 26 February 2009. Reception Football Manager 2009 's computer version received a 'Platinum' sales award from the (ELSPA), indicating sales of at least 300,000 copies in the United Kingdom. Studio director Miles Jacobson stated that 'for every one person that bought Football Manager 2009 there were four who pirated it.' References. Archived from on 2008-09-13. Miles Jacobson, Sports Interactive studio director. Miles Jacobson, Sports Interactive studio director.</p>
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Lotus Suspension Analysis 5 03 Crackers How to Model and Design Vehicle Suspensions with Ease.md b/spaces/rorallitri/biomedical-language-models/logs/Lotus Suspension Analysis 5 03 Crackers How to Model and Design Vehicle Suspensions with Ease.md
deleted file mode 100644
index e3bc86541a3da0276c092c507c76d87444f2e313..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Lotus Suspension Analysis 5 03 Crackers How to Model and Design Vehicle Suspensions with Ease.md	
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diff --git a/spaces/ruangguru/ds-chatbot-internal/app.py b/spaces/ruangguru/ds-chatbot-internal/app.py
deleted file mode 100644
index d1d3b8da57b22fc0fc260d5944e6fe730a07122e..0000000000000000000000000000000000000000
--- a/spaces/ruangguru/ds-chatbot-internal/app.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import gradio as gr
-import json
-from uuid import uuid4
-import requests
-from pydantic import BaseModel
-from typing import List
-import ast
-import os
-import sseclient
-
-
-QUEUE_MAX_SIZE = int(os.getenv("QUEUE_MAX_SIZE", 20))
-QUEUE_CONCURENCY_COUNT = int(os.getenv("QUEUE_CONCURENCY_COUNT", 10))
-USERNAME = os.getenv("USERNAME")
-PASSWORD = os.getenv("PASSWORD")
-CHATBOT_ENDPOINT = os.getenv("CHATBOT_ENDPOINT", "http://localhost:5000")
-
-
-class LearningBotRequest(BaseModel):
-    message: List[dict]
-    persona: str
-    session_id: str
-    context: dict
-    user_serial: str
-    stream: bool
-
-
-def generate_uuid():
-    return str(uuid4())
-
-
-def construct_message(list_message):
-    messages = []
-    for i, pair_message in enumerate(list_message):
-        if len(pair_message) < 2:
-            continue
-        content_user = {"human": pair_message[0]}
-        content_human = {"AI": pair_message[1]}
-        messages.append(content_user)
-        messages.append(content_human)
-
-    return messages
-
-
-def send_message(url, request):
-    return requests.post(url, stream=True, data=request.json())
-
-
-def respond(chat_history, message, history, session_id, user_serial, persona, context, endpoint):
-    if history is None:
-        history = []
-    history.append(message)
-    if session_id is None:
-        session_id = generate_uuid()
-
-    context = ast.literal_eval(context)
-    messages = construct_message(chat_history)
-    messages.append(
-        {"human": message}
-    )
-
-    request = LearningBotRequest(
-        message=messages,
-        persona=persona,
-        session_id=session_id,
-        context=context,
-        user_serial=user_serial,
-        stream=True,
-        product="learning_companion"
-    )
-
-    response = send_message(endpoint, request)
-
-    token_counter = 0
-    partial_reply = ""
-    client = sseclient.SSEClient(response)
-    for event in client.events():
-        data = json.loads(event.data)["data"]
-        partial_reply = partial_reply + data["data"]["reply"]
-
-        if token_counter == 0:
-            history.append(" " + partial_reply)
-        else:
-            history[-1] = partial_reply
-
-        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, "Success", session_id, session_id
-
-
-def reset_textbox():
-    return gr.update(value='')
-
-
-with gr.Blocks() as demo:
-    session_id = gr.State(value=generate_uuid())
-
-    with gr.Row():
-        with gr.Column(scale=5):
-            clear = gr.Button("Clear all converstation")
-        with gr.Column(scale=5):
-            endpoint = gr.Textbox(label="Endpoint API", value=CHATBOT_ENDPOINT)
-            with gr.Accordion("Parameters", open=False):
-                user_serial = gr.Textbox(label="User serial")
-                context = gr.Textbox(label="context", value={})
-                persona = gr.Textbox(label="persona", value="a493700848d84d0dab8d0095c2477c1e")
-
-    chatbot = gr.Chatbot()
-    message = gr.Textbox(placeholder="Halo kak, aku mau bertanya", label="Chat Here")
-    state = gr.State([])
-    with gr.Row():
-        with gr.Column(scale=5):
-            send = gr.Button("Send")
-        with gr.Column(scale=5):
-            status_box = gr.Textbox(label="Status code from OpenAI server")
-            session = gr.Textbox(label="session_id")
-
-    message.submit(respond, [chatbot, message, state, session_id, user_serial, persona, context, endpoint], [chatbot, state, status_box, session, session_id])
-
-    clear.click(lambda: None, None, chatbot, queue=False)
-    clear.click(lambda: None, None, session_id, queue=False)
-    clear.click(lambda: None, None, state, queue=False)
-
-    send.click(respond, [chatbot, message, state, session_id, user_serial, persona, context, endpoint], [chatbot, state, status_box, session, session_id])
-    send.click(reset_textbox, [], [message])
-    message.submit(reset_textbox, [], [message])
-
-    (
-        demo
-        .queue(max_size=QUEUE_MAX_SIZE, concurrency_count=QUEUE_CONCURENCY_COUNT)
-        .launch(auth=(USERNAME, PASSWORD), debug=True)
-    )
diff --git a/spaces/sam-hq-team/sam-hq/sam-hq/segment_anything/modeling/sam.py b/spaces/sam-hq-team/sam-hq/sam-hq/segment_anything/modeling/sam.py
deleted file mode 100644
index b928dfd40507b059908ef8676ac35b90779366ad..0000000000000000000000000000000000000000
--- a/spaces/sam-hq-team/sam-hq/sam-hq/segment_anything/modeling/sam.py
+++ /dev/null
@@ -1,177 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from typing import Any, Dict, List, Tuple
-
-from .image_encoder import ImageEncoderViT
-from .mask_decoder import MaskDecoder
-from .prompt_encoder import PromptEncoder
-
-
-class Sam(nn.Module):
-    mask_threshold: float = 0.0
-    image_format: str = "RGB"
-
-    def __init__(
-        self,
-        image_encoder: ImageEncoderViT,
-        prompt_encoder: PromptEncoder,
-        mask_decoder: MaskDecoder,
-        pixel_mean: List[float] = [123.675, 116.28, 103.53],
-        pixel_std: List[float] = [58.395, 57.12, 57.375],
-    ) -> None:
-        """
-        SAM predicts object masks from an image and input prompts.
-
-        Arguments:
-          image_encoder (ImageEncoderViT): The backbone used to encode the
-            image into image embeddings that allow for efficient mask prediction.
-          prompt_encoder (PromptEncoder): Encodes various types of input prompts.
-          mask_decoder (MaskDecoder): Predicts masks from the image embeddings
-            and encoded prompts.
-          pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
-          pixel_std (list(float)): Std values for normalizing pixels in the input image.
-        """
-        super().__init__()
-        self.image_encoder = image_encoder
-        self.prompt_encoder = prompt_encoder
-        self.mask_decoder = mask_decoder
-        self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
-        self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
-
-    @property
-    def device(self) -> Any:
-        return self.pixel_mean.device
-
-    def forward(
-        self,
-        batched_input: List[Dict[str, Any]],
-        multimask_output: bool,
-        hq_token_only: bool =False,
-    ) -> List[Dict[str, torch.Tensor]]:
-        """
-        Predicts masks end-to-end from provided images and prompts.
-        If prompts are not known in advance, using SamPredictor is
-        recommended over calling the model directly.
-
-        Arguments:
-          batched_input (list(dict)): A list over input images, each a
-            dictionary with the following keys. A prompt key can be
-            excluded if it is not present.
-              'image': The image as a torch tensor in 3xHxW format,
-                already transformed for input to the model.
-              'original_size': (tuple(int, int)) The original size of
-                the image before transformation, as (H, W).
-              'point_coords': (torch.Tensor) Batched point prompts for
-                this image, with shape BxNx2. Already transformed to the
-                input frame of the model.
-              'point_labels': (torch.Tensor) Batched labels for point prompts,
-                with shape BxN.
-              'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.
-                Already transformed to the input frame of the model.
-              'mask_inputs': (torch.Tensor) Batched mask inputs to the model,
-                in the form Bx1xHxW.
-          multimask_output (bool): Whether the model should predict multiple
-            disambiguating masks, or return a single mask.
-
-        Returns:
-          (list(dict)): A list over input images, where each element is
-            as dictionary with the following keys.
-              'masks': (torch.Tensor) Batched binary mask predictions,
-                with shape BxCxHxW, where B is the number of input prompts,
-                C is determined by multimask_output, and (H, W) is the
-                original size of the image.
-              'iou_predictions': (torch.Tensor) The model's predictions
-                of mask quality, in shape BxC.
-              'low_res_logits': (torch.Tensor) Low resolution logits with
-                shape BxCxHxW, where H=W=256. Can be passed as mask input
-                to subsequent iterations of prediction.
-        """
-        input_images = torch.stack([self.preprocess(x["image"]) for x in batched_input], dim=0)
-        image_embeddings, interm_embeddings = self.image_encoder(input_images)
-        interm_embeddings = interm_embeddings[0] # early layer
-
-        outputs = []
-        for image_record, curr_embedding, curr_interm in zip(batched_input, image_embeddings, interm_embeddings):
-            if "point_coords" in image_record:
-                points = (image_record["point_coords"], image_record["point_labels"])
-            else:
-                points = None
-            sparse_embeddings, dense_embeddings = self.prompt_encoder(
-                points=points,
-                boxes=image_record.get("boxes", None),
-                masks=image_record.get("mask_inputs", None),
-            )
-            low_res_masks, iou_predictions = self.mask_decoder(
-                image_embeddings=curr_embedding.unsqueeze(0),
-                image_pe=self.prompt_encoder.get_dense_pe(),
-                sparse_prompt_embeddings=sparse_embeddings,
-                dense_prompt_embeddings=dense_embeddings,
-                multimask_output=multimask_output,
-                hq_token_only=hq_token_only,
-                interm_embeddings=curr_interm.unsqueeze(0).unsqueeze(0),
-            )
-            masks = self.postprocess_masks(
-                low_res_masks,
-                input_size=image_record["image"].shape[-2:],
-                original_size=image_record["original_size"],
-            )
-            masks = masks > self.mask_threshold
-            outputs.append(
-                {
-                    "masks": masks,
-                    "iou_predictions": iou_predictions,
-                    "low_res_logits": low_res_masks,
-                }
-            )
-        return outputs
-
-    def postprocess_masks(
-        self,
-        masks: torch.Tensor,
-        input_size: Tuple[int, ...],
-        original_size: Tuple[int, ...],
-    ) -> torch.Tensor:
-        """
-        Remove padding and upscale masks to the original image size.
-
-        Arguments:
-          masks (torch.Tensor): Batched masks from the mask_decoder,
-            in BxCxHxW format.
-          input_size (tuple(int, int)): The size of the image input to the
-            model, in (H, W) format. Used to remove padding.
-          original_size (tuple(int, int)): The original size of the image
-            before resizing for input to the model, in (H, W) format.
-
-        Returns:
-          (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)
-            is given by original_size.
-        """
-        masks = F.interpolate(
-            masks,
-            (self.image_encoder.img_size, self.image_encoder.img_size),
-            mode="bilinear",
-            align_corners=False,
-        )
-        masks = masks[..., : input_size[0], : input_size[1]]
-        masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False)
-        return masks
-
-    def preprocess(self, x: torch.Tensor) -> torch.Tensor:
-        """Normalize pixel values and pad to a square input."""
-        # Normalize colors
-        x = (x - self.pixel_mean) / self.pixel_std
-
-        # Pad
-        h, w = x.shape[-2:]
-        padh = self.image_encoder.img_size - h
-        padw = self.image_encoder.img_size - w
-        x = F.pad(x, (0, padw, 0, padh))
-        return x
diff --git a/spaces/scedlatioru/img-to-music/Sarin-Advisor-5-1-Crack-HOT.md b/spaces/scedlatioru/img-to-music/Sarin-Advisor-5-1-Crack-HOT.md
deleted file mode 100644
index 14778c66366a75382a7a90cc718744cddc64df0e..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/Sarin-Advisor-5-1-Crack-HOT.md
+++ /dev/null
@@ -1,86 +0,0 @@
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-
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diff --git a/spaces/scedlatioru/img-to-music/example/Devon Ke Dev Mahadev - Episodes 101-150 ( Hindi ) 60.md b/spaces/scedlatioru/img-to-music/example/Devon Ke Dev Mahadev - Episodes 101-150 ( Hindi ) 60.md
deleted file mode 100644
index 4d494ecadb3cb43ebe5f5fd58cc095c1f66c2779..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Devon Ke Dev Mahadev - Episodes 101-150 ( Hindi ) 60.md	
+++ /dev/null
@@ -1,22 +0,0 @@
-<br />
-<h1>Devon Ke Dev Mahadev: A Mythological Drama Series Based on Lord Shiva</h1>
-<p>Devon Ke Dev Mahadev is a popular Hindi television show that depicts the life and legends of Lord Shiva, also known as Mahadev, the supreme god of Hinduism. The show premiered on 18 December 2011 on Life OK channel and ran for 820 episodes until 14 December 2014. The show covers various aspects of Shiva's mythology, such as his marriage to Parvati, his sons Ganesha and Kartikeya, his battles with demons, his role as the destroyer and the transformer, and his association with other gods and goddesses.</p>
-<p>The show features Mohit Raina as Shiva, Mouni Roy as Parvati/Sati, Sonarika Bhadoria as Parvati/Adi Shakti, Saurabh Raj Jain as Vishnu, Pooja Bose as Parvati/Adi Shakti/Durga/Kali, Radha Krishna Dutt as Brahma, Rushiraj Pawar as Kartikeya, Sadhil Kapoor as Ganesha, and many other actors in supporting roles. The show has been praised for its visual effects, costumes, sets, music, and performances. It has also won several awards, such as the Indian Telly Awards, the ITA Awards, the BIG Star Entertainment Awards, and the STAR Parivaar Awards.</p>
-<h2>Devon Ke Dev Mahadev - Episodes 101-150 ( Hindi ) 60</h2><br /><p><b><b>Download Zip</b> &#9881; <a href="https://gohhs.com/2uEzhL">https://gohhs.com/2uEzhL</a></b></p><br /><br />
-<p>In this article, we will focus on episodes 101-150 of Devon Ke Dev Mahadev, which aired from 7 May 2012 to 24 August 2012. These episodes cover some of the major events in Shiva's life, such as his marriage to Parvati after her severe penance, his fight with Tarakasura and Jalandhar, his dance of destruction at Daksha's yagna, his encounter with Ravana, his journey to Kailash with Parvati and their children, and his blessing to Markandeya. These episodes also show the interactions between Shiva and other gods and goddesses, such as Vishnu, Lakshmi, Saraswati, Indra, Narada, Nandi, and the Sapta Rishis.</p>
-<p>If you are interested in watching these episodes of Devon Ke Dev Mahadev, you can find them online on various platforms. One of them is Vidyabalaji's Blog[^1^], where you can download or stream the episodes in MP4 format with Hindi audio. Another option is Sway Office[^2^], where you can watch the episodes online with English subtitles. You can also buy the DVDs of the show from online stores or watch them on Star Bharat channel.</p>
-<p>Devon Ke Dev Mahadev is a show that will inspire you with its portrayal of Shiva's devotion, wisdom, power, compassion, and love. It will also entertain you with its captivating stories, stunning visuals, and melodious songs. If you are a fan of mythology or spirituality, you should definitely watch this show and enjoy its divine message.</p>
-
-<p>Some of the highlights of episodes 101-150 of Devon Ke Dev Mahadev are:</p>
-<p></p>
-<ul>
-<li>Episode 101: Parvati decides to leave her palace and perform penance to win Shiva's love. She faces many hardships and obstacles in her path, but remains determined and faithful.</li>
-<li>Episode 110: Shiva tests Parvati's devotion by sending seven sages to dissuade her from her penance. Parvati impresses them with her knowledge and devotion, and they bless her with their support.</li>
-<li>Episode 120: Shiva finally agrees to marry Parvati after she passes his final test. He appears before her in his true form and asks her to accept him as he is. Parvati happily agrees and embraces him.</li>
-<li>Episode 131: Shiva and Parvati get married in a grand ceremony attended by all the gods and goddesses. They exchange vows and garlands, and perform the rituals of the wedding.</li>
-<li>Episode 140: Shiva and Parvati fight against Tarakasura, a powerful demon who has a boon that he can only be killed by Shiva's son. Shiva and Parvati use their combined power to create a son, Kartikeya, who slays Tarakasura.</li>
-<li>Episode 150: Shiva and Parvati face another challenge from Jalandhar, a demon who is born from Shiva's sweat. Jalandhar tries to seduce Parvati and defeat Shiva, but fails in both attempts. Shiva kills Jalandhar with his trident.</li>
-</ul>
-<p>These episodes show the love story of Shiva and Parvati, as well as their role as the protectors of the universe. They also show how Shiva balances his ascetic and household life, and how Parvati supports him in his endeavors. They also show how Shiva deals with his past memories of Sati, his first wife who died at Daksha's yagna.</p> d5da3c52bf<br />
-<br />
-<br />
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diff --git a/spaces/shabnam91/Sanskrit-TTS/attentions.py b/spaces/shabnam91/Sanskrit-TTS/attentions.py
deleted file mode 100644
index 86bc73b5fe98cc7b443e9078553920346c996707..0000000000000000000000000000000000000000
--- a/spaces/shabnam91/Sanskrit-TTS/attentions.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/shi-labs/Prompt-Free-Diffusion/lib/utils.py b/spaces/shi-labs/Prompt-Free-Diffusion/lib/utils.py
deleted file mode 100644
index bd37cb9a7e670aefd46d473ea5b5958a827ca443..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/Prompt-Free-Diffusion/lib/utils.py
+++ /dev/null
@@ -1,651 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.backends.cudnn as cudnn
-# cudnn.enabled = True
-# cudnn.benchmark = True
-import torch.distributed as dist
-import torch.multiprocessing as mp
-
-import os
-import os.path as osp
-import sys
-import numpy as np
-import random
-import pprint
-import timeit
-import time
-import copy
-import matplotlib.pyplot as plt
-
-from .cfg_holder import cfg_unique_holder as cfguh
-
-from .data_factory import \
-    get_dataset, collate, \
-    get_loader, \
-    get_transform, \
-    get_estimator, \
-    get_formatter, \
-    get_sampler
-
-from .model_zoo import \
-    get_model, get_optimizer, get_scheduler
-
-from .log_service import print_log, distributed_log_manager
-
-from .evaluator import get_evaluator
-from . import sync
-
-class train_stage(object):
-    """
-    This is a template for a train stage,
-        (can be either train or test or anything)
-    Usually, it takes RANK
-        one dataloader, one model, one optimizer, one scheduler.
-    But it is not limited to these parameters. 
-    """
-    def __init__(self):
-        self.nested_eval_stage = None
-        self.rv_keep = None
-
-    def is_better(self, x):
-        return (self.rv_keep is None) or (x>self.rv_keep)
-
-    def set_model(self, net, mode):
-        if mode == 'train':
-            return net.train()
-        elif mode == 'eval':
-            return net.eval()
-        else:
-            raise ValueError
-
-    def __call__(self,
-                 **paras):
-        cfg = cfguh().cfg
-        cfgt = cfg.train
-        logm = distributed_log_manager()
-        epochn, itern_local, itern, samplen = 0, 0, 0, 0
-
-        step_type = cfgt.get('step_type', 'iter')
-        assert step_type in ['epoch', 'iter', 'sample'], \
-            'Step type must be in [epoch, iter, sample]'
- 
-        step_num      = cfgt.get('step_num'     , None)
-        gradacc_every = cfgt.get('gradacc_every', 1   )
-        log_every     = cfgt.get('log_every'    , None)
-        ckpt_every    = cfgt.get('ckpt_every'   , None)
-        eval_start    = cfgt.get('eval_start'   , 0   )
-        eval_every    = cfgt.get('eval_every'   , None)
-
-        if paras.get('resume_step', None) is not None:
-            resume_step = paras['resume_step']
-            assert step_type == resume_step['type']
-            epochn = resume_step['epochn']
-            itern = resume_step['itern']
-            itern_local = itern * gradacc_every
-            samplen = resume_step['samplen']
-            del paras['resume_step']
-
-        trainloader = paras['trainloader']
-        if trainloader is None:
-            import itertools
-            trainloader = itertools.cycle([None])
-        optimizer   = paras['optimizer']
-        scheduler   = paras['scheduler']
-        net         = paras['net']
-
-        GRANK, LRANK, NRANK = sync.get_rank('all')
-        GWSIZE, LWSIZE, NODES = sync.get_world_size('all')
-
-        weight_path = osp.join(cfgt.log_dir, 'weight')
-        if (GRANK==0) and (not osp.isdir(weight_path)):
-            os.makedirs(weight_path)
-        if (GRANK==0) and (cfgt.save_init_model):
-            self.save(net, is_init=True, step=0, optimizer=optimizer)
-
-        epoch_time = timeit.default_timer()
-        end_flag = False 
-        net.train()
-
-        while True:
-            if step_type == 'epoch':
-                lr = scheduler[epochn] if scheduler is not None else None
-            for batch in trainloader:
-                # so first element of batch (usually image) can be [tensor]
-                if batch is None:
-                    bs = cfgt.batch_size_per_gpu
-                elif not isinstance(batch[0], list):
-                    bs = batch[0].shape[0]
-                else:
-                    bs = len(batch[0])
-                if cfgt.skip_partial_batch and (bs != cfgt.batch_size_per_gpu):
-                    continue
-
-                itern_local_next = itern_local + 1
-                samplen_next = samplen + bs*GWSIZE
-
-                if step_type == 'iter':
-                    lr = scheduler[itern] if scheduler is not None else None
-                    grad_update = itern_local%gradacc_every==(gradacc_every-1)
-                elif step_type == 'sample':
-                    lr = scheduler[samplen] if scheduler is not None else None
-                    # TODO: 
-                    # grad_update = samplen%gradacc_every==(gradacc_every-1) 
-
-                itern_next = itern + 1 if grad_update else itern
-
-                # timeDebug = timeit.default_timer()
-                paras_new = self.main(
-                    batch=batch, 
-                    lr=lr,
-                    itern_local=itern_local,
-                    itern=itern,
-                    epochn=epochn,
-                    samplen=samplen,
-                    isinit=False,
-                    grad_update=grad_update,
-                    **paras)
-                # print_log(timeit.default_timer() - timeDebug)
-
-                paras.update(paras_new)
-                logm.accumulate(bs, **paras['log_info'])
-
-                #######
-                # log #
-                #######
-
-                display_flag = False
-                if log_every is not None:
-                    display_i = (itern//log_every) != (itern_next//log_every)
-                    display_s = (samplen//log_every) != (samplen_next//log_every)
-                    display_flag = (display_i and (step_type=='iter')) \
-                        or (display_s and (step_type=='sample'))
-
-                if display_flag:
-                    tbstep = itern_next if step_type=='iter' else samplen_next
-                    console_info = logm.train_summary(
-                        itern_next, epochn, samplen_next, lr, tbstep=tbstep)
-                    logm.clear()
-                    print_log(console_info)
-
-                ########
-                # eval #
-                ########
-
-                eval_flag = False
-                if (self.nested_eval_stage is not None) and (eval_every is not None) and (NRANK == 0):
-                    if step_type=='iter':
-                        eval_flag = (itern//eval_every) != (itern_next//eval_every)
-                        eval_flag = eval_flag and (itern_next>=eval_start)
-                        eval_flag = eval_flag or itern_local==0
-                    if step_type=='sample':
-                        eval_flag = (samplen//eval_every) != (samplen_next//eval_every)
-                        eval_flag = eval_flag and (samplen_next>=eval_start)
-                        eval_flag = eval_flag or samplen==0
-
-                if eval_flag:
-                    eval_cnt = itern_next if step_type=='iter' else samplen_next
-                    net = self.set_model(net, 'eval')
-                    rv = self.nested_eval_stage(
-                        eval_cnt=eval_cnt, **paras)
-                    rv = rv.get('eval_rv', None)
-                    if rv is not None:
-                        logm.tensorboard_log(eval_cnt, rv, mode='eval')
-                    if self.is_better(rv):
-                        self.rv_keep = rv
-                        if GRANK==0:
-                            step = {'epochn':epochn, 'itern':itern_next, 
-                                    'samplen':samplen_next, 'type':step_type, }
-                            self.save(net, is_best=True, step=step, optimizer=optimizer)
-                    net = self.set_model(net, 'train')
-
-                ########
-                # ckpt # 
-                ########
-
-                ckpt_flag = False
-                if (GRANK==0) and (ckpt_every is not None):
-                    # not distributed
-                    ckpt_i = (itern//ckpt_every) != (itern_next//ckpt_every)
-                    ckpt_s = (samplen//ckpt_every) != (samplen_next//ckpt_every)
-                    ckpt_flag = (ckpt_i and (step_type=='iter')) \
-                        or (ckpt_s and (step_type=='sample'))
-
-                if ckpt_flag:
-                    if step_type == 'iter':
-                        print_log('Checkpoint... {}'.format(itern_next))
-                        step = {'epochn':epochn, 'itern':itern_next, 
-                                'samplen':samplen_next, 'type':step_type, }
-                        self.save(net, itern=itern_next, step=step, optimizer=optimizer)
-                    else:
-                        print_log('Checkpoint... {}'.format(samplen_next))
-                        step = {'epochn':epochn, 'itern':itern_next, 
-                                'samplen':samplen_next, 'type':step_type, }
-                        self.save(net, samplen=samplen_next, step=step, optimizer=optimizer)
-
-                #######
-                # end #
-                #######
-
-                itern_local = itern_local_next
-                itern = itern_next
-                samplen = samplen_next
-
-                if step_type is not None:
-                    end_flag = (itern>=step_num and (step_type=='iter')) \
-                        or (samplen>=step_num and (step_type=='sample'))
-                if end_flag:
-                    break
-                # loop end
-
-            epochn += 1
-            print_log('Epoch {} time:{:.2f}s.'.format(
-                epochn, timeit.default_timer()-epoch_time))
-            epoch_time = timeit.default_timer()
-
-            if end_flag:
-                break
-            elif step_type != 'epoch':
-                # This is temporarily added to resolve the data issue
-                trainloader = self.trick_update_trainloader(trainloader)
-                continue
-
-            #######
-            # log #
-            #######
-
-            display_flag = False
-            if (log_every is not None) and (step_type=='epoch'):
-                display_flag = (epochn==1) or (epochn%log_every==0)
-
-            if display_flag:
-                console_info = logm.train_summary(
-                    itern, epochn, samplen, lr, tbstep=epochn)
-                logm.clear()
-                print_log(console_info)
-
-            ########
-            # eval #
-            ########
-
-            eval_flag = False
-            if (self.nested_eval_stage is not None) and (eval_every is not None) \
-                    and (step_type=='epoch') and (NRANK==0):
-                eval_flag = (epochn%eval_every==0) and (itern_next>=eval_start)
-                eval_flag = (epochn==1) or eval_flag
-
-            if eval_flag:
-                net = self.set_model(net, 'eval')
-                rv = self.nested_eval_stage(
-                    eval_cnt=epochn,
-                    **paras)['eval_rv']
-                if rv is not None:
-                    logm.tensorboard_log(epochn, rv, mode='eval')
-                if self.is_better(rv):
-                    self.rv_keep = rv
-                    if (GRANK==0):
-                        step = {'epochn':epochn, 'itern':itern, 
-                                'samplen':samplen, 'type':step_type, }
-                        self.save(net, is_best=True, step=step, optimizer=optimizer)
-                net = self.set_model(net, 'train')
-
-            ########
-            # ckpt # 
-            ########
-
-            ckpt_flag = False
-            if (ckpt_every is not None) and (GRANK==0) and (step_type=='epoch'):
-                # not distributed
-                ckpt_flag = epochn%ckpt_every==0
-
-            if ckpt_flag:
-                print_log('Checkpoint... {}'.format(itern_next))
-                step = {'epochn':epochn, 'itern':itern, 
-                        'samplen':samplen, 'type':step_type, }
-                self.save(net, epochn=epochn, step=step, optimizer=optimizer)
-
-            #######
-            # end #
-            #######
-            if (step_type=='epoch') and (epochn>=step_num):
-                break
-            # loop end
-
-            # This is temporarily added to resolve the data issue
-            trainloader = self.trick_update_trainloader(trainloader)
-
-        logm.tensorboard_close()
-        return {}
-
-    def main(self, **paras):
-        raise NotImplementedError
-
-    def trick_update_trainloader(self, trainloader):
-        return trainloader
-
-    def save_model(self, net, path_noext, **paras):
-        cfgt = cfguh().cfg.train
-        path = path_noext+'.pth'
-        if isinstance(net, (torch.nn.DataParallel,
-                            torch.nn.parallel.DistributedDataParallel)):
-            netm = net.module
-        else:
-            netm = net
-        torch.save(netm.state_dict(), path)
-        print_log('Saving model file {0}'.format(path))
-
-    def save(self, net, itern=None, epochn=None, samplen=None, 
-             is_init=False, is_best=False, is_last=False, **paras):
-        exid = cfguh().cfg.env.experiment_id
-        cfgt = cfguh().cfg.train
-        cfgm = cfguh().cfg.model
-        if isinstance(net, (torch.nn.DataParallel,
-                            torch.nn.parallel.DistributedDataParallel)):
-            netm = net.module
-        else:
-            netm = net
-        net_symbol = cfgm.symbol
-
-        check = sum([
-            itern is not None, samplen is not None, epochn is not None, 
-            is_init, is_best, is_last])
-        assert check<2
-
-        if itern is not None:
-            path_noexp = '{}_{}_iter_{}'.format(exid, net_symbol, itern)
-        elif samplen is not None:
-            path_noexp = '{}_{}_samplen_{}'.format(exid, net_symbol, samplen)
-        elif epochn is not None:
-            path_noexp = '{}_{}_epoch_{}'.format(exid, net_symbol, epochn)
-        elif is_init:
-            path_noexp = '{}_{}_init'.format(exid, net_symbol)
-        elif is_best:
-            path_noexp = '{}_{}_best'.format(exid, net_symbol)
-        elif is_last:
-            path_noexp = '{}_{}_last'.format(exid, net_symbol)
-        else:
-            path_noexp = '{}_{}_default'.format(exid, net_symbol)
-
-        path_noexp = osp.join(cfgt.log_dir, 'weight', path_noexp)
-        self.save_model(net, path_noexp, **paras)
-
-class eval_stage(object):
-    def __init__(self):
-        self.evaluator = None
-
-    def create_dir(self, path):
-        grank = sync.get_rank('global')
-        if (not osp.isdir(path)) and (grank == 0):
-            os.makedirs(path)
-        sync.nodewise_sync().barrier()
-
-    def __call__(self, 
-                 evalloader,
-                 net,
-                 **paras):
-        cfgt = cfguh().cfg.eval
-        local_rank = sync.get_rank('local')
-        if self.evaluator is None:
-            evaluator = get_evaluator()(cfgt.evaluator)
-            self.evaluator = evaluator
-        else:
-            evaluator = self.evaluator
-
-        time_check = timeit.default_timer()
-
-        for idx, batch in enumerate(evalloader): 
-            rv = self.main(batch, net)
-            evaluator.add_batch(**rv)
-            if cfgt.output_result:
-                try:
-                    self.output_f(**rv, cnt=paras['eval_cnt'])
-                except:
-                    self.output_f(**rv)
-            if idx%cfgt.log_display == cfgt.log_display-1:
-                print_log('processed.. {}, Time:{:.2f}s'.format(
-                    idx+1, timeit.default_timer() - time_check))
-                time_check = timeit.default_timer()
-            # break
-
-        evaluator.set_sample_n(len(evalloader.dataset))
-        eval_rv = evaluator.compute()
-        if local_rank == 0:
-            evaluator.one_line_summary()
-            evaluator.save(cfgt.log_dir)
-        evaluator.clear_data()
-        return {
-            'eval_rv' : eval_rv
-        }
-
-class exec_container(object):
-    """
-    This is the base functor for all types of executions.
-        One execution can have multiple stages, 
-        but are only allowed to use the same 
-        config, network, dataloader. 
-    Thus, in most of the cases, one exec_container is one
-        training/evaluation/demo...
-    If DPP is in use, this functor should be spawn.
-    """
-    def __init__(self,
-                 cfg,
-                 **kwargs):
-        self.cfg = cfg
-        self.registered_stages = []
-        self.node_rank = None
-        self.local_rank = None
-        self.global_rank = None
-        self.local_world_size = None
-        self.global_world_size = None
-        self.nodewise_sync_global_obj = sync.nodewise_sync_global()
-
-    def register_stage(self, stage):
-        self.registered_stages.append(stage)
-
-    def __call__(self, 
-                 local_rank,
-                 **kwargs):
-        cfg = self.cfg
-        cfguh().save_cfg(cfg)
-
-        self.node_rank = cfg.env.node_rank
-        self.local_rank = local_rank
-        self.nodes = cfg.env.nodes
-        self.local_world_size = cfg.env.gpu_count
-
-        self.global_rank = self.local_rank + self.node_rank * self.local_world_size
-        self.global_world_size = self.nodes * self.local_world_size
-
-        print('init {}/{}'.format(self.global_rank, self.global_world_size))
-        dist.init_process_group(
-            backend = cfg.env.dist_backend,
-            init_method = cfg.env.dist_url,
-            rank = self.global_rank,
-            world_size = self.global_world_size,)
-        torch.cuda.set_device(local_rank)
-        sync.nodewise_sync().copy_global(self.nodewise_sync_global_obj).local_init()
-        
-        if isinstance(cfg.env.rnd_seed, int):
-            random.seed(cfg.env.rnd_seed + self.global_rank + 200)
-            np.random.seed(cfg.env.rnd_seed + self.global_rank + 100)
-            torch.manual_seed(cfg.env.rnd_seed + self.global_rank)
-
-        time_start = timeit.default_timer()
-
-        para = {'itern_total' : 0,}
-        dl_para = self.prepare_dataloader()
-        assert isinstance(dl_para, dict)
-        para.update(dl_para)
-
-        md_para = self.prepare_model()
-        assert isinstance(md_para, dict)
-        para.update(md_para)
-
-        for stage in self.registered_stages:
-            stage_para = stage(**para)
-            if stage_para is not None:
-                para.update(stage_para)
-
-        if self.global_rank==0:
-            self.save_last_model(**para)
-
-        print_log(
-            'Total {:.2f} seconds'.format(timeit.default_timer() - time_start))
-        dist.destroy_process_group()
-
-    def prepare_dataloader(self):
-        """
-        Prepare the dataloader from config.
-        """
-        return {
-            'trainloader' : None,
-            'evalloader' : None}
-
-    def prepare_model(self):
-        """
-        Prepare the model from config.
-        """
-        return {'net' : None}
-
-    def save_last_model(self, **para):
-        return
-
-    def destroy(self):
-        self.nodewise_sync_global_obj.destroy()
-
-class train(exec_container):
-    def prepare_dataloader(self):
-        cfg = cfguh().cfg
-        trainset = get_dataset()(cfg.train.dataset)
-        trainloader = None
-        if trainset is not None:
-            sampler = get_sampler()(
-                dataset=trainset, cfg=cfg.train.dataset.get('sampler', 'default_train'))
-            trainloader = torch.utils.data.DataLoader(
-                trainset, 
-                batch_size = cfg.train.batch_size_per_gpu, 
-                sampler = sampler, 
-                num_workers = cfg.train.dataset_num_workers_per_gpu, 
-                drop_last = False, 
-                pin_memory = cfg.train.dataset.get('pin_memory', False),
-                collate_fn = collate(),)
-
-        evalloader = None
-        if 'eval' in cfg:
-            evalset = get_dataset()(cfg.eval.dataset)
-            if evalset is not None:
-                sampler = get_sampler()(
-                    dataset=evalset, cfg=cfg.eval.dataset.get('sampler', 'default_eval'))
-                evalloader = torch.utils.data.DataLoader(
-                    evalset, 
-                    batch_size = cfg.eval.batch_size_per_gpu,
-                    sampler = sampler,
-                    num_workers = cfg.eval.dataset_num_workers_per_gpu,
-                    drop_last = False, 
-                    pin_memory = cfg.eval.dataset.get('pin_memory', False),
-                    collate_fn = collate(),)
-            
-        return {
-            'trainloader' : trainloader,
-            'evalloader'  : evalloader,}
-
-    def prepare_model(self):
-        cfg = cfguh().cfg
-        net = get_model()(cfg.model)
-        find_unused_parameters=cfg.model.get('find_unused_parameters', False)
-        if cfg.env.cuda:
-            net.to(self.local_rank)
-            net = torch.nn.parallel.DistributedDataParallel(
-                net, device_ids=[self.local_rank], 
-                find_unused_parameters=find_unused_parameters)
-        net.train() 
-        scheduler = get_scheduler()(cfg.train.scheduler)
-        optimizer = get_optimizer()(net, cfg.train.optimizer)
-        return {
-            'net'       : net,
-            'optimizer' : optimizer,
-            'scheduler' : scheduler,}
-
-    def save_last_model(self, **para):
-        cfgt = cfguh().cfg.train
-        net = para['net']
-        net_symbol = cfguh().cfg.model.symbol
-        if isinstance(net, (torch.nn.DataParallel,
-                            torch.nn.parallel.DistributedDataParallel)):
-            netm = net.module
-        else:
-            netm = net
-        path = osp.join(cfgt.log_dir, '{}_{}_last.pth'.format(
-            cfgt.experiment_id, net_symbol))
-        torch.save(netm.state_dict(), path)
-        print_log('Saving model file {0}'.format(path))
-
-class eval(exec_container):
-    def prepare_dataloader(self):
-        cfg = cfguh().cfg
-        evalloader = None
-        if cfg.eval.get('dataset', None) is not None:
-            evalset = get_dataset()(cfg.eval.dataset)
-            if evalset is None:
-                return
-            sampler = get_sampler()(
-                dataset=evalset, cfg=getattr(cfg.eval.dataset, 'sampler', 'default_eval'))
-            evalloader = torch.utils.data.DataLoader(
-                evalset, 
-                batch_size = cfg.eval.batch_size_per_gpu,
-                sampler = sampler,
-                num_workers = cfg.eval.dataset_num_workers_per_gpu,
-                drop_last = False, 
-                pin_memory = False,
-                collate_fn = collate(), )
-        return {
-            'trainloader' : None,
-            'evalloader'  : evalloader,}
-
-    def prepare_model(self):
-        cfg = cfguh().cfg
-        net = get_model()(cfg.model)
-        if cfg.env.cuda:
-            net.to(self.local_rank)
-            net = torch.nn.parallel.DistributedDataParallel(
-                net, device_ids=[self.local_rank], 
-                find_unused_parameters=True)
-        net.eval()
-        return {'net' : net,}
-
-    def save_last_model(self, **para):
-        return
-
-###############
-# some helper #
-###############
-
-def torch_to_numpy(*argv):
-    if len(argv) > 1:
-        data = list(argv)
-    else:
-        data = argv[0]
-
-    if isinstance(data, torch.Tensor):
-        return data.to('cpu').detach().numpy()
-    elif isinstance(data, (list, tuple)):
-        out = []
-        for di in data:
-            out.append(torch_to_numpy(di))
-        return out
-    elif isinstance(data, dict):
-        out = {}
-        for ni, di in data.items():
-            out[ni] = torch_to_numpy(di)
-        return out
-    else:
-        return data
-
-import importlib
-
-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)
diff --git a/spaces/shikunl/prismer/prismer/experts/obj_detection/unidet/data/datasets/voc_cocoformat.py b/spaces/shikunl/prismer/prismer/experts/obj_detection/unidet/data/datasets/voc_cocoformat.py
deleted file mode 100644
index 4be4b26da8c8bf08f120a47e32cf36834870309c..0000000000000000000000000000000000000000
--- a/spaces/shikunl/prismer/prismer/experts/obj_detection/unidet/data/datasets/voc_cocoformat.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from detectron2.data.datasets.register_coco import register_coco_instances
-import os
-
-categories = [
-    {'id': 1, 'name': 'aeroplane'},
-    {'id': 2, 'name': 'bicycle'},
-    {'id': 3, 'name': 'bird'},
-    {'id': 4, 'name': 'boat'},
-    {'id': 5, 'name': 'bottle'},
-    {'id': 6, 'name': 'bus'},
-    {'id': 7, 'name': 'car'},
-    {'id': 8, 'name': 'cat'},
-    {'id': 9, 'name': 'chair'},
-    {'id': 10, 'name': 'cow'},
-    {'id': 11, 'name': 'diningtable'},
-    {'id': 12, 'name': 'dog'},
-    {'id': 13, 'name': 'horse'},
-    {'id': 14, 'name': 'motorbike'},
-    {'id': 15, 'name': 'person'},
-    {'id': 16, 'name': 'pottedplant'},
-    {'id': 17, 'name': 'sheep'},
-    {'id': 18, 'name': 'sofa'},
-    {'id': 19, 'name': 'train'},
-    {'id': 20, 'name': 'tvmonitor'},
-]
-
-def _get_builtin_metadata():
-    thing_dataset_id_to_contiguous_id = {
-        x['id']: i for i, x in enumerate(sorted(categories, key=lambda x: x['id']))}
-    thing_classes = [x['name'] for x in sorted(categories, key=lambda x: x['id'])]
-    return {
-        "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id,
-        "thing_classes": thing_classes}
-
-_PREDEFINED_SPLITS_VOC = {
-    "voc_cocoformat_test": ("voc/images/", "voc/annotations/pascal_test2007.json"),
-}
-
-for key, (image_root, json_file) in _PREDEFINED_SPLITS_VOC.items():
-    register_coco_instances(
-        key,
-        _get_builtin_metadata(),
-        os.path.join("datasets", json_file) if "://" not in json_file else json_file,
-        os.path.join("datasets", image_root),
-    )
diff --git a/spaces/shivammehta25/Diff-TTSG/diff_ttsg/train.py b/spaces/shivammehta25/Diff-TTSG/diff_ttsg/train.py
deleted file mode 100644
index 1a0d8fb4761be7b5d99e984ef530279dc1cb172c..0000000000000000000000000000000000000000
--- a/spaces/shivammehta25/Diff-TTSG/diff_ttsg/train.py
+++ /dev/null
@@ -1,128 +0,0 @@
-from typing import List, Optional, Tuple
-
-import hydra
-import lightning as L
-import pyrootutils
-import torch
-from lightning import Callback, LightningDataModule, LightningModule, Trainer
-from lightning.pytorch.loggers import Logger
-from omegaconf import DictConfig
-
-pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
-# ------------------------------------------------------------------------------------ #
-# the setup_root above is equivalent to:
-# - adding project root dir to PYTHONPATH
-#       (so you don't need to force user to install project as a package)
-#       (necessary before importing any local modules e.g. `from src import utils`)
-# - setting up PROJECT_ROOT environment variable
-#       (which is used as a base for paths in "configs/paths/default.yaml")
-#       (this way all filepaths are the same no matter where you run the code)
-# - loading environment variables from ".env" in root dir
-#
-# you can remove it if you:
-# 1. either install project as a package or move entry files to project root dir
-# 2. set `root_dir` to "." in "configs/paths/default.yaml"
-#
-# more info: https://github.com/ashleve/pyrootutils
-# ------------------------------------------------------------------------------------ #
-
-from diff_ttsg import utils
-
-log = utils.get_pylogger(__name__)
-
-
-@utils.task_wrapper
-def train(cfg: DictConfig) -> Tuple[dict, dict]:
-    """Trains the model. Can additionally evaluate on a testset, using best weights obtained during
-    training.
-
-    This method is wrapped in optional @task_wrapper decorator, that controls the behavior during
-    failure. Useful for multiruns, saving info about the crash, etc.
-
-    Args:
-        cfg (DictConfig): Configuration composed by Hydra.
-
-    Returns:
-        Tuple[dict, dict]: Dict with metrics and dict with all instantiated objects.
-    """
-
-    # set seed for random number generators in pytorch, numpy and python.random
-    if cfg.get("seed"):
-        L.seed_everything(cfg.seed, workers=True)
-
-    log.info(f"Instantiating datamodule <{cfg.data._target_}>")
-    datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data)
-
-    log.info(f"Instantiating model <{cfg.model._target_}>")
-    model: LightningModule = hydra.utils.instantiate(cfg.model)
-
-    log.info("Instantiating callbacks...")
-    callbacks: List[Callback] = utils.instantiate_callbacks(cfg.get("callbacks"))
-
-    log.info("Instantiating loggers...")
-    logger: List[Logger] = utils.instantiate_loggers(cfg.get("logger"))
-
-    log.info(f"Instantiating trainer <{cfg.trainer._target_}>")
-    trainer: Trainer = hydra.utils.instantiate(cfg.trainer, callbacks=callbacks, logger=logger)
-
-    object_dict = {
-        "cfg": cfg,
-        "datamodule": datamodule,
-        "model": model,
-        "callbacks": callbacks,
-        "logger": logger,
-        "trainer": trainer,
-    }
-
-    if logger:
-        log.info("Logging hyperparameters!")
-        utils.log_hyperparameters(object_dict)
-
-    if cfg.get("compile"):
-        log.info("Compiling model!")
-        model = torch.compile(model)
-
-    if cfg.get("train"):
-        log.info("Starting training!")
-        trainer.fit(model=model, datamodule=datamodule, ckpt_path=cfg.get("ckpt_path"))
-
-    train_metrics = trainer.callback_metrics
-
-    if cfg.get("test"):
-        log.info("Starting testing!")
-        ckpt_path = trainer.checkpoint_callback.best_model_path
-        if ckpt_path == "":
-            log.warning("Best ckpt not found! Using current weights for testing...")
-            ckpt_path = None
-        trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path)
-        log.info(f"Best ckpt path: {ckpt_path}")
-
-    test_metrics = trainer.callback_metrics
-
-    # merge train and test metrics
-    metric_dict = {**train_metrics, **test_metrics}
-
-    return metric_dict, object_dict
-
-
-@hydra.main(version_base="1.3", config_path="../configs", config_name="train.yaml")
-def main(cfg: DictConfig) -> Optional[float]:
-    # apply extra utilities
-    # (e.g. ask for tags if none are provided in cfg, print cfg tree, etc.)
-    utils.extras(cfg)
-
-    # train the model
-    metric_dict, _ = train(cfg)
-
-    # safely retrieve metric value for hydra-based hyperparameter optimization
-    metric_value = utils.get_metric_value(
-        metric_dict=metric_dict, metric_name=cfg.get("optimized_metric")
-    )
-
-    # return optimized metric
-    return metric_value
-
-
-if __name__ == "__main__":
-    torch.set_float32_matmul_precision('medium')
-    main()
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/create.py b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/create.py
deleted file mode 100644
index 7ac069c1a5e53648b9b457b0f07248de3d5d717e..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/create.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import os
-from pathlib import Path
-
-import streamlit as st
-from utils import get_rendered_model_card
-
-from skops import hub_utils
-
-
-def _add_back_button():
-    def fn():
-        st.session_state.screen.state = "edit"
-
-    st.button("Back", help="continue editing the model card", on_click=fn)
-
-
-def _add_delete_button():
-    def fn():
-        if "hf_path" in st.session_state:
-            del st.session_state["hf_path"]
-        if "model_card" in st.session_state:
-            del st.session_state["model_card"]
-        if "task_state" in st.session_state:
-            st.session_state.task_state.reset()
-        if "create_repo_name" in st.session_state:
-            del st.session_state["create_repo_name"]
-        if "hf_token" in st.session_state:
-            del st.session_state["hf_token"]
-        st.session_state.screen.state = "start"
-
-    st.button("Delete", on_click=fn, help="Start over from scratch (lose all progress)")
-
-
-def _save_model_card(path: Path) -> None:
-    model_card = st.session_state.get("model_card")
-    if model_card:
-        # do not use model_card.save, see doc of get_rendered_model_card
-        rendered = get_rendered_model_card(
-            model_card, hf_path=str(st.session_state.hf_path)
-        )
-        with open(path / "README.md", "w") as f:
-            f.write(rendered)
-
-
-def _display_repo_overview(path: Path) -> None:
-    text = "Files included in the repository:\n"
-    for file in os.listdir(path):
-        size = os.path.getsize(path / file)
-        text += f"- `{file} ({size:,} bytes)`\n"
-    st.markdown(text)
-
-
-def _display_private_box():
-    tip = (
-        "Private repositories can only seen by you or members of the same "
-        "organization, see https://huggingface.co/docs/hub/repositories-settings"
-    )
-    st.checkbox(
-        "Make repository private", value=True, help=tip, key="create_repo_private"
-    )
-
-
-def _repo_id_field():
-    st.text_input("Name of the repository (e.g. 'User/MyRepo')", key="create_repo_name")
-
-
-def _hf_token_field():
-    tip = "The Hugging Face token can be found at https://hf.co/settings/token"
-    st.text_input("Enter your Hugging Face token ('hf_***')", key="hf_token", help=tip)
-
-
-def _create_hf_repo(path, repo_name, hf_token, private):
-    try:
-        hub_utils.push(
-            repo_id=repo_name,
-            source=path,
-            token=hf_token,
-            private=private,
-            create_remote=True,
-        )
-    except Exception as exc:
-        st.error(
-            "Oops, something went wrong, please create an issue. "
-            f"The error message is:\n\n{exc}"
-        )
-        return
-
-    st.success(f"Successfully created the repo 'https://huggingface.co/{repo_name}'")
-
-
-def _add_create_repo_button():
-    private = bool(st.session_state.get("create_repo_private"))
-    repo_name = st.session_state.get("create_repo_name")
-    hf_token = st.session_state.get("hf_token")
-    disabled = (not repo_name) or (not hf_token)
-
-    button_text = "Create a new repository"
-    tip = "Creating a repo requires a name and a token"
-    path = st.session_state.get("hf_path")
-    st.button(
-        button_text,
-        help=tip,
-        disabled=disabled,
-        on_click=_create_hf_repo,
-        args=(path, repo_name, hf_token, private),
-    )
-
-    if not repo_name:
-        st.info("Repository name is required")
-    if not hf_token:
-        st.info("Token is required")
-
-
-def create_repo_input_form():
-    if not st.session_state.screen.state == "create_repo":
-        return
-
-    col_0, col_1, *_ = st.columns([2, 2, 2, 2])
-    with col_0:
-        _add_back_button()
-    with col_1:
-        _add_delete_button()
-
-    hf_path = st.session_state.hf_path
-    _save_model_card(hf_path)
-    _display_repo_overview(hf_path)
-    _display_private_box()
-    st.markdown("---")
-    _repo_id_field()
-    _hf_token_field()
-    _add_create_repo_button()
diff --git a/spaces/skf15963/summary/fengshen/examples/summary/seq2seq_summary.py b/spaces/skf15963/summary/fengshen/examples/summary/seq2seq_summary.py
deleted file mode 100644
index c0c725c215d61dc5c6fa0fbf6603b7f06f0a317b..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/examples/summary/seq2seq_summary.py
+++ /dev/null
@@ -1,197 +0,0 @@
-
-import torch
-import os
-import argparse
-import json
-import pytorch_lightning as pl
-from fengshen.models.model_utils import add_module_args
-from fengshen.data.task_dataloader.task_datasets import AbstractCollator
-from fengshen.data.universal_datamodule import UniversalDataModule
-from fengshen.utils.universal_checkpoint import UniversalCheckpoint
-from fengshen.utils.utils import chinese_char_tokenize
-from torchmetrics.text.rouge import ROUGEScore
-from pytorch_lightning import Trainer, loggers
-from pytorch_lightning.callbacks import LearningRateMonitor
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import sys
-sys.path.append('../../../')
-
-
-# os.environ["CUDA_VISIBLE_DEVICES"] = '3,4'
-
-
-class FinetuneSummary(pl.LightningModule):
-    @staticmethod
-    def add_model_specific_args(parent_args):
-        parser = parent_args.add_argument_group('BaseModel')
-        parser.add_argument('--rouge_keys', default='rougeL,rouge1,rouge2', type=str)
-        return parent_args
-
-    def __init__(self, args, tokenizer=None):
-        super().__init__()
-        self.save_hyperparameters(args)
-        self.model = AutoModelForSeq2SeqLM.from_pretrained(
-            args.pretrained_model_path)
-        self.tokenizer = tokenizer
-        assert self.tokenizer, "tokenizer is None!"
-        self.rouge_keys = tuple(args.rouge_keys.split(','))
-        self.rouge_metric = ROUGEScore(rouge_keys=self.rouge_keys, normalizer=lambda x: x)
-
-    def setup(self, stage) -> None:
-        if stage == 'fit':
-            train_loader = self.trainer._data_connector._train_dataloader_source.dataloader()
-
-            # Calculate total steps
-            tb_size = self.hparams.train_batchsize * max(1, self.trainer.gpus)
-            ab_size = self.trainer.accumulate_grad_batches * \
-                float(self.trainer.max_epochs)
-            self.total_steps = (
-                len(train_loader.dataset) // tb_size) // ab_size
-            print('total_steps is :', self.total_steps)
-
-    def training_step(self, batch, batch_idx):
-        output = self.model(input_ids=batch['input_ids'],
-                            attention_mask=batch['attention_mask'], labels=batch['labels'])
-        self.log('train_loss', output.loss, sync_dist=True)
-        return output.loss
-
-    def on_validation_start(self) -> None:
-        # rm file at validation start
-        prefix, ext = os.path.splitext(self.hparams.output_save_path)
-        file_path_rank = '{}_{}{}'.format(
-            prefix, self.trainer._accelerator_connector.cluster_environment.global_rank(), ext)
-        if os.path.exists(file_path_rank):
-            print('rm {}'.format(file_path_rank))
-            os.remove(file_path_rank)
-
-    def validation_step(self, batch, batch_idx):
-        output = self.model(input_ids=batch['input_ids'],
-                            attention_mask=batch['attention_mask'], labels=batch['labels'])
-        generated_ids = self.model.generate(
-            input_ids=batch['input_ids'],
-            attention_mask=batch['attention_mask'],
-            max_length=self.hparams.max_dec_length
-        )
-
-        preds = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-        labels = torch.where(batch['labels'] != -100, batch['labels'],
-                             self.tokenizer.pad_token_id)
-        labels = self.tokenizer.batch_decode(
-            labels, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-        # save preds for every rank
-        prefix, ext = os.path.splitext(self.hparams.output_save_path)
-        file_path_rank = '{}_{}{}'.format(
-            prefix, self.trainer._accelerator_connector.cluster_environment.global_rank(), ext)
-        self.save_prediction_to_file(preds=preds, texts=batch['text'],
-                                     summarys=batch['summary'], file_path=file_path_rank)
-        # you need to split chinese char with space for rouge metric
-        new_preds = [chinese_char_tokenize(p) for p in preds]
-        new_labels = [chinese_char_tokenize(label) for label in labels]
-        # update metric
-        self.rouge_metric.update(preds=new_preds, target=new_labels)
-        self.log('val_loss', output.loss, sync_dist=True)
-
-    def validation_epoch_end(self, outputs):
-        # compute metric for all process
-        rouge_dict = self.rouge_metric.compute()
-        # reset the metric after once validation
-        self.rouge_metric.reset()
-        for k, v in rouge_dict.items():
-            self.log('val_{}'.format(k), v, sync_dist=True)
-        if self.trainer._accelerator_connector.cluster_environment.global_rank() == 0:
-            print('rouge:\n', rouge_dict)
-
-    def on_save_checkpoint(self, checkpoint) -> None:
-        if self.trainer._accelerator_connector.cluster_environment.global_rank() == 0:
-            self.model.save_pretrained(os.path.join(
-                self.trainer.checkpoint_callback.dirpath,
-                'hf_pretrained_epoch{}_step{}'.format(checkpoint['epoch'], checkpoint['global_step'])))
-
-    def save_prediction_to_file(self, preds, texts, summarys, file_path):
-        with open(file_path, 'a', encoding='utf-8') as f:
-            for idx, pred in enumerate(preds):
-                text = texts[idx]
-                summary = summarys[idx]
-                tmp_result = dict()
-                tmp_result['pred'] = pred
-                tmp_result['label'] = summary
-                tmp_result['text'] = text
-                json_data = json.dumps(tmp_result, ensure_ascii=False)
-                f.write(json_data + '\n')
-
-    def predict_step(self, batch, batch_idx):
-        # print(batch)
-        texts = batch['text']
-        # output summary and metrics
-        generated_ids = self.model.generate(
-            input_ids=batch['input_ids'],
-            attention_mask=batch['attention_mask'],
-            max_length=self.hparams.max_dec_length
-        )
-        preds = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-        labels = self.tokenizer.batch_decode(
-            batch['labels'], skip_special_tokens=True, clean_up_tokenization_spaces=True)
-        print(batch_idx, len(preds), len(labels))
-        self.save_prediction_to_file(preds, texts, labels)
-
-    def configure_optimizers(self):
-        from fengshen.models.model_utils import configure_optimizers
-        return configure_optimizers(self)
-
-
-def main():
-    total_parser = argparse.ArgumentParser("Summary Task")
-    total_parser.add_argument('--do_eval_only',
-                              action='store_true',
-                              default=False)
-    total_parser.add_argument('--pretrained_model_path',
-                              default='google/mt5-small',
-                              type=str)
-    total_parser.add_argument('--output_save_path',
-                              default='./predict.json',
-                              type=str)
-    total_parser.add_argument('--self_tokenizer',
-                              action='store_true',
-                              default=False)
-    total_parser.add_argument('--max_enc_length', default=1024, type=int)
-    total_parser.add_argument('--max_dec_length', default=256, type=int)
-    total_parser.add_argument('--prompt', default='summarize:', type=str)
-    # * Args for data preprocessing
-    # from fengshen.data.task_dataloader.task_datasets import LCSTSDataModel
-    total_parser = UniversalDataModule.add_data_specific_args(total_parser)
-    # * Args for training
-    total_parser = add_module_args(total_parser)
-    total_parser = Trainer.add_argparse_args(total_parser)
-    total_parser = UniversalCheckpoint.add_argparse_args(total_parser)
-    total_parser = FinetuneSummary.add_model_specific_args(total_parser)
-    # * Args for base model
-    args = total_parser.parse_args()
-
-    if args.self_tokenizer:
-        from fengshen.examples.pegasus.tokenizers_pegasus import PegasusTokenizer
-        tokenizer = PegasusTokenizer.from_pretrained(args.pretrained_model_path)
-    else:
-        tokenizer = AutoTokenizer.from_pretrained(args.pretrained_model_path, use_fast=False)
-    collator = AbstractCollator(tokenizer, args.max_enc_length,
-                                args.max_dec_length, args.prompt)
-    data_model = UniversalDataModule(tokenizer=tokenizer, args=args, collate_fn=collator)
-    model = FinetuneSummary(args, tokenizer)
-    if not args.do_eval_only:
-        lr_monitor = LearningRateMonitor(logging_interval='step')
-        logger = loggers.TensorBoardLogger(save_dir=os.path.join(
-            args.default_root_dir, 'log/'))
-        checkpoint_callback = UniversalCheckpoint(args)
-        trainer = Trainer.from_argparse_args(args,
-                                             logger=logger,
-                                             callbacks=[lr_monitor,
-                                                        checkpoint_callback]
-                                             )
-        trainer.fit(model, data_model)
-    else:
-        trainer = Trainer.from_argparse_args(args)
-        # trainer.predict(model, data_model)
-        trainer.validate(model, data_model)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/skf15963/summary/fengshen/examples/ubert/README.md b/spaces/skf15963/summary/fengshen/examples/ubert/README.md
deleted file mode 100644
index fdad2ca0d948830c51bf141dceb907c4531a4690..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/examples/ubert/README.md
+++ /dev/null
@@ -1,280 +0,0 @@
-# Ubert: 统一 NLU 任务新范式
-- 论文：[https://arxiv.org/pdf/2206.12094.pdf](https://arxiv.org/pdf/2206.12094.pdf)
-- 知乎：[https://zhuanlan.zhihu.com/p/539958182?](https://zhuanlan.zhihu.com/p/539958182?)
-
-### 简介
-Ubert 是我们在做 [2022AIWIN 世界人工智能创新大赛：中文保险小样本多任务](http://ailab.aiwin.org.cn/competitions/68#results) 时提出的一种解决方案。并取得A/B榜榜首的成绩，且B榜综合成绩领先第二名超过 1 个百分点，领先第三名接近 5 个百分点。相比于官方提供的 baseline，提高 20 个百分点。Ubert 不仅可以完成 实体识别、事件抽取等常见抽取任务，还可以完成新闻分类、自然语言推理等分类任务，且所有任务是共享一个统一框架、统一任务、统一训练目标的模型。解题思路和方案可以参考我们的答辩PPT，或者参考我们的[知乎文章](https://zhuanlan.zhihu.com/p/539958182?)
-
-## 开源模型列表
- 开源的模型是我们在比赛模型的基础上重新整理 70+ 份数据，共 100万+条样本，进行预训练而得到的，可直接开箱即用。开源模型地址如下：
-| 模型 | 地址   |
-|:---------:|:--------------:|
-| Erlangshen-Ubert-110M-Chinese  | [https://huggingface.co/IDEA-CCNL/Erlangshen-Ubert-110M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-Ubert-110M-Chinese)       |
-| Erlangshen-Ubert-330M-Chinese  | [https://huggingface.co/IDEA-CCNL/Erlangshen-Ubert-330M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-Ubert-330M-Chinese)   |
-
-
-## 快速开箱使用
-安装我们的 fengshen 框架，我们暂且提供如下方式安装
-```python
-git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git
-cd Fengshenbang-LM
-pip install --editable ./
-```
-
-一键运行下面代码得到预测结果, 你可以任意修改示例 text 和要抽取的 entity_type，体验一下 Zero-Shot 性能
-```python
-import argparse
-from fengshen import UbertPiplines
-
-total_parser = argparse.ArgumentParser("TASK NAME")
-total_parser = UbertPiplines.piplines_args(total_parser)
-args = total_parser.parse_args()
-
-test_data=[
-    {
-        "task_type": "抽取任务", 
-        "subtask_type": "实体识别", 
-        "text": "这也让很多业主据此认为，雅清苑是政府公务员挤对了国家的经适房政策。", 
-        "choices": [ 
-            {"entity_type": "小区名字"}, 
-            {"entity_type": "岗位职责"}
-            ],
-        "id": 0}
-]
-
-model = UbertPiplines(args)
-result = model.predict(test_data)
-for line in result:
-    print(line)
-```
-
-## 继续 finetune 使用
-
-开源的模型我们已经经过大量的数据进行预训练而得到，可以直接进行 Zero-Shot，如果你还想继续finetune,可以参考我们的 [example.py](https://github.com/IDEA-CCNL/Fengshenbang-LM/blob/main/fengshen/examples/ubert/example.py)。你只需要将我们数据预处理成为我们定义的格式，即可使用简单的几行代码完成模型的训练和推理。我们是复用 pytorch-lightning 的 trainer 。在训练时，可以直接传入 trainer 的参数，此外我们还定义了一些其他参数。常用的参数如下：
-
-
-```sh
---pretrained_model_path       #预训练模型的路径，默认
---load_checkpoints_path       #加载模型的路径，如果你finetune完，想加载模型进行预测可以传入这个参数
---batchsize                   #批次大小, 默认 8
---monitor                     #保存模型需要监控的变量，例如我们可监控 val_span_acc
---checkpoint_path             #模型保存的路径, 默认 ./checkpoint
---save_top_k                  #最多保存几个模型, 默认 3
---every_n_train_steps         #多少步保存一次模型, 默认 100
---learning_rate               #学习率, 默认 2e-5
---warmup                      #预热的概率, 默认 0.01
---default_root_dir            #模型日子默认输出路径
---gradient_clip_val           #梯度截断， 默认 0.25
---gpus                        #gpu 的数量
---check_val_every_n_epoch     #多少次验证一次， 默认 100
---max_epochs                  #多少个 epochs， 默认 5
---max_length                  #句子最大长度， 默认 512
---num_labels                  #训练每条样本最多取多少个label，超过则进行随机采样负样本， 默认 10
-```
-
-## 数据预处理示例
-
-整个模型的 Piplines 我们已经写好，所以为了方便，我们定义了数据格式。目前我们在预训练中主要含有一下几种任务类型
-
-| task_type | subtask_type   |
-|:---------:|:--------------:|
-| 分类任务  | 文本分类       |
-|           | 自然语言推理   |
-|           | 情感分析       |
-|           | 多项式阅读理解 |
-| 抽取任务  | 实体识别       |
-|           | 事件抽取       |
-|           | 抽取式阅读理解 |
-|           | 关系抽取       |
-
-### 分类任务
-
-#### 普通分类任务
-对于分类任务，我们把类别描述当作是 entity_type，我们主要关注 label 字段，label为 1 表示该该标签是正确的标签。如下面示例所示
-```json
-{
-	"task_type": "分类任务",
-	"subtask_type": "文本分类",
-	"text": "7000亿美元救市方案将成期市毒药",
-	"choices": [{
-		"entity_type": "一则股票新闻",
-		"label": 1,
-		"entity_list": []
-	}, {
-		"entity_type": "一则教育新闻",
-		"label": 0,
-		"entity_list": []
-	}, {
-		"entity_type": "一则科学新闻",
-		"label": 0,
-		"entity_list": []
-	}],
-	"id": 0
-}
-
-```
-
-#### 自然语言推理
-```json
-{
-	"task_type": "分类任务",
-	"subtask_type": "自然语言推理",
-	"text": "在白云的蓝天下，一个孩子伸手摸着停在草地上的一架飞机的螺旋桨。",
-	"choices": [{
-		"entity_type": "可以推断出：一个孩子正伸手摸飞机的螺旋桨。",
-		"label": 1,
-		"entity_list": []
-	}, {
-		"entity_type": "不能推断出：一个孩子正伸手摸飞机的螺旋桨。",
-		"label": 0,
-		"entity_list": []
-	}, {
-		"entity_type": "很难推断出：一个孩子正伸手摸飞机的螺旋桨。",
-		"label": 0,
-		"entity_list": []
-	}],
-	"id": 0
-}
-```
-
-
-#### 语义匹配
-
-```json
-{
-	"task_type": "分类任务",
-	"subtask_type": "语义匹配",
-	"text": "不要借了我是试试看能否操作的",
-	"choices": [{
-		"entity_type": "不能理解为：借款审核期间能否取消借款",
-		"label": 1,
-		"entity_list": []
-	}, {
-		"entity_type": "可以理解为：借款审核期间能否取消借款",
-		"label": 0,
-		"entity_list": []
-	}],
-	"id": 0
-}
-
-```
-
-### 抽取任务
-对于抽取任务，label 字段是无效的
-#### 实体识别
-```json
-{
-	"task_type": "抽取任务",
-	"subtask_type": "实体识别",
-	"text": "彭小军认为，国内银行现在走的是台湾的发卡模式，先通过跑马圈地再在圈的地里面选择客户，",
-	"choices": [{
-		"entity_type": "地址",
-		"label": 0,
-		"entity_list": [{
-			"entity_name": "台湾",
-			"entity_type": "地址",
-			"entity_idx": [
-				[15, 16]
-			]
-		}]
-	}{
-		"entity_type": "政府机构",
-		"label": 0,
-		"entity_list": []
-	}, {
-		"entity_type": "电影名称",
-		"label": 0,
-		"entity_list": []
-	}, {
-		"entity_type": "人物姓名",
-		"label": 0,
-		"entity_list": [{
-			"entity_name": "彭小军",
-			"entity_type": "人物姓名",
-			"entity_idx": [
-				[0, 2]
-			]
-		}]
-	},
-	"id": 0
-}
-
-```
-#### 事件抽取
-```json
-
-{
-	"task_type": "抽取任务",
-	"subtask_type": "事件抽取",
-	"text": "小米9价格首降，6GB+128GB跌了200，却不如红米新机值得买",
-	"choices": [{
-		"entity_type": "降价的时间",
-		"label": 0,
-		"entity_list": []
-	}, {
-		"entity_type": "降价的降价方",
-		"label": 0,
-		"entity_list": []
-	}, {
-		"entity_type": "降价的降价物",
-		"label": 0,
-		"entity_list": [{
-			"entity_name": "小米9",
-			"entity_type": "降价的降价物",
-			"entity_idx": [
-				[0, 2]
-			]
-		}, {
-			"entity_name": "小米9",
-			"entity_type": "降价的降价物",
-			"entity_idx": [
-				[0, 2]
-			]
-		}]
-	}, {
-		"entity_type": "降价的降价幅度",
-		"label": 0,
-		"entity_list": []
-	}],
-	"id": 0
-}
-```
-#### 抽取式阅读理解
-
-```json
-{
-	"task_type": "抽取任务",
-	"subtask_type": "抽取式阅读理解",
-	"text": "截至2014年7月1日，圣地亚哥人口估计为1381069人，是美国第八大城市，加利福尼亚州第二大城市。它是圣迭戈-蒂华纳城市群的一部分，是美国与底特律-温莎之后的第二大跨境城市群，人口4922723。圣地亚哥是加州的出生地，以全年温和的气候、天然的深水港、广阔的海滩、与美国海军的长期联系以及最近作为医疗和生物技术发展中心而闻名。",
-	"choices": [{
-		"entity_type": "除了医疗保健，圣迭戈哪个就业部门已经强势崛起？",
-		"label": 0,
-		"entity_list": [{
-			"entity_name": "生物技术发展",
-			"entity_idx": [
-				[153, 158]
-			]
-		}]
-	}, {
-		"entity_type": "在所有的军事部门中，哪一个在圣地亚哥的存在最为强大？",
-		"label": 0,
-		"entity_list": [{
-			"entity_name": "美国海军",
-			"entity_idx": [
-				[135, 138]
-			]
-		}]
-	}, {
-		"entity_type": "在美国十大城市中，圣迭戈排名哪一位？",
-		"label": 0,
-		"entity_list": [{
-			"entity_name": "第八",
-			"entity_idx": [
-				[33, 34]
-			]
-		}]
-	}],
-	"id": 0
-}
-```
-
diff --git a/spaces/sklkd93/CodeFormer/CodeFormer/basicsr/utils/realesrgan_utils.py b/spaces/sklkd93/CodeFormer/CodeFormer/basicsr/utils/realesrgan_utils.py
deleted file mode 100644
index a2a5ab3b787212e4731e2a91b1493430a4a3664e..0000000000000000000000000000000000000000
--- a/spaces/sklkd93/CodeFormer/CodeFormer/basicsr/utils/realesrgan_utils.py
+++ /dev/null
@@ -1,299 +0,0 @@
-import cv2
-import math
-import numpy as np
-import os
-import queue
-import threading
-import torch
-from basicsr.utils.download_util import load_file_from_url
-from torch.nn import functional as F
-
-# ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-
-class RealESRGANer():
-    """A helper class for upsampling images with RealESRGAN.
-
-    Args:
-        scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
-        model_path (str): The path to the pretrained model. It can be urls (will first download it automatically).
-        model (nn.Module): The defined network. Default: None.
-        tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
-            input images into tiles, and then process each of them. Finally, they will be merged into one image.
-            0 denotes for do not use tile. Default: 0.
-        tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
-        pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
-        half (float): Whether to use half precision during inference. Default: False.
-    """
-
-    def __init__(self,
-                 scale,
-                 model_path,
-                 model=None,
-                 tile=0,
-                 tile_pad=10,
-                 pre_pad=10,
-                 half=False,
-                 device=None,
-                 gpu_id=None):
-        self.scale = scale
-        self.tile_size = tile
-        self.tile_pad = tile_pad
-        self.pre_pad = pre_pad
-        self.mod_scale = None
-        self.half = half
-
-        # initialize model
-        if gpu_id:
-            self.device = torch.device(
-                f'cuda:{gpu_id}' if torch.cuda.is_available() else 'cpu') if device is None else device
-        else:
-            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device
-        # if the model_path starts with https, it will first download models to the folder: realesrgan/weights
-        if model_path.startswith('https://'):
-            model_path = load_file_from_url(
-                url=model_path, model_dir=os.path.join('weights/realesrgan'), progress=True, file_name=None)
-        loadnet = torch.load(model_path, map_location=torch.device('cpu'))
-        # prefer to use params_ema
-        if 'params_ema' in loadnet:
-            keyname = 'params_ema'
-        else:
-            keyname = 'params'
-        model.load_state_dict(loadnet[keyname], strict=True)
-        model.eval()
-        self.model = model.to(self.device)
-        if self.half:
-            self.model = self.model.half()
-
-    def pre_process(self, img):
-        """Pre-process, such as pre-pad and mod pad, so that the images can be divisible
-        """
-        img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
-        self.img = img.unsqueeze(0).to(self.device)
-        if self.half:
-            self.img = self.img.half()
-
-        # pre_pad
-        if self.pre_pad != 0:
-            self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
-        # mod pad for divisible borders
-        if self.scale == 2:
-            self.mod_scale = 2
-        elif self.scale == 1:
-            self.mod_scale = 4
-        if self.mod_scale is not None:
-            self.mod_pad_h, self.mod_pad_w = 0, 0
-            _, _, h, w = self.img.size()
-            if (h % self.mod_scale != 0):
-                self.mod_pad_h = (self.mod_scale - h % self.mod_scale)
-            if (w % self.mod_scale != 0):
-                self.mod_pad_w = (self.mod_scale - w % self.mod_scale)
-            self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')
-
-    def process(self):
-        # model inference
-        self.output = self.model(self.img)
-
-    def tile_process(self):
-        """It will first crop input images to tiles, and then process each tile.
-        Finally, all the processed tiles are merged into one images.
-
-        Modified from: https://github.com/ata4/esrgan-launcher
-        """
-        batch, channel, height, width = self.img.shape
-        output_height = height * self.scale
-        output_width = width * self.scale
-        output_shape = (batch, channel, output_height, output_width)
-
-        # start with black image
-        self.output = self.img.new_zeros(output_shape)
-        tiles_x = math.ceil(width / self.tile_size)
-        tiles_y = math.ceil(height / self.tile_size)
-
-        # loop over all tiles
-        for y in range(tiles_y):
-            for x in range(tiles_x):
-                # extract tile from input image
-                ofs_x = x * self.tile_size
-                ofs_y = y * self.tile_size
-                # input tile area on total image
-                input_start_x = ofs_x
-                input_end_x = min(ofs_x + self.tile_size, width)
-                input_start_y = ofs_y
-                input_end_y = min(ofs_y + self.tile_size, height)
-
-                # input tile area on total image with padding
-                input_start_x_pad = max(input_start_x - self.tile_pad, 0)
-                input_end_x_pad = min(input_end_x + self.tile_pad, width)
-                input_start_y_pad = max(input_start_y - self.tile_pad, 0)
-                input_end_y_pad = min(input_end_y + self.tile_pad, height)
-
-                # input tile dimensions
-                input_tile_width = input_end_x - input_start_x
-                input_tile_height = input_end_y - input_start_y
-                tile_idx = y * tiles_x + x + 1
-                input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]
-
-                # upscale tile
-                try:
-                    with torch.no_grad():
-                        output_tile = self.model(input_tile)
-                except RuntimeError as error:
-                    print('Error', error)
-                # print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')
-
-                # output tile area on total image
-                output_start_x = input_start_x * self.scale
-                output_end_x = input_end_x * self.scale
-                output_start_y = input_start_y * self.scale
-                output_end_y = input_end_y * self.scale
-
-                # output tile area without padding
-                output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
-                output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
-                output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
-                output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
-
-                # put tile into output image
-                self.output[:, :, output_start_y:output_end_y,
-                            output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
-                                                                       output_start_x_tile:output_end_x_tile]
-
-    def post_process(self):
-        # remove extra pad
-        if self.mod_scale is not None:
-            _, _, h, w = self.output.size()
-            self.output = self.output[:, :, 0:h - self.mod_pad_h * self.scale, 0:w - self.mod_pad_w * self.scale]
-        # remove prepad
-        if self.pre_pad != 0:
-            _, _, h, w = self.output.size()
-            self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale]
-        return self.output
-
-    @torch.no_grad()
-    def enhance(self, img, outscale=None, alpha_upsampler='realesrgan'):
-        h_input, w_input = img.shape[0:2]
-        # img: numpy
-        img = img.astype(np.float32)
-        if np.max(img) > 256:  # 16-bit image
-            max_range = 65535
-            print('\tInput is a 16-bit image')
-        else:
-            max_range = 255
-        img = img / max_range
-        if len(img.shape) == 2:  # gray image
-            img_mode = 'L'
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
-        elif img.shape[2] == 4:  # RGBA image with alpha channel
-            img_mode = 'RGBA'
-            alpha = img[:, :, 3]
-            img = img[:, :, 0:3]
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-            if alpha_upsampler == 'realesrgan':
-                alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
-        else:
-            img_mode = 'RGB'
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
-        # ------------------- process image (without the alpha channel) ------------------- #
-        try:
-            with torch.no_grad():
-                self.pre_process(img)
-                if self.tile_size > 0:
-                    self.tile_process()
-                else:
-                    self.process()
-                output_img_t = self.post_process()
-                output_img = output_img_t.data.squeeze().float().cpu().clamp_(0, 1).numpy()
-                output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
-                if img_mode == 'L':
-                    output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
-            del output_img_t
-            torch.cuda.empty_cache()        
-        except RuntimeError as error:
-            print(f"Failed inference for RealESRGAN: {error}")
-
-        # ------------------- process the alpha channel if necessary ------------------- #
-        if img_mode == 'RGBA':
-            if alpha_upsampler == 'realesrgan':
-                self.pre_process(alpha)
-                if self.tile_size > 0:
-                    self.tile_process()
-                else:
-                    self.process()
-                output_alpha = self.post_process()
-                output_alpha = output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
-                output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
-                output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
-            else:  # use the cv2 resize for alpha channel
-                h, w = alpha.shape[0:2]
-                output_alpha = cv2.resize(alpha, (w * self.scale, h * self.scale), interpolation=cv2.INTER_LINEAR)
-
-            # merge the alpha channel
-            output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
-            output_img[:, :, 3] = output_alpha
-
-        # ------------------------------ return ------------------------------ #
-        if max_range == 65535:  # 16-bit image
-            output = (output_img * 65535.0).round().astype(np.uint16)
-        else:
-            output = (output_img * 255.0).round().astype(np.uint8)
-
-        if outscale is not None and outscale != float(self.scale):
-            output = cv2.resize(
-                output, (
-                    int(w_input * outscale),
-                    int(h_input * outscale),
-                ), interpolation=cv2.INTER_LANCZOS4)
-
-        return output, img_mode
-
-
-class PrefetchReader(threading.Thread):
-    """Prefetch images.
-
-    Args:
-        img_list (list[str]): A image list of image paths to be read.
-        num_prefetch_queue (int): Number of prefetch queue.
-    """
-
-    def __init__(self, img_list, num_prefetch_queue):
-        super().__init__()
-        self.que = queue.Queue(num_prefetch_queue)
-        self.img_list = img_list
-
-    def run(self):
-        for img_path in self.img_list:
-            img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
-            self.que.put(img)
-
-        self.que.put(None)
-
-    def __next__(self):
-        next_item = self.que.get()
-        if next_item is None:
-            raise StopIteration
-        return next_item
-
-    def __iter__(self):
-        return self
-
-
-class IOConsumer(threading.Thread):
-
-    def __init__(self, opt, que, qid):
-        super().__init__()
-        self._queue = que
-        self.qid = qid
-        self.opt = opt
-
-    def run(self):
-        while True:
-            msg = self._queue.get()
-            if isinstance(msg, str) and msg == 'quit':
-                break
-
-            output = msg['output']
-            save_path = msg['save_path']
-            cv2.imwrite(save_path, output)
-        print(f'IO worker {self.qid} is done.')
\ No newline at end of file
diff --git a/spaces/sklkd93/CodeFormer/CodeFormer/facelib/detection/yolov5face/utils/general.py b/spaces/sklkd93/CodeFormer/CodeFormer/facelib/detection/yolov5face/utils/general.py
deleted file mode 100644
index 1c8e14f56a107ec3a4269c382cfc5168ad780ffc..0000000000000000000000000000000000000000
--- a/spaces/sklkd93/CodeFormer/CodeFormer/facelib/detection/yolov5face/utils/general.py
+++ /dev/null
@@ -1,271 +0,0 @@
-import math
-import time
-
-import numpy as np
-import torch
-import torchvision
-
-
-def check_img_size(img_size, s=32):
-    # Verify img_size is a multiple of stride s
-    new_size = make_divisible(img_size, int(s))  # ceil gs-multiple
-    # if new_size != img_size:
-    #     print(f"WARNING: --img-size {img_size:g} must be multiple of max stride {s:g}, updating to {new_size:g}")
-    return new_size
-
-
-def make_divisible(x, divisor):
-    # Returns x evenly divisible by divisor
-    return math.ceil(x / divisor) * divisor
-
-
-def xyxy2xywh(x):
-    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
-    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center
-    y[:, 2] = x[:, 2] - x[:, 0]  # width
-    y[:, 3] = x[:, 3] - x[:, 1]  # height
-    return y
-
-
-def xywh2xyxy(x):
-    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
-    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
-    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
-    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
-    return y
-
-
-def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
-    # Rescale coords (xyxy) from img1_shape to img0_shape
-    if ratio_pad is None:  # calculate from img0_shape
-        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
-        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
-    else:
-        gain = ratio_pad[0][0]
-        pad = ratio_pad[1]
-
-    coords[:, [0, 2]] -= pad[0]  # x padding
-    coords[:, [1, 3]] -= pad[1]  # y padding
-    coords[:, :4] /= gain
-    clip_coords(coords, img0_shape)
-    return coords
-
-
-def clip_coords(boxes, img_shape):
-    # Clip bounding xyxy bounding boxes to image shape (height, width)
-    boxes[:, 0].clamp_(0, img_shape[1])  # x1
-    boxes[:, 1].clamp_(0, img_shape[0])  # y1
-    boxes[:, 2].clamp_(0, img_shape[1])  # x2
-    boxes[:, 3].clamp_(0, img_shape[0])  # y2
-
-
-def box_iou(box1, box2):
-    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
-    """
-    Return intersection-over-union (Jaccard index) of boxes.
-    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
-    Arguments:
-        box1 (Tensor[N, 4])
-        box2 (Tensor[M, 4])
-    Returns:
-        iou (Tensor[N, M]): the NxM matrix containing the pairwise
-            IoU values for every element in boxes1 and boxes2
-    """
-
-    def box_area(box):
-        return (box[2] - box[0]) * (box[3] - box[1])
-
-    area1 = box_area(box1.T)
-    area2 = box_area(box2.T)
-
-    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
-    return inter / (area1[:, None] + area2 - inter)
-
-
-def non_max_suppression_face(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
-    """Performs Non-Maximum Suppression (NMS) on inference results
-    Returns:
-         detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
-    """
-
-    nc = prediction.shape[2] - 15  # number of classes
-    xc = prediction[..., 4] > conf_thres  # candidates
-
-    # Settings
-    # (pixels) maximum box width and height
-    max_wh = 4096
-    time_limit = 10.0  # seconds to quit after
-    redundant = True  # require redundant detections
-    multi_label = nc > 1  # multiple labels per box (adds 0.5ms/img)
-    merge = False  # use merge-NMS
-
-    t = time.time()
-    output = [torch.zeros((0, 16), device=prediction.device)] * prediction.shape[0]
-    for xi, x in enumerate(prediction):  # image index, image inference
-        # Apply constraints
-        x = x[xc[xi]]  # confidence
-
-        # Cat apriori labels if autolabelling
-        if labels and len(labels[xi]):
-            label = labels[xi]
-            v = torch.zeros((len(label), nc + 15), device=x.device)
-            v[:, :4] = label[:, 1:5]  # box
-            v[:, 4] = 1.0  # conf
-            v[range(len(label)), label[:, 0].long() + 15] = 1.0  # cls
-            x = torch.cat((x, v), 0)
-
-        # If none remain process next image
-        if not x.shape[0]:
-            continue
-
-        # Compute conf
-        x[:, 15:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
-
-        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
-        box = xywh2xyxy(x[:, :4])
-
-        # Detections matrix nx6 (xyxy, conf, landmarks, cls)
-        if multi_label:
-            i, j = (x[:, 15:] > conf_thres).nonzero(as_tuple=False).T
-            x = torch.cat((box[i], x[i, j + 15, None], x[:, 5:15], j[:, None].float()), 1)
-        else:  # best class only
-            conf, j = x[:, 15:].max(1, keepdim=True)
-            x = torch.cat((box, conf, x[:, 5:15], j.float()), 1)[conf.view(-1) > conf_thres]
-
-        # Filter by class
-        if classes is not None:
-            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
-
-        # If none remain process next image
-        n = x.shape[0]  # number of boxes
-        if not n:
-            continue
-
-        # Batched NMS
-        c = x[:, 15:16] * (0 if agnostic else max_wh)  # classes
-        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
-        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
-
-        if merge and (1 < n < 3e3):  # Merge NMS (boxes merged using weighted mean)
-            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
-            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
-            weights = iou * scores[None]  # box weights
-            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
-            if redundant:
-                i = i[iou.sum(1) > 1]  # require redundancy
-
-        output[xi] = x[i]
-        if (time.time() - t) > time_limit:
-            break  # time limit exceeded
-
-    return output
-
-
-def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
-    """Performs Non-Maximum Suppression (NMS) on inference results
-
-    Returns:
-         detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
-    """
-
-    nc = prediction.shape[2] - 5  # number of classes
-    xc = prediction[..., 4] > conf_thres  # candidates
-
-    # Settings
-    # (pixels) maximum box width and height
-    max_wh = 4096
-    time_limit = 10.0  # seconds to quit after
-    redundant = True  # require redundant detections
-    multi_label = nc > 1  # multiple labels per box (adds 0.5ms/img)
-    merge = False  # use merge-NMS
-
-    t = time.time()
-    output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
-    for xi, x in enumerate(prediction):  # image index, image inference
-        x = x[xc[xi]]  # confidence
-
-        # Cat apriori labels if autolabelling
-        if labels and len(labels[xi]):
-            label_id = labels[xi]
-            v = torch.zeros((len(label_id), nc + 5), device=x.device)
-            v[:, :4] = label_id[:, 1:5]  # box
-            v[:, 4] = 1.0  # conf
-            v[range(len(label_id)), label_id[:, 0].long() + 5] = 1.0  # cls
-            x = torch.cat((x, v), 0)
-
-        # If none remain process next image
-        if not x.shape[0]:
-            continue
-
-        # Compute conf
-        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
-
-        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
-        box = xywh2xyxy(x[:, :4])
-
-        # Detections matrix nx6 (xyxy, conf, cls)
-        if multi_label:
-            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
-            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
-        else:  # best class only
-            conf, j = x[:, 5:].max(1, keepdim=True)
-            x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
-
-        # Filter by class
-        if classes is not None:
-            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
-
-        # Check shape
-        n = x.shape[0]  # number of boxes
-        if not n:  # no boxes
-            continue
-
-        x = x[x[:, 4].argsort(descending=True)]  # sort by confidence
-
-        # Batched NMS
-        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
-        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
-        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
-        if merge and (1 < n < 3e3):  # Merge NMS (boxes merged using weighted mean)
-            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
-            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
-            weights = iou * scores[None]  # box weights
-            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
-            if redundant:
-                i = i[iou.sum(1) > 1]  # require redundancy
-
-        output[xi] = x[i]
-        if (time.time() - t) > time_limit:
-            print(f"WARNING: NMS time limit {time_limit}s exceeded")
-            break  # time limit exceeded
-
-    return output
-
-
-def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None):
-    # Rescale coords (xyxy) from img1_shape to img0_shape
-    if ratio_pad is None:  # calculate from img0_shape
-        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
-        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
-    else:
-        gain = ratio_pad[0][0]
-        pad = ratio_pad[1]
-
-    coords[:, [0, 2, 4, 6, 8]] -= pad[0]  # x padding
-    coords[:, [1, 3, 5, 7, 9]] -= pad[1]  # y padding
-    coords[:, :10] /= gain
-    coords[:, 0].clamp_(0, img0_shape[1])  # x1
-    coords[:, 1].clamp_(0, img0_shape[0])  # y1
-    coords[:, 2].clamp_(0, img0_shape[1])  # x2
-    coords[:, 3].clamp_(0, img0_shape[0])  # y2
-    coords[:, 4].clamp_(0, img0_shape[1])  # x3
-    coords[:, 5].clamp_(0, img0_shape[0])  # y3
-    coords[:, 6].clamp_(0, img0_shape[1])  # x4
-    coords[:, 7].clamp_(0, img0_shape[0])  # y4
-    coords[:, 8].clamp_(0, img0_shape[1])  # x5
-    coords[:, 9].clamp_(0, img0_shape[0])  # y5
-    return coords
diff --git a/spaces/sohojoe/project_charles/local_speaker_service.py b/spaces/sohojoe/project_charles/local_speaker_service.py
deleted file mode 100644
index 68afdd5faf88f8f6161b2f0a8ce59f636568bbcf..0000000000000000000000000000000000000000
--- a/spaces/sohojoe/project_charles/local_speaker_service.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import subprocess
-from threading import Thread
-from queue import Queue, Empty
-from typing import Iterator
-import threading
-import time
-
-class LocalSpeakerService:
-    def __init__(self):
-        self.queue = Queue()
-        self._is_running = threading.Event()
-        self._is_running.set()
-        self.mpv_process = None
-        self.thread = Thread(target=self._process_audio_streams)
-        self.thread.start()
-
-    def add_audio_stream(self, audio_stream: Iterator[bytes]):
-        if self._is_running.is_set():
-            self.queue.put(audio_stream)
-
-    def _process_audio_streams(self):
-        self._start_mpv()
-        while self._is_running.is_set() or not self.queue.empty():
-            try:
-                audio_stream = self.queue.get(timeout=1)
-            except Empty:
-                continue
-            self._stream(audio_stream)
-        self._close_mpv()
-
-    def _stream(self, audio_stream: Iterator[bytes]):
-        for chunk in audio_stream:
-            if chunk is not None:
-                self.mpv_process.stdin.write(chunk)
-                self.mpv_process.stdin.flush()
-
-    def _start_mpv(self):
-        mpv_command = ["mpv", "--no-cache", "--no-terminal", "--", "fd://0"]
-        self.mpv_process = subprocess.Popen(
-            mpv_command,
-            stdin=subprocess.PIPE,
-            stdout=subprocess.DEVNULL,
-            stderr=subprocess.DEVNULL,
-        )
-
-    def _close_mpv(self):
-        if self.mpv_process.stdin:
-            self.mpv_process.stdin.close()
-        self.mpv_process.wait()
-
-    def close(self):
-        self._is_running.clear()
-        while not self.queue.empty():  # Wait until the queue is empty
-            time.sleep(0.1)
-        self.thread.join()
diff --git a/spaces/sohojoe/soho-clip-embeddings-explorer/experimental/fast_inference.py b/spaces/sohojoe/soho-clip-embeddings-explorer/experimental/fast_inference.py
deleted file mode 100644
index 250d0af20511bb557f5616221489b348953a8beb..0000000000000000000000000000000000000000
--- a/spaces/sohojoe/soho-clip-embeddings-explorer/experimental/fast_inference.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import time
-import numpy as np
-import torch
-from PIL import Image
-import ray
-from ray import serve
-from clip_retrieval.load_clip import load_clip, get_tokenizer 
-# from clip_retrieval.clip_client import ClipClient, Modality
-
-
-
-class CLIPModel:
-    def __init__(self):
-        self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
-        self._test_image_url = "https://static.wixstatic.com/media/4d6b49_42b9435ce1104008b1b5f7a3c9bfcd69~mv2.jpg/v1/fill/w_454,h_333,fp_0.50_0.50,q_90/4d6b49_42b9435ce1104008b1b5f7a3c9bfcd69~mv2.jpg"
-        self._clip_model="ViT-L/14"
-        self._clip_model_id ="laion5B-L-14"
-
-        self.model, self.preprocess = load_clip(self._clip_model, use_jit=True, device=self.device)
-        self.tokenizer = get_tokenizer(self._clip_model)
-
-        print ("using device", self.device)
-
-
-    def test_to_embeddings(self, prompt):
-        text = self.tokenizer([prompt]).to(self.device)
-        with torch.no_grad():
-            prompt_embededdings = self.model.encode_text(text)
-        prompt_embededdings /= prompt_embededdings.norm(dim=-1, keepdim=True)
-        return(prompt_embededdings)
-
-    def image_to_embeddings(self, input_im):
-        input_im = Image.fromarray(input_im)
-        prepro = self.preprocess(input_im).unsqueeze(0).to(self.device)
-        with torch.no_grad():
-            image_embeddings = self.model.encode_image(prepro)
-        image_embeddings /= image_embeddings.norm(dim=-1, keepdim=True)
-        return(image_embeddings)
-
-    def preprocessed_image_to_emdeddings(self, prepro):
-        with torch.no_grad():
-            image_embeddings = self.model.encode_image(prepro)
-        image_embeddings /= image_embeddings.norm(dim=-1, keepdim=True)
-        return(image_embeddings)
-
-    # simple regression test
-    def regression_test(self):
-        text_embeddings = self.test_to_embeddings("Howdy!")
-        print("text embeddings", text_embeddings)
-
-        # download image from url
-        import requests
-        from io import BytesIO
-        response = requests.get(self._test_image_url)
-        input_image = Image.open(BytesIO(response.content))
-        input_image = input_image.convert('RGB')
-        # convert image to numpy array
-        input_image = np.array(input_image)
-        image_embeddings = self.image_to_embeddings(input_image)
-        print("image embeddings", image_embeddings)
-
-        input_im = Image.fromarray(input_image)
-        prepro = self.preprocess(input_im).unsqueeze(0).to(self.device)
-        image_embeddings = self.preprocessed_image_to_emdeddings(prepro)
-        print("image embeddings", image_embeddings)
-
-# regression test
-test_instance = CLIPModel()
-test_instance.regression_test()
-
-ray.init()
-serve.start()
-# Register the model with Ray Serve
-serve.create_backend("clip_model", CLIPModel)
-serve.create_endpoint("clip_model", backend="clip_model", route="/clip_model")
-
-
-# You can now call the endpoint with your input
-import requests
-
-input_prompt = "Howdy!"
-response = requests.get("http://localhost:8000/clip_model", json={"prompt": input_prompt})
-print(response.json())
-
-
diff --git a/spaces/spark-ds549/fal2022-videoanalysis-v2/visualization.py b/spaces/spark-ds549/fal2022-videoanalysis-v2/visualization.py
deleted file mode 100644
index 5641ad496f634c0840fd9815a5b167c18c926541..0000000000000000000000000000000000000000
--- a/spaces/spark-ds549/fal2022-videoanalysis-v2/visualization.py
+++ /dev/null
@@ -1,706 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# Note: This file has been barrowed from facebookresearch/slowfast repo. And it is used to add the bounding boxes and predictions to the frame. 
-# TODO: Migrate this into the core PyTorchVideo libarary.
-from __future__ import annotations
-
-import itertools
-# import logging
-from types import SimpleNamespace
-from typing import Dict, List, Optional, Tuple, Union
-
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-from detectron2.utils.visualizer import Visualizer
-
-
-# logger = logging.getLogger(__name__)
-
-
-def _create_text_labels(
-    classes: List[int],
-    scores: List[float],
-    class_names: List[str],
-    ground_truth: bool = False,
-) -> List[str]:
-    """
-    Create text labels.
-    Args:
-        classes (list[int]): a list of class ids for each example.
-        scores (list[float] or None): list of scores for each example.
-        class_names (list[str]): a list of class names, ordered by their ids.
-        ground_truth (bool): whether the labels are ground truth.
-    Returns:
-        labels (list[str]): formatted text labels.
-    """
-    try:
-        labels = [class_names.get(c, "n/a") for c in classes]
-    except IndexError:
-        # logger.error("Class indices get out of range: {}".format(classes))
-        return None
-
-    if ground_truth:
-        labels = ["[{}] {}".format("GT", label) for label in labels]
-    elif scores is not None:
-        assert len(classes) == len(scores)
-        labels = ["[{:.2f}] {}".format(s, label) for s, label in zip(scores, labels)]
-    return labels
-
-
-class ImgVisualizer(Visualizer):
-    def __init__(
-        self, img_rgb: torch.Tensor, meta: Optional[SimpleNamespace] = None, **kwargs
-    ) -> None:
-        """
-        See https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/visualizer.py
-        for more details.
-        Args:
-            img_rgb: a tensor or 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].
-            meta (MetadataCatalog): image metadata.
-                See https://github.com/facebookresearch/detectron2/blob/81d5a87763bfc71a492b5be89b74179bd7492f6b/detectron2/data/catalog.py#L90
-        """
-        super(ImgVisualizer, self).__init__(img_rgb, meta, **kwargs)
-
-    def draw_text(
-        self,
-        text: str,
-        position: List[int],
-        *,
-        font_size: Optional[int] = None,
-        color: str = "w",
-        horizontal_alignment: str = "center",
-        vertical_alignment: str = "bottom",
-        box_facecolor: str = "black",
-        alpha: float = 0.5,
-    ) -> None:
-        """
-        Draw text at the specified position.
-        Args:
-            text (str): the text to draw on image.
-            position (list of 2 ints): the x,y coordinate to place the text.
-            font_size (Optional[int]): font of the text. If not provided, a font size
-                proportional to the image width is calculated and used.
-            color (str): color of the text. Refer to `matplotlib.colors` for full list
-                of formats that are accepted.
-            horizontal_alignment (str): see `matplotlib.text.Text`.
-            vertical_alignment (str): see `matplotlib.text.Text`.
-            box_facecolor (str): color of the box wrapped around the text. Refer to
-                `matplotlib.colors` for full list of formats that are accepted.
-            alpha (float): transparency level of the box.
-        """
-        if not font_size:
-            font_size = self._default_font_size
-        x, y = position
-        self.output.ax.text(
-            x,
-            y,
-            text,
-            size=font_size * self.output.scale,
-            family="monospace",
-            bbox={
-                "facecolor": box_facecolor,
-                "alpha": alpha,
-                "pad": 0.7,
-                "edgecolor": "none",
-            },
-            verticalalignment=vertical_alignment,
-            horizontalalignment=horizontal_alignment,
-            color=color,
-            zorder=10,
-        )
-
-    def draw_multiple_text(
-        self,
-        text_ls: List[str],
-        box_coordinate: torch.Tensor,
-        *,
-        top_corner: bool = True,
-        font_size: Optional[int] = None,
-        color: str = "w",
-        box_facecolors: str = "black",
-        alpha: float = 0.5,
-    ) -> None:
-        """
-        Draw a list of text labels for some bounding box on the image.
-        Args:
-            text_ls (list of strings): a list of text labels.
-            box_coordinate (tensor): shape (4,). The (x_left, y_top, x_right, y_bottom)
-                coordinates of the box.
-            top_corner (bool): If True, draw the text labels at (x_left, y_top) of the box.
-                Else, draw labels at (x_left, y_bottom).
-            font_size (Optional[int]): font of the text. If not provided, a font size
-                proportional to the image width is calculated and used.
-            color (str): color of the text. Refer to `matplotlib.colors` for full list
-                of formats that are accepted.
-            box_facecolors (str): colors of the box wrapped around the text. Refer to
-                `matplotlib.colors` for full list of formats that are accepted.
-            alpha (float): transparency level of the box.
-        """
-        if not isinstance(box_facecolors, list):
-            box_facecolors = [box_facecolors] * len(text_ls)
-        assert len(box_facecolors) == len(
-            text_ls
-        ), "Number of colors provided is not equal to the number of text labels."
-        if not font_size:
-            font_size = self._default_font_size
-        text_box_width = font_size + font_size // 2
-        # If the texts does not fit in the assigned location,
-        # we split the text and draw it in another place.
-        if top_corner:
-            num_text_split = self._align_y_top(
-                box_coordinate, len(text_ls), text_box_width
-            )
-            y_corner = 1
-        else:
-            num_text_split = len(text_ls) - self._align_y_bottom(
-                box_coordinate, len(text_ls), text_box_width
-            )
-            y_corner = 3
-
-        text_color_sorted = sorted(
-            zip(text_ls, box_facecolors), key=lambda x: x[0], reverse=True
-        )
-        if len(text_color_sorted) != 0:
-            text_ls, box_facecolors = zip(*text_color_sorted)
-        else:
-            text_ls, box_facecolors = [], []
-        text_ls, box_facecolors = list(text_ls), list(box_facecolors)
-        self.draw_multiple_text_upward(
-            text_ls[:num_text_split][::-1],
-            box_coordinate,
-            y_corner=y_corner,
-            font_size=font_size,
-            color=color,
-            box_facecolors=box_facecolors[:num_text_split][::-1],
-            alpha=alpha,
-        )
-        self.draw_multiple_text_downward(
-            text_ls[num_text_split:],
-            box_coordinate,
-            y_corner=y_corner,
-            font_size=font_size,
-            color=color,
-            box_facecolors=box_facecolors[num_text_split:],
-            alpha=alpha,
-        )
-
-    def draw_multiple_text_upward(
-        self,
-        text_ls: List[str],
-        box_coordinate: torch.Tensor,
-        *,
-        y_corner: int = 1,
-        font_size: Optional[int] = None,
-        color: str = "w",
-        box_facecolors: str = "black",
-        alpha: float = 0.5,
-    ) -> None:
-        """
-        Draw a list of text labels for some bounding box on the image in upward direction.
-        The next text label will be on top of the previous one.
-        Args:
-            text_ls (list of strings): a list of text labels.
-            box_coordinate (tensor): shape (4,). The (x_left, y_top, x_right, y_bottom)
-                coordinates of the box.
-            y_corner (int): Value of either 1 or 3. Indicate the index of the y-coordinate of
-                the box to draw labels around.
-            font_size (Optional[int]): font of the text. If not provided, a font size
-                proportional to the image width is calculated and used.
-            color (str): color of the text. Refer to `matplotlib.colors` for full list
-                of formats that are accepted.
-            box_facecolors (str or list of strs): colors of the box wrapped around the
-                text. Refer to `matplotlib.colors` for full list of formats that
-                are accepted.
-            alpha (float): transparency level of the box.
-        """
-        if not isinstance(box_facecolors, list):
-            box_facecolors = [box_facecolors] * len(text_ls)
-        assert len(box_facecolors) == len(
-            text_ls
-        ), "Number of colors provided is not equal to the number of text labels."
-
-        assert y_corner in [1, 3], "Y_corner must be either 1 or 3"
-        if not font_size:
-            font_size = self._default_font_size
-
-        x, horizontal_alignment = self._align_x_coordinate(box_coordinate)
-        y = box_coordinate[y_corner].item()
-        for i, text in enumerate(text_ls):
-            self.draw_text(
-                text,
-                (x, y),
-                font_size=font_size,
-                color=color,
-                horizontal_alignment=horizontal_alignment,
-                vertical_alignment="bottom",
-                box_facecolor=box_facecolors[i],
-                alpha=alpha,
-            )
-            y -= font_size + font_size // 2
-
-    def draw_multiple_text_downward(
-        self,
-        text_ls: List[str],
-        box_coordinate: torch.Tensor,
-        *,
-        y_corner: int = 1,
-        font_size: Optional[int] = None,
-        color: str = "w",
-        box_facecolors: str = "black",
-        alpha: float = 0.5,
-    ) -> None:
-        """
-        Draw a list of text labels for some bounding box on the image in downward direction.
-        The next text label will be below the previous one.
-        Args:
-            text_ls (list of strings): a list of text labels.
-            box_coordinate (tensor): shape (4,). The (x_left, y_top, x_right, y_bottom)
-                coordinates of the box.
-            y_corner (int): Value of either 1 or 3. Indicate the index of the y-coordinate of
-                the box to draw labels around.
-            font_size (Optional[int]): font of the text. If not provided, a font size
-                proportional to the image width is calculated and used.
-            color (str): color of the text. Refer to `matplotlib.colors` for full list
-                of formats that are accepted.
-            box_facecolors (str): colors of the box wrapped around the text. Refer to
-                `matplotlib.colors` for full list of formats that are accepted.
-            alpha (float): transparency level of the box.
-        """
-        if not isinstance(box_facecolors, list):
-            box_facecolors = [box_facecolors] * len(text_ls)
-        assert len(box_facecolors) == len(
-            text_ls
-        ), "Number of colors provided is not equal to the number of text labels."
-
-        assert y_corner in [1, 3], "Y_corner must be either 1 or 3"
-        if not font_size:
-            font_size = self._default_font_size
-
-        x, horizontal_alignment = self._align_x_coordinate(box_coordinate)
-        y = box_coordinate[y_corner].item()
-        for i, text in enumerate(text_ls):
-            self.draw_text(
-                text,
-                (x, y),
-                font_size=font_size,
-                color=color,
-                horizontal_alignment=horizontal_alignment,
-                vertical_alignment="top",
-                box_facecolor=box_facecolors[i],
-                alpha=alpha,
-            )
-            y += font_size + font_size // 2
-
-    def _align_x_coordinate(self, box_coordinate: torch.Tensor) -> Tuple[float, str]:
-        """
-        Choose an x-coordinate from the box to make sure the text label
-        does not go out of frames. By default, the left x-coordinate is
-        chosen and text is aligned left. If the box is too close to the
-        right side of the image, then the right x-coordinate is chosen
-        instead and the text is aligned right.
-        Args:
-            box_coordinate (array-like): shape (4,). The (x_left, y_top, x_right, y_bottom)
-            coordinates of the box.
-        Returns:
-            x_coordinate (float): the chosen x-coordinate.
-            alignment (str): whether to align left or right.
-        """
-        # If the x-coordinate is greater than 5/6 of the image width,
-        # then we align test to the right of the box. This is
-        # chosen by heuristics.
-        if box_coordinate[0] > (self.output.width * 5) // 6:
-            return box_coordinate[2], "right"
-
-        return box_coordinate[0], "left"
-
-    def _align_y_top(
-        self, box_coordinate: torch.Tensor, num_text: int, textbox_width: float
-    ) -> int:
-        """
-        Calculate the number of text labels to plot on top of the box
-        without going out of frames.
-        Args:
-            box_coordinate (array-like): shape (4,). The (x_left, y_top, x_right, y_bottom)
-            coordinates of the box.
-            num_text (int): the number of text labels to plot.
-            textbox_width (float): the width of the box wrapped around text label.
-        """
-        dist_to_top = box_coordinate[1]
-        num_text_top = dist_to_top // textbox_width
-
-        if isinstance(num_text_top, torch.Tensor):
-            num_text_top = int(num_text_top.item())
-
-        return min(num_text, num_text_top)
-
-    def _align_y_bottom(
-        self, box_coordinate: torch.Tensor, num_text: int, textbox_width: float
-    ) -> int:
-        """
-        Calculate the number of text labels to plot at the bottom of the box
-        without going out of frames.
-        Args:
-            box_coordinate (array-like): shape (4,). The (x_left, y_top, x_right, y_bottom)
-            coordinates of the box.
-            num_text (int): the number of text labels to plot.
-            textbox_width (float): the width of the box wrapped around text label.
-        """
-        dist_to_bottom = self.output.height - box_coordinate[3]
-        num_text_bottom = dist_to_bottom // textbox_width
-
-        if isinstance(num_text_bottom, torch.Tensor):
-            num_text_bottom = int(num_text_bottom.item())
-
-        return min(num_text, num_text_bottom)
-
-
-class VideoVisualizer:
-    def __init__(
-        self,
-        num_classes: int,
-        class_names: Dict,
-        top_k: int = 1,
-        colormap: str = "rainbow",
-        thres: float = 0.7,
-        lower_thres: float = 0.3,
-        common_class_names: Optional[List[str]] = None,
-        mode: str = "top-k",
-    ) -> None:
-        """
-        Args:
-            num_classes (int): total number of classes.
-            class_names (dict): Dict mapping classID to name.
-            top_k (int): number of top predicted classes to plot.
-            colormap (str): the colormap to choose color for class labels from.
-                See https://matplotlib.org/tutorials/colors/colormaps.html
-            thres (float): threshold for picking predicted classes to visualize.
-            lower_thres (Optional[float]): If `common_class_names` if given,
-                this `lower_thres` will be applied to uncommon classes and
-                `thres` will be applied to classes in `common_class_names`.
-            common_class_names (Optional[list of str]): list of common class names
-                to apply `thres`. Class names not included in `common_class_names` will
-                have `lower_thres` as a threshold. If None, all classes will have
-                `thres` as a threshold. This is helpful for model trained on
-                highly imbalanced dataset.
-            mode (str): Supported modes are {"top-k", "thres"}.
-                This is used for choosing predictions for visualization.
-        """
-        assert mode in ["top-k", "thres"], "Mode {} is not supported.".format(mode)
-        self.mode = mode
-        self.num_classes = num_classes
-        self.class_names = class_names
-        self.top_k = top_k
-        self.thres = thres
-        self.lower_thres = lower_thres
-
-        if mode == "thres":
-            self._get_thres_array(common_class_names=common_class_names)
-
-        self.color_map = plt.get_cmap(colormap)
-
-    def _get_color(self, class_id: int) -> List[float]:
-        """
-        Get color for a class id.
-        Args:
-            class_id (int): class id.
-        """
-        return self.color_map(class_id / self.num_classes)[:3]
-
-    def draw_one_frame(
-        self,
-        frame: Union[torch.Tensor, np.ndarray],
-        preds: Union[torch.Tensor, List[float]],
-        bboxes: Optional[torch.Tensor] = None,
-        alpha: float = 0.5,
-        text_alpha: float = 0.7,
-        ground_truth: bool = False,
-    ) -> np.ndarray:
-        """
-        Draw labels and bouding boxes for one image. By default, predicted
-        labels are drawn in the top left corner of the image or corresponding
-        bounding boxes. For ground truth labels (setting True for ground_truth flag),
-        labels will be drawn in the bottom left corner.
-        Args:
-            frame (array-like): a tensor or 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].
-            preds (tensor or list): If ground_truth is False, provide a float tensor of
-                shape (num_boxes, num_classes) that contains all of the confidence
-                scores of the model. For recognition task, input shape can be (num_classes,).
-                To plot true label (ground_truth is True), preds is a list contains int32
-                of the shape (num_boxes, true_class_ids) or (true_class_ids,).
-            bboxes (Optional[tensor]): shape (num_boxes, 4) that contains the coordinates
-                of the bounding boxes.
-            alpha (Optional[float]): transparency level of the bounding boxes.
-            text_alpha (Optional[float]): transparency level of the box wrapped around
-                text labels.
-            ground_truth (bool): whether the prodived bounding boxes are ground-truth.
-        Returns:
-            An image with bounding box annotations and corresponding bbox
-            labels plotted on it.
-        """
-        if isinstance(preds, torch.Tensor):
-            if preds.ndim == 1:
-                preds = preds.unsqueeze(0)
-            n_instances = preds.shape[0]
-        elif isinstance(preds, list):
-            n_instances = len(preds)
-        else:
-            # logger.error("Unsupported type of prediction input.")
-            return
-
-        if ground_truth:
-            top_scores, top_classes = [None] * n_instances, preds
-
-        elif self.mode == "top-k":
-            top_scores, top_classes = torch.topk(preds, k=self.top_k)
-            top_scores, top_classes = top_scores.tolist(), top_classes.tolist()
-        elif self.mode == "thres":
-            top_scores, top_classes = [], []
-            for pred in preds:
-                mask = pred >= self.thres
-                top_scores.append(pred[mask].tolist())
-                top_class = torch.squeeze(torch.nonzero(mask), dim=-1).tolist()
-                top_classes.append(top_class)
-
-        # Create labels top k predicted classes with their scores.
-        text_labels = []
-        for i in range(n_instances):
-            text_labels.append(
-                _create_text_labels(
-                    top_classes[i],
-                    top_scores[i],
-                    self.class_names,
-                    ground_truth=ground_truth,
-                )
-            )
-        frame_visualizer = ImgVisualizer(frame, meta=None)
-        font_size = min(max(np.sqrt(frame.shape[0] * frame.shape[1]) // 25, 5), 9)
-        top_corner = not ground_truth
-        if bboxes is not None:
-            assert len(preds) == len(
-                bboxes
-            ), "Encounter {} predictions and {} bounding boxes".format(
-                len(preds), len(bboxes)
-            )
-            for i, box in enumerate(bboxes):
-                text = text_labels[i]
-                pred_class = top_classes[i]
-                colors = [self._get_color(pred) for pred in pred_class]
-
-                box_color = "r" if ground_truth else "g"
-                line_style = "--" if ground_truth else "-."
-                frame_visualizer.draw_box(
-                    box,
-                    alpha=alpha,
-                    edge_color=box_color,
-                    line_style=line_style,
-                )
-                frame_visualizer.draw_multiple_text(
-                    text,
-                    box,
-                    top_corner=top_corner,
-                    font_size=font_size,
-                    box_facecolors=colors,
-                    alpha=text_alpha,
-                )
-        else:
-            text = text_labels[0]
-            pred_class = top_classes[0]
-            colors = [self._get_color(pred) for pred in pred_class]
-            frame_visualizer.draw_multiple_text(
-                text,
-                torch.Tensor([0, 5, frame.shape[1], frame.shape[0] - 5]),
-                top_corner=top_corner,
-                font_size=font_size,
-                box_facecolors=colors,
-                alpha=text_alpha,
-            )
-
-        return frame_visualizer.output.get_image()
-
-    def draw_clip_range(
-        self,
-        frames: Union[torch.Tensor, np.ndarray],
-        preds: Union[torch.Tensor, List[float]],
-        bboxes: Optional[torch.Tensor] = None,
-        text_alpha: float = 0.5,
-        ground_truth: bool = False,
-        keyframe_idx: Optional[int] = None,
-        draw_range: Optional[List[int]] = None,
-        repeat_frame: int = 1,
-    ) -> List[np.ndarray]:
-        """
-        Draw predicted labels or ground truth classes to clip.
-        Draw bouding boxes to clip if bboxes is provided. Boxes will gradually
-        fade in and out the clip, centered around the clip's central frame,
-        within the provided `draw_range`.
-        Args:
-            frames (array-like): video data in the shape (T, H, W, C).
-            preds (tensor): a tensor of shape (num_boxes, num_classes) that
-                contains all of the confidence scores of the model. For recognition
-                task or for ground_truth labels, input shape can be (num_classes,).
-            bboxes (Optional[tensor]): shape (num_boxes, 4) that contains the coordinates
-                of the bounding boxes.
-            text_alpha (float): transparency label of the box wrapped around text labels.
-            ground_truth (bool): whether the prodived bounding boxes are ground-truth.
-            keyframe_idx (int): the index of keyframe in the clip.
-            draw_range (Optional[list[ints]): only draw frames in range
-                [start_idx, end_idx] inclusively in the clip. If None, draw on
-                the entire clip.
-            repeat_frame (int): repeat each frame in draw_range for `repeat_frame`
-                time for slow-motion effect.
-        Returns:
-            A list of frames with bounding box annotations and corresponding
-            bbox labels ploted on them.
-        """
-        if draw_range is None:
-            draw_range = [0, len(frames) - 1]
-        if draw_range is not None:
-            draw_range[0] = max(0, draw_range[0])
-            left_frames = frames[: draw_range[0]]
-            right_frames = frames[draw_range[1] + 1 :]
-
-        draw_frames = frames[draw_range[0] : draw_range[1] + 1]
-        if keyframe_idx is None:
-            keyframe_idx = len(frames) // 2
-
-        img_ls = (
-            list(left_frames)
-            + self.draw_clip(
-                draw_frames,
-                preds,
-                bboxes=bboxes,
-                text_alpha=text_alpha,
-                ground_truth=ground_truth,
-                keyframe_idx=keyframe_idx - draw_range[0],
-                repeat_frame=repeat_frame,
-            )
-            + list(right_frames)
-        )
-
-        return img_ls
-
-    def draw_clip(
-        self,
-        frames: Union[torch.Tensor, np.ndarray],
-        preds: Union[torch.Tensor, List[float]],
-        bboxes: Optional[torch.Tensor] = None,
-        text_alpha: float = 0.5,
-        ground_truth: bool = False,
-        keyframe_idx: Optional[int] = None,
-        repeat_frame: int = 1,
-    ) -> List[np.ndarray]:
-        """
-        Draw predicted labels or ground truth classes to clip. Draw bouding boxes to clip
-        if bboxes is provided. Boxes will gradually fade in and out the clip, centered
-        around the clip's central frame.
-        Args:
-            frames (array-like): video data in the shape (T, H, W, C).
-            preds (tensor): a tensor of shape (num_boxes, num_classes) that contains
-                all of the confidence scores of the model. For recognition task or for
-                ground_truth labels, input shape can be (num_classes,).
-            bboxes (Optional[tensor]): shape (num_boxes, 4) that contains the coordinates
-                of the bounding boxes.
-            text_alpha (float): transparency label of the box wrapped around text labels.
-            ground_truth (bool): whether the prodived bounding boxes are ground-truth.
-            keyframe_idx (int): the index of keyframe in the clip.
-            repeat_frame (int): repeat each frame in draw_range for `repeat_frame`
-                time for slow-motion effect.
-        Returns:
-            A list of frames with bounding box annotations and corresponding
-            bbox labels plotted on them.
-        """
-        assert repeat_frame >= 1, "`repeat_frame` must be a positive integer."
-
-        repeated_seq = range(0, len(frames))
-        repeated_seq = list(
-            itertools.chain.from_iterable(
-                itertools.repeat(x, repeat_frame) for x in repeated_seq
-            )
-        )
-
-        frames, adjusted = self._adjust_frames_type(frames)
-        if keyframe_idx is None:
-            half_left = len(repeated_seq) // 2
-            half_right = (len(repeated_seq) + 1) // 2
-        else:
-            mid = int((keyframe_idx / len(frames)) * len(repeated_seq))
-            half_left = mid
-            half_right = len(repeated_seq) - mid
-
-        alpha_ls = np.concatenate(
-            [
-                np.linspace(0, 1, num=half_left),
-                np.linspace(1, 0, num=half_right),
-            ]
-        )
-        text_alpha = text_alpha
-        frames = frames[repeated_seq]
-        img_ls = []
-        for alpha, frame in zip(alpha_ls, frames):
-            draw_img = self.draw_one_frame(
-                frame,
-                preds,
-                bboxes,
-                alpha=alpha,
-                text_alpha=text_alpha,
-                ground_truth=ground_truth,
-            )
-            if adjusted:
-                draw_img = draw_img.astype("float32") / 255
-
-            img_ls.append(draw_img)
-
-        return img_ls
-
-    def _adjust_frames_type(
-        self, frames: torch.Tensor
-    ) -> Tuple[List[np.ndarray], bool]:
-        """
-        Modify video data to have dtype of uint8 and values range in [0, 255].
-        Args:
-            frames (array-like): 4D array of shape (T, H, W, C).
-        Returns:
-            frames (list of frames): list of frames in range [0, 1].
-            adjusted (bool): whether the original frames need adjusted.
-        """
-        assert (
-            frames is not None and len(frames) != 0
-        ), "Frames does not contain any values"
-        frames = np.array(frames)
-        assert np.array(frames).ndim == 4, "Frames must have 4 dimensions"
-        adjusted = False
-        if frames.dtype in [np.float32, np.float64]:
-            frames *= 255
-            frames = frames.astype(np.uint8)
-            adjusted = True
-
-        return frames, adjusted
-
-    def _get_thres_array(self, common_class_names: Optional[List[str]] = None) -> None:
-        """
-        Compute a thresholds array for all classes based on `self.thes` and `self.lower_thres`.
-        Args:
-            common_class_names (Optional[list of str]): a list of common class names.
-        """
-        common_class_ids = []
-        if common_class_names is not None:
-            common_classes = set(common_class_names)
-
-            for key, name in self.class_names.items():
-                if name in common_classes:
-                    common_class_ids.append(key)
-        else:
-            common_class_ids = list(range(self.num_classes))
-
-        thres_array = np.full(shape=(self.num_classes,), fill_value=self.lower_thres)
-        thres_array[common_class_ids] = self.thres
-        self.thres = torch.from_numpy(thres_array)
\ No newline at end of file
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/README.md b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/README.md
deleted file mode 100644
index 90741f42b0b070f2a91b63c8badb817c6aa24230..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/README.md
+++ /dev/null
@@ -1,87 +0,0 @@
-# ASR-based evaluation
-
-Overall, the life cycle of the ASR-based evaluation for an ULM contains the following steps:
- 1. Training an ULM and sampling from it [[description]](./../../ulm)
- 2. Running UTS on the sampled unit sequences [[description]](./../../unit2speech)
- 3. Pre-processing for the ASR (down-sampling to 16 KHz, aligning length of the generated audio with ground-truth utterances)
- 4. Running ASR
- 5. Calculation of the post-ASR evaluation metrics
-
-Here we assume that you have already went throught the first two steps and focus on the rest.
-
-## Preprocessing
-### Down-sampling to 16KHz
-The bulk conversion can be done by running
-```bash
- python $FAIRSEQ_ROOT/examples/textless_nlp/gslm/unit2speech/convert_to_16k.py $UTS_OUTPUT $UTS_OUTPUT_DOWNSAMPLE
- ```
- where `$UTS_OUTPUT` specifies the directory with the generated audio and `$UTS_OUTPUT_DOWNSAMPLE` is the directory where downsampled audio would be saved.
-
- ### Matching by length
-This step is somewhat optional. However, if you want to compare the fluency and diversity of a generated speech utterance to that of the ground-truth speech with the same prefix, it is a good idea to force them to be of the same length.
-```bash
-python $FAIRSEQ_ROOT/examples/textless_nlp/asr_metrics/cut_as.py \
-    --samples_dir=$UTS_OUTPUT_DOWNSAMPLE --out_dir=$UTS_OUTPUT_DOWNSAMPLE_CUT \
-    --prompts_description=data/ground_truth_continuation_dev.json
-```
-
-Here `ground_truth_continuation_dev.json` is a json file with ground-truth text from LibriSpeech dev-clean, associated with some meta-data (assuming the evaluation is done on dev-clean). This file can be downloaded [[here]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/ground_truth_continuation_dev.json). A similar file for the test-clean is [[here]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/ground_truth_continuation_test.json). These files are used for the evaluation and contain texts for audio sequences that are at least 6s long.
-
-## Running ASR
-We use a pre-trained wav2vec model to run the ASR step. We firstly need to prepare manifest files which, roughly, tell the ASR system which files we want to transcribe. You can find more details and download the `960h_scratch.pt` checkpoint
-[[here]](https://github.com/pytorch/fairseq/blob/main/examples/wav2vec/README.md)). To run ASR, you would also need to
-install KenLM, Flashlight decoder, and download the KenLM 4-gram English language model.
-
-```bash
- python $FAIRSEQ_ROOT/examples/wav2vec/wav2vec_manifest.py  \
-    $UTS_OUTPUT_DOWNSAMPLE_CUT --valid-percent 0.0  --dest $MANIFEST_DIR --ext wav
-```
-where `$UTS_OUTPUT_DOWNSAMPLE_CUT` speficies the directory with the preprocessed UTS outputs and `$MANIFEST_DIR` is the output directory.
-
-We will be running an out-of-the-box evaluation script which requires ground-truth transcripts to measure quality metrics. We are only
-interested in the transcripts (and we don't have ground-truth outputs for when our ULM generated!), hence we will just generate
-some dummy transcripts instead:
-```bash
-cp $FAIRSEQ_ROOT/examples/textless_nlp/gslm/asr_metrics/misc/dict.ltr.txt $MANIFEST_DIR
-python $FAIRSEQ_ROOT/examples/textless_nlp/gslm/asr_metrics/misc/dummy_asr_data.py  --tsv=$MANIFEST_DIR/train.tsv \
- --output-dir=$MANIFEST_DIR
-```
-
-Now we are ready for running ASR:
-```
-mkdir -p asr
-python $FAIRSEQ_ROOT/examples/speech_recognition/infer.py  \
-    $MANIFEST_DIR \
-    --task audio_pretraining --nbest 1 --path 960h_scratch.pt \
-    --gen-subset=train --results-path $PATH_TO_ASR_OUTPUT \
-    --w2l-decoder kenlm --lm-model 4-gram.bin \
-    --lexicon librispeech/lexicon_ltr.lst --word-score -1 \
-    --sil-weight 0 --lm-weight 2 --criterion ctc --labels ltr --max-tokens 300000 --remove-bpe letter
-```
-where `lexicon_ltr.lst` is the LibriSpeech lexicon and `$PATH_TO_ASR_OUTPUT` is the output directory (can be downloaded [[here]](https://dl.fbaipublicfiles.com/textless_nlp/gslm/eval_data/lexicon_ltr.lst)).
-
-## Evaluation metrics
-We run evaluation on the 1_000 shortest sequences that are at least 6s long. To filter those from the ASR transcript, we additionally provide each metric script with the paths to the manifest and `ground_truth_continuation_*` files.
-
-### Perplexity (PPX)
-To get a PPX metric estimate on an ASR transcript, you need to run the following command:
-```bash
-python ppx.py $PATH_TO_ASR_OUTPUT/hypo.word-960h_scratch.pt-train.txt --cut-tail\
-  --manifest=$MANIFEST_DIR/train.tsv --prompts-description=data/ground_truth_continuation_dev.json
-```
-where `--cut-tail` tells the script to ignore the last token on each line (ASR puts the sequence ID there).
-
-### Self- and Auto-BLEU
-```bash
-python self_bleu.py $PATH_TO_ASR_OUTPUT/hypo.word-960h_scratch.pt-train.txt  --cut-tail \
-  --manifest=$MANIFEST_DIR/train.tsv --prompts-description=data/ground_truth_continuation_dev.json
-```
-
-### Continuation-BLEU
-```bash
-python continuation_eval.py --asr-transcript $PATH_TO_ASR_OUTPUT/hypo.word-960h_scratch.pt-train.txt \
-   --manifest=$MANIFEST_DIR/train.tsv --prompts-description=data/ground_truth_continuation_dev.json
-```
-
-### AUC
-Based on the metrics calculated above, we can estimate the AUC of the perplexity/diversity trade-off. We provide an illustration in a [Colab notebook](https://colab.research.google.com/drive/1pVPfOVax_PU3MkYdHRSsa-SI8GBUldNt?usp=sharing).
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/__init__.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/__init__.py
deleted file mode 100644
index dc9fd1886d55756b5bdfeccf1ad329bd419a706e..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/__init__.py
+++ /dev/null
@@ -1,44 +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.
-"""isort:skip_file"""
-
-import os
-import sys
-
-try:
-    from .version import __version__  # noqa
-except ImportError:
-    version_txt = os.path.join(os.path.dirname(__file__), "version.txt")
-    with open(version_txt) as f:
-        __version__ = f.read().strip()
-
-__all__ = ["pdb"]
-
-# backwards compatibility to support `from fairseq.X import Y`
-from fairseq.distributed import utils as distributed_utils
-from fairseq.logging import meters, metrics, progress_bar  # noqa
-
-sys.modules["fairseq.distributed_utils"] = distributed_utils
-sys.modules["fairseq.meters"] = meters
-sys.modules["fairseq.metrics"] = metrics
-sys.modules["fairseq.progress_bar"] = progress_bar
-
-# initialize hydra
-from fairseq.dataclass.initialize import hydra_init
-hydra_init()
-
-import fairseq.criterions  # noqa
-import fairseq.distributed  # noqa
-import fairseq.models  # noqa
-import fairseq.modules  # noqa
-import fairseq.optim  # noqa
-import fairseq.optim.lr_scheduler  # noqa
-import fairseq.pdb  # noqa
-import fairseq.scoring  # noqa
-import fairseq.tasks  # noqa
-import fairseq.token_generation_constraints  # noqa
-
-import fairseq.benchmark  # noqa
-import fairseq.model_parallel  # noqa
diff --git a/spaces/strickvl/redaction-detector-streamlit/README.md b/spaces/strickvl/redaction-detector-streamlit/README.md
deleted file mode 100644
index 799e21ea7c1a69633f2ea86e4e0801454a029cbd..0000000000000000000000000000000000000000
--- a/spaces/strickvl/redaction-detector-streamlit/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: "Redaction Detector for PDFs (Streamlit)"
-emoji: 👁
-colorFrom: red
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: streamlit_app.py
-pinned: false
-license: apache-2.0
----
diff --git a/spaces/studiobrn/SplitTrack/tests/data/test_audio_utils.py b/spaces/studiobrn/SplitTrack/tests/data/test_audio_utils.py
deleted file mode 100644
index 0480671bb17281d61ce02bce6373a5ccec89fece..0000000000000000000000000000000000000000
--- a/spaces/studiobrn/SplitTrack/tests/data/test_audio_utils.py
+++ /dev/null
@@ -1,110 +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 julius
-import torch
-import pytest
-
-from audiocraft.data.audio_utils import (
-    _clip_wav,
-    convert_audio_channels,
-    convert_audio,
-    normalize_audio
-)
-from ..common_utils import get_batch_white_noise
-
-
-class TestConvertAudioChannels:
-
-    def test_convert_audio_channels_downmix(self):
-        b, c, t = 2, 3, 100
-        audio = get_batch_white_noise(b, c, t)
-        mixed = convert_audio_channels(audio, channels=2)
-        assert list(mixed.shape) == [b, 2, t]
-
-    def test_convert_audio_channels_nochange(self):
-        b, c, t = 2, 3, 100
-        audio = get_batch_white_noise(b, c, t)
-        mixed = convert_audio_channels(audio, channels=c)
-        assert list(mixed.shape) == list(audio.shape)
-
-    def test_convert_audio_channels_upmix(self):
-        b, c, t = 2, 1, 100
-        audio = get_batch_white_noise(b, c, t)
-        mixed = convert_audio_channels(audio, channels=3)
-        assert list(mixed.shape) == [b, 3, t]
-
-    def test_convert_audio_channels_upmix_error(self):
-        b, c, t = 2, 2, 100
-        audio = get_batch_white_noise(b, c, t)
-        with pytest.raises(ValueError):
-            convert_audio_channels(audio, channels=3)
-
-
-class TestConvertAudio:
-
-    def test_convert_audio_channels_downmix(self):
-        b, c, dur = 2, 3, 4.
-        sr = 128
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=sr, to_channels=2)
-        assert list(out.shape) == [audio.shape[0], 2, audio.shape[-1]]
-
-    def test_convert_audio_channels_upmix(self):
-        b, c, dur = 2, 1, 4.
-        sr = 128
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=sr, to_channels=3)
-        assert list(out.shape) == [audio.shape[0], 3, audio.shape[-1]]
-
-    def test_convert_audio_upsample(self):
-        b, c, dur = 2, 1, 4.
-        sr = 2
-        new_sr = 3
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=new_sr, to_channels=c)
-        out_j = julius.resample.resample_frac(audio, old_sr=sr, new_sr=new_sr)
-        assert torch.allclose(out, out_j)
-
-    def test_convert_audio_resample(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        new_sr = 2
-        audio = get_batch_white_noise(b, c, int(sr * dur))
-        out = convert_audio(audio, from_rate=sr, to_rate=new_sr, to_channels=c)
-        out_j = julius.resample.resample_frac(audio, old_sr=sr, new_sr=new_sr)
-        assert torch.allclose(out, out_j)
-
-
-class TestNormalizeAudio:
-
-    def test_clip_wav(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        _clip_wav(audio)
-        assert audio.abs().max() <= 1
-
-    def test_normalize_audio_clip(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        norm_audio = normalize_audio(audio, strategy='clip')
-        assert norm_audio.abs().max() <= 1
-
-    def test_normalize_audio_rms(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        norm_audio = normalize_audio(audio, strategy='rms')
-        assert norm_audio.abs().max() <= 1
-
-    def test_normalize_audio_peak(self):
-        b, c, dur = 2, 1, 4.
-        sr = 3
-        audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur))
-        norm_audio = normalize_audio(audio, strategy='peak')
-        assert norm_audio.abs().max() <= 1
diff --git a/spaces/sub314xxl/MetaGPT/metagpt/prompts/use_lib_sop.py b/spaces/sub314xxl/MetaGPT/metagpt/prompts/use_lib_sop.py
deleted file mode 100644
index b43ed5125ec1c07ac0def6c2d752dacd429bb3da..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/metagpt/prompts/use_lib_sop.py
+++ /dev/null
@@ -1,88 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/30 10:45
-@Author  : alexanderwu
-@File    : use_lib_sop.py
-"""
-
-SOP_SYSTEM = """SYSTEM:
-You serve as an assistant that helps me play the game Minecraft.
-I will give you a goal in the game. Please think of a plan to achieve the goal, and then write a sequence of actions to realize the plan. The requirements and instructions are as follows:
-1. You can only use the following functions. Don’t make plans purely based on your experience, think about how to use these functions.
-explore(object, strategy)
-Move around to find the object with the strategy: used to find objects including block items and entities. This action is finished once the object is visible (maybe at the distance).
-Augments:
-- object: a string, the object to explore.
-- strategy: a string, the strategy for exploration.
-approach(object)
-Move close to a visible object: used to approach the object you want to attack or mine. It may fail if the target object is not accessible.
-Augments:
-- object: a string, the object to approach.
-craft(object, materials, tool)
-Craft the object with the materials and tool: used for crafting new object that is not in the inventory or is not enough. The required materials must be in the inventory and will be consumed, and the newly crafted objects will be added to the inventory. The tools like the crafting table and furnace should be in the inventory and this action will directly use them. Don’t try to place or approach the crafting table or furnace, you will get failed since this action does not support using tools placed on the ground. You don’t need to collect the items after crafting. If the quantity you require is more than a unit, this action will craft the objects one unit by one unit. If the materials run out halfway through, this action will stop, and you will only get part of the objects you want that have been crafted.
-Augments:
-- object: a dict, whose key is the name of the object and value is the object quantity.
-- materials: a dict, whose keys are the names of the materials and values are the quantities.
-- tool: a string, the tool used for crafting. Set to null if no tool is required.
-mine(object, tool)
-Mine the object with the tool: can only mine the object within reach, cannot mine object from a distance. If there are enough objects within reach, this action will mine as many as you specify. The obtained objects will be added to the inventory.
-Augments:
-- object: a string, the object to mine.
-- tool: a string, the tool used for mining. Set to null if no tool is required.
-attack(object, tool)
-Attack the object with the tool: used to attack the object within reach. This action will keep track of and attack the object until it is killed.
-Augments:
-- object: a string, the object to attack.
-- tool: a string, the tool used for mining. Set to null if no tool is required.
-equip(object)
-Equip the object from the inventory: used to equip equipment, including tools, weapons, and armor. The object must be in the inventory and belong to the items for equipping.
-Augments:
-- object: a string, the object to equip.
-digdown(object, tool)
-Dig down to the y-level with the tool: the only action you can take if you want to go underground for mining some ore.
-Augments:
-- object: an int, the y-level (absolute y coordinate) to dig to.
-- tool: a string, the tool used for digging. Set to null if no tool is required.
-go_back_to_ground(tool)
-Go back to the ground from underground: the only action you can take for going back to the ground if you are underground.
-Augments:
-- tool: a string, the tool used for digging. Set to null if no tool is required.
-apply(object, tool)
-Apply the tool on the object: used for fetching water, milk, lava with the tool bucket, pooling water or lava to the object with the tool water bucket or lava bucket, shearing sheep with the tool shears, blocking attacks with the tool shield.
-Augments:
-- object: a string, the object to apply to.
-- tool: a string, the tool used to apply.
-2. You cannot define any new function. Note that the "Generated structures" world creation option is turned off.
-3. There is an inventory that stores all the objects I have. It is not an entity, but objects can be added to it or retrieved from it anytime at anywhere without specific actions. The mined or crafted objects will be added to this inventory, and the materials and tools to use are also from this inventory. Objects in the inventory can be directly used. Don’t write the code to obtain them. If you plan to use some object not in the inventory, you should first plan to obtain it. You can view the inventory as one of my states, and it is written in form of a dictionary whose keys are the name of the objects I have and the values are their quantities.
-4. You will get the following information about my current state:
-- inventory: a dict representing the inventory mentioned above, whose keys are the name of the objects and the values are their quantities
-- environment: a string including my surrounding biome, the y-level of my current location, and whether I am on the ground or underground
-Pay attention to this information. Choose the easiest way to achieve the goal conditioned on my current state. Do not provide options, always make the final decision.
-5. You must describe your thoughts on the plan in natural language at the beginning. After that, you should write all the actions together. The response should follow the format:
-{
-"explanation": "explain why the last action failed, set to null for the first planning",
-"thoughts": "Your thoughts on the plan in natural languag",
-"action_list": [
-{"name": "action name", "args": {"arg name": value}, "expectation": "describe the expected results of this action"},
-{"name": "action name", "args": {"arg name": value}, "expectation": "describe the expected results of this action"},
-{"name": "action name", "args": {"arg name": value}, "expectation": "describe the expected results of this action"}
-]
-}
-The action_list can contain arbitrary number of actions. The args of each action should correspond to the type mentioned in the Arguments part. Remember to add “‘dict“‘ at the beginning and the end of the dict. Ensure that you response can be parsed by Python json.loads
-6. I will execute your code step by step and give you feedback. If some action fails, I will stop at that action and will not execute its following actions. The feedback will include error messages about the failed action. At that time, you should replan and write the new code just starting from that failed action.
-"""
-
-
-SOP_USER = """USER:
-My current state:
-- inventory: {inventory}
-- environment: {environment}
-The goal is to {goal}.
-Here is one plan to achieve similar goal for reference: {reference plan}.
-Begin your plan. Remember to follow the response format.
-or Action {successful action} succeeded, and {feedback message}. Continue your
-plan. Do not repeat successful action. Remember to follow the response format.
-or Action {failed action} failed, because {feedback message}. Revise your plan from
-the failed action. Remember to follow the response format.
-"""
diff --git a/spaces/supertori/files/stable-diffusion-webui/modules/txt2img.py b/spaces/supertori/files/stable-diffusion-webui/modules/txt2img.py
deleted file mode 100644
index 3927d8538f06c1ed270c9a6cfd55d4bb15705ee5..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/modules/txt2img.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import modules.scripts
-from modules import sd_samplers
-from modules.generation_parameters_copypaste import create_override_settings_dict
-from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
-    StableDiffusionProcessingImg2Img, process_images
-from modules.shared import opts, cmd_opts
-import modules.shared as shared
-import modules.processing as processing
-from modules.ui import plaintext_to_html
-
-
-def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, override_settings_texts, *args):
-    override_settings = create_override_settings_dict(override_settings_texts)
-
-    p = StableDiffusionProcessingTxt2Img(
-        sd_model=shared.sd_model,
-        outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
-        outpath_grids=opts.outdir_grids or opts.outdir_txt2img_grids,
-        prompt=prompt,
-        styles=prompt_styles,
-        negative_prompt=negative_prompt,
-        seed=seed,
-        subseed=subseed,
-        subseed_strength=subseed_strength,
-        seed_resize_from_h=seed_resize_from_h,
-        seed_resize_from_w=seed_resize_from_w,
-        seed_enable_extras=seed_enable_extras,
-        sampler_name=sd_samplers.samplers[sampler_index].name,
-        batch_size=batch_size,
-        n_iter=n_iter,
-        steps=steps,
-        cfg_scale=cfg_scale,
-        width=width,
-        height=height,
-        restore_faces=restore_faces,
-        tiling=tiling,
-        enable_hr=enable_hr,
-        denoising_strength=denoising_strength if enable_hr else None,
-        hr_scale=hr_scale,
-        hr_upscaler=hr_upscaler,
-        hr_second_pass_steps=hr_second_pass_steps,
-        hr_resize_x=hr_resize_x,
-        hr_resize_y=hr_resize_y,
-        override_settings=override_settings,
-    )
-
-    p.scripts = modules.scripts.scripts_txt2img
-    p.script_args = args
-
-    if cmd_opts.enable_console_prompts:
-        print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
-
-    processed = modules.scripts.scripts_txt2img.run(p, *args)
-
-    if processed is None:
-        processed = process_images(p)
-
-    p.close()
-
-    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(processed.comments)
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/EximiousSoft Business Card Designer 3.80 Serial Crack High Quality.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/EximiousSoft Business Card Designer 3.80 Serial Crack High Quality.md
deleted file mode 100644
index f7f5c55e7852b5aa36e584d27e77638dcaf215f1..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/EximiousSoft Business Card Designer 3.80 Serial Crack High Quality.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Rita Pmp Book Pdf Free Download UPDATED.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Rita Pmp Book Pdf Free Download UPDATED.md
deleted file mode 100644
index cdf5d1380fd1132dc593386951e483519c9a08a9..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Rita Pmp Book Pdf Free Download UPDATED.md	
+++ /dev/null
@@ -1,24 +0,0 @@
-
-<h1>How to Download Rita PMP Book PDF for Free</h1>
-<p>If you are preparing for the Project Management Professional (PMP) exam, you might have heard of the Rita PMP book. This is one of the most popular and comprehensive books on project management, written by Rita Mulcahy, a renowned expert and trainer in the field.</p>
-<h2>Rita Pmp Book Pdf Free Download</h2><br /><p><b><b>Download File</b> &#10026; <a href="https://cinurl.com/2uEXvv">https://cinurl.com/2uEXvv</a></b></p><br /><br />
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-<p>The best way to download Rita PMP book PDF for free is to use a legitimate and authorized source. One such source is the PMI website. PMI is the organization that administers the PMP exam and sets the standards for project management. If you are a PMI member, you can access a digital library of over 300 books on project management, including the Rita PMP book.</p>
-<p></p>
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-<br />
\ No newline at end of file
diff --git a/spaces/suqionglin/White-box-Cartoonization/app.py b/spaces/suqionglin/White-box-Cartoonization/app.py
deleted file mode 100644
index c55ced56bd87a85f59d1c8ef84b7eca87422720f..0000000000000000000000000000000000000000
--- a/spaces/suqionglin/White-box-Cartoonization/app.py
+++ /dev/null
@@ -1,108 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-import argparse
-import functools
-import os
-import pathlib
-import sys
-from typing import Callable
-import uuid
-
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import PIL.Image
-
-from io import BytesIO
-from wbc.cartoonize import Cartoonize
-
-ORIGINAL_REPO_URL = 'https://github.com/SystemErrorWang/White-box-Cartoonization'
-TITLE = 'SystemErrorWang/White-box-Cartoonization'
-DESCRIPTION = f"""This is a demo for {ORIGINAL_REPO_URL}.
-
-"""
-ARTICLE = """
-
-"""
-
-SAFEHASH = [x for x in "0123456789-abcdefghijklmnopqrstuvwxyz_ABCDEFGHIJKLMNOPQRSTUVWXYZ"]
-def compress_UUID():
-    '''
-    根据http://www.ietf.org/rfc/rfc1738.txt,由uuid编码扩bai大字符域生成du串
-    包括:[0-9a-zA-Z\-_]共64个
-    长度:(32-2)/3*2=20
-    备注:可在地球上人zhi人都用,使用100年不重复(2^120)
-    :return:String
-    '''
-    row = str(uuid.uuid4()).replace('-', '')
-    safe_code = ''
-    for i in range(10):
-        enbin = "%012d" % int(bin(int(row[i * 3] + row[i * 3 + 1] + row[i * 3 + 2], 16))[2:], 10)
-        safe_code += (SAFEHASH[int(enbin[0:6], 2)] + SAFEHASH[int(enbin[6:12], 2)])
-    safe_code = safe_code.replace('-', '')
-    return safe_code
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--theme', type=str)
-    parser.add_argument('--live', action='store_true')
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    parser.add_argument('--allow-flagging', type=str, default='never')
-    parser.add_argument('--allow-screenshot', action='store_true')
-    return parser.parse_args()
-
-def run(
-        image,
-        cartoonize : Cartoonize
-) -> tuple[PIL.Image.Image]:
-
-    out_path = compress_UUID()+'.png'
-    cartoonize.run_sigle(image.name, out_path)
-
-    return PIL.Image.open(out_path)
-
-
-def main():
-    gr.close_all()
-
-    args = parse_args()
-
-    cartoonize = Cartoonize(os.path.join(os.path.dirname(os.path.abspath(__file__)),'wbc/saved_models/'))
-
-    func = functools.partial(run, cartoonize=cartoonize)
-    func = functools.update_wrapper(func, run)
-
-    gr.Interface(
-        func,
-        [
-            gr.inputs.Image(type='file', label='Input Image'),
-        ],
-        [
-            gr.outputs.Image(
-                type='pil',
-                label='Result'),
-        ],
-        # examples=examples,
-        theme=args.theme,
-        title=TITLE,
-        description=DESCRIPTION,
-        article=ARTICLE,
-        allow_screenshot=args.allow_screenshot,
-        allow_flagging=args.allow_flagging,
-        live=args.live,
-    ).launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
-    )
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/surendra962/ranking/facility_ranking.py b/spaces/surendra962/ranking/facility_ranking.py
deleted file mode 100644
index 9ac40010fa9614610da1e018527b82d5e3e74365..0000000000000000000000000000000000000000
--- a/spaces/surendra962/ranking/facility_ranking.py
+++ /dev/null
@@ -1,392 +0,0 @@
-import pandas as pd
-import numpy as np
-import streamlit as st
-import base64  # Standard Python Module
-from io import StringIO, BytesIO  # Standard Python Module
-
-# hide menu and footer
-st.set_page_config(page_title=None, page_icon=None, layout="wide", initial_sidebar_state="auto", menu_items=None)
-hide_streamlit_style = """
-            <style>
-            #MainMenu {visibility: hidden;}
-            footer {visibility: hidden;}
-            </style>
-            """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)  # unsafe_allow_html allows us to embed html code
-
-
-
-
-
-def generate_excel_download_link(df):
-    # Credit Excel: https://discuss.streamlit.io/t/how-to-add-a-download-excel-csv-function-to-a-button/4474/5
-    towrite = BytesIO()
-    df.to_excel(towrite, encoding="utf-8", index=False, header=True)  # write to BytesIO buffer
-    towrite.seek(0)  # reset pointer
-    b64 = base64.b64encode(towrite.read()).decode()
-    href = f'<a href="data:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;base64,{b64}" download="facility_ranking_SSM.xlsx">Download </a>'
-    return st.markdown(href, unsafe_allow_html=True)
-
-
-
-
-
-# DE=pd.read_csv('C:/Python/1. Dashboard/Facility ranking/Data/DE.csv')
-# FPE=pd.read_csv('C:/Python/1. Dashboard/Facility ranking/Data/FPE.csv')
-# sd=pd.read_csv('C:/Python/1. Dashboard/Facility ranking/Data/SD.csv')
-
-# width in full screen
-
-
-st.title('AI Ranking-CPHC Project Jhpiego')
-# add two columns
-col1, col2 = st.columns(2)
-with col1:        
-    counts=0
-
-    # file upload
-    upload_file_FPE = st.file_uploader("Upload Facility Profile Entry Data in CSV format", type=['csv'])
-
-    # if upload_file_DE is not None highlight "file not uploaded"
-
-
-    if upload_file_FPE:
-        FPE = pd.read_csv(upload_file_FPE)
-        
-        # check the columns heading available in the file
-        required_col=['NIN_2_HFI', 'HFI_Name', 'PHC_CHC_Type',
-            'State_Name', 'District_Name', 'Taluka_Name', 'Block_Name',
-            'Proposed Date', 'Progressive Date',
-            'Total Population in catchment area of facility']
-        if set(required_col).issubset(FPE.columns):
-            st.subheader("Perfect")
-            st.write(FPE.head(3))
-        else:
-            st.subheader(f"Please upload correct file missing columns are {set(required_col)-set(FPE.columns)}")
-            counts=1
-
-
-    # file upload
-    upload_file_DE = st.file_uploader("Upload Daily Entry Data in CSV format", type=['csv'])
-    if upload_file_DE:
-        DE = pd.read_csv(upload_file_DE)
-        
-        required_col=['NIN ID',  'Entry Date', 'Footfall Male', 'Footfall Female ',
-        'Footfall Others ', 'Footfall Total', 'Patients received medicines ',
-        'Patients availed diagnostic tests',
-        ' Patients availed tele-consulation services ',
-        'Wellness sessions conducted ',
-        'Total participants of Wellness session']
-        if set(required_col).issubset(DE.columns):
-            st.subheader("Perfect")
-            st.write(DE.head(3))
-        else:
-            st.subheader(f"Please upload correct file missing columns are {set(required_col)-set(DE.columns)}")
-            counts=1
-
-
-    # file upload
-    upload_file_SD = st.file_uploader("Upload Service Delivery Data in CSV format", type=['csv'])
-    if upload_file_SD:
-        sd = pd.read_csv(upload_file_SD)
-        
-        required_col=['NIN ID',  'Entry Month', 
-        'HTN Individuals screened Male', 'HTN Individuals screened Female',
-        'HTN Individuals screened Other', 'HTN Newly diagnosed Male',
-        'HTN Newly diagnosed Female', 'HTN Newly diagnosed Other',
-        'HTN On treatment Male', 'HTN On treatment Female',
-        'HTN On treatment Other', 'DM Individuals screened Male',
-        'DM Individuals screened Female', 'DM Individuals screened Other',
-        'DM Newly diagnosed Male', 'DM Newly diagnosed Female',
-        'DM Newly diagnosed Other', 'DM On treatment Male',
-        'DM On treatment Female', 'DM On treatment Other',
-        'OC Individuals screened Male', 'OC Individuals screened Female',
-        'OC Individuals screened Other', 'OC Newly diagnosed Male',
-        'OC Newly diagnosed Female', 'OC Newly diagnosed Other',
-        'OC On treatment male', 'OC On treatment Female',
-        'OC On treatment Other', 'BC Individuals screened female',
-        'BC Newly diagnosed female', 'BC On treatment female',
-        'CC Individuals screened female', 'CC Newly diagnosed female',
-        'CC On treatment female', 'Individuals referred for screening male',
-        'Individuals referred for screening female',
-        'Individuals referred for screening other', 'Newly diagnosed Male',
-        'Newly diagnosed Female', 'Newly diagnosed Other', 'On treatment Male',
-        'On treatment Female', 'On treatment Other',
-        'Total Patients received antihypertensive medicines at this centre',
-        'Total Patients received ant-diabetic medicines at this centre',
-        'Medicines_TPR_AO_M', 'Total TB patients received DOTS from the centre',
-        'Medicines_CSOM_EOM', 'Availability of functional BP apparatus',
-        'Availability of functional glucometer', 'Closing stock of glucostrips',
-        'Performance/team based incentives for MO/SN/CHO',
-        'Team based incentives for ASHA/MPW']
-        if set(required_col).issubset(sd.columns):
-            st.subheader("Perfect")
-            st.write(sd.head(3))
-        else:
-            st.subheader(f"Please upload correct file missing columns are {set(required_col)-set(sd.columns)}")
-            counts=1
-
-    # if upload_file_FPE and upload_file_DE and upload_file_SD is not None:
-            # markdown processing
-        st.markdown('''
-        # *************************Processing Done: Please check next section**************************
-        ''')
-    st.subheader("This tool is developed by : Surendra Mehta Data Analyst Jhpiego India all rights reserved")
-with col2:
-
-    if counts==0 and upload_file_FPE and upload_file_DE and upload_file_SD:
-    
-
-
-
-        # Facility PE cleaning **********************************************************************************************************
-        FPE.rename(columns={'NIN_2_HFI':'NIN ID'}, inplace=True)
-
-        # Total Population in catchment area of facility remove any commas and convert in int format
-        FPE['Total Population in catchment area of facility']=FPE['Total Population in catchment area of facility'].replace({',': ''}, regex=True)
-        FPE['Total Population in catchment area of facility']=FPE['Total Population in catchment area of facility'].astype(int)
-
-        col=['NIN ID', 'HFI_Name', 'PHC_CHC_Type',
-            'State_Name', 'District_Name', 'Taluka_Name', 'Block_Name',
-            'Proposed Date', 'Progressive Date',
-            'Total Population in catchment area of facility']
-        FPE=FPE[col]
-        geo=['HFI_Name', 'PHC_CHC_Type','State_Name', 'District_Name', 'Taluka_Name', 'Block_Name']
-
-        # fill NA if any value is missing in geo 
-        FPE[geo]=FPE[geo].fillna('NA')
-        # total facility 
-        total_facility=FPE.shape[0]
-        st.write(f"Total facility in facility profile entry is :{total_facility}")
-
-        
-        # DE cleaning *******************************************************************************************************************
-        #Entry Date convert in date format in format of 2022-05-09
-        
-        DE['Entry Date'] = pd.to_datetime(DE['Entry Date'], format='%Y-%m-%d')
-        DE.head(3)
-        # add month-year column from entry date
-        DE['Month-Year'] = DE['Entry Date'].dt.strftime('%b-%Y')
-        DE['Total'] = DE["Footfall Male"] + DE["Footfall Female "]+DE["Footfall Others "]
-        # if wellness session conducted is yes then 1 else 0
-        DE['Wellness sessions conducted ']=DE['Wellness sessions conducted '].replace({'Yes': 1, 'No': 0})
-        DE['Reporting']=1
-
-        DE_col=['NIN ID','Reporting', 'Total',
-            ' Patients availed tele-consulation services ',
-            'Wellness sessions conducted ',
-                'Month-Year']
-        DE=DE[DE_col]
-        #groupby
-        DE=DE.groupby(['NIN ID','Month-Year']).sum().reset_index()
-
-
-        #total facility in DE
-        st.write(f"Total facility in DE is :{DE['NIN ID'].nunique()}") 
-
-        # SD cleaning ********************************************************************************************************************
-
-        #Entry Date convert in date format in format of 2022-11-30
-        sd['Entry Month'] = pd.to_datetime(sd['Entry Month'], format='%Y-%m-%d')
-
-        # # add month-year column from entry date
-        sd['Month-Year'] = sd['Entry Month'].dt.strftime('%b-%Y')
-
-        #show all the columns in view
-        pd.set_option('display.max_columns', None)
-
-        int_col=['Individuals empanelled',
-            'Community Based Assessment Checklist filled',
-            'HTN Individuals screened Male', 'HTN Individuals screened Female',
-            'HTN Individuals screened Other', 'HTN Newly diagnosed Male',
-            'HTN Newly diagnosed Female', 'HTN Newly diagnosed Other',
-            'HTN On treatment Male', 'HTN On treatment Female',
-            'HTN On treatment Other', 'DM Individuals screened Male',
-            'DM Individuals screened Female', 'DM Individuals screened Other',
-            'DM Newly diagnosed Male', 'DM Newly diagnosed Female',
-            'DM Newly diagnosed Other', 'DM On treatment Male',
-            'DM On treatment Female', 'DM On treatment Other',
-            'OC Individuals screened Male', 'OC Individuals screened Female',
-            'OC Individuals screened Other', 'OC Newly diagnosed Male',
-            'OC Newly diagnosed Female', 'OC Newly diagnosed Other',
-            'OC On treatment male', 'OC On treatment Female',
-            'OC On treatment Other', 'BC Individuals screened female',
-            'BC Newly diagnosed female', 'BC On treatment female',
-            'CC Individuals screened female', 'CC Newly diagnosed female',
-            'CC On treatment female', 'Individuals referred for screening male',
-            'Individuals referred for screening female',
-            'Individuals referred for screening other', 'Newly diagnosed Male',
-            'Newly diagnosed Female', 'Newly diagnosed Other', 'On treatment Male',
-            'On treatment Female', 'On treatment Other',
-            'Total Patients received antihypertensive medicines at this centre',
-            'Total Patients received ant-diabetic medicines at this centre','Medicines_TPR_AO_M',"Closing stock of glucostrips"]
-
-
-        # convert all the int columns in int format
-        sd_int=sd[int_col].head()
-        # remove any commas in the int columns
-        sd[sd_int.columns] = sd[sd_int.columns].replace({',': ''}, regex=True)
-        #convert all the int columns in int format
-        sd[int_col] = sd[int_col].apply(pd.to_numeric, errors='coerce', axis=1)
-
-        # add total column in sd_int
-
-        #HTN
-        sd['HTN screened '] = sd['HTN Individuals screened Male']+ sd['HTN Individuals screened Female']+ sd['HTN Individuals screened Other']
-        sd['HTN diagnosed '] = sd['HTN Newly diagnosed Male']+ sd['HTN Newly diagnosed Female']+ sd['HTN Newly diagnosed Other']
-        sd['HTN on treatment '] = sd['HTN On treatment Male']+ sd['HTN On treatment Female']+ sd['HTN On treatment Other']
-
-        #DM
-        sd['DM screened '] = sd['DM Individuals screened Male']+ sd['DM Individuals screened Female']+ sd['DM Individuals screened Other']
-        sd['DM diagnosed '] = sd['DM Newly diagnosed Male']+ sd['DM Newly diagnosed Female']+ sd['DM Newly diagnosed Other']
-        sd['DM on treatment '] = sd['DM On treatment Male']+ sd['DM On treatment Female']+ sd['DM On treatment Other']
-
-        #DM
-        sd['OC screened '] = sd['OC Individuals screened Male']+ sd['OC Individuals screened Female']+ sd['OC Individuals screened Other']
-        sd['OC diagnosed '] = sd['OC Newly diagnosed Male']+ sd['OC Newly diagnosed Female']+ sd['OC Newly diagnosed Other']
-        sd['OC on treatment '] = sd['OC On treatment male']+ sd['OC On treatment Female']+ sd['OC On treatment Other']
-
-
-        #referred
-        sd['TB_Referred'] = sd['Individuals referred for screening male']+ sd['Individuals referred for screening female']+ sd['Individuals referred for screening other']
-
-        #Newly diagnosed
-        sd['TB_Newly diagnosed'] = sd['Newly diagnosed Male']+ sd['Newly diagnosed Female']+ sd['Newly diagnosed Other']
-
-        #On treatment
-        sd['TB_On treatment'] = sd['On treatment Male']+ sd['On treatment Female']+ sd['On treatment Other']
-        # if yes than 1 else 0 add both
-        sd["equipments_BP_gluco"]=sd["Availability of functional BP apparatus"].replace({'Yes': 1, 'No': 0})+sd["Availability of functional glucometer"].replace({'Yes': 1, 'No': 0})
-        sd["pbi_tbi"]=sd["Performance/team based incentives for MO/SN/CHO"].replace({'Yes': 1, 'No': 0})+sd["Team based incentives for ASHA/MPW"].replace({'Yes': 1, 'No': 0})
-
-
-        selected_col=['NIN ID', 'Facility Name', 'Facility Type', 'State', 'District',
-            'Taluka', 'Block', 'Entry Month',
-                    'Month-Year',
-            'HTN screened ', 'DM screened ',
-            'OC screened ','BC Individuals screened female', 'TB_Referred','equipments_BP_gluco',"pbi_tbi"]
-        sd=sd[selected_col]
-
-
-        # group by NIN-ID and month-year sum
-        sd=sd.groupby(['NIN ID','Month-Year']).sum().reset_index()
-
-        # total facility in SD
-        st.write("Total facility in SD",sd['NIN ID'].nunique())
-
-
-
-        df=pd.merge(DE, sd, on=['NIN ID','Month-Year'], how='outer')
-        #merge with FPE
-        df=pd.merge(FPE,df, on=['NIN ID'], how='outer')
-
-
-
-        #df add a target population column with 80 percent of the total population
-        df['Target Population']=round(df['Total Population in catchment area of facility']*0.8,0)
-
-
-
-        # # all float value in one list
-        # float_col=df.select_dtypes(include=['float']).columns.tolist()
-
-        # # convert all the float columns in int format
-        # df[float_col] = df[float_col].apply(pd.to_numeric, errors='coerce', axis=1)
-
-        # cal_Reporting map value>=20 than 15, IF value >=10 than 10 else 0
-        df['cal_Reporting']=np.where(df['Reporting']>=20,15,np.where(df['Reporting']>=10,10,0))
-        # cal_footfall map if 'Total'/'Target Population'>=1, 15,if 'Total'/'Target Population'>=.8, 10,if 'Total'/'Target Population'>=.5,5, else 0
-
-        df['cal_footfall']=np.where((df['Total']/df['Target Population'])>=1,15,np.where((df['Total']/df['Target Population'])>=.8,10,np.where((df['Total']/df['Target Population'])>=.5,5,0)))
-        # Equipment value AV4=2,10,IF value =1,5,0
-        df['cal_Equipment']=np.where(df['equipments_BP_gluco']==2,10,np.where(df['equipments_BP_gluco']==1,5,0))
-        # pbi_tbi value AV4=2,10,IF value =1,5,0
-        df['cal_pbi_tbi']=np.where(df['pbi_tbi']==2,10,np.where(df['pbi_tbi']==1,5,0))
-        # tb refered value referred/total >=30,10 ,if referred/total >=20,5, else 0
-        df['cal_tb']=np.where((df['TB_Referred']/df['Total'])>=.3,10,np.where((df['TB_Referred']/df['Total'])>=.2,5,0))
-        # teleconsultation value AM4>=30,5,IF(AM4>=15,3,IF(AM4>=1,1,0
-        df['cal_teleconsultation']=np.where(df[' Patients availed tele-consulation services ']>=30,5,np.where(df[' Patients availed tele-consulation services ']>=15,3,np.where(df[' Patients availed tele-consulation services ']>=1,1,0)))
-        # wellness and yoga AQ6>=10,5,IF(AQ6>=5,3,0)
-        df['cal_wellness']=np.where(df['Wellness sessions conducted ']>=10,5,np.where(df['Wellness sessions conducted ']>=5,3,0))
-        # save the df in csv
-        
-
-        # pivot table 
-        df_pivot=df.pivot_table(index=["NIN ID","HFI_Name","PHC_CHC_Type","State_Name","District_Name","Taluka_Name","Block_Name"],columns='Month-Year',
-        values=['HTN screened ','DM screened ','OC screened ','BC Individuals screened female','cal_Reporting','cal_footfall','cal_Equipment',
-            'cal_pbi_tbi','cal_tb','cal_teleconsultation','cal_wellness'],aggfunc='sum').reset_index()
-
-        # total unique facility in all 3 uploaded data
-        st.write(f"Total facility in all 3 uploaded data {df_pivot['NIN ID'].nunique()}")
-
-        col=[col[0] + '_' + col[1] for col in df_pivot.columns]
-        df_pivot.columns = col
-        df_pivot = df_pivot.reset_index()
-
-
-
-        # filter all columns having "screened" or refered
-        screened_col=[col for col in df_pivot.columns if 'screened' in col]
-
-        # import minmaxscaler
-        from sklearn.preprocessing import MinMaxScaler
-
-        # apply minmaxscaler on screened_col
-        scaler = MinMaxScaler()
-        df_pivot[screened_col] = scaler.fit_transform(df_pivot[screened_col])
-
-        # for each month-year calculate the sum of all the columns 
-        unique_month=df['Month-Year'].unique()
-        #drop blank value
-        unique_month=unique_month[~pd.isnull(unique_month)]
-
-        for i in unique_month:
-            df_pivot[i+'_sum']=df_pivot[[col for col in df_pivot.columns if i in col]].sum(axis=1)
-            # add rank column and based on sum of each month-year rank the facility
-            df_pivot[i+'_rank']=df_pivot[i+'_sum'].rank(ascending=False)
-
-
-
-        # For Below month ranking will be calculated st.write(unique_month)
-        st.subheader('Below month ranking will be calculated')
-        st.write(unique_month)
-        
-        # calculate the sum of all the month-year sum
-        df_pivot['Overall Score']=df_pivot[[col for col in df_pivot.columns if 'sum' in col]].sum(axis=1)
-
-
-        
-        
-
-        # keep only ranking and overall score columns
-        sum=[col for col in df_pivot.columns if 'sum' in col]
-        rank=[col for col in df_pivot.columns if 'rank' in col]
-
-        # add both list in one list
-        sum_rank=sum+rank
-
-
-        # all sum and rank columns in df_pivot convert in int format
-        df_pivot[sum_rank] = df_pivot[sum_rank].apply(pd.to_numeric, errors='coerce', axis=1)
-
-        df_rank=df_pivot[['NIN ID_','HFI_Name_','PHC_CHC_Type_', 'State_Name_','District_Name_','Taluka_Name_',
-        'Block_Name_','Overall Score']+sum_rank]
-
-            # df subheader 
-        st.subheader("Download the Ranking")
-        generate_excel_download_link(df_rank)
-        
-            # df subheader pivot table
-        st.subheader("Detailed calculation download")
-        generate_excel_download_link(df_pivot)
-        
-            # subheader download all 3 files analysis
-        st.subheader("Download all 3 files analysis")
-        generate_excel_download_link(df)
-
-        # df subheader
-        
-
-            
-        
-
diff --git a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Activador Windows 8 Pro (todas Las Versiones) Serial Key.md b/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Activador Windows 8 Pro (todas Las Versiones) Serial Key.md
deleted file mode 100644
index 11ab25e9a4551c1f96cb297310b26a03b0975916..0000000000000000000000000000000000000000
--- a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Activador Windows 8 Pro (todas Las Versiones) Serial Key.md	
+++ /dev/null
@@ -1,12 +0,0 @@
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-<br />
-<p></p>
diff --git a/spaces/svdiff-library/SVDiff-Training-UI/app.py b/spaces/svdiff-library/SVDiff-Training-UI/app.py
deleted file mode 100644
index 80da0a5762e6ef85a672a45677a853e339802b63..0000000000000000000000000000000000000000
--- a/spaces/svdiff-library/SVDiff-Training-UI/app.py
+++ /dev/null
@@ -1,78 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-
-import gradio as gr
-import torch
-
-from app_inference import create_inference_demo
-from app_training import create_training_demo
-from app_upload import create_upload_demo
-from inference import InferencePipeline
-from trainer import Trainer
-
-TITLE = """# SVDiff-pytorch Training UI
-This demo is based on https://github.com/mkshing/svdiff-pytorch, which is an implementation of "SVDiff: Compact Parameter Space for Diffusion Fine-Tuning" by [mkshing](https://twitter.com/mk1stats)
-"""
-
-ORIGINAL_SPACE_ID = 'mshing/SVDiff-pytorch-UI'
-SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
-SHARED_UI_WARNING = f'''# Attention - This Space doesn't work in this shared UI. You can duplicate and use it with a paid private T4 GPU.
-
-<center><a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></center>
-'''
-
-if os.getenv('SYSTEM') == 'spaces' and SPACE_ID != ORIGINAL_SPACE_ID:
-    SETTINGS = f'<a href="https://huggingface.co/spaces/{SPACE_ID}/settings">Settings</a>'
-else:
-    SETTINGS = 'Settings'
-CUDA_NOT_AVAILABLE_WARNING = f'''# Attention - Running on CPU.
-<center>
-You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces.
-"T4 small" is sufficient to run this demo.
-</center>
-'''
-
-HF_TOKEN_NOT_SPECIFIED_WARNING = f'''# Attention - The environment variable `HF_TOKEN` is not specified. Please specify your Hugging Face token with write permission as the value of it.
-<center>
-You can check and create your Hugging Face tokens <a href="https://huggingface.co/settings/tokens" target="_blank">here</a>.
-You can specify environment variables in the "Repository secrets" section of the {SETTINGS} tab.
-</center>
-'''
-
-HF_TOKEN = os.getenv('HF_TOKEN')
-
-
-def show_warning(warning_text: str) -> gr.Blocks:
-    with gr.Blocks() as demo:
-        with gr.Box():
-            gr.Markdown(warning_text)
-    return demo
-
-
-pipe = InferencePipeline(HF_TOKEN)
-trainer = Trainer(HF_TOKEN)
-
-with gr.Blocks(css='style.css') as demo:
-    if os.getenv('IS_SHARED_UI'):
-        show_warning(SHARED_UI_WARNING)
-    if not torch.cuda.is_available():
-        show_warning(CUDA_NOT_AVAILABLE_WARNING)
-    if not HF_TOKEN:
-        show_warning(HF_TOKEN_NOT_SPECIFIED_WARNING)
-
-    gr.Markdown(TITLE)
-    with gr.Tabs():
-        with gr.TabItem('Train'):
-            create_training_demo(trainer, pipe)
-        with gr.TabItem('Test'):
-            create_inference_demo(pipe, HF_TOKEN)
-        with gr.TabItem('Upload'):
-            gr.Markdown('''
-            - You can use this tab to upload models later if you choose not to upload models in training time or if upload in training time failed.
-            ''')
-            create_upload_demo(HF_TOKEN)
-
-demo.queue(max_size=1).launch(share=False)
diff --git a/spaces/svdiff-library/SVDiff-Training-UI/trainer.py b/spaces/svdiff-library/SVDiff-Training-UI/trainer.py
deleted file mode 100644
index d53bd71b48eac6411ec60fe61c2b1fbe9c0c4840..0000000000000000000000000000000000000000
--- a/spaces/svdiff-library/SVDiff-Training-UI/trainer.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from __future__ import annotations
-
-import datetime
-import os
-import pathlib
-import shlex
-import shutil
-import subprocess
-
-import gradio as gr
-import PIL.Image
-import slugify
-import torch
-from huggingface_hub import HfApi
-from accelerate.utils import write_basic_config
-
-
-from app_upload import ModelUploader
-from utils import save_model_card
-
-URL_TO_JOIN_LIBRARY_ORG = 'https://huggingface.co/organizations/svdiff-library/share/PZBRRkosXikenXUdjMcvcoFmpWjcWnZjKL'
-
-
-def pad_image(image: PIL.Image.Image) -> PIL.Image.Image:
-    w, h = image.size
-    if w == h:
-        return image
-    elif w > h:
-        new_image = PIL.Image.new(image.mode, (w, w), (0, 0, 0))
-        new_image.paste(image, (0, (w - h) // 2))
-        return new_image
-    else:
-        new_image = PIL.Image.new(image.mode, (h, h), (0, 0, 0))
-        new_image.paste(image, ((h - w) // 2, 0))
-        return new_image
-
-
-class Trainer:
-    def __init__(self, hf_token: str | None = None):
-        self.hf_token = hf_token
-        self.api = HfApi(token=hf_token)
-        self.model_uploader = ModelUploader(hf_token)
-
-    def prepare_dataset(self, instance_images: list, resolution: int,
-                        instance_data_dir: pathlib.Path) -> None:
-        shutil.rmtree(instance_data_dir, ignore_errors=True)
-        instance_data_dir.mkdir(parents=True)
-        for i, temp_path in enumerate(instance_images):
-            image = PIL.Image.open(temp_path.name)
-            image = pad_image(image)
-            image = image.resize((resolution, resolution))
-            image = image.convert('RGB')
-            out_path = instance_data_dir / f'{i:03d}.jpg'
-            image.save(out_path, format='JPEG', quality=100)
-
-    def join_library_org(self) -> None:
-        subprocess.run(
-            shlex.split(
-                f'curl -X POST -H "Authorization: Bearer {self.hf_token}" -H "Content-Type: application/json" {URL_TO_JOIN_LIBRARY_ORG}'
-            ))
-
-    def run(
-        self,
-        instance_images: list | None,
-        instance_prompt: str,
-        output_model_name: str,
-        overwrite_existing_model: bool,
-        validation_prompt: str,
-        base_model: str,
-        resolution_s: str,
-        n_steps: int,
-        learning_rate: float,
-        gradient_accumulation: int,
-        seed: int,
-        fp16: bool,
-        use_8bit_adam: bool,
-        gradient_checkpointing: bool,
-        # enable_xformers_memory_efficient_attention: bool,
-        checkpointing_steps: int,
-        use_wandb: bool,
-        validation_epochs: int,
-        upload_to_hub: bool,
-        use_private_repo: bool,
-        delete_existing_repo: bool,
-        upload_to: str,
-        remove_gpu_after_training: bool,
-    ) -> str:
-        if not torch.cuda.is_available():
-            raise gr.Error('CUDA is not available.')
-        if instance_images is None:
-            raise gr.Error('You need to upload images.')
-        if not instance_prompt:
-            raise gr.Error('The instance prompt is missing.')
-        if not validation_prompt:
-            raise gr.Error('The validation prompt is missing.')
-
-        resolution = int(resolution_s)
-
-        if not output_model_name:
-            timestamp = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
-            output_model_name = f'svdiff-pytorch-{timestamp}'
-        output_model_name = slugify.slugify(output_model_name)
-
-        repo_dir = pathlib.Path(__file__).parent
-        output_dir = repo_dir / 'experiments' / output_model_name
-        if overwrite_existing_model or upload_to_hub:
-            shutil.rmtree(output_dir, ignore_errors=True)
-        output_dir.mkdir(parents=True)
-
-        instance_data_dir = repo_dir / 'training_data' / output_model_name
-        self.prepare_dataset(instance_images, resolution, instance_data_dir)
-
-        if upload_to_hub:
-            self.join_library_org()
-        # accelerate config 
-        write_basic_config()
-        command = f'''
-        accelerate launch train_svdiff.py \
-          --pretrained_model_name_or_path={base_model}  \
-          --instance_data_dir={instance_data_dir} \
-          --output_dir={output_dir} \
-          --instance_prompt="{instance_prompt}" \
-          --resolution={resolution} \
-          --train_batch_size=1 \
-          --gradient_accumulation_steps={gradient_accumulation} \
-          --learning_rate={learning_rate} \
-          --learning_rate_1d=1e-6 \
-          --train_text_encoder \
-          --lr_scheduler=constant \
-          --lr_warmup_steps=0 \
-          --max_train_steps={n_steps} \
-          --checkpointing_steps={checkpointing_steps} \
-          --validation_prompt="{validation_prompt}" \
-          --validation_epochs={validation_epochs} \
-          --seed={seed}
-        '''
-        if fp16:
-            command += ' --mixed_precision="fp16"'
-        if use_8bit_adam:
-            command += ' --use_8bit_adam'
-        if gradient_checkpointing:
-            command += ' --gradient_checkpointing'
-        # if enable_xformers_memory_efficient_attention:
-        #     command += ' --enable_xformers_memory_efficient_attention'
-        if use_wandb:
-            command += ' --report_to wandb'
-
-        with open(output_dir / 'train.sh', 'w') as f:
-            command_s = ' '.join(command.split())
-            f.write(command_s)
-        subprocess.run(shlex.split(command))
-        save_model_card(save_dir=output_dir,
-                        base_model=base_model,
-                        instance_prompt=instance_prompt,
-                        test_prompt=validation_prompt,
-                        test_image_dir='test_images')
-        
-        message = 'Training completed!'
-        print(message)
-
-        if upload_to_hub:
-            upload_message = self.model_uploader.upload_model(
-                folder_path=output_dir.as_posix(),
-                repo_name=output_model_name,
-                upload_to=upload_to,
-                private=use_private_repo,
-                delete_existing_repo=delete_existing_repo)
-            print(upload_message)
-            message = message + '\n' + upload_message
-
-        if remove_gpu_after_training:
-            space_id = os.getenv('SPACE_ID')
-            if space_id:
-                self.api.request_space_hardware(repo_id=space_id,
-                                                hardware='cpu-basic')
-
-        return message
diff --git a/spaces/syaz01/rvc-anigames-v2/lib/infer_pack/onnx_inference.py b/spaces/syaz01/rvc-anigames-v2/lib/infer_pack/onnx_inference.py
deleted file mode 100644
index c78324cbc08414fffcc689f325312de0e51bd6b4..0000000000000000000000000000000000000000
--- a/spaces/syaz01/rvc-anigames-v2/lib/infer_pack/onnx_inference.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import onnxruntime
-import librosa
-import numpy as np
-import soundfile
-
-
-class ContentVec:
-    def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
-        print("load model(s) from {}".format(vec_path))
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(vec_path, providers=providers)
-
-    def __call__(self, wav):
-        return self.forward(wav)
-
-    def forward(self, wav):
-        feats = wav
-        if feats.ndim == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.ndim == 1, feats.ndim
-        feats = np.expand_dims(np.expand_dims(feats, 0), 0)
-        onnx_input = {self.model.get_inputs()[0].name: feats}
-        logits = self.model.run(None, onnx_input)[0]
-        return logits.transpose(0, 2, 1)
-
-
-def get_f0_predictor(f0_predictor, hop_length, sampling_rate, **kargs):
-    if f0_predictor == "pm":
-        from lib.infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor
-
-        f0_predictor_object = PMF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "harvest":
-        from lib.infer_pack.modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor
-
-        f0_predictor_object = HarvestF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "dio":
-        from lib.infer_pack.modules.F0Predictor.DioF0Predictor import DioF0Predictor
-
-        f0_predictor_object = DioF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    else:
-        raise Exception("Unknown f0 predictor")
-    return f0_predictor_object
-
-
-class OnnxRVC:
-    def __init__(
-        self,
-        model_path,
-        sr=40000,
-        hop_size=512,
-        vec_path="vec-768-layer-12",
-        device="cpu",
-    ):
-        vec_path = f"pretrained/{vec_path}.onnx"
-        self.vec_model = ContentVec(vec_path, device)
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(model_path, providers=providers)
-        self.sampling_rate = sr
-        self.hop_size = hop_size
-
-    def forward(self, hubert, hubert_length, pitch, pitchf, ds, rnd):
-        onnx_input = {
-            self.model.get_inputs()[0].name: hubert,
-            self.model.get_inputs()[1].name: hubert_length,
-            self.model.get_inputs()[2].name: pitch,
-            self.model.get_inputs()[3].name: pitchf,
-            self.model.get_inputs()[4].name: ds,
-            self.model.get_inputs()[5].name: rnd,
-        }
-        return (self.model.run(None, onnx_input)[0] * 32767).astype(np.int16)
-
-    def inference(
-        self,
-        raw_path,
-        sid,
-        f0_method="dio",
-        f0_up_key=0,
-        pad_time=0.5,
-        cr_threshold=0.02,
-    ):
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        f0_predictor = get_f0_predictor(
-            f0_method,
-            hop_length=self.hop_size,
-            sampling_rate=self.sampling_rate,
-            threshold=cr_threshold,
-        )
-        wav, sr = librosa.load(raw_path, sr=self.sampling_rate)
-        org_length = len(wav)
-        if org_length / sr > 50.0:
-            raise RuntimeError("Reached Max Length")
-
-        wav16k = librosa.resample(wav, orig_sr=self.sampling_rate, target_sr=16000)
-        wav16k = wav16k
-
-        hubert = self.vec_model(wav16k)
-        hubert = np.repeat(hubert, 2, axis=2).transpose(0, 2, 1).astype(np.float32)
-        hubert_length = hubert.shape[1]
-
-        pitchf = f0_predictor.compute_f0(wav, hubert_length)
-        pitchf = pitchf * 2 ** (f0_up_key / 12)
-        pitch = pitchf.copy()
-        f0_mel = 1127 * np.log(1 + pitch / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        pitch = np.rint(f0_mel).astype(np.int64)
-
-        pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32)
-        pitch = pitch.reshape(1, len(pitch))
-        ds = np.array([sid]).astype(np.int64)
-
-        rnd = np.random.randn(1, 192, hubert_length).astype(np.float32)
-        hubert_length = np.array([hubert_length]).astype(np.int64)
-
-        out_wav = self.forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze()
-        out_wav = np.pad(out_wav, (0, 2 * self.hop_size), "constant")
-        return out_wav[0:org_length]
diff --git a/spaces/taesiri/LatexDiff/README.md b/spaces/taesiri/LatexDiff/README.md
deleted file mode 100644
index 77ac7d6d1ef60085c7de38b02082b77d4f7b8f48..0000000000000000000000000000000000000000
--- a/spaces/taesiri/LatexDiff/README.md
+++ /dev/null
@@ -1,46 +0,0 @@
----
-title: Latex Diff
-emoji: 🚀
-colorFrom: pink
-colorTo: yellow
-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/tcapelle/calculadora_impuestos/README.md b/spaces/tcapelle/calculadora_impuestos/README.md
deleted file mode 100644
index 5814bcd3bdad7a61888e6741e4a0b6c6a9e481da..0000000000000000000000000000000000000000
--- a/spaces/tcapelle/calculadora_impuestos/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Calculadora Impuestos
-emoji: 📊
-colorFrom: gray
-colorTo: green
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: true
-license: mit
----
\ No newline at end of file
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Bejeweled 3 UPD CrackED Lucky Patcher.md b/spaces/terfces0erbo/CollegeProjectV2/Bejeweled 3 UPD CrackED Lucky Patcher.md
deleted file mode 100644
index 2d68493aa4fa09cae0b99bb20fd92bb56a052b30..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Bejeweled 3 UPD CrackED Lucky Patcher.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Bejeweled 3 CRACKED lucky patcher</h2><br /><p><b><b>Download</b> &#9734;&#9734;&#9734; <a href="https://bytlly.com/2uGlkd">https://bytlly.com/2uGlkd</a></b></p><br /><br />
-<br />
-3 Cracked APK Download [No Root] 2020. Lucky Patcher APK 8.6.3 App Cracker Free download is the best as well as faultless working APK. 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Download !!HOT!! Sublime Text 4 For Windows 10 64 Bit.md b/spaces/tialenAdioni/chat-gpt-api/logs/Download !!HOT!! Sublime Text 4 For Windows 10 64 Bit.md
deleted file mode 100644
index 866b4042c8dfce0e9e561b5ad97317d21bded1cf..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Download !!HOT!! Sublime Text 4 For Windows 10 64 Bit.md	
+++ /dev/null
@@ -1,31 +0,0 @@
-
-<h1>How to Download Sublime Text 4 for Windows 10 64 Bit</h1>
-<p>Sublime Text 4 is a sophisticated text editor for code, markup and prose. It has a slick user interface, extraordinary features and amazing performance. It supports many programming languages and has a powerful plugin system. If you are looking for a code editor that can boost your productivity and creativity, Sublime Text 4 is a great choice.</p>
-<h2>download sublime text 4 for windows 10 64 bit</h2><br /><p><b><b>Download</b> &#9913; <a href="https://urlcod.com/2uK38C">https://urlcod.com/2uK38C</a></b></p><br /><br />
-<p>In this article, we will show you how to download Sublime Text 4 for Windows 10 64 bit. We will also explain some of the benefits of using Sublime Text 4 and some of its key features.</p>
-<h2>Why Use Sublime Text 4?</h2>
-<p>Sublime Text 4 is not just a text editor, it is a tool that can help you write better code faster and easier. Here are some of the reasons why you should use Sublime Text 4:</p>
-<ul>
-<li>It has a minimalist and customizable interface that lets you focus on your code.</li>
-<li>It has a fast and responsive performance that can handle large files and complex projects.</li>
-<li>It has a built-in package manager that lets you install and update plugins with ease.</li>
-<li>It has a powerful syntax highlighting engine that supports many languages and formats.</li>
-<li>It has a smart auto-completion feature that suggests words, symbols and snippets as you type.</li>
-<li>It has a versatile multi-selection feature that lets you edit multiple lines or regions at once.</li>
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-<li>It has a distraction-free mode that lets you write code in full screen without any distractions.</li>
-<li>It has a cross-platform compatibility that lets you use it on Windows, Mac and Linux.</li>
-</ul>
-<h2>How to Download Sublime Text 4 for Windows 10 64 Bit</h2>
-<p>Downloading Sublime Text 4 for Windows 10 64 bit is easy and straightforward. Here are the steps you need to follow:</p>
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-<p></p> ddb901b051<br />
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-<h1>Bleach vs Naruto 540+ Characters: A Legendary Anime Fighting Game for Android</h1>
- <p>If you are a fan of anime, especially Bleach and Naruto, you might have heard of a game called Bleach vs Naruto. It is a free online 2D flash anime fighting game developed by the Chinese company 5Dplay, which is playable on internet browser, PC, and Android. It is a crossover anime fighting game featuring characters from both Bleach and Naruto Shippuden, with guest characters from other anime series such as Rurouni Kenshin, One Piece, Fairy Tail, Dragon Ball, and more.</p>
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- <p>Bleach vs Naruto has been one of the most popular anime fighting games among anime fans for years, thanks to its huge roster of characters, various game modes, smooth and fast gameplay, stunning graphics and effects, and frequent updates. However, there is a modded version of the game that takes it to a whole new level. It is called Bleach vs Naruto 540+ Characters, and it is made by Kizuma Gaming and Makoto Itou, two passionate fans of the game who have added more than 500 characters, more than 150 stages, more assists, new game modes, new interface, new effects, and more to the original game.</p>
- <p>In this article, we will tell you everything you need to know about Bleach vs Naruto 540+ Characters, including its features, how to download and install it on your Android device, how to fix common errors and issues, and some tips and tricks for playing it. If you are ready to experience the ultimate anime fighting game on your Android device, read on!</p>
- <h2>Features of Bleach vs Naruto 540+ Characters</h2>
- <h3>A huge roster of characters from Bleach, Naruto, and other anime series</h3>
- <p>One of the main attractions of Bleach vs Naruto 540+ Characters is its massive roster of characters. The game features more than 500 characters from Bleach, Naruto, and other anime series such as Rurouni Kenshin, One Piece, Fairy Tail, Dragon Ball, Hunter x Hunter, My Hero Academia, Demon Slayer, Jujutsu Kaisen, Attack on Titan, Tokyo Ghoul, One Punch Man, Fullmetal Alchemist, Death Note, Black Clover, JoJo's Bizarre Adventure, Yu-Gi-Oh!, Inuyasha, Soul Eater, D.Gray-man and more. You can find almost any character you can think of in this game.</p>
- <p>Each character has their own unique skills, abilities, combos, and special moves that are faithful to their original anime counterparts. You can unleash powerful attacks such as Ichigo's Getsuga Tensho, Naruto's Rasengan, Luffy's Gear Fourth, Goku's Kamehameha, Saitama's Serious Punch, Edward's Alchemy, Light's Death Note, Asta's Anti-Magic, Jotaro's Star Platinum, Yugi's Dark Magician, and many more. You can also use assists to summon other characters to help you in battle.</p>
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- <h3>Various game modes and stages to choose from</h3>
- <p>Bleach vs Naruto 540+ Characters also offers various game modes for you to enjoy. You can play solo or with a friend in local or online multiplayer mode. You can choose from different types of matches such as 1v1, 2v2, 3v3, team battle, survival, arcade, training, and more. You can also customize the difficulty, time limit, health, and other settings to suit your preference.</p>
- <p>The game also features more than 150 stages from various anime worlds for you to fight on. You can battle in iconic locations such as Soul Society, Konoha, Marineford, Namek, U.A. High School, Demon Slayer Corps Headquarters, Shibuya, Wall Maria, Anteiku, Hero Association Headquarters, Central City, Death Note World, Clover Kingdom, Morioh Town, Duelist Kingdom, Feudal Japan, Death City, Black Order Headquarters and more. Each stage has its own background music and sound effects that match the anime atmosphere.</p>
- <h3>Smooth and fast gameplay with stunning graphics and effects</h3>
- <p>Bleach vs Naruto 540+ Characters is not only a game with a lot of content, but also a game with a lot of quality. The game runs smoothly and fast on Android devices, with no lag or glitches. The game also has stunning graphics and effects that make the game look like a real anime. The characters are well-designed and animated, with realistic expressions and movements. The game also has impressive visual effects such as lighting, shadows, explosions, blood splashes, sparks, smoke, and more. The game also has a dynamic camera that zooms in and out depending on the action.</p>
- <p>The game also has a great sound system that enhances the gameplay experience. The game has original voice acting from the anime actors for each character, which makes the game more authentic and immersive. The game also has epic soundtracks and sound effects that match the anime style and mood. You can hear the characters' shouts, grunts, taunts, and catchphrases as they fight. You can also hear the sound of their attacks hitting or missing their targets. The game also has a narrator that announces the start and end of each match.</p>
- <h2>How to Download and Install Bleach vs Naruto 540+ Characters on Android</h2>
- <h3>Requirements and precautions before downloading</h3>
- <p>Before you download and install Bleach vs Naruto 540+ Characters on your Android device, you need to make sure that your device meets the following requirements:</p>
- <ul>
-<li>Your device must have Android 4.4 or higher.</li>
-<li>Your device must have at least 2 GB of RAM and 4 GB of free storage space.</li>
-<li>Your device must have a stable internet connection for online multiplayer mode.</li>
-</ul>
- <p>You also need to take some precautions before downloading the game:</p>
- <ul>
-<li>You need to enable unknown sources on your device settings to allow the installation of third-party apps.</li>
-<li>You need to disable any antivirus or security apps that might interfere with the installation process.</li>
-<li>You need to backup your data before installing the game in case something goes wrong.</li>
-</ul>
- <h3>Download links and instructions for installation</h3>
- <p>Once you have checked the requirements and precautions, you can proceed to download and install Bleach vs Naruto 540+ Characters on your Android device. Here are the download links and instructions for installation:</p>
- <ol>
-<li>Download the APK file of Bleach vs Naruto 540+ Characters from this link: </li>
-<li>Download the OBB file of Bleach vs Naruto 540+ Characters from this link: </li>
-<li>After downloading both files, locate them in your device's file manager and extract them using a ZIP extractor app such as ZArchiver or RAR.</li>
-<li>Copy the extracted OBB folder named "com.kizuma.makotoitou.bvn540" and paste it in your device's internal storage/Android/obb folder.</li>
-<li>Install the APK file by tapping on it and following the instructions on the screen.</li>
-<li>Launch the game from your app drawer or home screen and enjoy!</li>
-</ol>
- <h3>How to fix common errors and issues</h3>
- <p>If you encounter any errors or issues while downloading or installing Bleach vs Naruto 540+ Characters on your Android device, here are some possible solutions you can try:</p>
- <ul>
-<li>Make sure you have downloaded the correct and latest version of the APK and OBB files from the links provided above.</li>
-<li>Make sure you have extracted and copied the OBB folder correctly to your device's internal storage/Android/obb folder.</li>
-<li>Make sure you have enough storage space and RAM available on your device.</li>
-<li>Make sure you have a stable internet connection for online multiplayer mode.</li>
-<li>Make sure you have disabled any antivirus or security apps that might block or delete the game files.</li>
-<li>Make sure you have enabled unknown sources on your device settings to allow the installation of third-party apps.</li>
-<li>Restart your device and try to launch the game again.</li>
-<li>Clear the cache and data of the game from your device settings/apps/Bleach vs Naruto 540+ Characters/storage.</li>
-<li>Uninstall and reinstall the game following the instructions above.</li>
-</ul>
- <p>If none of these solutions work, you can contact the developers of Bleach vs Naruto 540+ Characters on their YouTube channels: Kizuma Gaming and Makoto Itou. They might be able to help you with your problem or update the game to fix it.</p>
- <h2>Tips and Tricks for Playing Bleach vs Naruto 540+ Characters on Android</h2>
- <h3>How to unlock hidden characters and assists</h3>
- <p>Bleach vs Naruto 540+ Characters has a lot of hidden characters and assists that you can unlock by completing certain tasks or using certain codes. Here are some of them:</p>
- <ul>
-<li>To unlock Ichigo's final form, Mugetsu, you need to win 10 matches in a row with Ichigo in any game mode.</li>
-<li>To unlock Naruto's six paths mode, you need to win 10 matches in a row with Naruto in any game mode.</li>
-<li>To unlock Luffy's gear fifth mode, you need to win 10 matches in a row with Luffy in any game mode.</li>
-<li>To unlock Goku's ultra instinct mode, you need to win 10 matches in a row with Goku in any game mode.</li>
-<li>To unlock Saitama's serious mode, you need to win 10 matches in a row with Saitama in any game mode.</li>
-<li>To unlock Light Yagami as an assist, you need to enter the code "DEATH" in the character selection screen.</li>
-<li>To unlock Yugi Muto as an assist, you need to enter the code "DUEL" in the character selection screen.</li>
-<li>To unlock Jotaro Kujo as an assist, you need to enter the code "ORA" in the character selection screen.</li>
-</ul>
- <p>There are many more hidden characters and assists that you can discover by playing the game or watching videos from the developers' YouTube channels.</p>
- <h3>How to use combos and special moves effectively</h3>
- <p>Bleach vs Naruto 540+ Characters is a game that requires skill and strategy to master. You need to know how to use combos and special moves effectively to defeat your opponents. Here are some tips:</p>
- <ul>
-<li>Each character has their own combo list that you can view by pressing the pause button during a match. You can also practice combos in training mode or arcade mode.</li>
-<li>Combos are sequences of attacks that can deal more damage and stun your opponent. You can perform combos by pressing different buttons in a certain order or timing. For example, Naruto's basic combo is A-A-A-A-D-J-A-A-A-A-D-J-A-A-A-A-D-J-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S. You can also mix up combos with different buttons or directions to create your own style.</li>
-<li>Special moves are powerful attacks that consume energy or chakra. You can perform special moves by pressing different buttons with a direction or holding a button. For example, Ichigo's Getsuga Tensho is W-J, Naruto's Rasengan is D-J, Luffy's Gomu Gomu no Pistol is S-J, Goku's Kamehameha is A-J, Saitama's Serious Punch is W-J, Edward's Alchemy is D-J, Light's Death Note is W-L, Yugi's Dark Magician is W-L, Jotaro's Star Platinum is W-L. You can also use assists to perform special moves with them by pressing L instead of J.</li>
-<li>You can use combos and special moves together to create devastating attacks. For example, you can start with a combo and end with a special move, or vice versa. You can also use assists to extend your combos or special moves, or interrupt your opponent's attacks. You can also use counters or dodges to avoid or reverse your opponent's attacks. You can also use transformations to enhance your stats and abilities.</li>
-</ul>
- <p>The key to using combos and special moves effectively is to know your character's strengths and weaknesses, and to adapt to your opponent's moves and strategies. You can also watch videos from the developers' YouTube channels or other players to learn from their techniques and tips.</p>
- <h3>How to play online with other players</h3>
- <p>Bleach vs Naruto 540+ Characters also allows you to play online with other players from around the world. You can join or create a room and invite or challenge other players to a match. You can also chat with other players and make friends or rivals. Here are some tips for playing online:</p>
- <ul>
-<li>You need to have a stable internet connection for online multiplayer mode. If your connection is slow or unstable, you might experience lag, disconnection, or other problems.</li>
-<li>You need to be respectful and polite to other players. Do not spam, insult, cheat, or troll other players. If you encounter any toxic or abusive players, you can report or block them.</li>
-<li>You need to follow the rules and settings of the room you join or create. Do not change the settings without the consent of the host or other players. Do not quit or disconnect during a match unless you have a valid reason.</li>
-<li>You need to have fun and enjoy the game. Do not take the game too seriously or get angry if you lose. Learn from your mistakes and improve your skills. Congratulate and compliment other players if they win or do well. Share your feedback and suggestions with the developers or other players.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Bleach vs Naruto 540+ Characters is a legendary anime fighting game for Android that you should not miss if you are a fan of anime, especially Bleach and Naruto. It is a game that has everything you could ask for in an anime fighting game: a huge roster of characters, various game modes, smooth and fast gameplay, stunning graphics and effects, and frequent updates. It is a game that will keep you entertained and challenged for hours.</p>
- <p>If you want to download and install Bleach vs Naruto 540+ Characters on your Android device, you can follow the instructions above. If you encounter any errors or issues, you can try the solutions above or contact the developers. If you want to learn how to use combos and special moves effectively, or how to play online with other players, you can follow the tips above or watch videos from the developers' YouTube channels or other players.</p>
- <p>Bleach vs Naruto 540+ Characters is a game that deserves your attention and appreciation. It is a game that is made by fans for fans, with passion and dedication. It is a game that celebrates the anime culture and community. It is a game that you will love and enjoy.</p>
- <p>So what are you waiting for? Download Bleach vs Naruto 540+ Characters now and join the ultimate anime fighting game on your Android device!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Bleach vs Naruto 540+ Characters:</p>
- <ol>
-<li>Q: Is Bleach vs Naruto 540+ Characters free?</li>
-<li>A: Yes, Bleach vs Naruto 540+ Characters is free to download and play on Android devices. However, you might see some ads in the game that support the developers.</li>
-<li>Q: Is Bleach vs Naruto 540+ Characters safe?</li>
-<li>A: Yes, Bleach vs Naruto 540+ Characters is safe to download and install on Android devices. It does not contain any viruses, malware, spyware, or other harmful elements. However, you should always download the game from the official links provided above and not from any unknown sources.</li>
-<li>Q: Is Bleach vs Naruto 540+ Characters official?</li>
-<li>A: No, Bleach vs Naruto 540+ Characters is not an official game from the creators of Bleach or Naruto anime series. It is a fan-made modded version of Bleach vs Naruto, which is an original game developed by 5Dplay. The developers of Bleach vs Naruto 540+ Characters do not own any rights to the characters, images, sounds, or music used in the game. They are only using them for entertainment purposes under fair use.</li>
-<li>Q: How often is Bleach vs Naruto 540+ Characters updated?</li>
-<li>A: Bleach vs Naruto 540+ Characters is updated regularly by the developers Kizuma Gaming and Makoto Itou. They usually release new updates every month or every few months, depending on their progress and availability. They usually announce their updates on their YouTube channels or social media accounts.</li>
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-<li>A: You can support the developers of Bleach vs Naruto 540+ Characters by subscribing to their YouTube channels, liking and commenting on their videos, sharing their game with your friends, giving them feedback and suggestions, donating to them via PayPal or Patreon, or simply playing and enjoying their game.</li>
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Accounting P1 Everything You Need to Know about the NSC and CIMA Syllabus.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Accounting P1 Everything You Need to Know about the NSC and CIMA Syllabus.md
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-<p>If you prefer a more structured and interactive way of learning Accounting P1, you can enroll in an online course or tutorial that covers the syllabus. There are many online providers that offer courses and tutorials for Accounting P1, such as:</p>
-<ul>
-<li>CIMAstudy.com, which is the official online learning platform of CIMA that offers comprehensive and flexible courses for Accounting P1, featuring interactive lessons, videos, quizzes, feedback, mock exams, and tutor support</li>
-<li>Astranti, which is a leading online provider of CIMA courses that offers a complete course for Accounting P1, featuring study text, videos, practice questions, mock exams, tutor support, and pass guarantee</li>
-<li>OpenTuition, which is a free online platform that offers lectures, notes, flashcards, quizzes, forums, and articles for Accounting P1</li>
-</ul>
-<p>You can choose the online course or tutorial that suits your learning style, budget, and schedule. However, you should always check the quality and validity of the course or tutorial before enrolling.</p>
- <h4>Mock exams and practice questions</h4>
-<p>One of the most effective ways to prepare for Accounting P1 exam is to practice as many mock exams and questions as possible. This can help you test your knowledge and understanding of the syllabus, improve your exam technique and time management skills, identify your strengths and weaknesses, and boost your confidence. There are many sources and platforms that offer mock exams and practice questions for Accounting P1, such as:</p>
-<ul>
-<li>CIMA Aptitude, which is an online tool that offers personalized practice tests for Accounting P1 based on your performance and feedback</li>
-<li>Practice Tests Academy, which is an online platform that offers over 500 practice questions for Accounting P1 with detailed explanations and progress tracking</li>
-<li>VIVA Financial Tuition, which is an online platform that offers mock exams for Accounting P1 with instant marking and feedback</li>
-</ul>
-<p>You can access some of these mock exams and practice questions for free or at a low cost. However, you should always use them in conjunction with other resources and materials to ensure a comprehensive preparation.</p>
- <h3>The tips and strategies for passing Accounting P1</h3>
-<p>Besides having access to high-quality and relevant resources and materials, you also need to follow some tips and strategies that can help you pass Accounting P1 exam. Here are some of them:</p>
- <h4>Plan your study schedule and set your goals</h4> <p>Continuing the article:</p>
-<p>One of the first things you should do before starting your preparation for Accounting P1 is to plan your study schedule and set your goals. You should allocate enough time and resources for each topic and subtopic in the syllabus, taking into account your current level of knowledge, learning pace, and availability. You should also set realistic and specific goals for each study session, such as completing a chapter, solving a practice question, or reviewing a concept. This can help you stay focused, motivated, and organized throughout your preparation.</p>
- <h4>Review the key concepts and topics</h4>
-<p>Another important thing you should do during your preparation for Accounting P1 is to review the key concepts and topics covered in the syllabus. You should make sure that you understand the underlying principles and techniques of management accounting, such as costing methods, budgeting processes, variance analysis, decision making models, risk management tools, and performance measurement systems. You should also be able to apply these concepts and topics to different scenarios and situations that may arise in the exam. You can use various resources and materials, such as study guides, notes, videos, lectures, and articles, to review the key concepts and topics.</p>
- <h4>Practice your exam technique and time management</h4>
-<p>One of the most crucial things you should do before taking the Accounting P1 exam is to practice your exam technique and time management skills. You should familiarize yourself with the format and content of the exam, such as the number and type of questions, the marking scheme, and the time limit. You should also practice answering multiple choice questions under exam conditions, such as using a timer, eliminating wrong answers, guessing intelligently, and checking your work. You can use various resources and materials, such as past papers, mock exams, practice tests, and quizzes, to practice your exam technique and time management skills.</p>
- <h4>Revise and refresh your knowledge before the exam</h4>
-<p>One of the final things you should do before sitting for the Accounting P1 exam is to revise and refresh your knowledge of the syllabus. You should review the main points and formulas of each topic and subtopic, as well as any areas that you are weak or unsure about. You should also revise any questions or concepts that you have previously answered incorrectly or found difficult. You can use various resources and materials, such as flashcards, summaries, mind maps, diagrams, and mnemonics, to revise and refresh your knowledge before the exam.</p>
- <h2>How to download Accounting P1 materials?</h2>
-<h3>The sources and platforms for downloading Accounting P1 materials</h3>
-<p>If you are looking for Accounting P1 materials to download for your preparation or reference, you have many options to choose from. There are many sources and platforms that offer various types of materials for Accounting P1, such as:</p>
- <h4>Official websites and portals</h4>
-<p>The most reliable and authentic source for downloading Accounting P1 materials is the official CIMA website or portal. As mentioned earlier, you can find the syllabus and assessment guide, study guide, past papers and answers for Accounting P1 on the CIMA website. You can also find other useful materials for Accounting P1 on the CIMA portal, such as:</p>
-<ul>
-<li>The pre-seen material for the Management level case study exam</li>
-<li>The examiner's reports for Accounting P1</li>
-<li>The technical articles for Accounting P1</li>
-<li>The ethical guidance for Accounting P1</li>
-</ul>
-<p>You can download these materials for free from the CIMA website or portal. However, you need to register as a CIMA student or member to access them.</p>
- <h4>Online libraries and databases</h4>
-<p>Another good source for downloading Accounting P1 materials is online libraries or databases that offer academic or professional publications related to accounting. These online libraries or databases can provide you with access to various types of materials for Accounting P1, such as:</p>
-<ul>
-<li>Books and textbooks on management accounting</li>
-<li>Journals and articles on management accounting research</li>
-<li>Case studies and examples on management accounting practice</li>
-<li>Reports and standards on management accounting regulation</li>
-</ul>
-<p>You can download these materials from online libraries or databases by searching for relevant keywords or filters. However, you may need to pay a fee or subscribe to access some of these online libraries or databases.</p>
- <h4>File sharing and cloud storage services</h4>
-<p>A third source for downloading Accounting P1 materials is file sharing or cloud storage services that allow users to upload, store, share, or download files online. These file sharing or cloud storage services can offer you access to various types of materials for Accounting P1 that are uploaded by other users or providers. For example:</p>
-<ul>
-<li>Dropbox: A cloud storage <p>Continuing the article:</p>
-<p>service that lets you store and access your files from any device. You can find various materials for Accounting P1 that are shared by other Dropbox users or providers, such as study guides, notes, mock exams, and practice questions.</li>
-<li>Google Drive: A cloud storage service that lets you store and access your files from any device. You can also create and edit documents, spreadsheets, presentations, and forms online. You can find various materials for Accounting P1 that are shared by other Google Drive users or providers, such as study guides, notes, mock exams, and practice questions.</li>
-<li>MediaFire: A file sharing service that lets you upload and download files up to 10 GB for free. You can find various materials for Accounting P1 that are uploaded by other MediaFire users or providers, such as study guides, notes, mock exams, and practice questions.</li>
-</ul>
-<p>You can download these materials from file sharing or cloud storage services by searching for relevant keywords or filters. However, you should always be careful and cautious when downloading files from these services, as they may contain viruses, malware, or inaccurate or outdated information.</p>
- <h3>The steps and procedures for downloading Accounting P1 materials</h3>
-<p>Once you have chosen the source or platform for downloading Accounting P1 materials, you need to follow some steps and procedures to download the materials successfully and safely. Here are some of them:</p>
- <h4>Search for the relevant materials using keywords and filters</h4>
-<p>The first step is to search for the relevant materials using keywords and filters that match your needs and preferences. For example, if you are looking for past papers and answers for Accounting P1, you can use keywords such as "Accounting P1 past papers", "Accounting P1 exam questions", or "Accounting P1 solutions". You can also use filters such as date, format, size, or language to narrow down your search results.</p>
- <h4>Check the quality and validity of the materials before downloading</h4>
-<p>The second step is to check the quality and validity of the materials before downloading them. You should make sure that the materials are accurate, reliable, relevant, and up-to-date. You should also check the source or provider of the materials to ensure their credibility and reputation. You can do this by reading the reviews, ratings, comments, or feedback from other users or customers. You can also verify the information or data in the materials with other sources or references.</p>
- <h4>Follow the instructions and terms of use of the source or platform</h4>
-<p>The third step is to follow the instructions and terms of use of the source or platform that offers the materials. You should read and understand the terms and conditions, privacy policy, and disclaimer of the source or platform before downloading any materials. You should also comply with any rules or regulations regarding the access, download, use, or distribution of the materials. You should also respect the intellectual property rights and copyrights of the source or provider of the materials.</p>
- <h4>Save and organize the materials in a secure location</h4>
-<p>The fourth step is to save and organize the materials in a secure location on your device or online. You should choose a location that is easy to access, manage, and backup. You should also create folders or categories to sort and classify your materials according to their type, topic, or purpose. You should also label your files with clear and descriptive names to help you identify them easily. You should also protect your files with passwords or encryption to prevent unauthorized access or loss.</p>
- <h2>Conclusion</h2>
-<p>Accounting P1 is a challenging but rewarding paper that can help you develop your skills and knowledge in management accounting. To pass this paper, you need to have access to high-quality and relevant resources and materials that can help you learn, practice, and revise the syllabus. You also need to follow some tips and strategies that can help you prepare for and pass the exam.</p>
-<p>In this article, we have provided you with a comprehensive guide on how to download Accounting P1 materials from various sources and platforms. We have also given you some tips and strategies on how to prepare for and pass Accounting P1 exam. We hope that this article has been helpful and informative for you.</p>
-<p>If you have any questions or comments about this article or Accounting P1 in general, please feel free to contact us. We would love to hear from you.</p>
- <h2>FAQs</h2>
-<h3>What is CIMA?</h3>
-<p>CIMA stands for Chartered Institute of Management Accountants. It is the world's largest professional body of management accountants, with over 600,000 members and students in more than 170 countries. CIMA offers a range of qualifications <p>materials, you have many options to choose from. There are many sources and platforms that offer various types of materials for Accounting P1, such as official websites and portals, online libraries and databases, and file sharing and cloud storage services. You can download these materials by following some steps and procedures, such as searching for the relevant materials using keywords and filters, checking the quality and validity of the materials before downloading, following the instructions and terms of use of the source or platform, and saving and organizing the materials in a secure location.</p>
- <h3>How to prepare for Accounting P1 exam?</h3>
-<p>To prepare for Accounting P1 exam, you need to have access to high-quality and relevant resources and materials that can help you learn, practice, and revise the syllabus. You also need to follow some tips and strategies that can help you pass the exam. Some of the resources and materials that you can use are official study guides and past papers, online courses and tutorials, and mock exams and practice questions. Some of the tips and strategies that you can follow are planning your study schedule and setting your goals, reviewing the key concepts and topics, practicing your exam technique and time management, and revising and refreshing your knowledge before the exam.</p>
- <h2></h2></p> 401be4b1e0<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Autodesk AutoCAD Plant 3D 2019 With Activation The Latest Version !FREE!.md b/spaces/tioseFevbu/cartoon-converter/scripts/Autodesk AutoCAD Plant 3D 2019 With Activation The Latest Version !FREE!.md
deleted file mode 100644
index ddbb4cbdb045a7ebb4d33f2b56355d74940ec131..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Autodesk AutoCAD Plant 3D 2019 With Activation The Latest Version !FREE!.md	
+++ /dev/null
@@ -1,51 +0,0 @@
-<br />
-<h1>How to Download and Activate Autodesk AutoCAD Plant 3D 2019 - The Latest Version of the Professional 3D Design Tool</h1>
-
-<p>If you are looking for a powerful and versatile 3D design tool for plant engineering, you might want to check out Autodesk AutoCAD Plant 3D 2019. This software allows you to create and edit piping and instrumentation diagrams, 3D models, orthographic drawings, and isometric drawings with ease and accuracy. You can also collaborate with other project stakeholders using cloud-based services and integrate with other Autodesk products such as Revit, Civil 3D, and Navisworks.</p>
-
-<p>But how can you get started with Autodesk AutoCAD Plant 3D 2019? In this article, we will show you how to download and activate the latest version of this software in a few simple steps.</p>
-<h2>Autodesk AutoCAD Plant 3D 2019 with activation the latest version</h2><br /><p><b><b>Download</b> &#128504; <a href="https://urlcod.com/2uHvkz">https://urlcod.com/2uHvkz</a></b></p><br /><br />
-
-<h2>Step 1: Download the Free Trial Version</h2>
-
-<p>The first thing you need to do is to download the free trial version of Autodesk AutoCAD Plant 3D 2019 from the official website[^3^]. You can choose from a variety of languages and operating systems. The trial version will let you use the software for 30 days without any limitations.</p>
-
-<p>To download the free trial version, you need to create an Autodesk account or sign in with your existing one. You also need to provide some basic information such as your name, email address, country, and industry. After that, you will receive a confirmation email with a download link and instructions.</p>
-
-<h2>Step 2: Install the Software</h2>
-
-<p>Once you have downloaded the software, you need to install it on your computer. To do that, you need to follow these steps:</p>
-
-<ul>
-<li>Double-click the downloaded file to start the installation process.</li>
-<li>Select your preferred language and click <b>Install</b>.</li>
-<li>Accept the license agreement and click <b>Next</b>.</li>
-<li>Select the components you want to install and click <b>Next</b>.</li>
-<li>Choose the installation location and click <b>Next</b>.</li>
-<li>Wait for the installation to complete and click <b>Finish</b>.</li>
-</ul>
-
-<p>Congratulations! You have successfully installed Autodesk AutoCAD Plant 3D 2019 on your computer.</p>
-
-<h2>Step 3: Activate the Software</h2>
-
-<p>The last step is to activate the software using a product key. A product key is a unique code that identifies your software license and allows you to use it without any restrictions. You can find your product key in your Autodesk account or in the confirmation email you received after downloading the software.</p>
-
-<p>To activate the software, you need to follow these steps:</p>
-<p></p>
-
-<ul>
-<li>Launch Autodesk AutoCAD Plant 3D 2019 on your computer.</li>
-<li>Click <b>Enter a Serial Number</b>.</li>
-<li>Type your serial number and product key in the corresponding fields and click <b>Next</b>.</li>
-<li>Select your preferred activation method (online or offline) and follow the instructions on the screen.</li>
-<li>Wait for the activation to complete and click <b>Finish</b>.</li>
-</ul>
-
-<p>Congratulations! You have successfully activated Autodesk AutoCAD Plant 3D 2019 on your computer.</p>
-
-<h2>Conclusion</h2>
-
-<p>In this article, we have shown you how to download and activate Autodesk AutoCAD Plant 3D 2019 - the latest version of the professional 3D design tool for plant engineering. We hope this guide was helpful and easy to follow. If you have any questions or feedback, please feel free to leave a comment below.</p> e93f5a0c3f<br />
-<br />
-<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Cricket.LG.G.Stylo.[H634].Firmware.Android.5.1.1 12 !FULL!.md b/spaces/tioseFevbu/cartoon-converter/scripts/Cricket.LG.G.Stylo.[H634].Firmware.Android.5.1.1 12 !FULL!.md
deleted file mode 100644
index 282501bd8fabf5f11b723db7f6c50220d1a5d00e..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Cricket.LG.G.Stylo.[H634].Firmware.Android.5.1.1 12 !FULL!.md	
+++ /dev/null
@@ -1,34 +0,0 @@
-
-Here is a possible title and article with html formatting for the keyword "Cricket.LG.G.Stylo.[H634].Firmware.Android.5.1.1 12":
-
-<h1>How to Update Cricket LG G Stylo H634 to Android 5.1.1 Lollipop</h1>
-<p>The LG G Stylo is a mid-range smartphone that was released by LG in 2015. It features a 5.7-inch HD display, a quad-core processor, 1 GB of RAM, 8 GB of internal storage, a 13 MP rear camera, a 5 MP front camera, and a stylus pen. The phone runs on Android 5.0 Lollipop out of the box, but it can be updated to Android 5.1.1 Lollipop with the official firmware from Cricket Wireless.</p>
-<p>If you own the Cricket LG G Stylo H634 and want to enjoy the latest features and improvements of Android 5.1.1 Lollipop, you can follow this guide to install the firmware on your device. Before you proceed, make sure you have backed up your data, charged your battery to at least 50%, and enabled USB debugging on your phone.</p>
-<h2>Cricket.LG.G.Stylo.[H634].Firmware.Android.5.1.1 12</h2><br /><p><b><b>Download</b> - <a href="https://urlcod.com/2uHxYQ">https://urlcod.com/2uHxYQ</a></b></p><br /><br />
-<h2>Steps to Update Cricket LG G Stylo H634 to Android 5.1.1 Lollipop</h2>
-<ol>
-<li>Download the firmware file from <a href="https://www.lg.com/us/support/software-firmware-drivers">LG's official website</a>. The file name should be H63410g_00_0406.kdz and the size should be about 1 GB.</li>
-<li>Download and install <a href="https://lgflashtool.com/">LG Flash Tool</a> on your computer.</li>
-<li>Connect your phone to your computer using a USB cable and launch LG Flash Tool.</li>
-<li>Select "CDMA" as the device type and "DIAG" as the phone mode.</li>
-<li>Click on the folder icon and browse to the location where you saved the firmware file.</li>
-<li>Click on "CSE Flash" and then "Start".</li>
-<li>A pop-up window will appear asking you to confirm the firmware update. Click on "OK".</li>
-<li>The flashing process will begin and it may take several minutes to complete.</li>
-<li>Once the flashing is done, your phone will reboot automatically.</li>
-<li>Disconnect your phone from your computer and enjoy Android 5.1.1 Lollipop on your Cricket LG G Stylo H634.</li>
-</ol>
-<p>Congratulations! You have successfully updated your Cricket LG G Stylo H634 to Android 5.1.1 Lollipop with the official firmware from LG. If you have any questions or issues, feel free to leave a comment below.</p>Here is a possible continuation of the article:
-
-<h2>Benefits of Android 5.1.1 Lollipop on Cricket LG G Stylo H634</h2>
-<p>By updating your Cricket LG G Stylo H634 to Android 5.1.1 Lollipop, you can enjoy some of the following benefits:</p>
-<ul>
-<li>Better performance and stability: Android 5.1.1 Lollipop brings various bug fixes and optimizations that improve the speed and smoothness of your device.</li>
-<li>Enhanced security: Android 5.1.1 Lollipop introduces new features such as device protection, which prevents unauthorized access to your phone even if it is lost or stolen, and Smart Lock, which allows you to unlock your phone with trusted devices or locations.</li>
-<li>Improved battery life: Android 5.1.1 Lollipop includes a battery saver mode that reduces the power consumption of your device when the battery level is low.</li>
-<li>More customization options: Android 5.1.1 Lollipop allows you to change the system theme, icons, fonts, and sounds according to your preference.</li>
-<li>New features and apps: Android 5.1.1 Lollipop adds new features and apps such as Quick Settings, Notifications, Google Now on Tap, Do Not Disturb mode, Screen Pinning, Ambient Display, and more.</li>
-</ul>
-<p>These are some of the benefits of updating your Cricket LG G Stylo H634 to Android 5.1.1 Lollipop with the official firmware from LG. If you want to learn more about Android 5.1.1 Lollipop and its features, you can visit <a href="https://www.android.com/versions/lollipop-5-0/">Android's official website</a>.</p> 7196e7f11a<br />
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-<br />
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diff --git a/spaces/tomg-group-umd/pez-dispenser/open_clip/version.py b/spaces/tomg-group-umd/pez-dispenser/open_clip/version.py
deleted file mode 100644
index b03f5b5da69b5ce97d063b5b275068237ccf41b4..0000000000000000000000000000000000000000
--- a/spaces/tomg-group-umd/pez-dispenser/open_clip/version.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = '2.9.1'
diff --git a/spaces/tomofi/MMOCR/mmocr/models/textdet/postprocess/textsnake_postprocessor.py b/spaces/tomofi/MMOCR/mmocr/models/textdet/postprocess/textsnake_postprocessor.py
deleted file mode 100644
index 3e37154c7d267db146a07fc03496c616d12d6f71..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MMOCR/mmocr/models/textdet/postprocess/textsnake_postprocessor.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-
-import cv2
-import numpy as np
-import torch
-from skimage.morphology import skeletonize
-
-from mmocr.models.builder import POSTPROCESSOR
-from .base_postprocessor import BasePostprocessor
-from .utils import centralize, fill_hole, merge_disks
-
-
-@POSTPROCESSOR.register_module()
-class TextSnakePostprocessor(BasePostprocessor):
-    """Decoding predictions of TextSnake to instances. This was partially
-    adapted from https://github.com/princewang1994/TextSnake.pytorch.
-
-    Args:
-        text_repr_type (str): The boundary encoding type 'poly' or 'quad'.
-        min_text_region_confidence (float): The confidence threshold of text
-            region in TextSnake.
-        min_center_region_confidence (float): The confidence threshold of text
-            center region in TextSnake.
-        min_center_area (int): The minimal text center region area.
-        disk_overlap_thr (float): The radius overlap threshold for merging
-            disks.
-        radius_shrink_ratio (float): The shrink ratio of ordered disks radii.
-    """
-
-    def __init__(self,
-                 text_repr_type='poly',
-                 min_text_region_confidence=0.6,
-                 min_center_region_confidence=0.2,
-                 min_center_area=30,
-                 disk_overlap_thr=0.03,
-                 radius_shrink_ratio=1.03,
-                 **kwargs):
-        super().__init__(text_repr_type)
-        assert text_repr_type == 'poly'
-        self.min_text_region_confidence = min_text_region_confidence
-        self.min_center_region_confidence = min_center_region_confidence
-        self.min_center_area = min_center_area
-        self.disk_overlap_thr = disk_overlap_thr
-        self.radius_shrink_ratio = radius_shrink_ratio
-
-    def __call__(self, preds):
-        """
-        Args:
-            preds (Tensor): Prediction map with shape :math:`(C, H, W)`.
-
-        Returns:
-            list[list[float]]: The instance boundary and its confidence.
-        """
-        assert preds.dim() == 3
-
-        preds[:2, :, :] = torch.sigmoid(preds[:2, :, :])
-        preds = preds.detach().cpu().numpy()
-
-        pred_text_score = preds[0]
-        pred_text_mask = pred_text_score > self.min_text_region_confidence
-        pred_center_score = preds[1] * pred_text_score
-        pred_center_mask = \
-            pred_center_score > self.min_center_region_confidence
-        pred_sin = preds[2]
-        pred_cos = preds[3]
-        pred_radius = preds[4]
-        mask_sz = pred_text_mask.shape
-
-        scale = np.sqrt(1.0 / (pred_sin**2 + pred_cos**2 + 1e-8))
-        pred_sin = pred_sin * scale
-        pred_cos = pred_cos * scale
-
-        pred_center_mask = fill_hole(pred_center_mask).astype(np.uint8)
-        center_contours, _ = cv2.findContours(pred_center_mask, cv2.RETR_TREE,
-                                              cv2.CHAIN_APPROX_SIMPLE)
-
-        boundaries = []
-        for contour in center_contours:
-            if cv2.contourArea(contour) < self.min_center_area:
-                continue
-            instance_center_mask = np.zeros(mask_sz, dtype=np.uint8)
-            cv2.drawContours(instance_center_mask, [contour], -1, 1, -1)
-            skeleton = skeletonize(instance_center_mask)
-            skeleton_yx = np.argwhere(skeleton > 0)
-            y, x = skeleton_yx[:, 0], skeleton_yx[:, 1]
-            cos = pred_cos[y, x].reshape((-1, 1))
-            sin = pred_sin[y, x].reshape((-1, 1))
-            radius = pred_radius[y, x].reshape((-1, 1))
-
-            center_line_yx = centralize(skeleton_yx, cos, -sin, radius,
-                                        instance_center_mask)
-            y, x = center_line_yx[:, 0], center_line_yx[:, 1]
-            radius = (pred_radius[y, x] * self.radius_shrink_ratio).reshape(
-                (-1, 1))
-            score = pred_center_score[y, x].reshape((-1, 1))
-            instance_disks = np.hstack(
-                [np.fliplr(center_line_yx), radius, score])
-            instance_disks = merge_disks(instance_disks, self.disk_overlap_thr)
-
-            instance_mask = np.zeros(mask_sz, dtype=np.uint8)
-            for x, y, radius, score in instance_disks:
-                if radius > 1:
-                    cv2.circle(instance_mask, (int(x), int(y)), int(radius), 1,
-                               -1)
-            contours, _ = cv2.findContours(instance_mask, cv2.RETR_TREE,
-                                           cv2.CHAIN_APPROX_SIMPLE)
-
-            score = np.sum(instance_mask * pred_text_score) / (
-                np.sum(instance_mask) + 1e-8)
-            if (len(contours) > 0 and cv2.contourArea(contours[0]) > 0
-                    and contours[0].size > 8):
-                boundary = contours[0].flatten().tolist()
-                boundaries.append(boundary + [score])
-
-        return boundaries
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py
deleted file mode 100644
index ef749ccc8ddafd84da852c56821d7624a0111eb2..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py
+++ /dev/null
@@ -1,105 +0,0 @@
-_base_ = [
-    '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
-]
-
-# model settings
-model = dict(
-    type='CornerNet',
-    backbone=dict(
-        type='HourglassNet',
-        downsample_times=5,
-        num_stacks=2,
-        stage_channels=[256, 256, 384, 384, 384, 512],
-        stage_blocks=[2, 2, 2, 2, 2, 4],
-        norm_cfg=dict(type='BN', requires_grad=True)),
-    neck=None,
-    bbox_head=dict(
-        type='CornerHead',
-        num_classes=80,
-        in_channels=256,
-        num_feat_levels=2,
-        corner_emb_channels=1,
-        loss_heatmap=dict(
-            type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1),
-        loss_embedding=dict(
-            type='AssociativeEmbeddingLoss',
-            pull_weight=0.10,
-            push_weight=0.10),
-        loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1)),
-    # training and testing settings
-    train_cfg=None,
-    test_cfg=dict(
-        corner_topk=100,
-        local_maximum_kernel=3,
-        distance_threshold=0.5,
-        score_thr=0.05,
-        max_per_img=100,
-        nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian')))
-# data settings
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile', to_float32=True),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='PhotoMetricDistortion',
-        brightness_delta=32,
-        contrast_range=(0.5, 1.5),
-        saturation_range=(0.5, 1.5),
-        hue_delta=18),
-    dict(
-        type='RandomCenterCropPad',
-        crop_size=(511, 511),
-        ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
-        test_mode=False,
-        test_pad_mode=None,
-        **img_norm_cfg),
-    dict(type='Resize', img_scale=(511, 511), keep_ratio=False),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile', to_float32=True),
-    dict(
-        type='MultiScaleFlipAug',
-        scale_factor=1.0,
-        flip=True,
-        transforms=[
-            dict(type='Resize'),
-            dict(
-                type='RandomCenterCropPad',
-                crop_size=None,
-                ratios=None,
-                border=None,
-                test_mode=True,
-                test_pad_mode=['logical_or', 127],
-                **img_norm_cfg),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(
-                type='Collect',
-                keys=['img'],
-                meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape',
-                           'scale_factor', 'flip', 'img_norm_cfg', 'border')),
-        ])
-]
-data = dict(
-    samples_per_gpu=6,
-    workers_per_gpu=3,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
-# optimizer
-optimizer = dict(type='Adam', lr=0.0005)
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=500,
-    warmup_ratio=1.0 / 3,
-    step=[180])
-runner = dict(type='EpochBasedRunner', max_epochs=210)
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py
deleted file mode 100644
index 19efa0dd756993c9f51a3b9589e558beb2eb5f83..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
diff --git a/spaces/toraleap/chatbot/app.py b/spaces/toraleap/chatbot/app.py
deleted file mode 100644
index 57dd8c74bd7c1e5441f3893bb63fcb04dcef4407..0000000000000000000000000000000000000000
--- a/spaces/toraleap/chatbot/app.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import openai
-import os
-import gradio as gr
-
-openai.api_key = os.environ.get("OPENAI_API_KEY")
-
-class Conversation:
-    def __init__(self, prompt, num_of_round):
-        self.prompt = prompt
-        self.num_of_round = num_of_round
-        self.messages = []
-        self.messages.append({"role": "system", "content": self.prompt})
-
-    def ask(self, question):
-        try:
-            self.messages.append( {"role": "user", "content": question})
-            response = openai.ChatCompletion.create(
-                model="gpt-3.5-turbo",
-                messages=self.messages,
-                temperature=0.5,
-                max_tokens=2048,
-                top_p=1,
-            )
-        except Exception as e:
-            print(e)
-            return e
-
-        message = response["choices"][0]["message"]["content"]
-        self.messages.append({"role": "assistant", "content": message})
-        
-        if len(self.messages) > self.num_of_round*2 + 1:
-            del self.messages[1:3]
-        return message
-    
-
-prompt = """你是一个中国厨师，用中文回答做菜的问题。你的回答需要满足以下要求:
-1. 你的回答必须是中文
-2. 回答限制在100个字以内"""
-
-conv = Conversation(prompt, 5)
-
-def predict(input, history=[]):
-    history.append(input)
-    response = conv.ask(input)
-    history.append(response)
-    responses = [(u,b) for u,b in zip(history[::2], history[1::2])]
-    return responses, history
-
-with gr.Blocks(css="#chatbot{height:350px} .overflow-y-auto{height:500px}") 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)
-
-    txt.submit(predict, [txt, state], [chatbot, state])
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/trttung1610/musicgen/audiocraft/models/lm.py b/spaces/trttung1610/musicgen/audiocraft/models/lm.py
deleted file mode 100644
index 8cefd2c58c3a337378579d6cd6469fd038cbb1ee..0000000000000000000000000000000000000000
--- a/spaces/trttung1610/musicgen/audiocraft/models/lm.py
+++ /dev/null
@@ -1,531 +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 dataclasses import dataclass
-from functools import partial
-import logging
-import math
-import typing as tp
-
-import torch
-from torch import nn
-
-from ..utils import utils
-from ..modules.streaming import StreamingModule, State
-from ..modules.transformer import StreamingTransformer, create_norm_fn
-from ..modules.conditioners import (
-    ConditionFuser,
-    ClassifierFreeGuidanceDropout,
-    AttributeDropout,
-    ConditioningProvider,
-    ConditioningAttributes,
-    ConditionType,
-)
-from ..modules.codebooks_patterns import CodebooksPatternProvider
-from ..modules.activations import get_activation_fn
-
-
-logger = logging.getLogger(__name__)
-ConditionTensors = tp.Dict[str, ConditionType]
-CFGConditions = tp.Union[ConditionTensors, tp.Tuple[ConditionTensors, ConditionTensors]]
-
-
-def get_init_fn(method: str, input_dim: int, init_depth: tp.Optional[int] = None):
-    """LM layer initialization.
-    Inspired from xlformers: https://github.com/fairinternal/xlformers
-
-    Args:
-        method (str): Method name for init function. Valid options are:
-            'gaussian', 'uniform'.
-        input_dim (int): Input dimension of the initialized module.
-        init_depth (int, optional): Optional init depth value used to rescale
-            the standard deviation if defined.
-    """
-    # Compute std
-    std = 1 / math.sqrt(input_dim)
-    # Rescale with depth
-    if init_depth is not None:
-        std = std / math.sqrt(2 * init_depth)
-
-    if method == 'gaussian':
-        return partial(
-            torch.nn.init.trunc_normal_, mean=0.0, std=std, a=-3 * std, b=3 * std
-        )
-    elif method == 'uniform':
-        bound = math.sqrt(3) * std  # ensure the standard deviation is `std`
-        return partial(torch.nn.init.uniform_, a=-bound, b=bound)
-    else:
-        raise ValueError("Unsupported layer initialization method")
-
-
-def init_layer(m: nn.Module,
-               method: str,
-               init_depth: tp.Optional[int] = None,
-               zero_bias_init: bool = False):
-    """Wrapper around ``get_init_fn`` for proper initialization of LM modules.
-
-    Args:
-        m (nn.Module): Module to initialize.
-        method (str): Method name for the init function.
-        init_depth (int, optional): Optional init depth value used to rescale
-            the standard deviation if defined.
-        zero_bias_init (bool): Whether to initialize the bias to 0 or not.
-    """
-    if isinstance(m, nn.Linear):
-        init_fn = get_init_fn(method, m.in_features, init_depth=init_depth)
-        if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
-            weight = m.weight.float()
-            init_fn(weight)
-            m.weight.data[:] = weight.half()
-        else:
-            init_fn(m.weight)
-        if zero_bias_init and m.bias is not None:
-            nn.init.constant_(m.bias, 0)
-    elif isinstance(m, nn.Embedding):
-        init_fn = get_init_fn(method, m.embedding_dim, init_depth=None)
-        if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
-            weight = m.weight.float()
-            init_fn(weight)
-            m.weight.data[:] = weight.half()
-        else:
-            init_fn(m.weight)
-
-
-class ScaledEmbedding(nn.Embedding):
-    """Boost learning rate for embeddings (with `scale`).
-    """
-    def __init__(self, *args, lr=None, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.lr = lr
-
-    def make_optim_group(self):
-        group = {"params": list(self.parameters())}
-        if self.lr is not None:
-            group["lr"] = self.lr
-        return group
-
-
-@dataclass
-class LMOutput:
-    # The logits are already re-aligned with the input codes
-    # hence no extra shift is required, e.g. when computing CE
-    logits: torch.Tensor  # [B, K, T, card]
-    mask: torch.Tensor  # [B, K, T]
-
-
-class LMModel(StreamingModule):
-    """Transformer-based language model on multiple streams of codes.
-
-    Args:
-        pattern_provider (CodebooksPatternProvider): Pattern provider for codebook interleaving.
-        condition_provider (MusicConditioningProvider): Conditioning provider from metadata.
-        fuser (ConditionFuser): Fuser handling the fusing of conditions with language model input.
-        n_q (int): Number of parallel streams to model.
-        card (int): Cardinality, vocabulary size.
-        dim (int): Dimension of the transformer encoder.
-        num_heads (int): Number of heads for the transformer encoder.
-        hidden_scale (int): Scale for hidden feed forward dimension of the transformer encoder.
-        norm (str): Normalization method.
-        norm_first (bool): Use pre-norm instead of post-norm.
-        emb_lr (float, optional): Embedding-specific learning rate.
-        bias_proj (bool): Use bias for output projections.
-        weight_init (str, optional): Method for weight initialization.
-        depthwise_init (str, optional): Method for depthwise weight initialization.
-        zero_bias_init (bool): If true and bias in Linears, initialize bias to zeros.
-        cfg_dropout (float): Classifier-free guidance dropout.
-        cfg_coef (float): Classifier-free guidance coefficient.
-        attribute_dropout (dict): Attribute dropout probabilities.
-        two_step_cfg (bool): Whether to run classifier free-guidance with 2 distinct steps.
-        **kwargs: Additional parameters for the transformer encoder.
-    """
-    def __init__(self, pattern_provider: CodebooksPatternProvider, condition_provider: ConditioningProvider,
-                 fuser: ConditionFuser, n_q: int = 8, card: int = 1024, dim: int = 128, num_heads: int = 8,
-                 hidden_scale: int = 4, norm: str = 'layer_norm', norm_first: bool = False,
-                 emb_lr: tp.Optional[float] = None, bias_proj: bool = True,
-                 weight_init: tp.Optional[str] = None, depthwise_init: tp.Optional[str] = None,
-                 zero_bias_init: bool = False, cfg_dropout: float = 0, cfg_coef: float = 1.0,
-                 attribute_dropout: tp.Dict[str, tp.Dict[str, float]] = {}, two_step_cfg: bool = False,
-                 **kwargs):
-        super().__init__()
-        self.cfg_coef = cfg_coef
-        self.cfg_dropout = ClassifierFreeGuidanceDropout(p=cfg_dropout)
-        self.att_dropout = AttributeDropout(p=attribute_dropout)
-        self.condition_provider = condition_provider
-        self.fuser = fuser
-        self.card = card
-        embed_dim = self.card + 1
-        self.n_q = n_q
-        self.dim = dim
-        self.pattern_provider = pattern_provider
-        self.two_step_cfg = two_step_cfg
-        self.emb = nn.ModuleList([ScaledEmbedding(embed_dim, dim, lr=emb_lr) for _ in range(n_q)])
-        if 'activation' in kwargs:
-            kwargs['activation'] = get_activation_fn(kwargs['activation'])
-        self.transformer = StreamingTransformer(
-            d_model=dim, num_heads=num_heads, dim_feedforward=int(hidden_scale * dim),
-            norm=norm, norm_first=norm_first, **kwargs)
-        self.out_norm: tp.Optional[nn.Module] = None
-        if norm_first:
-            self.out_norm = create_norm_fn(norm, dim)
-        self.linears = nn.ModuleList([nn.Linear(dim, self.card, bias=bias_proj) for _ in range(n_q)])
-        self._init_weights(weight_init, depthwise_init, zero_bias_init)
-        self._fsdp: tp.Optional[nn.Module]
-        self.__dict__['_fsdp'] = None
-
-    def _init_weights(self, weight_init: tp.Optional[str], depthwise_init: tp.Optional[str], zero_bias_init: bool):
-        """Initialization of the transformer module weights.
-
-        Args:
-            weight_init (str, optional): Weight initialization strategy. See ``get_init_fn`` for valid options.
-            depthwise_init (str, optional): Depthwise initialization strategy. The following options are valid:
-                'current' where the depth corresponds to the current layer index or 'global' where the total number
-                of layer is used as depth. If not set, no depthwise initialization strategy is used.
-            zero_bias_init (bool): Whether to initialize bias to zero or not.
-        """
-        assert depthwise_init is None or depthwise_init in ['current', 'global']
-        assert depthwise_init is None or weight_init is not None, \
-            "If 'depthwise_init' is defined, a 'weight_init' method should be provided."
-        assert not zero_bias_init or weight_init is not None, \
-            "If 'zero_bias_init', a 'weight_init' method should be provided"
-
-        if weight_init is None:
-            return
-
-        for emb_layer in self.emb:
-            init_layer(emb_layer, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
-        for layer_idx, tr_layer in enumerate(self.transformer.layers):
-            depth = None
-            if depthwise_init == 'current':
-                depth = layer_idx + 1
-            elif depthwise_init == 'global':
-                depth = len(self.transformer.layers)
-            init_fn = partial(init_layer, method=weight_init, init_depth=depth, zero_bias_init=zero_bias_init)
-            tr_layer.apply(init_fn)
-
-        for linear in self.linears:
-            init_layer(linear, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
-    @property
-    def special_token_id(self) -> int:
-        return self.card
-
-    @property
-    def num_codebooks(self) -> int:
-        return self.n_q
-
-    def forward(self, sequence: torch.Tensor,
-                conditions: tp.List[ConditioningAttributes],
-                condition_tensors: tp.Optional[ConditionTensors] = None) -> torch.Tensor:
-        """Apply language model on sequence and conditions.
-        Given a tensor of sequence of shape [B, K, S] with K the number of codebooks and
-        S the sequence steps, return the logits with shape [B, card, K, S].
-
-        Args:
-            indices (torch.Tensor): Indices of the codes to model.
-            conditions (list of ConditioningAttributes): Conditions to use when modeling
-                the given codes. Note that when evaluating multiple time with the same conditioning
-                you should pre-compute those and pass them as `condition_tensors`.
-            condition_tensors (dict[str, ConditionType], optional): Pre-computed conditioning
-                tensors, see `conditions`.
-        Returns:
-            torch.Tensor: Logits.
-        """
-        B, K, S = sequence.shape
-        assert K == self.num_codebooks, "Sequence shape must match the specified number of codebooks"
-        input_ = sum([self.emb[k](sequence[:, k]) for k in range(K)])
-        if condition_tensors is None:
-            assert not self._is_streaming, "Conditions tensors should be precomputed when streaming."
-            # apply dropout modules
-            conditions = self.cfg_dropout(conditions)
-            conditions = self.att_dropout(conditions)
-            tokenized = self.condition_provider.tokenize(conditions)
-            # encode conditions and fuse, both have a streaming cache to not recompute when generating.
-            condition_tensors = self.condition_provider(tokenized)
-        else:
-            assert not conditions, "Shouldn't pass both conditions and condition_tensors."
-
-        input_, cross_attention_input = self.fuser(input_, condition_tensors)
-
-        out = self.transformer(input_, cross_attention_src=cross_attention_input)
-        if self.out_norm:
-            out = self.out_norm(out)
-        logits = torch.stack([self.linears[k](out) for k in range(K)], dim=1)  # [B, K, S, card]
-
-        # remove the prefix from the model outputs
-        if len(self.fuser.fuse2cond['prepend']) > 0:
-            logits = logits[:, :, -S:]
-
-        return logits  # [B, K, S, card]
-
-    def compute_predictions(
-            self, codes: torch.Tensor,
-            conditions: tp.List[ConditioningAttributes],
-            condition_tensors: tp.Optional[ConditionTensors] = None) -> LMOutput:
-        """Given an input tensor of codes [B, K, T] and list of conditions, runs the model
-        forward using the specified codes interleaving pattern.
-
-        Args:
-            codes (torch.Tensor): Input codes of shape [B, K, T] with B the batch size,
-                K the number of codebooks and T the number of timesteps.
-            conditions (list of ConditioningAttributes): conditionings to use when modeling
-                the given codes. Note that when evaluating multiple time with the same conditioning
-                you should pre-compute those and pass them as `condition_tensors`.
-            condition_tensors (dict[str, ConditionType], optional): pre-computed conditioning
-                tensors, see `conditions`.
-        Returns:
-            LMOutput: Language model outputs
-                logits (torch.Tensor) of shape [B, K, T, card] corresponding to the provided codes,
-                    i.e. the first item corresponds to logits to predict the first code, meaning that
-                    no additional shifting of codes and logits is required.
-                mask (torch.Tensor) of shape [B, K, T], mask over valid and invalid positions.
-                    Given the specified interleaving strategies, parts of the logits and codes should
-                    not be considered as valid predictions because of invalid context.
-        """
-        B, K, T = codes.shape
-        codes = codes.contiguous()
-        # map codes [B, K, T] into pattern sequence [B, K, S] using special_token_id for masked tokens
-        pattern = self.pattern_provider.get_pattern(T)
-        sequence_codes, sequence_indexes, sequence_mask = pattern.build_pattern_sequence(
-            codes, self.special_token_id, keep_only_valid_steps=True
-        )
-        # apply model on pattern sequence
-        model = self if self._fsdp is None else self._fsdp
-        logits = model(sequence_codes, conditions, condition_tensors)  # [B, K, S, card]
-        # map back the logits on pattern sequence to logits on original codes: [B, K, S, card] -> [B, K, T, card]
-        # and provide the corresponding mask over invalid positions of tokens
-        logits = logits.permute(0, 3, 1, 2)  # [B, card, K, S]
-        # note: we use nans as special token to make it obvious if we feed unexpected logits
-        logits, logits_indexes, logits_mask = pattern.revert_pattern_logits(
-            logits, float('nan'), keep_only_valid_steps=True
-        )
-        logits = logits.permute(0, 2, 3, 1)  # [B, K, T, card]
-        logits_mask = logits_mask[None, :, :].expand(B, -1, -1)  # [K, T] -> [B, K, T]
-        return LMOutput(logits, logits_mask)
-
-    def _sample_next_token(self,
-                           sequence: torch.Tensor,
-                           cfg_conditions: CFGConditions,
-                           unconditional_state: State,
-                           use_sampling: bool = False,
-                           temp: float = 1.0,
-                           top_k: int = 0,
-                           top_p: float = 0.0,
-                           cfg_coef: tp.Optional[float] = None) -> torch.Tensor:
-        """Sample next token from the model given a sequence and a set of conditions. The model supports
-        multiple sampling strategies (greedy sampling, softmax, top-k, top-p...).
-
-        Args:
-            sequence (torch.Tensor): Current sequence of shape [B, K, S]
-                with K corresponding to the number of codebooks and S the number of sequence steps.
-                S = 1 in streaming mode, except for the first step that contains a bigger prompt.
-            condition_tensors (dict[str, ConditionType): Set of conditions. If CFG is used,
-                should be twice the batch size, being the concatenation of the conditions + null conditions.
-            use_sampling (bool): Whether to use a sampling strategy or not.
-            temp (float): Sampling temperature.
-            top_k (int): K for "top-k" sampling.
-            top_p (float): P for "top-p" sampling.
-            cfg_coef (float, optional): classifier free guidance coefficient
-        Returns:
-            next_token (torch.Tensor): Next token tensor of shape [B, K, 1].
-        """
-        B = sequence.shape[0]
-        cfg_coef = self.cfg_coef if cfg_coef is None else cfg_coef
-        model = self if self._fsdp is None else self._fsdp
-        if self.two_step_cfg and cfg_conditions != {}:
-            assert isinstance(cfg_conditions, tuple), type(cfg_conditions)
-            condition_tensors, null_condition_tensors = cfg_conditions
-            cond_logits = model(sequence, conditions=[], condition_tensors=condition_tensors)
-            state = self.get_streaming_state()
-            self.set_streaming_state(unconditional_state)
-            uncond_logits = model(sequence, conditions=[], condition_tensors=null_condition_tensors)
-            unconditional_state.update(self.get_streaming_state())
-            self.set_streaming_state(state)
-            logits = uncond_logits + (cond_logits - uncond_logits) * self.cfg_coef
-        else:
-            assert isinstance(cfg_conditions, dict)
-            condition_tensors = cfg_conditions
-            if condition_tensors:
-                # Preparing for CFG, predicting both conditional and unconditional logits.
-                sequence = torch.cat([sequence, sequence], dim=0)
-            all_logits = model(
-                sequence,
-                conditions=[], condition_tensors=condition_tensors)
-            if condition_tensors:
-                cond_logits, uncond_logits = all_logits.split(B, dim=0)  # [B, K, T, card]
-                logits = uncond_logits + (cond_logits - uncond_logits) * cfg_coef
-            else:
-                logits = all_logits
-
-        logits = logits.permute(0, 1, 3, 2)  # [B, K, card, T]
-        logits = logits[..., -1]  # [B x K x card]
-
-        # Apply softmax for sampling if temp > 0. Else, do greedy sampling to avoid zero division error.
-        if use_sampling and temp > 0.0:
-            probs = torch.softmax(logits / temp, dim=-1)
-            if top_p > 0.0:
-                next_token = utils.sample_top_p(probs, p=top_p)
-            elif top_k > 0:
-                next_token = utils.sample_top_k(probs, k=top_k)
-            else:
-                next_token = utils.multinomial(probs, num_samples=1)
-        else:
-            next_token = torch.argmax(logits, dim=-1, keepdim=True)
-
-        return next_token
-
-    @torch.no_grad()
-    def generate(self,
-                 prompt: tp.Optional[torch.Tensor] = None,
-                 conditions: tp.List[ConditioningAttributes] = [],
-                 num_samples: tp.Optional[int] = None,
-                 max_gen_len: int = 256,
-                 use_sampling: bool = True,
-                 temp: float = 1.0,
-                 top_k: int = 250,
-                 top_p: float = 0.0,
-                 cfg_coef: tp.Optional[float] = None,
-                 two_step_cfg: tp.Optional[bool] = None,
-                 remove_prompts: bool = False,
-                 check: bool = False,
-                 callback: tp.Optional[tp.Callable[[int, int], None]] = None) -> torch.Tensor:
-        """Generate tokens sampling from the model given a prompt or unconditionally. Generation can
-        be perform in a greedy fashion or using sampling with top K and top P strategies.
-
-        Args:
-            prompt (torch.Tensor, optional): Prompt tokens of shape [B, K, T].
-            conditions_tensors (list of ConditioningAttributes, optional): List of conditions.
-            num_samples (int, optional): Number of samples to generate when no prompt and no conditions are given.
-            max_gen_len (int): Maximum generation length.
-            use_sampling (bool): Whether to use a sampling strategy or not.
-            temp (float): Sampling temperature.
-            top_k (int): K for "top-k" sampling.
-            top_p (float): P for "top-p" sampling.
-            cfg_coeff (float, optional): Classifier-free guidance coefficient.
-            two_step_cfg (bool, optional): Whether to perform classifier-free guidance with two steps generation.
-            remove_prompts (bool): Whether to remove prompts from generation or not.
-            check (bool): Whether to apply further checks on generated sequence.
-            callback (Callback, optional): Callback function to report generation progress.
-        Returns:
-            torch.Tensor: Generated tokens.
-        """
-        assert not self.training, "generation shouldn't be used in training mode."
-        first_param = next(iter(self.parameters()))
-        device = first_param.device
-
-        # Checking all input shapes are consistent.
-        possible_num_samples = []
-        if num_samples is not None:
-            possible_num_samples.append(num_samples)
-        elif prompt is not None:
-            possible_num_samples.append(prompt.shape[0])
-        elif conditions:
-            possible_num_samples.append(len(conditions))
-        else:
-            possible_num_samples.append(1)
-        assert [x == possible_num_samples[0] for x in possible_num_samples], "Inconsistent inputs shapes"
-        num_samples = possible_num_samples[0]
-
-        # below we create set of conditions: one conditional and one unconditional
-        # to do that we merge the regular condition together with the null condition
-        # we then do 1 forward pass instead of 2.
-        # the reason for that is two-fold:
-        # 1. it is about x2 faster than doing 2 forward passes
-        # 2. avoid the streaming API treating the 2 passes as part of different time steps
-        # We also support doing two different passes, in particular to ensure that
-        # the padding structure is exactly the same between train and test.
-        # With a batch size of 1, this can be slower though.
-        cfg_conditions: CFGConditions
-        two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg
-        if conditions:
-            null_conditions = ClassifierFreeGuidanceDropout(p=1.0)(conditions)
-            if two_step_cfg:
-                cfg_conditions = (
-                    self.condition_provider(self.condition_provider.tokenize(conditions)),
-                    self.condition_provider(self.condition_provider.tokenize(null_conditions)),
-                )
-            else:
-                conditions = conditions + null_conditions
-                tokenized = self.condition_provider.tokenize(conditions)
-                cfg_conditions = self.condition_provider(tokenized)
-        else:
-            cfg_conditions = {}
-
-        if prompt is None:
-            assert num_samples > 0
-            prompt = torch.zeros((num_samples, self.num_codebooks, 0), dtype=torch.long, device=device)
-
-        B, K, T = prompt.shape
-        start_offset = T
-        assert start_offset < max_gen_len
-
-        pattern = self.pattern_provider.get_pattern(max_gen_len)
-        # this token is used as default value for codes that are not generated yet
-        unknown_token = -1
-
-        # we generate codes up to the max_gen_len that will be mapped to the pattern sequence
-        gen_codes = torch.full((B, K, max_gen_len), unknown_token, dtype=torch.long, device=device)
-        # filling the gen_codes with the prompt if needed
-        gen_codes[..., :start_offset] = prompt
-        # create the gen_sequence with proper interleaving from the pattern: [B, K, S]
-        gen_sequence, indexes, mask = pattern.build_pattern_sequence(gen_codes, self.special_token_id)
-        # retrieve the start_offset in the sequence:
-        # it is the first sequence step that contains the `start_offset` timestep
-        start_offset_sequence = pattern.get_first_step_with_timesteps(start_offset)
-        assert start_offset_sequence is not None
-
-        with self.streaming():
-            unconditional_state = self.get_streaming_state()
-            prev_offset = 0
-            gen_sequence_len = gen_sequence.shape[-1]  # gen_sequence shape is [B, K, S]
-            for offset in range(start_offset_sequence, gen_sequence_len):
-                # get current sequence (note that the streaming API is providing the caching over previous offsets)
-                curr_sequence = gen_sequence[..., prev_offset:offset]
-                curr_mask = mask[None, ..., prev_offset:offset].expand(B, -1, -1)
-                if check:
-                    # check coherence between mask and sequence
-                    assert (curr_sequence == torch.where(curr_mask, curr_sequence, self.special_token_id)).all()
-                    # should never happen as gen_sequence is filled progressively
-                    assert not (curr_sequence == unknown_token).any()
-                # sample next token from the model, next token shape is [B, K, 1]
-                next_token = self._sample_next_token(
-                    curr_sequence, cfg_conditions, unconditional_state, use_sampling, temp, top_k, top_p,
-                    cfg_coef=cfg_coef)
-                # ensure the tokens that should be masked are properly set to special_token_id
-                # as the model never output special_token_id
-                valid_mask = mask[..., offset:offset+1].expand(B, -1, -1)
-                next_token[~valid_mask] = self.special_token_id
-                # ensure we don't overwrite prompt tokens, we only write over unknown tokens
-                # (then mask tokens should be left as is as well, which is correct)
-                gen_sequence[..., offset:offset+1] = torch.where(
-                    gen_sequence[..., offset:offset+1] == unknown_token,
-                    next_token, gen_sequence[..., offset:offset+1]
-                )
-                prev_offset = offset
-                if callback is not None:
-                    callback(1 + offset - start_offset_sequence, gen_sequence_len - start_offset_sequence)
-        unconditional_state.clear()
-
-        # ensure sequence has been entirely filled
-        assert not (gen_sequence == unknown_token).any()
-        # ensure gen_sequence pattern and mask are matching
-        # which means the gen_sequence is valid according to the pattern
-        assert (
-            gen_sequence == torch.where(mask[None, ...].expand(B, -1, -1), gen_sequence, self.special_token_id)
-        ).all()
-        # get back the codes, trimming the prompt if needed and cutting potentially incomplete timesteps
-        out_codes, out_indexes, out_mask = pattern.revert_pattern_sequence(gen_sequence, special_token=unknown_token)
-
-        # sanity checks over the returned codes and corresponding masks
-        assert (out_codes[..., :max_gen_len] != unknown_token).all()
-        assert (out_mask[..., :max_gen_len] == 1).all()
-
-        out_start_offset = start_offset if remove_prompts else 0
-        out_codes = out_codes[..., out_start_offset:max_gen_len]
-
-        # ensure the returned codes are all valid
-        assert (out_codes >= 0).all() and (out_codes <= self.card).all()
-        return out_codes
diff --git a/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/app.py b/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/app.py
deleted file mode 100644
index a8fc415bb28b3c6b4b716964e63033066b621198..0000000000000000000000000000000000000000
--- a/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/app.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# import gradio as gr
-# def modelscope_quickstart(name):
-#     return "Welcome to modelscope, " + name + "!!"
-# demo = gr.Interface(fn=modelscope_quickstart, inputs="text", outputs="text")
-# demo.launch()
-
-import os
-os.chdir(os.path.dirname(__file__))
-
-import webui
-webui.demo.launch()
diff --git a/spaces/ulysses115/Nogizaka46-so/modules/modules.py b/spaces/ulysses115/Nogizaka46-so/modules/modules.py
deleted file mode 100644
index 54290fd207b25e93831bd21005990ea137e6b50e..0000000000000000000000000000000000000000
--- a/spaces/ulysses115/Nogizaka46-so/modules/modules.py
+++ /dev/null
@@ -1,342 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import modules.commons as commons
-from modules.commons import init_weights, get_padding
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
diff --git a/spaces/ulysses115/diffsvc_test/utils/training_utils.py b/spaces/ulysses115/diffsvc_test/utils/training_utils.py
deleted file mode 100644
index 409b15388790b1aadb24632313bdd1f41b4b06ac..0000000000000000000000000000000000000000
--- a/spaces/ulysses115/diffsvc_test/utils/training_utils.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from utils.hparams import hparams
-
-
-class RSQRTSchedule(object):
-    def __init__(self, optimizer):
-        super().__init__()
-        self.optimizer = optimizer
-        self.constant_lr = hparams['lr']
-        self.warmup_updates = hparams['warmup_updates']
-        self.hidden_size = hparams['hidden_size']
-        self.lr = hparams['lr']
-        for param_group in optimizer.param_groups:
-            param_group['lr'] = self.lr
-        self.step(0)
-
-    def step(self, num_updates):
-        constant_lr = self.constant_lr
-        warmup = min(num_updates / self.warmup_updates, 1.0)
-        rsqrt_decay = max(self.warmup_updates, num_updates) ** -0.5
-        rsqrt_hidden = self.hidden_size ** -0.5
-        self.lr = max(constant_lr * warmup * rsqrt_decay * rsqrt_hidden, 1e-7)
-        for param_group in self.optimizer.param_groups:
-            param_group['lr'] = self.lr
-        return self.lr
-
-    def get_lr(self):
-        return self.optimizer.param_groups[0]['lr']
diff --git a/spaces/unity/ML-Agents-PushBlock/index.html b/spaces/unity/ML-Agents-PushBlock/index.html
deleted file mode 100644
index 3d91fa31016163b223979daa4859b96e6e0dc733..0000000000000000000000000000000000000000
--- a/spaces/unity/ML-Agents-PushBlock/index.html
+++ /dev/null
@@ -1,92 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-us">
-  <head>
-    <meta charset="utf-8">
-    <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no">
-    <title>Unity-MLAgents-Pyramids</title>
-    <link rel="shortcut icon" href="TemplateData/favicon.ico">
-    <link rel="stylesheet" href="TemplateData/style.css">
-  </head>
-  <body class="dark">
-    <div id="unity-container" class="unity-desktop">
-      <canvas id="unity-canvas"></canvas>
-    </div>
-    <div id="loading-cover" style="display:none;">
-      <div id="unity-loading-bar">
-        <div id="unity-logo"><img src="logo.png"></div>
-        <div id="unity-progress-bar-empty" style="display: none;">
-          <div id="unity-progress-bar-full"></div>
-        </div>
-        <div class="spinner"></div>
-      </div>
-    </div>
-    <div id="unity-fullscreen-button" style="display: none;"></div>
-    <script>
-      const hideFullScreenButton = "";
-      const buildUrl = "Build";
-      const loaderUrl = buildUrl + "/ML-Agents-PushBlock.loader.js";
-      const config = {
-        dataUrl: buildUrl + "/ML-Agents-PushBlock.data.unityweb",
-        frameworkUrl: buildUrl + "/ML-Agents-PushBlock.framework.js.unityweb",
-        codeUrl: buildUrl + "/ML-Agents-PushBlock.wasm.unityweb",
-        streamingAssetsUrl: "StreamingAssets",
-        companyName: "Unity Technologies",
-        productName: "Unity-MLAgents-Pyramids",
-        productVersion: "1.0",
-      };
-
-      const container = document.querySelector("#unity-container");
-      const canvas = document.querySelector("#unity-canvas");
-      const loadingCover = document.querySelector("#loading-cover");
-      const progressBarEmpty = document.querySelector("#unity-progress-bar-empty");
-      const progressBarFull = document.querySelector("#unity-progress-bar-full");
-      const fullscreenButton = document.querySelector("#unity-fullscreen-button");
-      const spinner = document.querySelector('.spinner');
-
-      const canFullscreen = (function() {
-        for (const key of [
-            'exitFullscreen',
-            'webkitExitFullscreen',
-            'webkitCancelFullScreen',
-            'mozCancelFullScreen',
-            'msExitFullscreen',
-          ]) {
-          if (key in document) {
-            return true;
-          }
-        }
-        return false;
-      }());
-
-      if (/iPhone|iPad|iPod|Android/i.test(navigator.userAgent)) {
-        container.className = "unity-mobile";
-        config.devicePixelRatio = 1;
-      }
-      loadingCover.style.display = "";
-
-      const script = document.createElement("script");
-      script.src = loaderUrl;
-      script.onload = () => {
-        createUnityInstance(canvas, config, (progress) => {
-          spinner.style.display = "none";
-          progressBarEmpty.style.display = "";
-          progressBarFull.style.width = `${100 * progress}%`;
-        }).then((unityInstance) => {
-          loadingCover.style.display = "none";
-          if (canFullscreen) {
-            if (!hideFullScreenButton) {
-              fullscreenButton.style.display = "";
-            }
-            fullscreenButton.onclick = () => {
-              unityInstance.SetFullscreen(1);
-            };
-          }
-        }).catch((message) => {
-          alert(message);
-        });
-      };
-      document.body.appendChild(script);
-    </script>
-  </body>
-</html>
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Ansys Maxwell 15.0 (64bit).torrent ((LINK)).md b/spaces/usbethFlerru/sovits-modelsV2/example/Ansys Maxwell 15.0 (64bit).torrent ((LINK)).md
deleted file mode 100644
index f32f0bb26be36809111507a926b6067a97894cfe..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Ansys Maxwell 15.0 (64bit).torrent ((LINK)).md	
+++ /dev/null
@@ -1,127 +0,0 @@
-<br />
-<h1>Ansys Maxwell 15.0 (64bit).torrent: A Torrent Link for Downloading the Engineering Software</h1>
-
-<p>Ansys Maxwell 15.0 (64bit) is the latest version of the engineering software developed by Ansys, a leading provider of solutions for electromagnetic field simulation. Ansys Maxwell 15.0 (64bit) is a software that allows engineers to design and analyze 3-D and 2-D electromagnetic and electromechanical devices, such as motors, actuators, transformers, sensors and coils.</p>
-
-<p>If you are looking for a torrent link for downloading Ansys Maxwell 15.0 (64bit), you might find it difficult to find a reliable and safe source. Many torrent sites are full of malware, viruses, and fake files that can harm your system or compromise your data. Therefore, you need to be careful and cautious when searching for Ansys Maxwell 15.0 (64bit).torrent.</p>
-<h2>Ansys Maxwell 15.0 (64bit).torrent</h2><br /><p><b><b>DOWNLOAD</b> &#9658;&#9658;&#9658; <a href="https://urlcod.com/2uyVSY">https://urlcod.com/2uyVSY</a></b></p><br /><br />
-
-<p>In this article, we will show you some of the best methods and sources for finding Ansys Maxwell 15.0 (64bit).torrent, as well as how to download and install it on your system. Let's get started!</p>
-
-<h2>Methods and Sources for Finding Ansys Maxwell 15.0 (64bit).torrent</h2>
-
-<p>There are different methods and sources for finding Ansys Maxwell 15.0 (64bit).torrent, such as:</p>
-
-<ul>
-<li>Search engines: You can use search engines like Google or Bing to search for Ansys Maxwell 15.0 (64bit).torrent. However, you need to be careful and check the credibility and reputation of the sites that offer the torrent link. You can also use advanced search operators or filters to narrow down your results.</li>
-<li>Torrent sites: You can use torrent sites like The Pirate Bay or Kickass Torrents to search for Ansys Maxwell 15.0 (64bit).torrent. However, you need to be aware of the risks and dangers of using torrent sites, such as malware, viruses, fake files, legal issues, etc. You can also use torrent trackers or indexes to find more torrent sites.</li>
-<li>Torrent clients: You can use torrent clients like uTorrent or BitTorrent to search for Ansys Maxwell 15.0 (64bit).torrent. However, you need to have a torrent client installed on your system and configure it properly to download torrents safely and efficiently. You can also use torrent plugins or extensions to enhance your torrent client.</li>
-</ul>
-
-<p>These are some of the methods and sources for finding Ansys Maxwell 15.0 (64bit).torrent. However, you need to be careful and cautious when using them, as they might not be reliable or safe.</p>
-
-<h2>How to Download and Install Ansys Maxwell 15.0 (64bit)</h2>
-
-<p>If you have found a reliable and safe source for Ansys Maxwell 15.0 (64bit).torrent, you can download and install it on your system by following these steps:</p>
-
-<ol>
-<li>Download the torrent file or magnet link from the source.</li>
-<li>Open the torrent file or magnet link with your torrent client.</li>
-<li>Select the destination folder and start the download.</li>
-<li>Wait until the download is complete.</li>
-<li>Run the setup.exe file and follow the instructions on the screen.</li>
-<li>Select the components you want to install (client/server/database).</li>
-<li>Specify the installation path and settings.</li>
-<li>Restart your system after the installation is complete.</li>
-</ol>
-
-<p>Before you download or install Ansys Maxwell 15.0 (64bit) on your system, make sure you meet the minimum system requirements:</p>
-
-<ul>
-<li>Operating system: Windows 10 (64-bit)</li>
-<li>Processor: Intel Core i5 or equivalent</li>
-<li>Memory: 8 GB RAM or more</li>
-<li>Disk space: 10 GB free space or more</li>
-<li>Graphics card: DirectX 11 compatible with 1 GB VRAM or more</li>
-<li>Database server: Microsoft SQL Server 2016 or higher</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>Ansys Maxwell 15.0 (64bit) is the latest version of the engineering software developed by Ansys, a leading provider of solutions for electromagnetic field simulation. Ansys Maxwell 15.0 (64bit) is a software that allows engineers to design and analyze 3-D and 2-D electromagnetic and electromechanical devices.</p>
-
-<p>If you are looking for a torrent link for downloading Ansys Maxwell 15.0 (64bit), you might find it difficult to find a reliable and safe source. Therefore, you need to be careful and cautious when searching for Ansys Maxwell 15.0 (64bit).torrent.</p>
-<p></p>
-
-<p>In this article, we have shown you some of the best methods and sources for finding Ansys Maxwell 15.0 (64bit).torrent, as well as how to download and install it on your system. We hope this article has helped you find Ansys Maxwell 15.0 (64bit).torrent.</p>
-<h2>Advantages and Disadvantages of Using Ansys Maxwell 15.0 (64bit).torrent</h2>
-
-<p>Using Ansys Maxwell 15.0 (64bit).torrent can have some advantages and disadvantages for your engineering projects. Here are some of them:</p>
-
-<ul>
-<li>Advantages: Using Ansys Maxwell 15.0 (64bit).torrent can help you save time and money by downloading the software faster and easier. You can also access the latest version of the software without waiting for official updates or releases. You can also share the torrent link with other users who need the software.</li>
-<li>Disadvantages: Using Ansys Maxwell 15.0 (64bit).torrent can also pose some risks and challenges for your engineering projects. You might encounter malware, viruses, fake files, or corrupted files that can harm your system or compromise your data. You might also face legal issues or ethical dilemmas for using or distributing pirated software. You might also have compatibility or performance issues with the software.</li>
-</ul>
-
-<p>These are some of the advantages and disadvantages of using Ansys Maxwell 15.0 (64bit).torrent. You need to weigh them carefully and decide whether using Ansys Maxwell 15.0 (64bit).torrent is worth it or not.</p>
-
-<h2>Tips and Tricks for Using Ansys Maxwell 15.0 (64bit)</h2>
-
-<p>If you have decided to use Ansys Maxwell 15.0 (64bit) for your engineering projects, here are some tips and tricks that can help you use it more effectively and efficiently:</p>
-
-<ul>
-<li>Use a reliable and safe source for downloading Ansys Maxwell 15.0 (64bit).torrent. Check the credibility and reputation of the site that offers the torrent link. Use advanced search operators or filters to narrow down your results.</li>
-<li>Use a reputable and secure torrent client for downloading Ansys Maxwell 15.0 (64bit).torrent. Check the reviews and ratings of the torrent client. Configure it properly to download torrents safely and efficiently. Use torrent plugins or extensions to enhance your torrent client.</li>
-<li>Scan the downloaded files with an antivirus or anti-malware program before installing them on your system. Check the file size, format, and extension of the downloaded files. Avoid opening or running suspicious or unknown files.</li>
-<li>Follow the installation instructions carefully and correctly. Select the components you want to install (client/server/database). Specify the installation path and settings. Restart your system after the installation is complete.</li>
-<li>Use online help and tutorials to learn how to use Ansys Maxwell 15.0 (64bit) for your engineering projects. Contact Ansys for technical support or training if you encounter any issues or difficulties.</li>
-</ul>
-
-<p>These are some of the tips and tricks for using Ansys Maxwell 15.0 (64bit) for your engineering projects. We hope they can help you use Ansys Maxwell 15.0 (64bit) more effectively and efficiently.</p>
-<h2>Alternatives to Ansys Maxwell 15.0 (64bit).torrent</h2>
-
-<p>If you are not comfortable or satisfied with using Ansys Maxwell 15.0 (64bit).torrent for your engineering projects, you might want to consider some alternatives to it. Here are some of them:</p>
-
-<ul>
-<li>Official Ansys website: You can download Ansys Maxwell 15.0 (64bit) from the official Ansys website or contact Ansys for a license or a trial version. This way, you can get the original and authentic software without any risks or issues.</li>
-<li>Other torrent links: You can search for other torrent links for Ansys Maxwell 15.0 (64bit) that might be more reliable or safe than the one you have found. However, you still need to be careful and cautious when using torrent links, as they might not be trustworthy or legal.</li>
-<li>Other engineering software: You can use other engineering software that can perform similar or better functions than Ansys Maxwell 15.0 (64bit). You can compare the features, benefits, and drawbacks of different engineering software and choose the one that suits your needs and preferences.</li>
-</ul>
-
-<p>These are some of the alternatives to Ansys Maxwell 15.0 (64bit).torrent. You need to evaluate them carefully and decide whether they are better or worse than using Ansys Maxwell 15.0 (64bit).torrent.</p>
-
-<h2>Conclusion</h2>
-
-<p>Ansys Maxwell 15.0 (64bit) is the latest version of the engineering software developed by Ansys, a leading provider of solutions for electromagnetic field simulation. Ansys Maxwell 15.0 (64bit) is a software that allows engineers to design and analyze 3-D and 2-D electromagnetic and electromechanical devices.</p>
-
-<p>If you are looking for a torrent link for downloading Ansys Maxwell 15.0 (64bit), you might find it difficult to find a reliable and safe source. Therefore, you need to be careful and cautious when searching for Ansys Maxwell 15.0 (64bit).torrent.</p>
-
-<p>In this article, we have shown you some of the best methods and sources for finding Ansys Maxwell 15.0 (64bit).torrent, as well as how to download and install it on your system. We have also shown you some of the advantages and disadvantages of using Ansys Maxwell 15.0 (64bit).torrent, as well as some tips and tricks for using it more effectively and efficiently. We have also shown you some of the alternatives to Ansys Maxwell 15.0 (64bit).torrent.</p>
-
-<p>We hope this article has helped you find Ansys Maxwell 15.0 (64bit).torrent and use it for your engineering projects.</p>
-<h2>FAQs about Ansys Maxwell 15.0 (64bit).torrent</h2>
-
-<p>Here are some of the frequently asked questions (FAQs) about Ansys Maxwell 15.0 (64bit).torrent:</p>
-
-<ul>
-<li>What is Ansys Maxwell 15.0 (64bit)? Ansys Maxwell 15.0 (64bit) is the latest version of the engineering software developed by Ansys, a leading provider of solutions for electromagnetic field simulation. Ansys Maxwell 15.0 (64bit) is a software that allows engineers to design and analyze 3-D and 2-D electromagnetic and electromechanical devices, such as motors, actuators, transformers, sensors and coils.</li>
-<li>What is a torrent link? A torrent link is a file or a magnet link that contains information about a file or a group of files that can be downloaded using a torrent client. A torrent client is a software that connects to a peer-to-peer network and downloads the file or files from other users who have the same torrent link.</li>
-<li>Why use a torrent link for downloading Ansys Maxwell 15.0 (64bit)? Using a torrent link for downloading Ansys Maxwell 15.0 (64bit) can help you save time and money by downloading the software faster and easier. You can also access the latest version of the software without waiting for official updates or releases. You can also share the torrent link with other users who need the software.</li>
-<li>What are the risks and challenges of using a torrent link for downloading Ansys Maxwell 15.0 (64bit)? Using a torrent link for downloading Ansys Maxwell 15.0 (64bit) can also pose some risks and challenges for your engineering projects. You might encounter malware, viruses, fake files, or corrupted files that can harm your system or compromise your data. You might also face legal issues or ethical dilemmas for using or distributing pirated software. You might also have compatibility or performance issues with the software.</li>
-<li>How to find a reliable and safe source for Ansys Maxwell 15.0 (64bit).torrent? You can find a reliable and safe source for Ansys Maxwell 15.0 (64bit).torrent by using search engines, torrent sites, torrent clients, or other methods and sources that we have discussed in this article. However, you need to be careful and cautious when using them, as they might not be trustworthy or legal.</li>
-<li>How to download and install Ansys Maxwell 15.0 (64bit) on your system? You can download and install Ansys Maxwell 15.0 (64bit) on your system by following the steps that we have discussed in this article. However, you need to have a torrent client installed on your system and configure it properly to download torrents safely and efficiently. You also need to scan the downloaded files with an antivirus or anti-malware program before installing them on your system.</li>
-<li>How to use Ansys Maxwell 15.0 (64bit) for your engineering projects? You can use Ansys Maxwell 15.0 (64bit) for your engineering projects by following the steps that we have discussed in this article. You can also use online help and tutorials to learn how to use Ansys Maxwell 15.0 (64bit) for your engineering projects. You can also contact Ansys for technical support or training if you encounter any issues or difficulties.</li>
-</ul>
-
-<p>These are some of the FAQs about Ansys Maxwell 15.0 (64bit).torrent. If you have any other questions or concerns about Ansys Maxwell 15.0 (64bit).torrent, you can contact us or leave a comment below.</p>
-<h2>Conclusion</h2>
-
-<p>Ansys Maxwell 15.0 (64bit) is the latest version of the engineering software developed by Ansys, a leading provider of solutions for electromagnetic field simulation. Ansys Maxwell 15.0 (64bit) is a software that allows engineers to design and analyze 3-D and 2-D electromagnetic and electromechanical devices.</p>
-
-<p>If you are looking for a torrent link for downloading Ansys Maxwell 15.0 (64bit), you might find it difficult to find a reliable and safe source. Therefore, you need to be careful and cautious when searching for Ansys Maxwell 15.0 (64bit).torrent.</p>
-
-<p>In this article, we have shown you some of the best methods and sources for finding Ansys Maxwell 15.0 (64bit).torrent, as well as how to download and install it on your system. We have also shown you some of the advantages and disadvantages of using Ansys Maxwell 15.0 (64bit).torrent, as well as some tips and tricks for using it more effectively and efficiently. We have also shown you some of the alternatives to Ansys Maxwell 15.0 (64bit).torrent. We have also answered some of the FAQs about Ansys Maxwell 15.0 (64bit).torrent.</p>
-
-<p>We hope this article has helped you find Ansys Maxwell 15.0 (64bit).torrent and use it for your engineering projects.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Descargar Torrents De Juegos Para Xbox 360.md b/spaces/usbethFlerru/sovits-modelsV2/example/Descargar Torrents De Juegos Para Xbox 360.md
deleted file mode 100644
index 3cf52a1844c3deed2eba7fe35134fa35324ab44b..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Descargar Torrents De Juegos Para Xbox 360.md	
+++ /dev/null
@@ -1,11 +0,0 @@
-<h2>descargar torrents de juegos para xbox 360</h2><br /><p><b><b>DOWNLOAD</b> &#10002; &#10002; &#10002; <a href="https://urlcod.com/2uyVua">https://urlcod.com/2uyVua</a></b></p><br /><br />
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-6 days ago — Cada mes, Microsoft renueva su selección de juegos disponibles con el . This is the first item you bought for Xbox One and Series X|S pero, . 3069
-3 months, 1 week ago
-1 day ago - Microsoft renueva su selección de juegos disponibles con el . This is the first one you bought for Xbox One and Series X|S pero, .
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-5 days ago - Microsoft renueva su selección de juegos disponibles con el . This is the first one you bought for Xbox One and Series X|S pero, .
-5 days ago - Microsoft renueva su selección de juegos disponibles con el . This is the first thing 8a78ff9644<br />
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diff --git a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/rife/inference_video.py b/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/rife/inference_video.py
deleted file mode 100644
index 5f2358610802582f9681de236ea29b4a37186685..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/rife/inference_video.py
+++ /dev/null
@@ -1,282 +0,0 @@
-# thanks to https://github.com/n00mkrad for the inspiration and a bit of code. Also thanks for https://github.com/XmYx for the initial reorganization of this script
-import os, sys
-from types import SimpleNamespace
-import cv2
-import torch
-import argparse
-import shutil
-import numpy as np
-from tqdm import tqdm
-from torch.nn import functional as F
-import warnings
-import _thread
-from queue import Queue, Empty
-import subprocess
-import time
-from .model.pytorch_msssim import ssim_matlab
-
-sys.path.append('../../')
-from deforum_helpers.video_audio_utilities import ffmpeg_stitch_video
-from deforum_helpers.general_utils import duplicate_pngs_from_folder
-
-warnings.filterwarnings("ignore")
-
-def run_rife_new_video_infer(
-        output=None,
-        model=None,
-        fp16=False,
-        UHD=False, # *Will be received as *True* if imgs/vid resolution is 2K or higher*
-        scale=1.0,
-        fps=None,
-        deforum_models_path=None,
-        raw_output_imgs_path=None,
-        img_batch_id=None,
-        ffmpeg_location=None,
-        audio_track=None,
-        interp_x_amount=2,
-        slow_mo_enabled=False,
-        slow_mo_x_amount=2,
-        ffmpeg_crf=17,
-        ffmpeg_preset='veryslow',
-        keep_imgs=False,
-        orig_vid_name = None):
-
-    args = SimpleNamespace()
-    args.output = output
-    args.modelDir = model
-    args.fp16 = fp16
-    args.UHD = UHD
-    args.scale = scale
-    args.fps = fps
-    args.deforum_models_path = deforum_models_path
-    args.raw_output_imgs_path = raw_output_imgs_path
-    args.img_batch_id = img_batch_id
-    args.ffmpeg_location = ffmpeg_location
-    args.audio_track = audio_track
-    args.interp_x_amount = interp_x_amount
-    args.slow_mo_enabled = slow_mo_enabled
-    args.slow_mo_x_amount = slow_mo_x_amount
-    args.ffmpeg_crf = ffmpeg_crf
-    args.ffmpeg_preset = ffmpeg_preset
-    args.keep_imgs = keep_imgs
-    args.orig_vid_name = orig_vid_name
-
-    if args.UHD and args.scale == 1.0:
-        args.scale = 0.5
-        
-    start_time = time.time()
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    torch.set_grad_enabled(False)
-    if torch.cuda.is_available():
-        torch.backends.cudnn.enabled = True
-        torch.backends.cudnn.benchmark = True
-        # TODO: Can/ need to handle this? currently it's always False and give errors if True but faster speeds on tensortcore equipped gpus?
-        if (args.fp16):
-            torch.set_default_tensor_type(torch.cuda.HalfTensor)
-    if args.modelDir is not None:
-        try:
-            from .rife_new_gen.RIFE_HDv3 import Model
-        except ImportError as e:
-            raise ValueError(f"{args.modelDir} could not be found. Please contact deforum support {e}")
-        except Exception as e:
-            raise ValueError(f"An error occured while trying to import {args.modelDir}: {e}")
-    else:
-        print("Got a request to frame-interpolate but no valid frame interpolation engine value provided. Doing... nothing")
-        return
-   
-    model = Model()
-    if not hasattr(model, 'version'):
-        model.version = 0
-    model.load_model(args.modelDir, -1, deforum_models_path)
-    model.eval()
-    model.device()
-    
-    print(f"{args.modelDir}.pkl model successfully loaded into memory")
-    print("Interpolation progress (it's OK if it finishes before 100%):")
-   
-    interpolated_path = os.path.join(args.raw_output_imgs_path, 'interpolated_frames_rife')
-    # set custom name depending on if we interpolate after a run, or interpolate a video (related/unrelated to deforum, we don't know) directly from within the RIFE tab
-    if args.orig_vid_name is not None: # interpolating a video (deforum or unrelated)
-        custom_interp_path = "{}_{}".format(interpolated_path, args.orig_vid_name)
-    else: # interpolating after a deforum run:
-        custom_interp_path = "{}_{}".format(interpolated_path, args.img_batch_id)
-
-    # In this folder we temporarily keep the original frames (converted/ copy-pasted and img format depends on scenario)
-    # the convertion case is done to avert a problem with 24 and 32 mixed outputs from the same animation run
-    temp_convert_raw_png_path = os.path.join(args.raw_output_imgs_path, "tmp_rife_folder")
-    
-    duplicate_pngs_from_folder(args.raw_output_imgs_path, temp_convert_raw_png_path, args.img_batch_id, args.orig_vid_name)
-    
-    videogen = []
-    for f in os.listdir(temp_convert_raw_png_path):
-        # double check for old _depth_ files, not really needed probably but keeping it for now
-        if '_depth_' not in f:
-            videogen.append(f)
-    tot_frame = len(videogen)
-    videogen.sort(key= lambda x:int(x.split('.')[0]))
-    img_path = os.path.join(temp_convert_raw_png_path, videogen[0])
-    lastframe = cv2.imdecode(np.fromfile(img_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)[:, :, ::-1].copy()
-    videogen = videogen[1:]    
-    h, w, _ = lastframe.shape
-    vid_out = None
-
-    if not os.path.exists(custom_interp_path):
-        os.mkdir(custom_interp_path)
-
-    tmp = max(128, int(128 / args.scale))
-    ph = ((h - 1) // tmp + 1) * tmp
-    pw = ((w - 1) // tmp + 1) * tmp
-    padding = (0, pw - w, 0, ph - h)
-    pbar = tqdm(total=tot_frame)
-
-    write_buffer = Queue(maxsize=500)
-    read_buffer  = Queue(maxsize=500)
-    
-    _thread.start_new_thread(build_read_buffer, (args, read_buffer, videogen, temp_convert_raw_png_path))
-    _thread.start_new_thread(clear_write_buffer, (args, write_buffer, custom_interp_path))
-
-    I1 = torch.from_numpy(np.transpose(lastframe, (2, 0, 1))).to(device, non_blocking=True).unsqueeze(0).float() / 255.
-    I1 = pad_image(I1, args.fp16, padding)
-    temp = None  # save lastframe when processing static frame
-    
-    while True:
-        if temp is not None:
-            frame = temp
-            temp = None
-        else:
-            frame = read_buffer.get()
-        if frame is None:
-            break
-        I0 = I1
-        I1 = torch.from_numpy(np.transpose(frame, (2, 0, 1))).to(device, non_blocking=True).unsqueeze(0).float() / 255.
-        I1 = pad_image(I1, args.fp16, padding)
-        I0_small = F.interpolate(I0, (32, 32), mode='bilinear', align_corners=False)
-        I1_small = F.interpolate(I1, (32, 32), mode='bilinear', align_corners=False)
-        ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
-
-        break_flag = False
-        if ssim > 0.996:
-            frame = read_buffer.get()  # read a new frame
-            if frame is None:
-                break_flag = True
-                frame = lastframe
-            else:
-                temp = frame
-            I1 = torch.from_numpy(np.transpose(frame, (2, 0, 1))).to(device, non_blocking=True).unsqueeze(0).float() / 255.
-            I1 = pad_image(I1, args.fp16, padding)
-            I1 = model.inference(I0, I1, args.scale)
-            I1_small = F.interpolate(I1, (32, 32), mode='bilinear', align_corners=False)
-            ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
-            frame = (I1[0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w]
-
-        if ssim < 0.2:
-            output = []
-            for i in range(args.interp_x_amount - 1):
-                output.append(I0)
-        else:
-            output = make_inference(model, I0, I1, args.interp_x_amount - 1, scale)
-        
-        write_buffer.put(lastframe)
-        for mid in output:
-            mid = (((mid[0] * 255.).byte().cpu().numpy().transpose(1, 2, 0)))
-            write_buffer.put(mid[:h, :w])
-        pbar.update(1)
-        lastframe = frame
-        if break_flag:
-            break
-
-    write_buffer.put(lastframe)
-
-    while (not write_buffer.empty()):
-        time.sleep(0.1)
-    pbar.close()
-    shutil.rmtree(temp_convert_raw_png_path)
-    
-    print(f"Interpolation \033[0;32mdone\033[0m in {time.time()-start_time:.2f} seconds!")
-    # stitch video from interpolated frames, and add audio if needed
-    try:
-        print (f"*Passing interpolated frames to ffmpeg...*")
-        vid_out_path = stitch_video(args.img_batch_id, args.fps, custom_interp_path, args.audio_track, args.ffmpeg_location, args.interp_x_amount, args.slow_mo_enabled, args.slow_mo_x_amount, args.ffmpeg_crf, args.ffmpeg_preset, args.keep_imgs, args.orig_vid_name)
-        # remove folder with raw (non-interpolated) vid input frames in case of input VID and not PNGs
-        if orig_vid_name is not None:
-            shutil.rmtree(raw_output_imgs_path)
-    except Exception as e:
-        print(f'Video stitching gone wrong. *Interpolated frames were saved to HD as backup!*. Actual error: {e}')
-    
-def clear_write_buffer(user_args, write_buffer, custom_interp_path):
-    cnt = 0
-
-    while True:
-        item = write_buffer.get()
-        if item is None:
-            break
-        filename = '{}/{:0>7d}.png'.format(custom_interp_path, cnt)
-
-        cv2.imwrite(filename, item[:, :, ::-1])
-
-        cnt += 1
-
-def build_read_buffer(user_args, read_buffer, videogen, temp_convert_raw_png_path):
-    for frame in videogen:
-        if not temp_convert_raw_png_path is None:
-            img_path = os.path.join(temp_convert_raw_png_path, frame)
-            frame = cv2.imdecode(np.fromfile(img_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)[:, :, ::-1].copy()
-        read_buffer.put(frame)
-    read_buffer.put(None)
-
-def make_inference(model, I0, I1, n, scale):
-    if model.version >= 3.9:
-        res = []
-        for i in range(n):
-            res.append(model.inference(I0, I1, (i + 1) * 1. / (n + 1), scale))
-        return res
-    else:
-        middle = model.inference(I0, I1, scale)
-        if n == 1:
-            return [middle]
-        first_half = make_inference(model, I0, middle, n=n // 2, scale=scale)
-        second_half = make_inference(model, middle, I1, n=n // 2, scale=scale)
-        if n % 2:
-            return [*first_half, middle, *second_half]
-        else:
-            return [*first_half, *second_half]
-
-def pad_image(img, fp16, padding):
-    if (fp16):
-        return F.pad(img, padding).half()
-    else:
-        return F.pad(img, padding)
-
-# TODO: move to fream_interpolation and add FILM to it!
-def stitch_video(img_batch_id, fps, img_folder_path, audio_path, ffmpeg_location, interp_x_amount, slow_mo_enabled, slow_mo_x_amount, f_crf, f_preset, keep_imgs, orig_vid_name):        
-    parent_folder = os.path.dirname(img_folder_path)
-    grandparent_folder = os.path.dirname(parent_folder)
-    if orig_vid_name is not None:
-        mp4_path = os.path.join(grandparent_folder, str(orig_vid_name) +'_RIFE_' + 'x' + str(interp_x_amount))
-    else:
-        mp4_path = os.path.join(parent_folder, str(img_batch_id) +'_RIFE_' + 'x' + str(interp_x_amount))
-
-    if slow_mo_enabled:
-        mp4_path = mp4_path + '_slomo_x' + str(slow_mo_x_amount)
-    mp4_path = mp4_path + '.mp4'
-
-    t = os.path.join(img_folder_path, "%07d.png")
-    add_soundtrack = 'None'
-    if not audio_path is None:
-        add_soundtrack = 'File'
-        
-    exception_raised = False
-    try:
-        ffmpeg_stitch_video(ffmpeg_location=ffmpeg_location, fps=fps, outmp4_path=mp4_path, stitch_from_frame=0, stitch_to_frame=1000000, imgs_path=t, add_soundtrack=add_soundtrack, audio_path=audio_path, crf=f_crf, preset=f_preset)
-    except Exception as e:
-        exception_raised = True
-        print(f"An error occurred while stitching the video: {e}")
-
-    if not exception_raised and not keep_imgs:
-        shutil.rmtree(img_folder_path)
-
-    if (keep_imgs and orig_vid_name is not None) or (orig_vid_name is not None and exception_raised is True):
-        shutil.move(img_folder_path, grandparent_folder)
-
-    return mp4_path
\ No newline at end of file
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/utils/loss.py b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/utils/loss.py
deleted file mode 100644
index 71ed0a5263df16163173a5fd4f84815c1de3cd57..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/utils/loss.py
+++ /dev/null
@@ -1,392 +0,0 @@
-# Ultralytics YOLO 🚀, AGPL-3.0 license
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ultralytics.yolo.utils.metrics import OKS_SIGMA
-from ultralytics.yolo.utils.ops import crop_mask, xywh2xyxy, xyxy2xywh
-from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
-
-from .metrics import bbox_iou
-from .tal import bbox2dist
-
-
-class VarifocalLoss(nn.Module):
-    """Varifocal loss by Zhang et al. https://arxiv.org/abs/2008.13367."""
-
-    def __init__(self):
-        """Initialize the VarifocalLoss class."""
-        super().__init__()
-
-    def forward(self, pred_score, gt_score, label, alpha=0.75, gamma=2.0):
-        """Computes varfocal loss."""
-        weight = alpha * pred_score.sigmoid().pow(gamma) * (1 - label) + gt_score * label
-        with torch.cuda.amp.autocast(enabled=False):
-            loss = (F.binary_cross_entropy_with_logits(pred_score.float(), gt_score.float(), reduction='none') *
-                    weight).mean(1).sum()
-        return loss
-
-
-# Losses
-class FocalLoss(nn.Module):
-    """Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)."""
-
-    def __init__(self, ):
-        super().__init__()
-
-    def forward(self, pred, label, gamma=1.5, alpha=0.25):
-        """Calculates and updates confusion matrix for object detection/classification tasks."""
-        loss = F.binary_cross_entropy_with_logits(pred, label, reduction='none')
-        # p_t = torch.exp(-loss)
-        # loss *= self.alpha * (1.000001 - p_t) ** self.gamma  # non-zero power for gradient stability
-
-        # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
-        pred_prob = pred.sigmoid()  # prob from logits
-        p_t = label * pred_prob + (1 - label) * (1 - pred_prob)
-        modulating_factor = (1.0 - p_t) ** gamma
-        loss *= modulating_factor
-        if alpha > 0:
-            alpha_factor = label * alpha + (1 - label) * (1 - alpha)
-            loss *= alpha_factor
-        return loss.mean(1).sum()
-
-
-class BboxLoss(nn.Module):
-
-    def __init__(self, reg_max, use_dfl=False):
-        """Initialize the BboxLoss module with regularization maximum and DFL settings."""
-        super().__init__()
-        self.reg_max = reg_max
-        self.use_dfl = use_dfl
-
-    def forward(self, pred_dist, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask):
-        """IoU loss."""
-        weight = target_scores.sum(-1)[fg_mask].unsqueeze(-1)
-        iou = bbox_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask], xywh=False, CIoU=True)
-        loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum
-
-        # DFL loss
-        if self.use_dfl:
-            target_ltrb = bbox2dist(anchor_points, target_bboxes, self.reg_max)
-            loss_dfl = self._df_loss(pred_dist[fg_mask].view(-1, self.reg_max + 1), target_ltrb[fg_mask]) * weight
-            loss_dfl = loss_dfl.sum() / target_scores_sum
-        else:
-            loss_dfl = torch.tensor(0.0).to(pred_dist.device)
-
-        return loss_iou, loss_dfl
-
-    @staticmethod
-    def _df_loss(pred_dist, target):
-        """Return sum of left and right DFL losses."""
-        # Distribution Focal Loss (DFL) proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391
-        tl = target.long()  # target left
-        tr = tl + 1  # target right
-        wl = tr - target  # weight left
-        wr = 1 - wl  # weight right
-        return (F.cross_entropy(pred_dist, tl.view(-1), reduction='none').view(tl.shape) * wl +
-                F.cross_entropy(pred_dist, tr.view(-1), reduction='none').view(tl.shape) * wr).mean(-1, keepdim=True)
-
-
-class KeypointLoss(nn.Module):
-
-    def __init__(self, sigmas) -> None:
-        super().__init__()
-        self.sigmas = sigmas
-
-    def forward(self, pred_kpts, gt_kpts, kpt_mask, area):
-        """Calculates keypoint loss factor and Euclidean distance loss for predicted and actual keypoints."""
-        d = (pred_kpts[..., 0] - gt_kpts[..., 0]) ** 2 + (pred_kpts[..., 1] - gt_kpts[..., 1]) ** 2
-        kpt_loss_factor = (torch.sum(kpt_mask != 0) + torch.sum(kpt_mask == 0)) / (torch.sum(kpt_mask != 0) + 1e-9)
-        # e = d / (2 * (area * self.sigmas) ** 2 + 1e-9)  # from formula
-        e = d / (2 * self.sigmas) ** 2 / (area + 1e-9) / 2  # from cocoeval
-        return kpt_loss_factor * ((1 - torch.exp(-e)) * kpt_mask).mean()
-
-
-# Criterion class for computing Detection training losses
-class v8DetectionLoss:
-
-    def __init__(self, model):  # model must be de-paralleled
-
-        device = next(model.parameters()).device  # get model device
-        h = model.args  # hyperparameters
-
-        m = model.model[-1]  # Detect() module
-        self.bce = nn.BCEWithLogitsLoss(reduction='none')
-        self.hyp = h
-        self.stride = m.stride  # model strides
-        self.nc = m.nc  # number of classes
-        self.no = m.no
-        self.reg_max = m.reg_max
-        self.device = device
-
-        self.use_dfl = m.reg_max > 1
-
-        self.assigner = TaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
-        self.bbox_loss = BboxLoss(m.reg_max - 1, use_dfl=self.use_dfl).to(device)
-        self.proj = torch.arange(m.reg_max, dtype=torch.float, device=device)
-
-    def preprocess(self, targets, batch_size, scale_tensor):
-        """Preprocesses the target counts and matches with the input batch size to output a tensor."""
-        if targets.shape[0] == 0:
-            out = torch.zeros(batch_size, 0, 5, device=self.device)
-        else:
-            i = targets[:, 0]  # image index
-            _, counts = i.unique(return_counts=True)
-            counts = counts.to(dtype=torch.int32)
-            out = torch.zeros(batch_size, counts.max(), 5, device=self.device)
-            for j in range(batch_size):
-                matches = i == j
-                n = matches.sum()
-                if n:
-                    out[j, :n] = targets[matches, 1:]
-            out[..., 1:5] = xywh2xyxy(out[..., 1:5].mul_(scale_tensor))
-        return out
-
-    def bbox_decode(self, anchor_points, pred_dist):
-        """Decode predicted object bounding box coordinates from anchor points and distribution."""
-        if self.use_dfl:
-            b, a, c = pred_dist.shape  # batch, anchors, channels
-            pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
-            # pred_dist = pred_dist.view(b, a, c // 4, 4).transpose(2,3).softmax(3).matmul(self.proj.type(pred_dist.dtype))
-            # pred_dist = (pred_dist.view(b, a, c // 4, 4).softmax(2) * self.proj.type(pred_dist.dtype).view(1, 1, -1, 1)).sum(2)
-        return dist2bbox(pred_dist, anchor_points, xywh=False)
-
-    def __call__(self, preds, batch):
-        """Calculate the sum of the loss for box, cls and dfl multiplied by batch size."""
-        loss = torch.zeros(3, device=self.device)  # box, cls, dfl
-        feats = preds[1] if isinstance(preds, tuple) else preds
-        pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
-            (self.reg_max * 4, self.nc), 1)
-
-        pred_scores = pred_scores.permute(0, 2, 1).contiguous()
-        pred_distri = pred_distri.permute(0, 2, 1).contiguous()
-
-        dtype = pred_scores.dtype
-        batch_size = pred_scores.shape[0]
-        imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0]  # image size (h,w)
-        anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
-
-        # targets
-        targets = torch.cat((batch['batch_idx'].view(-1, 1), batch['cls'].view(-1, 1), batch['bboxes']), 1)
-        targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
-        gt_labels, gt_bboxes = targets.split((1, 4), 2)  # cls, xyxy
-        mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
-
-        # pboxes
-        pred_bboxes = self.bbox_decode(anchor_points, pred_distri)  # xyxy, (b, h*w, 4)
-
-        _, target_bboxes, target_scores, fg_mask, _ = self.assigner(
-            pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
-            anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt)
-
-        target_scores_sum = max(target_scores.sum(), 1)
-
-        # cls loss
-        # loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum  # VFL way
-        loss[1] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum  # BCE
-
-        # bbox loss
-        if fg_mask.sum():
-            target_bboxes /= stride_tensor
-            loss[0], loss[2] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores,
-                                              target_scores_sum, fg_mask)
-
-        loss[0] *= self.hyp.box  # box gain
-        loss[1] *= self.hyp.cls  # cls gain
-        loss[2] *= self.hyp.dfl  # dfl gain
-
-        return loss.sum() * batch_size, loss.detach()  # loss(box, cls, dfl)
-
-
-# Criterion class for computing training losses
-class v8SegmentationLoss(v8DetectionLoss):
-
-    def __init__(self, model):  # model must be de-paralleled
-        super().__init__(model)
-        self.nm = model.model[-1].nm  # number of masks
-        self.overlap = model.args.overlap_mask
-
-    def __call__(self, preds, batch):
-        """Calculate and return the loss for the YOLO model."""
-        loss = torch.zeros(4, device=self.device)  # box, cls, dfl
-        feats, pred_masks, proto = preds if len(preds) == 3 else preds[1]
-        batch_size, _, mask_h, mask_w = proto.shape  # batch size, number of masks, mask height, mask width
-        pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
-            (self.reg_max * 4, self.nc), 1)
-
-        # b, grids, ..
-        pred_scores = pred_scores.permute(0, 2, 1).contiguous()
-        pred_distri = pred_distri.permute(0, 2, 1).contiguous()
-        pred_masks = pred_masks.permute(0, 2, 1).contiguous()
-
-        dtype = pred_scores.dtype
-        imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0]  # image size (h,w)
-        anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
-
-        # targets
-        try:
-            batch_idx = batch['batch_idx'].view(-1, 1)
-            targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes']), 1)
-            targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
-            gt_labels, gt_bboxes = targets.split((1, 4), 2)  # cls, xyxy
-            mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
-        except RuntimeError as e:
-            raise TypeError('ERROR ❌ segment dataset incorrectly formatted or not a segment dataset.\n'
-                            "This error can occur when incorrectly training a 'segment' model on a 'detect' dataset, "
-                            "i.e. 'yolo train model=yolov8n-seg.pt data=coco128.yaml'.\nVerify your dataset is a "
-                            "correctly formatted 'segment' dataset using 'data=coco128-seg.yaml' "
-                            'as an example.\nSee https://docs.ultralytics.com/tasks/segment/ for help.') from e
-
-        # pboxes
-        pred_bboxes = self.bbox_decode(anchor_points, pred_distri)  # xyxy, (b, h*w, 4)
-
-        _, target_bboxes, target_scores, fg_mask, target_gt_idx = self.assigner(
-            pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
-            anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt)
-
-        target_scores_sum = max(target_scores.sum(), 1)
-
-        # cls loss
-        # loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum  # VFL way
-        loss[2] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum  # BCE
-
-        if fg_mask.sum():
-            # bbox loss
-            loss[0], loss[3] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes / stride_tensor,
-                                              target_scores, target_scores_sum, fg_mask)
-            # masks loss
-            masks = batch['masks'].to(self.device).float()
-            if tuple(masks.shape[-2:]) != (mask_h, mask_w):  # downsample
-                masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0]
-
-            for i in range(batch_size):
-                if fg_mask[i].sum():
-                    mask_idx = target_gt_idx[i][fg_mask[i]]
-                    if self.overlap:
-                        gt_mask = torch.where(masks[[i]] == (mask_idx + 1).view(-1, 1, 1), 1.0, 0.0)
-                    else:
-                        gt_mask = masks[batch_idx.view(-1) == i][mask_idx]
-                    xyxyn = target_bboxes[i][fg_mask[i]] / imgsz[[1, 0, 1, 0]]
-                    marea = xyxy2xywh(xyxyn)[:, 2:].prod(1)
-                    mxyxy = xyxyn * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)
-                    loss[1] += self.single_mask_loss(gt_mask, pred_masks[i][fg_mask[i]], proto[i], mxyxy, marea)  # seg
-
-                # WARNING: lines below prevents Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
-                else:
-                    loss[1] += (proto * 0).sum() + (pred_masks * 0).sum()  # inf sums may lead to nan loss
-
-        # WARNING: lines below prevent Multi-GPU DDP 'unused gradient' PyTorch errors, do not remove
-        else:
-            loss[1] += (proto * 0).sum() + (pred_masks * 0).sum()  # inf sums may lead to nan loss
-
-        loss[0] *= self.hyp.box  # box gain
-        loss[1] *= self.hyp.box / batch_size  # seg gain
-        loss[2] *= self.hyp.cls  # cls gain
-        loss[3] *= self.hyp.dfl  # dfl gain
-
-        return loss.sum() * batch_size, loss.detach()  # loss(box, cls, dfl)
-
-    def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
-        """Mask loss for one image."""
-        pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:])  # (n, 32) @ (32,80,80) -> (n,80,80)
-        loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction='none')
-        return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
-
-
-# Criterion class for computing training losses
-class v8PoseLoss(v8DetectionLoss):
-
-    def __init__(self, model):  # model must be de-paralleled
-        super().__init__(model)
-        self.kpt_shape = model.model[-1].kpt_shape
-        self.bce_pose = nn.BCEWithLogitsLoss()
-        is_pose = self.kpt_shape == [17, 3]
-        nkpt = self.kpt_shape[0]  # number of keypoints
-        sigmas = torch.from_numpy(OKS_SIGMA).to(self.device) if is_pose else torch.ones(nkpt, device=self.device) / nkpt
-        self.keypoint_loss = KeypointLoss(sigmas=sigmas)
-
-    def __call__(self, preds, batch):
-        """Calculate the total loss and detach it."""
-        loss = torch.zeros(5, device=self.device)  # box, cls, dfl, kpt_location, kpt_visibility
-        feats, pred_kpts = preds if isinstance(preds[0], list) else preds[1]
-        pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
-            (self.reg_max * 4, self.nc), 1)
-
-        # b, grids, ..
-        pred_scores = pred_scores.permute(0, 2, 1).contiguous()
-        pred_distri = pred_distri.permute(0, 2, 1).contiguous()
-        pred_kpts = pred_kpts.permute(0, 2, 1).contiguous()
-
-        dtype = pred_scores.dtype
-        imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0]  # image size (h,w)
-        anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
-
-        # targets
-        batch_size = pred_scores.shape[0]
-        batch_idx = batch['batch_idx'].view(-1, 1)
-        targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes']), 1)
-        targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
-        gt_labels, gt_bboxes = targets.split((1, 4), 2)  # cls, xyxy
-        mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
-
-        # pboxes
-        pred_bboxes = self.bbox_decode(anchor_points, pred_distri)  # xyxy, (b, h*w, 4)
-        pred_kpts = self.kpts_decode(anchor_points, pred_kpts.view(batch_size, -1, *self.kpt_shape))  # (b, h*w, 17, 3)
-
-        _, target_bboxes, target_scores, fg_mask, target_gt_idx = self.assigner(
-            pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
-            anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt)
-
-        target_scores_sum = max(target_scores.sum(), 1)
-
-        # cls loss
-        # loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum  # VFL way
-        loss[3] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum  # BCE
-
-        # bbox loss
-        if fg_mask.sum():
-            target_bboxes /= stride_tensor
-            loss[0], loss[4] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores,
-                                              target_scores_sum, fg_mask)
-            keypoints = batch['keypoints'].to(self.device).float().clone()
-            keypoints[..., 0] *= imgsz[1]
-            keypoints[..., 1] *= imgsz[0]
-            for i in range(batch_size):
-                if fg_mask[i].sum():
-                    idx = target_gt_idx[i][fg_mask[i]]
-                    gt_kpt = keypoints[batch_idx.view(-1) == i][idx]  # (n, 51)
-                    gt_kpt[..., 0] /= stride_tensor[fg_mask[i]]
-                    gt_kpt[..., 1] /= stride_tensor[fg_mask[i]]
-                    area = xyxy2xywh(target_bboxes[i][fg_mask[i]])[:, 2:].prod(1, keepdim=True)
-                    pred_kpt = pred_kpts[i][fg_mask[i]]
-                    kpt_mask = gt_kpt[..., 2] != 0
-                    loss[1] += self.keypoint_loss(pred_kpt, gt_kpt, kpt_mask, area)  # pose loss
-                    # kpt_score loss
-                    if pred_kpt.shape[-1] == 3:
-                        loss[2] += self.bce_pose(pred_kpt[..., 2], kpt_mask.float())  # keypoint obj loss
-
-        loss[0] *= self.hyp.box  # box gain
-        loss[1] *= self.hyp.pose / batch_size  # pose gain
-        loss[2] *= self.hyp.kobj / batch_size  # kobj gain
-        loss[3] *= self.hyp.cls  # cls gain
-        loss[4] *= self.hyp.dfl  # dfl gain
-
-        return loss.sum() * batch_size, loss.detach()  # loss(box, cls, dfl)
-
-    def kpts_decode(self, anchor_points, pred_kpts):
-        """Decodes predicted keypoints to image coordinates."""
-        y = pred_kpts.clone()
-        y[..., :2] *= 2.0
-        y[..., 0] += anchor_points[:, [0]] - 0.5
-        y[..., 1] += anchor_points[:, [1]] - 0.5
-        return y
-
-
-class v8ClassificationLoss:
-
-    def __call__(self, preds, batch):
-        """Compute the classification loss between predictions and true labels."""
-        loss = torch.nn.functional.cross_entropy(preds, batch['cls'], reduction='sum') / 64
-        loss_items = loss.detach()
-        return loss, loss_items
diff --git a/spaces/verkaDerkaDerk/face-mesh-workflow/quads.py b/spaces/verkaDerkaDerk/face-mesh-workflow/quads.py
deleted file mode 100644
index 443213720aed95e38f84e784a1e296174132d9de..0000000000000000000000000000000000000000
--- a/spaces/verkaDerkaDerk/face-mesh-workflow/quads.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# This was created by importing a MediaPipe tesselated mesh and manually converting it in blender
-
-QUADS = [
-	[300, 334, 333, 298] , [  1,  12, 303, 268] , [234, 233, 122, 129] , [270, 304, 305, 271] , [246, 129, 115, 189] ,
-	[112, 118, 229,  32] , [104,  55,  69, 105] , [228,  35, 128, 235] , [120, 102, 101, 121] , [ 74,  73,  38,  40] ,
-	[ 71,  47,  54,  64] , [135, 132, 116, 221] , [335, 294, 299, 334] , [ 73,  12,   1,  38] , [ 42,  43,  81,  82] ,
-	[166,  93,  41,  40] , [122, 233, 232, 121] , [215, 213, 217, 208] , [183,  84,  85, 182] , [376, 308, 321, 322] ,
-	[ 30, 161, 160,  28] , [ 57,  29, 159, 158] , [ 84, 202, 201,  19] , [117, 144,  35, 228] , [204, 207,  93, 166] ,
-	[139, 216,  59, 173] , [276, 282,   6,   5] , [ 25, 145, 164, 111] , [292, 307, 308, 376] , [143, 127,  48, 101] ,
-	[419, 422, 429, 263] , [147,  44, 107,  92] , [ 17,  86,  85,  18] , [ 78,  77,  62, 147] , [127, 210, 199, 218] ,
-	[397, 378, 401, 370] , [166,  40,  38, 168] , [245, 234, 129, 246] , [ 31, 248, 247, 162] , [ 34, 247, 248, 131] ,
-	[175, 218, 199, 237] , [418, 352, 413, 466] , [125, 114, 226,  47] , [225, 224,  53,  54] , [ 99,  65, 103, 130] ,
-	[193, 215, 208, 188] , [219,  80, 240, 238] , [134, 156, 113, 244] , [345, 361, 364, 441] , [141, 171, 150, 177] ,
-	[400, 413, 352, 420] , [119, 230, 229, 118] , [282, 276, 441, 364] , [ 71,  64,  69,  72] , [315, 314, 407, 406] ,
-	[222, 190, 194,  56] , [114, 248,  31, 226] , [106,  53,  66,  67] , [236,  60, 167, 220] , [108,  56,   9,  10] ,
-	[ 67,  66,  56, 108] , [ 69,  64, 106, 105] , [120, 119,  51, 102] , [242, 126,  45, 238] , [  6, 196,   4,  52] ,
-	[143, 130, 210, 127] , [ 34, 131,  26,   8] , [323, 271, 410, 411] , [ 33, 195, 205, 212] , [ 37, 102,  51, 206] ,
-	[195, 202,  84, 183] , [238, 240, 239, 242] , [ 26, 111, 164,   8] , [225,  54,  47, 226] , [154, 146,  24,  23] ,
-	[211, 203, 213, 215] , [246, 194, 190, 245] , [425, 336, 407, 419] , [318, 317, 404, 403] , [ 33, 212, 171, 141] ,
-	[ 12,  73,  39,  13] , [208, 217, 207, 206] , [238, 221, 116, 219] , [ 46, 221, 238,  45] , [184,  43,  75, 185] ,
-	[209, 202, 195,  33] , [269, 272, 304, 303] , [214, 148, 178, 216] , [235,  94, 138, 228] , [ 67, 108, 109,  70] ,
-	[  7, 352, 418, 169] , [193, 188, 148, 214] , [ 97,  63,  77,  78] , [125,  47,  71, 157] , [317,  16,  17, 316] ,
-	[180,  87,  88, 179] , [106,  64,  54,  53] , [119, 118, 124,  51] , [146, 145,  25,  24] , [325, 319, 320, 326] ,
-	[123, 189, 175, 197] , [293, 309, 325, 326] , [150, 171, 170, 151] , [178, 138,  94, 133] , [328, 295, 456, 461] ,
-	[361, 421, 457, 364] , [336, 274, 376, 322] , [396, 395, 431, 432] , [ 13,  39,  83,  14] , [278, 330, 350, 351] ,
-	[191,  57, 158, 174] , [117, 112,  36, 144] , [224, 223,  66,  53] , [140,  72,  22, 163] , [163, 128,  35, 140] ,
-	[366, 365, 395, 380] , [219, 116,  49, 220] , [430, 359, 372, 356] , [157, 144,  36, 125] , [377, 353, 281, 412] ,
-	[125,  36, 227, 114] , [355,  20,  95, 371] , [120, 231, 230, 119] , [249, 457, 400, 420] , [162, 161,  30,  31] ,
-	[ 46,  45,   2,   5] , [141, 172, 209,  33] , [394, 392, 328, 327] , [ 32,  26, 131, 227] , [300, 298, 339, 338] ,
-	[395, 396, 379, 380] , [102,  37, 143, 101] , [217, 213,  58, 187] , [327,   3, 165, 394] , [242, 239,  21, 243] ,
-	[186,  41,  93, 187] , [269, 303,  12,  13] , [192,  81,  43, 184] , [140,  35, 144, 157] , [223, 222,  56,  66] ,
-	[189, 115, 218, 175] , [323, 427, 424, 392] , [ 37, 204, 130, 143] , [280, 430, 421, 361] , [  2, 275, 276,   5] ,
-	[134, 244, 191, 174] , [241,  76,  60, 236] , [108,  10, 152, 109] , [ 27, 155, 154,  23] , [211, 215, 136, 170] ,
-	[355, 275,   2,  20] , [ 90,  89,  96,  97] , [321, 320, 404, 405] , [316, 315, 406, 405] , [107,  44, 203, 205] ,
-	[201, 422, 314,  19] , [153, 176, 172, 149] , [376, 274, 288, 292] , [292, 288, 411, 410] , [130, 204, 166,  99] ,
-	[115,  48, 127, 218] , [327, 328, 461, 329] , [105, 106,  67,  70] , [236,  65,  99, 241] , [200, 201, 202, 209] ,
-	[332, 295, 328, 359] , [100,  61,  76, 241] , [243, 142, 126, 242] , [329, 463, 371, 327] , [220, 167,  80, 219] ,
-	[233,  27,  23, 232] , [190, 222,  57, 191] , [223,  29,  57, 222] , [244, 113, 234, 245] , [ 32, 229, 111,  26] ,
-	[226,  31,  30, 225] , [232,  23,  24, 231] , [225,  30,  28, 224] , [114, 227, 131, 248] , [ 32, 227,  36, 112] ,
-	[234, 113,  27, 233] , [230,  25, 111, 229] , [224,  28,  29, 223] , [ 95,  20, 126, 142] , [239, 240,  80,  21] ,
-	[243,  21,  61, 100] , [157,  71,  72, 140] , [ 76,  61, 167,  60] , [189, 123, 194, 246] , [231,  24,  25, 230] ,
-	[232, 231, 120, 121] , [121, 101,  48, 122] , [208, 206,  51, 188] , [332, 280, 279, 295] , [196, 249, 420, 198] ,
-	[199, 210,  50, 132] , [177, 149, 172, 141] , [117, 124, 118, 112] , [ 28, 160, 159,  29] , [245, 190, 191, 244] ,
-	[379, 396, 370, 401] , [268, 303, 304, 270] , [351, 453, 454, 358] , [ 75,  74,  40,  41] , [169, 418, 286,   9] ,
-	[283, 444, 445, 284] , [397, 176, 153, 378] , [110,  68,  70, 109] , [301, 277, 354, 384] , [186,  62,  77, 185] ,
-	[299, 294, 301, 302] , [ 50,  49, 116, 132] , [422, 201, 200, 429] , [304, 272, 273, 305] , [271, 323, 392, 270] ,
-	[296, 443, 444, 283] , [427, 437, 428, 426] , [336, 322, 406, 407] , [ 19, 314, 315,  18] , [387, 388, 260, 258] ,
-	[255, 374, 375, 254] , [314, 422, 419, 407] , [297, 335, 334, 300] , [313, 312, 272, 269] , [ 55,  22,  72,  69] ,
-	[221,  46,  52, 135] , [391, 374, 255, 340] , [315, 316,  17,  18] , [372, 267, 331, 330] , [423, 274, 336, 425] ,
-	[ 58,  44, 147,  62] , [ 91,  78, 147,  92] , [182,  85,  86, 181] , [423, 425, 432, 431] , [357, 265, 448, 455] ,
-	[268, 270, 392, 394] , [358, 454, 465, 466] , [264, 360, 468, 467] , [264, 250, 256, 360] , [421, 430, 356, 438] ,
-	[194, 123,   7, 169] , [449, 450, 348, 347] , [277, 284, 445, 446] , [241,  99,  98, 100] , [281, 331, 267, 426] ,
-	[307, 292, 410, 409] , [260, 388, 389, 261] , [364, 457, 249, 282] , [338, 339,  11, 152] , [438, 344, 413, 400] ,
-	[349, 451, 452, 350] , [345, 279, 280, 361] , [402, 377, 434, 436] , [367, 324, 455, 448] , [182,  92, 107, 183] ,
-	[418, 414, 442, 286] , [360, 256, 262, 447] , [284, 277, 301, 294] , [291, 251, 463, 329] , [344, 358, 466, 413] ,
-	[179,  89,  90, 180] , [266, 341, 346, 373] , [429, 397, 370, 263] , [296, 283, 335, 297] , [275, 355, 462, 458] ,
-	[  4, 237, 135,  52] , [359, 424, 267, 372] , [386, 387, 258, 259] , [394, 165,   1, 268] , [207, 217, 187,  93] ,
-	[278, 356, 372, 330] , [ 44,  58, 213, 203] , [459, 460, 458, 462] , [381, 382, 257, 253] , [266, 447, 262, 341] ,
-	[399, 385, 287, 415] , [437, 433, 435, 428] , [447, 266, 354, 343] , [183, 107, 205, 195] , [ 43,  42,  74,  75] ,
-	[302, 301, 384, 369] , [425, 419, 263, 432] , [295, 279, 440, 456] , [ 49,  50, 103,  65] , [ 74,  42,  39,  73] ,
-	[433, 423, 431, 435] , [311, 273, 272, 312] , [353, 367, 448, 346] , [252, 302, 369, 390] , [209, 172, 176, 200] ,
-	[ 56, 194, 169,   9] , [377, 412, 417, 434] , [ 90,  97,  78,  91] , [330, 331, 349, 350] , [180,  90,  91, 181] ,
-	[281, 348, 349, 331] , [265, 373, 346, 448] , [324, 367, 402, 362] , [308, 326, 320, 321] , [ 16,  15,  88,  87] ,
-	[266, 373, 384, 354] , [353, 347, 348, 281] , [363, 399, 415, 464] , [318,  15,  16, 317] , [356, 278, 344, 438] ,
-	[ 96,  79,  63,  97] , [ 11, 110, 109, 152] , [398, 368, 365, 366] , [  2,  45, 126,  20] , [313, 269,  13,  14] ,
-	[237, 199, 132, 135] , [187,  58,  62, 186] , [152,  10, 337, 338] , [ 42,  82,  83,  39] , [414, 418, 466, 465] ,
-	[467, 468, 261, 389] , [  9, 286, 337,  10] , [446, 343, 354, 277] , [265, 357, 390, 369] , [436, 434, 417, 368] ,
-	[170, 136, 137, 151] , [458, 441, 276, 275] , [212, 205, 203, 211] , [347, 353, 346, 341] , [284, 294, 335, 283] ,
-	[452, 453, 351, 350] , [ 95,   3, 327, 371] , [450, 451, 349, 348] , [197,   4, 196, 198] , [254, 375, 381, 253] ,
-	[345, 441, 458, 439] , [367, 353, 377, 402] , [449, 347, 341, 262] , [360, 447, 343, 468] , [136, 139, 173, 137] ,
-	[289, 436, 368, 398] , [281, 426, 428, 412] , [288, 433, 437, 411] , [ 99, 166, 168,  98] , [142, 243, 100,  98] ,
-	[175, 237,   4, 197] , [185,  75,  41, 186] , [307, 293, 326, 308] , [396, 432, 263, 370] , [286, 442, 443, 296] ,
-	[428, 435, 417, 412] , [411, 437, 427, 323] , [421, 438, 400, 457] , [165,   3,  98, 168] , [279, 345, 439, 440] ,
-	[391, 340, 256, 250] , [306, 291, 329, 461] , [373, 265, 369, 384] , [386, 259, 287, 385] , [435, 365, 368, 417] ,
-	[251, 459, 462, 463] , [320, 319, 403, 404] , [ 17,  16,  87,  86] , [322, 321, 405, 406] , [ 85,  84,  19,  18] ,
-	[433, 288, 274, 423] , [362, 402, 436, 289] , [185,  77,  63, 184] , [293, 307, 409, 408] , [392, 424, 359, 328] ,
-	[352,   7, 198, 420] , [228, 138, 124, 117] , [393, 290, 456, 440] , [176, 397, 429, 200] , [220,  49,  65, 236] ,
-	[424, 427, 426, 267] , [332, 359, 430, 280] , [365, 435, 431, 395] , [310, 251, 291, 393] , [355, 371, 463, 462] ,
-	[ 98,   3,  95, 142] , [255, 254, 451, 450] , [415, 414, 465, 464] , [254, 253, 452, 451] , [261, 468, 343, 446] ,
-	[260, 261, 446, 445] , [258, 260, 445, 444] , [454, 342, 464, 465] , [198,   7, 123, 197] , [259, 258, 444, 443] ,
-	[287, 442, 414, 415] , [340, 449, 262, 256] , [340, 255, 450, 449] , [257, 342, 454, 453] , [ 61,  21,  80, 167] ,
-	[310, 393, 440, 439] , [338, 337, 297, 300] , [310, 460, 459, 251] , [ 51, 124, 148, 188] , [253, 257, 453, 452] ,
-	[215, 193, 139, 136] , [351, 358, 344, 278] , [113, 156, 155,  27] , [  6,  52,  46,   5] , [206, 207, 204,  37] ,
-	[249, 196,   6, 282] , [216, 178, 133,  59] , [286, 296, 297, 337] , [382, 383, 342, 257] , [287, 259, 443, 442] ,
-	[211, 170, 171, 212] , [306, 461, 456, 290] , [104, 105,  70,  68] , [271, 305, 409, 410] , [460, 310, 439, 458] ,
-	[214, 216, 139, 193] , [317, 316, 405, 404] , [181,  91,  92, 182] , [  1, 165, 168,  38] , [363, 464, 342, 383] ,
-	[210, 130, 103,  50] , [305, 273, 408, 409] , [311, 416, 408, 273] , [309, 293, 408, 416] , [184,  63,  79, 192] ,
-	[115, 129, 122,  48] , [148, 124, 138, 178] , [181,  86,  87, 180] , [290, 393, 291, 306] , [252, 285, 299, 302] ,
-    [285, 333, 334, 299]
-]
diff --git a/spaces/vialibre/edia_full_es/modules/module_crowsPairs.py b/spaces/vialibre/edia_full_es/modules/module_crowsPairs.py
deleted file mode 100644
index 55fe12e0cb36bb1f5ad85234934e75fdcdb98647..0000000000000000000000000000000000000000
--- a/spaces/vialibre/edia_full_es/modules/module_crowsPairs.py
+++ /dev/null
@@ -1,53 +0,0 @@
-from modules.module_customPllLabel import CustomPllLabel
-from modules.module_pllScore import PllScore
-from typing import Dict, List
-
-class CrowsPairs:
-    def __init__(
-        self, 
-        language_model, # LanguageModel class instance
-        errorManager    # ErrorManager class instance
-    ) -> None:
-
-        self.Label = CustomPllLabel()
-        self.pllScore = PllScore(
-            language_model=language_model
-        )
-        self.errorManager = errorManager
-
-    def errorChecking(
-        self, 
-        sent_list: List[str],
-    ) -> str:
-
-        out_msj = ""
-
-        mandatory_sents = [0,1]
-        for sent_id, sent in enumerate(sent_list):
-            c_sent = sent.strip()
-            if c_sent:
-                if not self.pllScore.sentIsCorrect(c_sent):
-                    out_msj = ['CROWS-PAIRS_BAD_FORMATTED_SENTENCE', sent_id+1]
-                    break
-            else:
-                if sent_id in mandatory_sents:
-                    out_msj = ['CROWS-PAIRS_MANDATORY_SENTENCE_MISSING', sent_id+1]
-                    break
-        
-        return self.errorManager.process(out_msj)
-
-    def rank(
-        self, 
-        sent_list: List[str],
-    ) -> Dict[str, float]:
-
-        err = self.errorChecking(sent_list)
-        if err:
-            raise Exception(err)
-        
-        all_plls_scores = {}
-        for sent in sent_list:
-            if sent:
-                all_plls_scores[sent] = self.pllScore.compute(sent)
-
-        return all_plls_scores
\ No newline at end of file
diff --git a/spaces/vishnu23/drone_image_segmentation/app.py b/spaces/vishnu23/drone_image_segmentation/app.py
deleted file mode 100644
index 341c66ade2acf1947e6b0ae49ff96407cea7be54..0000000000000000000000000000000000000000
--- a/spaces/vishnu23/drone_image_segmentation/app.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import os
-import cv2
-from PIL import Image
-import numpy as np
-import geopandas as gpd
-import matplotlib.pyplot as plt
-from keras.models import load_model
-from tensorflow.keras.preprocessing.image import load_img, img_to_array
-import geopandas as gpd
-from skimage.measure import regionprops, label
-from shapely.geometry import Polygon
-import shutil
-import gradio as gr
-
-def predict(img):
-    # model = load_model('drive/My Drive/building_footprint_extraction_model.h5')
-    model = load_model('building_footprint_extraction_model.h5')
-    img_array = img_to_array(img)
-    img_array = img_array.reshape((1, 256, 256, 3))
-    img_array = img_array / 255.0
-    predictions = model.predict(img_array)
-    predicted_image = np.argmax(predictions, axis=3)
-    predicted_image = predicted_image[0,:,:]
-    predicted_image = predicted_image * 255
-    return predictions,predicted_image
-
-def get_shape_files(img):
-  predictions,predicted_image= predict(img)
-  threshold = 0.5
-  binary_mask = (predictions > threshold).astype(np.uint8)[:, :, 1]
-  if np.sum(binary_mask) == 0:
-      print("No building pixels detected. Saving an empty shapefile.")
-  else:
-      labeled_mask = label(binary_mask)
-      building_polygons = []
-      props = regionprops(labeled_mask)
-      for prop in props:
-          polygon = Polygon([(point[1], point[0]) for point in prop.coords])
-          building_polygons.append(polygon)
-      gdf = gpd.GeoDataFrame(geometry=building_polygons, crs="EPSG:4326")
-      output_shapefile = "shapefiles/building_footprints.shp"
-      if os.path.exists('shapefiles'):
-        pass
-      else:
-        os.mkdir('shapefiles')
-      gdf.to_file(output_shapefile)
-
-      # To get Masked Image
-      cv2.imwrite('shapefiles/mask.jpg',predicted_image)
-      shutil.make_archive('shapefile', 'zip', 'shapefiles')
-      return 'shapefile.zip',predicted_image
-
-my_app = gr.Blocks()
-with my_app:
-  gr.Markdown("<center><h1>Building Footprint Extraction</h1></center>")
-  with gr.Tabs():
-    # with gr.TabItem("Get Mask Image"):
-    #   with gr.Row():
-    #     with gr.Column():
-    #         img_source = gr.Image(label="Please select source Image", shape=(256, 256))
-    #         source_image_loader = gr.Button("Load above Image")
-    #     with gr.Column():
-    #         img_output = gr.Image(label="Image Output")
-    # source_image_loader.click(predict,img_source,[img_output])
-    with gr.TabItem("Get Mask Image and Shapefiles"):
-      with gr.Row():
-        with gr.Column():
-            img_source = gr.Image(label="Please select source Image", shape=(256, 256))
-            get_shape_loader = gr.Button("Get Shape File")
-
-        with gr.Column():
-          with gr.Row():
-              mask_img=gr.Image(label="Image Output")
-          with gr.Row():
-            output_zip = gr.outputs.File()
-
-    get_shape_loader.click(get_shape_files,img_source,[output_zip,mask_img])
-my_app.launch(debug = True)
\ No newline at end of file
diff --git a/spaces/vishnun/SpellCorrectorT5/app.py b/spaces/vishnun/SpellCorrectorT5/app.py
deleted file mode 100644
index 66052f791991bfb7c3aca6b73f5b8543141be65d..0000000000000000000000000000000000000000
--- a/spaces/vishnun/SpellCorrectorT5/app.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import streamlit as st
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-
-st.title("SpellCorrectorT5")
-st.markdown('SpellCorrectorT5 is a fine-tuned version of **pre-trained t5-small model** modelled on randomly selected 50000 sentences modified by [imputing random noises/errors](./random_noiser.py) and trained using transformers. It not only looks for _spelling errors but also looks for the semantics_ in the sentence and suggest other possible words for the incorrect word.')
-m_name = "vishnun/tinygram"
-ttokenizer = AutoTokenizer.from_pretrained(m_name)
-tmodel = AutoModelForSeq2SeqLM.from_pretrained(m_name)
-form = st.form("T5-form")
-
-examples = ["I will return it to yu once it is donr",
-            "Iu is going to rain",
-            "Wheir do you live?",
-            "It wis great mieting with you all"]
-
-input_text = form.selectbox(label="Choose an example",
-        options=examples)        
-form.write("(or)")
-input_text = form.text_input(label='Enter your own sentence', value=input_text)
-submit = form.form_submit_button("Submit")
-
-if submit:
-  input_ids = ttokenizer.encode(input_text, return_tensors='pt')
-  
-  # generate text until the output length (which includes the context length) reaches 50
-  outputs = tmodel.generate(
-    input_ids,
-    do_sample=True, 
-    max_length=50,
-    top_p=0.999, 
-    top_k=45,
-    num_return_sequences=2
-  )
-  
-  st.subheader("Most probable: ")
-  
-  for y in outputs:
-    
-    out_text = ttokenizer.decode(y, skip_special_tokens=True)
-    st.success(out_text.capitalize())
-    c_text = ""
-    for x in out_text.lower().split(" "):
-      if x in input_text.lower().split(" "):
-        c_text = c_text + x + " "
-      else:
-        c_text = c_text + '<span style="font-weight:bold; color:rgb(150,255,100);">' + x + '</span>' + " "
-        
-    ct = c_text.capitalize()
-    st.markdown(str(ct), unsafe_allow_html=True)
-    st.markdown("***", unsafe_allow_html=True)
\ No newline at end of file
diff --git a/spaces/vitaliykinakh/Galaxy_Zoo_Generation/src/models/big/BigGAN2.py b/spaces/vitaliykinakh/Galaxy_Zoo_Generation/src/models/big/BigGAN2.py
deleted file mode 100644
index d4ed5cf354bcaff97e6d6e881aeae75b09f36828..0000000000000000000000000000000000000000
--- a/spaces/vitaliykinakh/Galaxy_Zoo_Generation/src/models/big/BigGAN2.py
+++ /dev/null
@@ -1,469 +0,0 @@
-import functools
-
-import torch
-import torch.nn as nn
-from torch.nn import init
-import torch.optim as optim
-import torch.nn.functional as F
-
-import src.models.big.layers as layers
-from src.models.parameter import labels_dim, parameter
-from src.models.neuralnetwork import NeuralNetwork
-
-
-# Architectures for G
-# Attention is passed in in the format '32_64' to mean applying an attention
-# block at both resolution 32x32 and 64x64. Just '64' will apply at 64x64.
-def G_arch(ch=64, attention='64', ksize='333333', dilation='111111'):
-  arch = {}
-  arch[512] = {'in_channels' :  [ch * item for item in [16, 16, 8, 8, 4, 2, 1]],
-               'out_channels' : [ch * item for item in [16,  8, 8, 4, 2, 1, 1]],
-               'upsample' : [True] * 7,
-               'resolution' : [8, 16, 32, 64, 128, 256, 512],
-               'attention' : {2**i: (2**i in [int(item) for item in attention.split('_')])
-                              for i in range(3,10)}}
-  arch[256] = {'in_channels' :  [ch * item for item in [16, 16, 8, 8, 4, 2]],
-               'out_channels' : [ch * item for item in [16,  8, 8, 4, 2, 1]],
-               'upsample' : [True] * 6,
-               'resolution' : [8, 16, 32, 64, 128, 256],
-               'attention' : {2**i: (2**i in [int(item) for item in attention.split('_')])
-                              for i in range(3,9)}}
-  arch[128] = {'in_channels' :  [ch * item for item in [16, 16, 8, 4, 2]],
-               'out_channels' : [ch * item for item in [16, 8, 4, 2, 1]],
-               'upsample' : [True] * 5,
-               'resolution' : [8, 16, 32, 64, 128],
-               'attention' : {2**i: (2**i in [int(item) for item in attention.split('_')])
-                              for i in range(3,8)}}
-  arch[64]  = {'in_channels' :  [ch * item for item in [16, 16, 8, 4]],
-               'out_channels' : [ch * item for item in [16, 8, 4, 2]],
-               'upsample' : [True] * 4,
-               'resolution' : [8, 16, 32, 64],
-               'attention' : {2**i: (2**i in [int(item) for item in attention.split('_')])
-                              for i in range(3,7)}}
-  arch[32]  = {'in_channels' :  [ch * item for item in [4, 4, 4]],
-               'out_channels' : [ch * item for item in [4, 4, 4]],
-               'upsample' : [True] * 3,
-               'resolution' : [8, 16, 32],
-               'attention' : {2**i: (2**i in [int(item) for item in attention.split('_')])
-                              for i in range(3,6)}}
-
-  return arch
-
-class Generator(NeuralNetwork):
-  def __init__(self, G_ch=64, dim_z=128, bottom_width=4, resolution=64, labels_dim=labels_dim,
-               G_kernel_size=3, G_attn='64', n_classes=1,
-               num_G_SVs=1, num_G_SV_itrs=1,
-               G_shared=True, shared_dim=0, hier=False,
-               cross_replica=False, mybn=False,
-               G_activation=nn.ReLU(inplace=False),
-               G_lr=5e-5, G_B1=0.0, G_B2=0.999, adam_eps=1e-8,
-               BN_eps=1e-5, SN_eps=1e-12, G_mixed_precision=False, G_fp16=False,
-               G_init='ortho', skip_init=False, no_optim=False,
-               G_param='SN', norm_style='bn',
-               **kwargs):
-    super(Generator, self).__init__()
-    # Channel width mulitplier
-    self.ch = G_ch
-    # Dimensionality of the latent space
-    self.dim_z = dim_z
-    # The initial spatial dimensions
-    self.bottom_width = bottom_width
-    # Resolution of the output
-    self.resolution = resolution
-    # Kernel size?
-    self.kernel_size = G_kernel_size
-    # Attention?
-    self.attention = G_attn
-    # number of classes, for use in categorical conditional generation
-    self.n_classes = n_classes
-    # Use shared embeddings?
-    self.G_shared = G_shared
-    # Dimensionality of the shared embedding? Unused if not using G_shared
-    self.shared_dim = shared_dim if shared_dim > 0 else dim_z
-    # Hierarchical latent space?
-    self.hier = hier
-    # Cross replica batchnorm?
-    self.cross_replica = cross_replica
-    # Use my batchnorm?
-    self.mybn = mybn
-    # nonlinearity for residual blocks
-    self.activation = G_activation
-    # Initialization style
-    self.init = G_init
-    # Parameterization style
-    self.G_param = G_param
-    # Normalization style
-    self.norm_style = norm_style
-    # Epsilon for BatchNorm?
-    self.BN_eps = BN_eps
-    # Epsilon for Spectral Norm?
-    self.SN_eps = SN_eps
-    # fp16?
-    self.fp16 = G_fp16
-    # Architecture dict
-    self.arch = G_arch(self.ch, self.attention)[resolution]
-
-    # If using hierarchical latents, adjust z
-    if self.hier:
-      # Number of places z slots into
-      self.num_slots = len(self.arch['in_channels']) + 1
-      self.z_chunk_size = (self.dim_z // self.num_slots)
-      # Recalculate latent dimensionality for even splitting into chunks
-      self.dim_z = self.z_chunk_size *  self.num_slots
-    else:
-      self.num_slots = 1
-      self.z_chunk_size = 0
-
-    # Which convs, batchnorms, and linear layers to use
-    if self.G_param == 'SN':
-      self.which_conv = functools.partial(layers.SNConv2d,
-                          kernel_size=3, padding=1,
-                          num_svs=num_G_SVs, num_itrs=num_G_SV_itrs,
-                          eps=self.SN_eps)
-      self.which_linear = functools.partial(layers.SNLinear,
-                          num_svs=num_G_SVs, num_itrs=num_G_SV_itrs,
-                          eps=self.SN_eps)
-    else:
-      self.which_conv = functools.partial(nn.Conv2d, kernel_size=3, padding=1)
-      self.which_linear = nn.Linear
-      
-    # We use a non-spectral-normed embedding here regardless;
-    # For some reason applying SN to G's embedding seems to randomly cripple G
-    self.which_embedding = nn.Embedding
-    bn_linear = (functools.partial(self.which_linear, bias=False) if self.G_shared
-                 else self.which_embedding)
-    self.which_bn = functools.partial(layers.ccbn,
-                          which_linear=bn_linear,
-                          cross_replica=self.cross_replica,
-                          mybn=self.mybn,
-                          input_size=(self.shared_dim + self.z_chunk_size if self.G_shared
-                                      else self.n_classes),
-                          norm_style=self.norm_style,
-                          eps=self.BN_eps)
-
-
-    # Prepare model
-    # prepare label input
-    self.transform_label_layer = torch.nn.Linear(labels_dim, 128)
-    # If not using shared embeddings, self.shared is just a passthrough
-    self.shared = (self.which_embedding(n_classes, self.shared_dim) if G_shared 
-                    else layers.identity())
-    # First linear layer
-    self.linear = self.which_linear(self.dim_z // self.num_slots,
-                                    self.arch['in_channels'][0] * (self.bottom_width **2))
-
-    # self.blocks is a doubly-nested list of modules, the outer loop intended
-    # to be over blocks at a given resolution (resblocks and/or self-attention)
-    # while the inner loop is over a given block
-    self.blocks = []
-    for index in range(len(self.arch['out_channels'])):
-      self.blocks += [[layers.GBlock(in_channels=self.arch['in_channels'][index],
-                             out_channels=self.arch['out_channels'][index],
-                             which_conv=self.which_conv,
-                             which_bn=self.which_bn,
-                             activation=self.activation,
-                             upsample=(functools.partial(F.interpolate, scale_factor=2)
-                                       if self.arch['upsample'][index] else None))]]
-
-      # If attention on this block, attach it to the end
-      if self.arch['attention'][self.arch['resolution'][index]]:
-        print('Adding attention layer in G at resolution %d' % self.arch['resolution'][index])
-        self.blocks[-1] += [layers.Attention(self.arch['out_channels'][index], self.which_conv)]
-
-    # Turn self.blocks into a ModuleList so that it's all properly registered.
-    self.blocks = nn.ModuleList([nn.ModuleList(block) for block in self.blocks])
-
-    # output layer: batchnorm-relu-conv.
-    # Consider using a non-spectral conv here
-    self.output_layer = nn.Sequential(layers.bn(self.arch['out_channels'][-1],
-                                                cross_replica=self.cross_replica,
-                                                mybn=self.mybn),
-                                    self.activation,
-                                    self.which_conv(self.arch['out_channels'][-1], 3))
-
-    # Initialize weights. Optionally skip init for testing.
-    if not skip_init:
-      self.init_weights()
-
-    # Set up optimizer
-    # If this is an EMA copy, no need for an optim, so just return now
-    if no_optim:
-      return
-    self.lr, self.B1, self.B2, self.adam_eps = G_lr, G_B1, G_B2, adam_eps
-    if G_mixed_precision:
-      print('Using fp16 adam in G...')
-      import utils
-      self.optim = utils.Adam16(params=self.parameters(), lr=self.lr,
-                           betas=(self.B1, self.B2), weight_decay=0,
-                           eps=self.adam_eps)
-    else:
-      self.optim = optim.Adam(params=self.parameters(), lr=self.lr,
-                           betas=(self.B1, self.B2), weight_decay=0,
-                           eps=self.adam_eps)
-
-    # LR scheduling, left here for forward compatibility
-    # self.lr_sched = {'itr' : 0}# if self.progressive else {}
-    # self.j = 0
-    self.set_optimizer(parameter.optimizer, lr=parameter.learning_rate, betas=parameter.betas)
-
-  # Initialize
-  def init_weights(self):
-    self.param_count = 0
-    for module in self.modules():
-      if (isinstance(module, nn.Conv2d) 
-          or isinstance(module, nn.Linear) 
-          or isinstance(module, nn.Embedding)):
-        if self.init == 'ortho':
-          init.orthogonal_(module.weight)
-        elif self.init == 'N02':
-          init.normal_(module.weight, 0, 0.02)
-        elif self.init in ['glorot', 'xavier']:
-          init.xavier_uniform_(module.weight)
-        else:
-          print('Init style not recognized...')
-        self.param_count += sum([p.data.nelement() for p in module.parameters()])
-    print('Param count for G''s initialized parameters: %d' % self.param_count)
-
-
-  def transform_labels(self, labels):
-    """ prepore labels for input to generator """
-    return self.transform_label_layer(labels)
-
-
-  # Note on this forward function: we pass in a y vector which has
-  # already been passed through G.shared to enable easy class-wise
-  # interpolation later. If we passed in the one-hot and then ran it through
-  # G.shared in this forward function, it would be harder to handle.
-  def forward(self, z, y):
-    # If hierarchical, concatenate zs and ys
-    y = self.transform_labels(y)
-    if self.hier:
-      zs = torch.split(z, self.z_chunk_size, 1)
-      z = zs[0]
-      ys = [torch.cat([y, item], 1) for item in zs[1:]]
-    else:
-      ys = [y] * len(self.blocks)
-      
-    # First linear layer
-    h = self.linear(z)
-    # Reshape
-    h = h.view(h.size(0), -1, self.bottom_width, self.bottom_width)
-    
-    # Loop over blocks
-    for index, blocklist in enumerate(self.blocks):
-      # Second inner loop in case block has multiple layers
-      for block in blocklist:
-        h = block(h, ys[index])
-        
-    # Apply batchnorm-relu-conv-tanh at output
-    return torch.sigmoid(self.output_layer(h))
-#    return torch.tanh(self.output_layer(h))
-
-
-# Discriminator architecture, same paradigm as G's above
-def D_arch(ch=64, attention='64',ksize='333333', dilation='111111'):
-  arch = {}
-  arch[256] = {'in_channels' :  [3] + [ch*item for item in [1, 2, 4, 8, 8, 16]],
-               'out_channels' : [item * ch for item in [1, 2, 4, 8, 8, 16, 16]],
-               'downsample' : [True] * 6 + [False],
-               'resolution' : [128, 64, 32, 16, 8, 4, 4 ],
-               'attention' : {2**i: 2**i in [int(item) for item in attention.split('_')]
-                              for i in range(2,8)}}
-  arch[128] = {'in_channels' :  [3] + [ch*item for item in [1, 2, 4, 8, 16]],
-               'out_channels' : [item * ch for item in [1, 2, 4, 8, 16, 16]],
-               'downsample' : [True] * 5 + [False],
-               'resolution' : [64, 32, 16, 8, 4, 4],
-               'attention' : {2**i: 2**i in [int(item) for item in attention.split('_')]
-                              for i in range(2,8)}}
-  arch[64]  = {'in_channels' :  [3] + [ch*item for item in [1, 2, 4, 8]],
-               'out_channels' : [item * ch for item in [1, 2, 4, 8, 16]],
-               'downsample' : [True] * 4 + [False],
-               'resolution' : [32, 16, 8, 4, 4],
-               'attention' : {2**i: 2**i in [int(item) for item in attention.split('_')]
-                              for i in range(2,7)}}
-  arch[32]  = {'in_channels' :  [3] + [item * ch for item in [4, 4, 4]],
-               'out_channels' : [item * ch for item in [4, 4, 4, 4]],
-               'downsample' : [True, True, False, False],
-               'resolution' : [16, 16, 16, 16],
-               'attention' : {2**i: 2**i in [int(item) for item in attention.split('_')]
-                              for i in range(2,6)}}
-  return arch
-
-class Discriminator(NeuralNetwork):
-
-  def __init__(self, D_ch=64, D_wide=True, resolution=64, labels_dim=labels_dim,
-               D_kernel_size=3, D_attn='64', n_classes=1,
-               num_D_SVs=1, num_D_SV_itrs=1, D_activation=nn.ReLU(inplace=False),
-               D_lr=2e-4, D_B1=0.0, D_B2=0.999, adam_eps=1e-8,
-               SN_eps=1e-12, output_dim=1, D_mixed_precision=False, D_fp16=False,
-               D_init='ortho', skip_init=False, D_param='SN', **kwargs):
-    super(Discriminator, self).__init__()
-    # Width multiplier
-    self.ch = D_ch
-    # Use Wide D as in BigGAN and SA-GAN or skinny D as in SN-GAN?
-    self.D_wide = D_wide
-    # Resolution
-    self.resolution = resolution
-    # Kernel size
-    self.kernel_size = D_kernel_size
-    # Attention?
-    self.attention = D_attn
-    # Number of classes
-    self.n_classes = n_classes
-    # Activation
-    self.activation = D_activation
-    # Initialization style
-    self.init = D_init
-    # Parameterization style
-    self.D_param = D_param
-    # Epsilon for Spectral Norm?
-    self.SN_eps = SN_eps
-    # Fp16?
-    self.fp16 = D_fp16
-    # Architecture
-    self.arch = D_arch(self.ch, self.attention)[resolution]
-
-    # Which convs, batchnorms, and linear layers to use
-    # No option to turn off SN in D right now
-    if self.D_param == 'SN':
-      self.which_conv = functools.partial(layers.SNConv2d,
-                          kernel_size=3, padding=1,
-                          num_svs=num_D_SVs, num_itrs=num_D_SV_itrs,
-                          eps=self.SN_eps)
-      self.which_linear = functools.partial(layers.SNLinear,
-                          num_svs=num_D_SVs, num_itrs=num_D_SV_itrs,
-                          eps=self.SN_eps)
-      self.which_embedding = functools.partial(layers.SNEmbedding,
-                              num_svs=num_D_SVs, num_itrs=num_D_SV_itrs,
-                              eps=self.SN_eps)
-    # Prepare model
-    # prepare label input
-    self.transform_label_layer = torch.nn.Linear(labels_dim, 1024)
-    # self.blocks is a doubly-nested list of modules, the outer loop intended
-    # to be over blocks at a given resolution (resblocks and/or self-attention)
-    self.blocks = []
-    for index in range(len(self.arch['out_channels'])):
-      self.blocks += [[layers.DBlock(in_channels=self.arch['in_channels'][index],
-                       out_channels=self.arch['out_channels'][index],
-                       which_conv=self.which_conv,
-                       wide=self.D_wide,
-                       activation=self.activation,
-                       preactivation=(index > 0),
-                       downsample=(nn.AvgPool2d(2) if self.arch['downsample'][index] else None))]]
-      # If attention on this block, attach it to the end
-      if self.arch['attention'][self.arch['resolution'][index]]:
-        print('Adding attention layer in D at resolution %d' % self.arch['resolution'][index])
-        self.blocks[-1] += [layers.Attention(self.arch['out_channels'][index],
-                                             self.which_conv)]
-    # Turn self.blocks into a ModuleList so that it's all properly registered.
-    self.blocks = nn.ModuleList([nn.ModuleList(block) for block in self.blocks])
-    # Linear output layer. The output dimension is typically 1, but may be
-    # larger if we're e.g. turning this into a VAE with an inference output
-    self.linear = self.which_linear(self.arch['out_channels'][-1], output_dim)
-    # Embedding for projection discrimination
-    self.embed = self.which_embedding(self.n_classes, self.arch['out_channels'][-1])
-
-    # Initialize weights
-    if not skip_init:
-      self.init_weights()
-
-    # Set up optimizer
-    self.lr, self.B1, self.B2, self.adam_eps = D_lr, D_B1, D_B2, adam_eps
-    if D_mixed_precision:
-      print('Using fp16 adam in D...')
-      import utils
-      self.optim = utils.Adam16(params=self.parameters(), lr=self.lr,
-                             betas=(self.B1, self.B2), weight_decay=0, eps=self.adam_eps)
-    else:
-      self.optim = optim.Adam(params=self.parameters(), lr=self.lr,
-                             betas=(self.B1, self.B2), weight_decay=0, eps=self.adam_eps)
-    # LR scheduling, left here for forward compatibility
-    # self.lr_sched = {'itr' : 0}# if self.progressive else {}
-    # self.j = 0
-    self.set_optimizer(parameter.optimizer, lr=parameter.learning_rate*3, betas=parameter.betas)
-
-  # Initialize
-  def init_weights(self):
-    self.param_count = 0
-    for module in self.modules():
-      if (isinstance(module, nn.Conv2d)
-          or isinstance(module, nn.Linear)
-          or isinstance(module, nn.Embedding)):
-        if self.init == 'ortho':
-          init.orthogonal_(module.weight)
-        elif self.init == 'N02':
-          init.normal_(module.weight, 0, 0.02)
-        elif self.init in ['glorot', 'xavier']:
-          init.xavier_uniform_(module.weight)
-        else:
-          print('Init style not recognized...')
-        self.param_count += sum([p.data.nelement() for p in module.parameters()])
-    print('Param count for D''s initialized parameters: %d' % self.param_count)
-
-  def transform_labels(self, labels):
-    """ prepore labels for input to discriminator """
-    return self.transform_label_layer(labels)
-
-
-  def forward(self, x, y=None):
-    # Stick x into h for cleaner for loops without flow control
-    h = x
-    # Loop over blocks
-    for index, blocklist in enumerate(self.blocks):
-      for block in blocklist:
-        h = block(h)
-    # Apply global sum pooling as in SN-GAN
-    h = torch.sum(self.activation(h), [2, 3])
-    # Get initial class-unconditional output
-    out = self.linear(h)
-    # Get projection of final featureset onto class vectors and add to evidence
-    y = self.transform_labels(y)
-    out = out + torch.sum(y * h, 1, keepdim=True)
-#    out = out + torch.sum(self.embed(y) * h, 1, keepdim=True)  ## use y = torch.tensor(0)
-    return out
-
-# Parallelized G_D to minimize cross-gpu communication
-# Without this, Generator outputs would get all-gathered and then rebroadcast.
-class G_D(nn.Module):
-  def __init__(self, G, D):
-    super(G_D, self).__init__()
-    self.G = G
-    self.D = D
-
-  def forward(self, z, gy, x=None, dy=None, train_G=False, return_G_z=False,
-              split_D=False):              
-    # If training G, enable grad tape
-    with torch.set_grad_enabled(train_G):
-      # Get Generator output given noise
-      G_z = self.G(z, self.G.shared(gy))
-      # Cast as necessary
-      if self.G.fp16 and not self.D.fp16:
-        G_z = G_z.float()
-      if self.D.fp16 and not self.G.fp16:
-        G_z = G_z.half()
-    # Split_D means to run D once with real data and once with fake,
-    # rather than concatenating along the batch dimension.
-    if split_D:
-      D_fake = self.D(G_z, gy)
-      if x is not None:
-        D_real = self.D(x, dy)
-        return D_fake, D_real
-      else:
-        if return_G_z:
-          return D_fake, G_z
-        else:
-          return D_fake
-    # If real data is provided, concatenate it with the Generator's output
-    # along the batch dimension for improved efficiency.
-    else:
-      D_input = torch.cat([G_z, x], 0) if x is not None else G_z
-      D_class = torch.cat([gy, dy], 0) if dy is not None else gy
-      # Get Discriminator output
-      D_out = self.D(D_input, D_class)
-      if x is not None:
-        return torch.split(D_out, [G_z.shape[0], x.shape[0]]) # D_fake, D_real
-      else:
-        if return_G_z:
-          return D_out, G_z
-        else:
-          return D_out
diff --git a/spaces/vivien/clip-slip/losses.py b/spaces/vivien/clip-slip/losses.py
deleted file mode 100644
index b69b5ad6af4d9c14a1c9f14fca846f442a45b41a..0000000000000000000000000000000000000000
--- a/spaces/vivien/clip-slip/losses.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-import utils
-
-
-class CLIPLoss(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.labels = None
-        self.last_local_batch_size = None
-
-    def forward(self, outputs):
-        image_embed = outputs['image_embed']
-        text_embed = outputs['text_embed']
-        logit_scale = outputs['logit_scale']
-        local_batch_size = image_embed.size(0)
-
-        if local_batch_size != self.last_local_batch_size:
-            self.labels = local_batch_size * utils.get_rank() + torch.arange(
-                local_batch_size, device=image_embed.device
-            )
-            self.last_local_batch_size = local_batch_size
-
-        # normalized features
-        image_embed = F.normalize(image_embed, dim=-1, p=2)
-        text_embed = F.normalize(text_embed, dim=-1, p=2)
-
-        # gather features from all GPUs
-        image_embed_all, text_embed_all = \
-            utils.all_gather_batch([image_embed, text_embed])
-
-        # cosine similarity as logits
-        logits_per_image = logit_scale * image_embed @ text_embed_all.t()
-        logits_per_text = logit_scale * text_embed @ image_embed_all.t()
-
-        loss = (F.cross_entropy(logits_per_image, self.labels) + \
-            F.cross_entropy(logits_per_text, self.labels)) / 2
-
-        # compute accuracy
-        with torch.no_grad():
-            pred = torch.argmax(logits_per_image, dim=-1)
-            correct = pred.eq(self.labels).sum()
-            acc = 100 * correct / local_batch_size
-
-        return {'loss': loss, 'clip_loss': loss, 'clip_acc': acc}
-
-
-class SIMCLRLoss(nn.Module):
-    """
-    This is the SimCLR loss in https://arxiv.org/abs/2002.05709
-    The embedding vectors are assumed to have size (2 x batch_size, embedding_dim) and
-    the memory layout that can be reshaped into shape (2, batch_size, embedding_dim).
-    This memory layout is consistent with the SimCLR collator in
-    https://github.com/facebookresearch/vissl/blob/master/vissl/data/collators/simclr_collator.py
-    Config params:
-        temperature (float): the temperature to be applied on the logits
-    """
-
-    def __init__(self, temperature=0.1):
-        super().__init__()
-        self.tau = temperature
-        self.labels = None
-        self.masks = None
-        self.last_local_batch_size = None
-
-    def forward(self, outputs):
-        q_a = outputs['aug1_embed']
-        q_b = outputs['aug2_embed']
-
-        q_a = F.normalize(q_a, dim=-1, p=2)
-        q_b = F.normalize(q_b, dim=-1, p=2)
-
-        local_batch_size = q_a.size(0)
-
-        k_a, k_b = utils.all_gather_batch_with_grad([q_a, q_b])
-
-        if local_batch_size != self.last_local_batch_size:
-            self.labels = local_batch_size * utils.get_rank() + torch.arange(
-                local_batch_size, device=q_a.device
-            )
-            total_batch_size = local_batch_size * utils.get_world_size()
-            self.masks = F.one_hot(self.labels, total_batch_size) * 1e9
-            self.last_local_batch_size = local_batch_size
-
-        logits_aa = torch.matmul(q_a, k_a.transpose(0, 1)) / self.tau
-        logits_aa = logits_aa - self.masks
-        logits_bb = torch.matmul(q_b, k_b.transpose(0, 1)) / self.tau
-        logits_bb = logits_bb - self.masks
-        logits_ab = torch.matmul(q_a, k_b.transpose(0, 1)) / self.tau
-        logits_ba = torch.matmul(q_b, k_a.transpose(0, 1)) / self.tau
-
-        loss_a = F.cross_entropy(torch.cat([logits_ab, logits_aa], dim=1), self.labels)
-        loss_b = F.cross_entropy(torch.cat([logits_ba, logits_bb], dim=1), self.labels)
-        loss = (loss_a + loss_b) / 2  # divide by 2 to average over all samples
-
-        # compute accuracy
-        with torch.no_grad():
-            pred = torch.argmax(torch.cat([logits_ab, logits_aa], dim=1), dim=-1)
-            correct = pred.eq(self.labels).sum()
-            acc = 100 * correct / local_batch_size
-
-        return {'loss': loss, 'ssl_loss': loss, 'ssl_acc': acc}
-
-
-class SLIPLoss(nn.Module):
-    def __init__(self, ssl_loss, ssl_scale):
-        super().__init__()
-        self.clip_loss = CLIPLoss()
-        self.ssl_loss = ssl_loss
-        self.ssl_scale = ssl_scale
-
-    def forward(self, outputs):
-        clip_loss_dict = self.clip_loss(outputs)
-        clip_loss = clip_loss_dict['clip_loss']
-        clip_acc = clip_loss_dict['clip_acc']
-
-        ssl_loss_dict = self.ssl_loss(outputs)
-        ssl_loss = ssl_loss_dict['ssl_loss']
-        ssl_acc = ssl_loss_dict['ssl_acc']
-
-        return {'loss': clip_loss + self.ssl_scale * ssl_loss,
-                'clip_loss': clip_loss,
-                'clip_acc': clip_acc,
-                'ssl_loss': ssl_loss,
-                'ssl_acc': ssl_acc}
diff --git a/spaces/vonbarnekowa/stable-diffusion/ldm/models/diffusion/ddpm.py b/spaces/vonbarnekowa/stable-diffusion/ldm/models/diffusion/ddpm.py
deleted file mode 100644
index ff53bd8a7b84bd4fc2cd1b41c8f6d5819d803d6e..0000000000000000000000000000000000000000
--- a/spaces/vonbarnekowa/stable-diffusion/ldm/models/diffusion/ddpm.py
+++ /dev/null
@@ -1,1796 +0,0 @@
-"""
-wild mixture of
-https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
-https://github.com/CompVis/taming-transformers
--- merci
-"""
-
-import torch
-import torch.nn as nn
-import numpy as np
-import pytorch_lightning as pl
-from torch.optim.lr_scheduler import LambdaLR
-from einops import rearrange, repeat
-from contextlib import contextmanager, nullcontext
-from functools import partial
-import itertools
-from tqdm import tqdm
-from torchvision.utils import make_grid
-from pytorch_lightning.utilities.distributed import rank_zero_only
-from omegaconf import ListConfig
-
-from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
-from ldm.modules.ema import LitEma
-from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
-from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
-from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
-from ldm.models.diffusion.ddim import DDIMSampler
-
-
-__conditioning_keys__ = {'concat': 'c_concat',
-                         'crossattn': 'c_crossattn',
-                         'adm': 'y'}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-def uniform_on_device(r1, r2, shape, device):
-    return (r1 - r2) * torch.rand(*shape, device=device) + r2
-
-
-class DDPM(pl.LightningModule):
-    # classic DDPM with Gaussian diffusion, in image space
-    def __init__(self,
-                 unet_config,
-                 timesteps=1000,
-                 beta_schedule="linear",
-                 loss_type="l2",
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 load_only_unet=False,
-                 monitor="val/loss",
-                 use_ema=True,
-                 first_stage_key="image",
-                 image_size=256,
-                 channels=3,
-                 log_every_t=100,
-                 clip_denoised=True,
-                 linear_start=1e-4,
-                 linear_end=2e-2,
-                 cosine_s=8e-3,
-                 given_betas=None,
-                 original_elbo_weight=0.,
-                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
-                 l_simple_weight=1.,
-                 conditioning_key=None,
-                 parameterization="eps",  # all assuming fixed variance schedules
-                 scheduler_config=None,
-                 use_positional_encodings=False,
-                 learn_logvar=False,
-                 logvar_init=0.,
-                 make_it_fit=False,
-                 ucg_training=None,
-                 reset_ema=False,
-                 reset_num_ema_updates=False,
-                 ):
-        super().__init__()
-        assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
-        self.parameterization = parameterization
-        print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
-        self.cond_stage_model = None
-        self.clip_denoised = clip_denoised
-        self.log_every_t = log_every_t
-        self.first_stage_key = first_stage_key
-        self.image_size = image_size  # try conv?
-        self.channels = channels
-        self.use_positional_encodings = use_positional_encodings
-        self.model = DiffusionWrapper(unet_config, conditioning_key)
-        count_params(self.model, verbose=True)
-        self.use_ema = use_ema
-        if self.use_ema:
-            self.model_ema = LitEma(self.model)
-            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
-        self.use_scheduler = scheduler_config is not None
-        if self.use_scheduler:
-            self.scheduler_config = scheduler_config
-
-        self.v_posterior = v_posterior
-        self.original_elbo_weight = original_elbo_weight
-        self.l_simple_weight = l_simple_weight
-
-        if monitor is not None:
-            self.monitor = monitor
-        self.make_it_fit = make_it_fit
-        if reset_ema: assert exists(ckpt_path)
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
-            if reset_ema:
-                assert self.use_ema
-                print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
-                self.model_ema = LitEma(self.model)
-        if reset_num_ema_updates:
-            print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
-            assert self.use_ema
-            self.model_ema.reset_num_updates()
-
-        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
-                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
-
-        self.loss_type = loss_type
-
-        self.learn_logvar = learn_logvar
-        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
-        if self.learn_logvar:
-            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
-
-        self.ucg_training = ucg_training or dict()
-        if self.ucg_training:
-            self.ucg_prng = np.random.RandomState()
-
-    def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        if exists(given_betas):
-            betas = given_betas
-        else:
-            betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
-                                       cosine_s=cosine_s)
-        alphas = 1. - betas
-        alphas_cumprod = np.cumprod(alphas, axis=0)
-        alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
-
-        timesteps, = betas.shape
-        self.num_timesteps = int(timesteps)
-        self.linear_start = linear_start
-        self.linear_end = linear_end
-        assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
-
-        to_torch = partial(torch.tensor, dtype=torch.float32)
-
-        self.register_buffer('betas', to_torch(betas))
-        self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
-
-        # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
-
-        # calculations for posterior q(x_{t-1} | x_t, x_0)
-        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
-                1. - alphas_cumprod) + self.v_posterior * betas
-        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
-        self.register_buffer('posterior_variance', to_torch(posterior_variance))
-        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
-        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
-        self.register_buffer('posterior_mean_coef1', to_torch(
-            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
-        self.register_buffer('posterior_mean_coef2', to_torch(
-            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
-
-        if self.parameterization == "eps":
-            lvlb_weights = self.betas ** 2 / (
-                    2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
-        elif self.parameterization == "x0":
-            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
-        elif self.parameterization == "v":
-            lvlb_weights = torch.ones_like(self.betas ** 2 / (
-                    2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
-        else:
-            raise NotImplementedError("mu not supported")
-        # TODO how to choose this term
-        lvlb_weights[0] = lvlb_weights[1]
-        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
-        assert not torch.isnan(self.lvlb_weights).all()
-
-    @contextmanager
-    def ema_scope(self, context=None):
-        if self.use_ema:
-            self.model_ema.store(self.model.parameters())
-            self.model_ema.copy_to(self.model)
-            if context is not None:
-                print(f"{context}: Switched to EMA weights")
-        try:
-            yield None
-        finally:
-            if self.use_ema:
-                self.model_ema.restore(self.model.parameters())
-                if context is not None:
-                    print(f"{context}: Restored training weights")
-
-    @torch.no_grad()
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-        if self.make_it_fit:
-            n_params = len([name for name, _ in
-                            itertools.chain(self.named_parameters(),
-                                            self.named_buffers())])
-            for name, param in tqdm(
-                    itertools.chain(self.named_parameters(),
-                                    self.named_buffers()),
-                    desc="Fitting old weights to new weights",
-                    total=n_params
-            ):
-                if not name in sd:
-                    continue
-                old_shape = sd[name].shape
-                new_shape = param.shape
-                assert len(old_shape) == len(new_shape)
-                if len(new_shape) > 2:
-                    # we only modify first two axes
-                    assert new_shape[2:] == old_shape[2:]
-                # assumes first axis corresponds to output dim
-                if not new_shape == old_shape:
-                    new_param = param.clone()
-                    old_param = sd[name]
-                    if len(new_shape) == 1:
-                        for i in range(new_param.shape[0]):
-                            new_param[i] = old_param[i % old_shape[0]]
-                    elif len(new_shape) >= 2:
-                        for i in range(new_param.shape[0]):
-                            for j in range(new_param.shape[1]):
-                                new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
-
-                        n_used_old = torch.ones(old_shape[1])
-                        for j in range(new_param.shape[1]):
-                            n_used_old[j % old_shape[1]] += 1
-                        n_used_new = torch.zeros(new_shape[1])
-                        for j in range(new_param.shape[1]):
-                            n_used_new[j] = n_used_old[j % old_shape[1]]
-
-                        n_used_new = n_used_new[None, :]
-                        while len(n_used_new.shape) < len(new_shape):
-                            n_used_new = n_used_new.unsqueeze(-1)
-                        new_param /= n_used_new
-
-                    sd[name] = new_param
-
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys:\n {missing}")
-        if len(unexpected) > 0:
-            print(f"\nUnexpected Keys:\n {unexpected}")
-
-    def q_mean_variance(self, x_start, t):
-        """
-        Get the distribution q(x_t | x_0).
-        :param x_start: the [N x C x ...] tensor of noiseless inputs.
-        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
-        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
-        """
-        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
-        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
-        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
-        return mean, variance, log_variance
-
-    def predict_start_from_noise(self, x_t, t, noise):
-        return (
-                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
-                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
-        )
-
-    def predict_start_from_z_and_v(self, x_t, t, v):
-        # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
-        # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
-        return (
-                extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
-        )
-
-    def predict_eps_from_z_and_v(self, x_t, t, v):
-        return (
-                extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
-        )
-
-    def q_posterior(self, x_start, x_t, t):
-        posterior_mean = (
-                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
-                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
-        )
-        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
-        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
-        return posterior_mean, posterior_variance, posterior_log_variance_clipped
-
-    def p_mean_variance(self, x, t, clip_denoised: bool):
-        model_out = self.model(x, t)
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
-        b, *_, device = *x.shape, x.device
-        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
-        noise = noise_like(x.shape, device, repeat_noise)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def p_sample_loop(self, shape, return_intermediates=False):
-        device = self.betas.device
-        b = shape[0]
-        img = torch.randn(shape, device=device)
-        intermediates = [img]
-        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
-            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
-                                clip_denoised=self.clip_denoised)
-            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
-                intermediates.append(img)
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, batch_size=16, return_intermediates=False):
-        image_size = self.image_size
-        channels = self.channels
-        return self.p_sample_loop((batch_size, channels, image_size, image_size),
-                                  return_intermediates=return_intermediates)
-
-    def q_sample(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
-
-    def get_v(self, x, noise, t):
-        return (
-                extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
-                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
-        )
-
-    def get_loss(self, pred, target, mean=True):
-        if self.loss_type == 'l1':
-            loss = (target - pred).abs()
-            if mean:
-                loss = loss.mean()
-        elif self.loss_type == 'l2':
-            if mean:
-                loss = torch.nn.functional.mse_loss(target, pred)
-            else:
-                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
-        else:
-            raise NotImplementedError("unknown loss type '{loss_type}'")
-
-        return loss
-
-    def p_losses(self, x_start, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_out = self.model(x_noisy, t)
-
-        loss_dict = {}
-        if self.parameterization == "eps":
-            target = noise
-        elif self.parameterization == "x0":
-            target = x_start
-        elif self.parameterization == "v":
-            target = self.get_v(x_start, noise, t)
-        else:
-            raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
-
-        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
-
-        log_prefix = 'train' if self.training else 'val'
-
-        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
-        loss_simple = loss.mean() * self.l_simple_weight
-
-        loss_vlb = (self.lvlb_weights[t] * loss).mean()
-        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
-
-        loss = loss_simple + self.original_elbo_weight * loss_vlb
-
-        loss_dict.update({f'{log_prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def forward(self, x, *args, **kwargs):
-        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
-        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        return self.p_losses(x, t, *args, **kwargs)
-
-    def get_input(self, batch, k):
-        x = batch[k]
-        if len(x.shape) == 3:
-            x = x[..., None]
-        x = rearrange(x, 'b h w c -> b c h w')
-        x = x.to(memory_format=torch.contiguous_format).float()
-        return x
-
-    def shared_step(self, batch):
-        x = self.get_input(batch, self.first_stage_key)
-        loss, loss_dict = self(x)
-        return loss, loss_dict
-
-    def training_step(self, batch, batch_idx):
-        for k in self.ucg_training:
-            p = self.ucg_training[k]["p"]
-            val = self.ucg_training[k]["val"]
-            if val is None:
-                val = ""
-            for i in range(len(batch[k])):
-                if self.ucg_prng.choice(2, p=[1 - p, p]):
-                    batch[k][i] = val
-
-        loss, loss_dict = self.shared_step(batch)
-
-        self.log_dict(loss_dict, prog_bar=True,
-                      logger=True, on_step=True, on_epoch=True)
-
-        self.log("global_step", self.global_step,
-                 prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        if self.use_scheduler:
-            lr = self.optimizers().param_groups[0]['lr']
-            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        return loss
-
-    @torch.no_grad()
-    def validation_step(self, batch, batch_idx):
-        _, loss_dict_no_ema = self.shared_step(batch)
-        with self.ema_scope():
-            _, loss_dict_ema = self.shared_step(batch)
-            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
-        self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
-        self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
-
-    def on_train_batch_end(self, *args, **kwargs):
-        if self.use_ema:
-            self.model_ema(self.model)
-
-    def _get_rows_from_list(self, samples):
-        n_imgs_per_row = len(samples)
-        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
-        log = dict()
-        x = self.get_input(batch, self.first_stage_key)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        x = x.to(self.device)[:N]
-        log["inputs"] = x
-
-        # get diffusion row
-        diffusion_row = list()
-        x_start = x[:n_row]
-
-        for t in range(self.num_timesteps):
-            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                t = t.to(self.device).long()
-                noise = torch.randn_like(x_start)
-                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-                diffusion_row.append(x_noisy)
-
-        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
-
-        if sample:
-            # get denoise row
-            with self.ema_scope("Plotting"):
-                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
-
-            log["samples"] = samples
-            log["denoise_row"] = self._get_rows_from_list(denoise_row)
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.learn_logvar:
-            params = params + [self.logvar]
-        opt = torch.optim.AdamW(params, lr=lr)
-        return opt
-
-
-class LatentDiffusion(DDPM):
-    """main class"""
-
-    def __init__(self,
-                 first_stage_config,
-                 cond_stage_config,
-                 num_timesteps_cond=None,
-                 cond_stage_key="image",
-                 cond_stage_trainable=False,
-                 concat_mode=True,
-                 cond_stage_forward=None,
-                 conditioning_key=None,
-                 scale_factor=1.0,
-                 scale_by_std=False,
-                 force_null_conditioning=False,
-                 *args, **kwargs):
-        self.force_null_conditioning = force_null_conditioning
-        self.num_timesteps_cond = default(num_timesteps_cond, 1)
-        self.scale_by_std = scale_by_std
-        assert self.num_timesteps_cond <= kwargs['timesteps']
-        # for backwards compatibility after implementation of DiffusionWrapper
-        if conditioning_key is None:
-            conditioning_key = 'concat' if concat_mode else 'crossattn'
-        if cond_stage_config == '__is_unconditional__' and not self.force_null_conditioning:
-            conditioning_key = None
-        ckpt_path = kwargs.pop("ckpt_path", None)
-        reset_ema = kwargs.pop("reset_ema", False)
-        reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False)
-        ignore_keys = kwargs.pop("ignore_keys", [])
-        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
-        self.concat_mode = concat_mode
-        self.cond_stage_trainable = cond_stage_trainable
-        self.cond_stage_key = cond_stage_key
-        try:
-            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
-            self.num_downs = 0
-        if not scale_by_std:
-            self.scale_factor = scale_factor
-        else:
-            self.register_buffer('scale_factor', torch.tensor(scale_factor))
-        self.instantiate_first_stage(first_stage_config)
-        self.instantiate_cond_stage(cond_stage_config)
-        self.cond_stage_forward = cond_stage_forward
-        self.clip_denoised = False
-        self.bbox_tokenizer = None
-
-        self.restarted_from_ckpt = False
-        if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys)
-            self.restarted_from_ckpt = True
-            if reset_ema:
-                assert self.use_ema
-                print(
-                    f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
-                self.model_ema = LitEma(self.model)
-        if reset_num_ema_updates:
-            print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
-            assert self.use_ema
-            self.model_ema.reset_num_updates()
-
-    def make_cond_schedule(self, ):
-        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
-        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
-        self.cond_ids[:self.num_timesteps_cond] = ids
-
-    @rank_zero_only
-    @torch.no_grad()
-    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
-        # only for very first batch
-        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
-            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
-            # set rescale weight to 1./std of encodings
-            print("### USING STD-RESCALING ###")
-            x = super().get_input(batch, self.first_stage_key)
-            x = x.to(self.device)
-            encoder_posterior = self.encode_first_stage(x)
-            z = self.get_first_stage_encoding(encoder_posterior).detach()
-            del self.scale_factor
-            self.register_buffer('scale_factor', 1. / z.flatten().std())
-            print(f"setting self.scale_factor to {self.scale_factor}")
-            print("### USING STD-RESCALING ###")
-
-    def register_schedule(self,
-                          given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
-
-        self.shorten_cond_schedule = self.num_timesteps_cond > 1
-        if self.shorten_cond_schedule:
-            self.make_cond_schedule()
-
-    def instantiate_first_stage(self, config):
-        model = instantiate_from_config(config)
-        self.first_stage_model = model.eval()
-        self.first_stage_model.train = disabled_train
-        for param in self.first_stage_model.parameters():
-            param.requires_grad = False
-
-    def instantiate_cond_stage(self, config):
-        if not self.cond_stage_trainable:
-            if config == "__is_first_stage__":
-                print("Using first stage also as cond stage.")
-                self.cond_stage_model = self.first_stage_model
-            elif config == "__is_unconditional__":
-                print(f"Training {self.__class__.__name__} as an unconditional model.")
-                self.cond_stage_model = None
-                # self.be_unconditional = True
-            else:
-                model = instantiate_from_config(config)
-                self.cond_stage_model = model.eval()
-                self.cond_stage_model.train = disabled_train
-                for param in self.cond_stage_model.parameters():
-                    param.requires_grad = False
-        else:
-            assert config != '__is_first_stage__'
-            assert config != '__is_unconditional__'
-            model = instantiate_from_config(config)
-            self.cond_stage_model = model
-
-    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
-        denoise_row = []
-        for zd in tqdm(samples, desc=desc):
-            denoise_row.append(self.decode_first_stage(zd.to(self.device),
-                                                       force_not_quantize=force_no_decoder_quantization))
-        n_imgs_per_row = len(denoise_row)
-        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
-        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
-        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
-        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
-        return denoise_grid
-
-    def get_first_stage_encoding(self, encoder_posterior):
-        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
-            z = encoder_posterior.sample()
-        elif isinstance(encoder_posterior, torch.Tensor):
-            z = encoder_posterior
-        else:
-            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
-        return self.scale_factor * z
-
-    def get_learned_conditioning(self, c):
-        if self.cond_stage_forward is None:
-            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
-                c = self.cond_stage_model.encode(c)
-                if isinstance(c, DiagonalGaussianDistribution):
-                    c = c.mode()
-            else:
-                c = self.cond_stage_model(c)
-        else:
-            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
-            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
-        return c
-
-    def meshgrid(self, h, w):
-        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
-        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
-
-        arr = torch.cat([y, x], dim=-1)
-        return arr
-
-    def delta_border(self, h, w):
-        """
-        :param h: height
-        :param w: width
-        :return: normalized distance to image border,
-         wtith min distance = 0 at border and max dist = 0.5 at image center
-        """
-        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
-        arr = self.meshgrid(h, w) / lower_right_corner
-        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
-        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
-        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
-        return edge_dist
-
-    def get_weighting(self, h, w, Ly, Lx, device):
-        weighting = self.delta_border(h, w)
-        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
-                               self.split_input_params["clip_max_weight"], )
-        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
-
-        if self.split_input_params["tie_braker"]:
-            L_weighting = self.delta_border(Ly, Lx)
-            L_weighting = torch.clip(L_weighting,
-                                     self.split_input_params["clip_min_tie_weight"],
-                                     self.split_input_params["clip_max_tie_weight"])
-
-            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
-            weighting = weighting * L_weighting
-        return weighting
-
-    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
-        """
-        :param x: img of size (bs, c, h, w)
-        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
-        """
-        bs, nc, h, w = x.shape
-
-        # number of crops in image
-        Ly = (h - kernel_size[0]) // stride[0] + 1
-        Lx = (w - kernel_size[1]) // stride[1] + 1
-
-        if uf == 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
-
-            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
-
-        elif uf > 1 and df == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
-                                dilation=1, padding=0,
-                                stride=(stride[0] * uf, stride[1] * uf))
-            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
-
-        elif df > 1 and uf == 1:
-            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
-            unfold = torch.nn.Unfold(**fold_params)
-
-            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
-                                dilation=1, padding=0,
-                                stride=(stride[0] // df, stride[1] // df))
-            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
-
-            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
-            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
-            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
-
-        else:
-            raise NotImplementedError
-
-        return fold, unfold, normalization, weighting
-
-    @torch.no_grad()
-    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
-                  cond_key=None, return_original_cond=False, bs=None, return_x=False):
-        x = super().get_input(batch, k)
-        if bs is not None:
-            x = x[:bs]
-        x = x.to(self.device)
-        encoder_posterior = self.encode_first_stage(x)
-        z = self.get_first_stage_encoding(encoder_posterior).detach()
-
-        if self.model.conditioning_key is not None and not self.force_null_conditioning:
-            if cond_key is None:
-                cond_key = self.cond_stage_key
-            if cond_key != self.first_stage_key:
-                if cond_key in ['caption', 'coordinates_bbox', "txt"]:
-                    xc = batch[cond_key]
-                elif cond_key in ['class_label', 'cls']:
-                    xc = batch
-                else:
-                    xc = super().get_input(batch, cond_key).to(self.device)
-            else:
-                xc = x
-            if not self.cond_stage_trainable or force_c_encode:
-                if isinstance(xc, dict) or isinstance(xc, list):
-                    c = self.get_learned_conditioning(xc)
-                else:
-                    c = self.get_learned_conditioning(xc.to(self.device))
-            else:
-                c = xc
-            if bs is not None:
-                c = c[:bs]
-
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                ckey = __conditioning_keys__[self.model.conditioning_key]
-                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
-
-        else:
-            c = None
-            xc = None
-            if self.use_positional_encodings:
-                pos_x, pos_y = self.compute_latent_shifts(batch)
-                c = {'pos_x': pos_x, 'pos_y': pos_y}
-        out = [z, c]
-        if return_first_stage_outputs:
-            xrec = self.decode_first_stage(z)
-            out.extend([x, xrec])
-        if return_x:
-            out.extend([x])
-        if return_original_cond:
-            out.append(xc)
-        return out
-
-    @torch.no_grad()
-    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
-        if predict_cids:
-            if z.dim() == 4:
-                z = torch.argmax(z.exp(), dim=1).long()
-            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
-            z = rearrange(z, 'b h w c -> b c h w').contiguous()
-
-        z = 1. / self.scale_factor * z
-        return self.first_stage_model.decode(z)
-
-    @torch.no_grad()
-    def encode_first_stage(self, x):
-        return self.first_stage_model.encode(x)
-
-    def shared_step(self, batch, **kwargs):
-        x, c = self.get_input(batch, self.first_stage_key)
-        loss = self(x, c)
-        return loss
-
-    def forward(self, x, c, *args, **kwargs):
-        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
-        if self.model.conditioning_key is not None:
-            assert c is not None
-            if self.cond_stage_trainable:
-                c = self.get_learned_conditioning(c)
-            if self.shorten_cond_schedule:  # TODO: drop this option
-                tc = self.cond_ids[t].to(self.device)
-                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
-        return self.p_losses(x, c, t, *args, **kwargs)
-
-    def apply_model(self, x_noisy, t, cond, return_ids=False):
-        if isinstance(cond, dict):
-            # hybrid case, cond is expected to be a dict
-            pass
-        else:
-            if not isinstance(cond, list):
-                cond = [cond]
-            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
-            cond = {key: cond}
-
-        x_recon = self.model(x_noisy, t, **cond)
-
-        if isinstance(x_recon, tuple) and not return_ids:
-            return x_recon[0]
-        else:
-            return x_recon
-
-    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
-        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
-               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
-
-    def _prior_bpd(self, x_start):
-        """
-        Get the prior KL term for the variational lower-bound, measured in
-        bits-per-dim.
-        This term can't be optimized, as it only depends on the encoder.
-        :param x_start: the [N x C x ...] tensor of inputs.
-        :return: a batch of [N] KL values (in bits), one per batch element.
-        """
-        batch_size = x_start.shape[0]
-        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
-        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
-        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
-        return mean_flat(kl_prior) / np.log(2.0)
-
-    def p_losses(self, x_start, cond, t, noise=None):
-        noise = default(noise, lambda: torch.randn_like(x_start))
-        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
-        model_output = self.apply_model(x_noisy, t, cond)
-
-        loss_dict = {}
-        prefix = 'train' if self.training else 'val'
-
-        if self.parameterization == "x0":
-            target = x_start
-        elif self.parameterization == "eps":
-            target = noise
-        elif self.parameterization == "v":
-            target = self.get_v(x_start, noise, t)
-        else:
-            raise NotImplementedError()
-
-        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
-        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
-
-        logvar_t = self.logvar[t].to(self.device)
-        loss = loss_simple / torch.exp(logvar_t) + logvar_t
-        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
-        if self.learn_logvar:
-            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
-            loss_dict.update({'logvar': self.logvar.data.mean()})
-
-        loss = self.l_simple_weight * loss.mean()
-
-        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
-        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
-        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
-        loss += (self.original_elbo_weight * loss_vlb)
-        loss_dict.update({f'{prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
-                        return_x0=False, score_corrector=None, corrector_kwargs=None):
-        t_in = t
-        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
-
-        if score_corrector is not None:
-            assert self.parameterization == "eps"
-            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
-
-        if return_codebook_ids:
-            model_out, logits = model_out
-
-        if self.parameterization == "eps":
-            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
-        elif self.parameterization == "x0":
-            x_recon = model_out
-        else:
-            raise NotImplementedError()
-
-        if clip_denoised:
-            x_recon.clamp_(-1., 1.)
-        if quantize_denoised:
-            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
-        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
-        if return_codebook_ids:
-            return model_mean, posterior_variance, posterior_log_variance, logits
-        elif return_x0:
-            return model_mean, posterior_variance, posterior_log_variance, x_recon
-        else:
-            return model_mean, posterior_variance, posterior_log_variance
-
-    @torch.no_grad()
-    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
-                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
-                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
-        b, *_, device = *x.shape, x.device
-        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
-                                       return_codebook_ids=return_codebook_ids,
-                                       quantize_denoised=quantize_denoised,
-                                       return_x0=return_x0,
-                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-        if return_codebook_ids:
-            raise DeprecationWarning("Support dropped.")
-            model_mean, _, model_log_variance, logits = outputs
-        elif return_x0:
-            model_mean, _, model_log_variance, x0 = outputs
-        else:
-            model_mean, _, model_log_variance = outputs
-
-        noise = noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        # no noise when t == 0
-        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
-
-        if return_codebook_ids:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
-        if return_x0:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
-        else:
-            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
-
-    @torch.no_grad()
-    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
-                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
-                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
-                              log_every_t=None):
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        timesteps = self.num_timesteps
-        if batch_size is not None:
-            b = batch_size if batch_size is not None else shape[0]
-            shape = [batch_size] + list(shape)
-        else:
-            b = batch_size = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=self.device)
-        else:
-            img = x_T
-        intermediates = []
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
-                        total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-        if type(temperature) == float:
-            temperature = [temperature] * timesteps
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img, x0_partial = self.p_sample(img, cond, ts,
-                                            clip_denoised=self.clip_denoised,
-                                            quantize_denoised=quantize_denoised, return_x0=True,
-                                            temperature=temperature[i], noise_dropout=noise_dropout,
-                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
-            if mask is not None:
-                assert x0 is not None
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(x0_partial)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-        return img, intermediates
-
-    @torch.no_grad()
-    def p_sample_loop(self, cond, shape, return_intermediates=False,
-                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, start_T=None,
-                      log_every_t=None):
-
-        if not log_every_t:
-            log_every_t = self.log_every_t
-        device = self.betas.device
-        b = shape[0]
-        if x_T is None:
-            img = torch.randn(shape, device=device)
-        else:
-            img = x_T
-
-        intermediates = [img]
-        if timesteps is None:
-            timesteps = self.num_timesteps
-
-        if start_T is not None:
-            timesteps = min(timesteps, start_T)
-        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
-            range(0, timesteps))
-
-        if mask is not None:
-            assert x0 is not None
-            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
-
-        for i in iterator:
-            ts = torch.full((b,), i, device=device, dtype=torch.long)
-            if self.shorten_cond_schedule:
-                assert self.model.conditioning_key != 'hybrid'
-                tc = self.cond_ids[ts].to(cond.device)
-                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
-
-            img = self.p_sample(img, cond, ts,
-                                clip_denoised=self.clip_denoised,
-                                quantize_denoised=quantize_denoised)
-            if mask is not None:
-                img_orig = self.q_sample(x0, ts)
-                img = img_orig * mask + (1. - mask) * img
-
-            if i % log_every_t == 0 or i == timesteps - 1:
-                intermediates.append(img)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
-
-        if return_intermediates:
-            return img, intermediates
-        return img
-
-    @torch.no_grad()
-    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
-               verbose=True, timesteps=None, quantize_denoised=False,
-               mask=None, x0=None, shape=None, **kwargs):
-        if shape is None:
-            shape = (batch_size, self.channels, self.image_size, self.image_size)
-        if cond is not None:
-            if isinstance(cond, dict):
-                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
-            else:
-                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
-        return self.p_sample_loop(cond,
-                                  shape,
-                                  return_intermediates=return_intermediates, x_T=x_T,
-                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
-                                  mask=mask, x0=x0)
-
-    @torch.no_grad()
-    def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
-        if ddim:
-            ddim_sampler = DDIMSampler(self)
-            shape = (self.channels, self.image_size, self.image_size)
-            samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,
-                                                         shape, cond, verbose=False, **kwargs)
-
-        else:
-            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
-                                                 return_intermediates=True, **kwargs)
-
-        return samples, intermediates
-
-    @torch.no_grad()
-    def get_unconditional_conditioning(self, batch_size, null_label=None):
-        if null_label is not None:
-            xc = null_label
-            if isinstance(xc, ListConfig):
-                xc = list(xc)
-            if isinstance(xc, dict) or isinstance(xc, list):
-                c = self.get_learned_conditioning(xc)
-            else:
-                if hasattr(xc, "to"):
-                    xc = xc.to(self.device)
-                c = self.get_learned_conditioning(xc)
-        else:
-            if self.cond_stage_key in ["class_label", "cls"]:
-                xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
-                return self.get_learned_conditioning(xc)
-            else:
-                raise NotImplementedError("todo")
-        if isinstance(c, list):  # in case the encoder gives us a list
-            for i in range(len(c)):
-                c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)
-        else:
-            c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
-        return c
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None,
-                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
-                   plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
-                   use_ema_scope=True,
-                   **kwargs):
-        ema_scope = self.ema_scope if use_ema_scope else nullcontext
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
-                                           return_first_stage_outputs=True,
-                                           force_c_encode=True,
-                                           return_original_cond=True,
-                                           bs=N)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x
-        log["reconstruction"] = xrec
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):
-                xc = self.cond_stage_model.decode(c)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ["caption", "txt"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ['class_label', "cls"]:
-                try:
-                    xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
-                    log['conditioning'] = xc
-                except KeyError:
-                    # probably no "human_label" in batch
-                    pass
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if plot_diffusion_rows:
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:
-            # get denoise row
-            with ema_scope("Sampling"):
-                samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                         ddim_steps=ddim_steps, eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
-                    self.first_stage_model, IdentityFirstStage):
-                # also display when quantizing x0 while sampling
-                with ema_scope("Plotting Quantized Denoised"):
-                    samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                             ddim_steps=ddim_steps, eta=ddim_eta,
-                                                             quantize_denoised=True)
-                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
-                    #                                      quantize_denoised=True)
-                x_samples = self.decode_first_stage(samples.to(self.device))
-                log["samples_x0_quantized"] = x_samples
-
-        if unconditional_guidance_scale > 1.0:
-            uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
-            if self.model.conditioning_key == "crossattn-adm":
-                uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
-            with ema_scope("Sampling with classifier-free guidance"):
-                samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                 ddim_steps=ddim_steps, eta=ddim_eta,
-                                                 unconditional_guidance_scale=unconditional_guidance_scale,
-                                                 unconditional_conditioning=uc,
-                                                 )
-                x_samples_cfg = self.decode_first_stage(samples_cfg)
-                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
-
-        if inpaint:
-            # make a simple center square
-            b, h, w = z.shape[0], z.shape[2], z.shape[3]
-            mask = torch.ones(N, h, w).to(self.device)
-            # zeros will be filled in
-            mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
-            mask = mask[:, None, ...]
-            with ema_scope("Plotting Inpaint"):
-                samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
-                                             ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-            x_samples = self.decode_first_stage(samples.to(self.device))
-            log["samples_inpainting"] = x_samples
-            log["mask"] = mask
-
-            # outpaint
-            mask = 1. - mask
-            with ema_scope("Plotting Outpaint"):
-                samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
-                                             ddim_steps=ddim_steps, x0=z[:N], mask=mask)
-            x_samples = self.decode_first_stage(samples.to(self.device))
-            log["samples_outpainting"] = x_samples
-
-        if plot_progressive_rows:
-            with ema_scope("Plotting Progressives"):
-                img, progressives = self.progressive_denoising(c,
-                                                               shape=(self.channels, self.image_size, self.image_size),
-                                                               batch_size=N)
-            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
-            log["progressive_row"] = prog_row
-
-        if return_keys:
-            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
-                return log
-            else:
-                return {key: log[key] for key in return_keys}
-        return log
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = list(self.model.parameters())
-        if self.cond_stage_trainable:
-            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
-            params = params + list(self.cond_stage_model.parameters())
-        if self.learn_logvar:
-            print('Diffusion model optimizing logvar')
-            params.append(self.logvar)
-        opt = torch.optim.AdamW(params, lr=lr)
-        if self.use_scheduler:
-            assert 'target' in self.scheduler_config
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [opt], scheduler
-        return opt
-
-    @torch.no_grad()
-    def to_rgb(self, x):
-        x = x.float()
-        if not hasattr(self, "colorize"):
-            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
-        x = nn.functional.conv2d(x, weight=self.colorize)
-        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
-        return x
-
-
-class DiffusionWrapper(pl.LightningModule):
-    def __init__(self, diff_model_config, conditioning_key):
-        super().__init__()
-        self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False)
-        self.diffusion_model = instantiate_from_config(diff_model_config)
-        self.conditioning_key = conditioning_key
-        assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm']
-
-    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None):
-        if self.conditioning_key is None:
-            out = self.diffusion_model(x, t)
-        elif self.conditioning_key == 'concat':
-            xc = torch.cat([x] + c_concat, dim=1)
-            out = self.diffusion_model(xc, t)
-        elif self.conditioning_key == 'crossattn':
-            if not self.sequential_cross_attn:
-                cc = torch.cat(c_crossattn, 1)
-            else:
-                cc = c_crossattn
-            out = self.diffusion_model(x, t, context=cc)
-        elif self.conditioning_key == 'hybrid':
-            xc = torch.cat([x] + c_concat, dim=1)
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(xc, t, context=cc)
-        elif self.conditioning_key == 'hybrid-adm':
-            assert c_adm is not None
-            xc = torch.cat([x] + c_concat, dim=1)
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(xc, t, context=cc, y=c_adm)
-        elif self.conditioning_key == 'crossattn-adm':
-            assert c_adm is not None
-            cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(x, t, context=cc, y=c_adm)
-        elif self.conditioning_key == 'adm':
-            cc = c_crossattn[0]
-            out = self.diffusion_model(x, t, y=cc)
-        else:
-            raise NotImplementedError()
-
-        return out
-
-
-class LatentUpscaleDiffusion(LatentDiffusion):
-    def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_level_key=None, **kwargs):
-        super().__init__(*args, **kwargs)
-        # assumes that neither the cond_stage nor the low_scale_model contain trainable params
-        assert not self.cond_stage_trainable
-        self.instantiate_low_stage(low_scale_config)
-        self.low_scale_key = low_scale_key
-        self.noise_level_key = noise_level_key
-
-    def instantiate_low_stage(self, config):
-        model = instantiate_from_config(config)
-        self.low_scale_model = model.eval()
-        self.low_scale_model.train = disabled_train
-        for param in self.low_scale_model.parameters():
-            param.requires_grad = False
-
-    @torch.no_grad()
-    def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False):
-        if not log_mode:
-            z, c = super().get_input(batch, k, force_c_encode=True, bs=bs)
-        else:
-            z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
-                                                  force_c_encode=True, return_original_cond=True, bs=bs)
-        x_low = batch[self.low_scale_key][:bs]
-        x_low = rearrange(x_low, 'b h w c -> b c h w')
-        x_low = x_low.to(memory_format=torch.contiguous_format).float()
-        zx, noise_level = self.low_scale_model(x_low)
-        if self.noise_level_key is not None:
-            # get noise level from batch instead, e.g. when extracting a custom noise level for bsr
-            raise NotImplementedError('TODO')
-
-        all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level}
-        if log_mode:
-            # TODO: maybe disable if too expensive
-            x_low_rec = self.low_scale_model.decode(zx)
-            return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level
-        return z, all_conds
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
-                   plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True,
-                   unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True,
-                   **kwargs):
-        ema_scope = self.ema_scope if use_ema_scope else nullcontext
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(batch, self.first_stage_key, bs=N,
-                                                                          log_mode=True)
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x
-        log["reconstruction"] = xrec
-        log["x_lr"] = x_low
-        log[f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"] = x_low_rec
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):
-                xc = self.cond_stage_model.decode(c)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ["caption", "txt"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ['class_label', 'cls']:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
-                log['conditioning'] = xc
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if plot_diffusion_rows:
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:
-            # get denoise row
-            with ema_scope("Sampling"):
-                samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                         ddim_steps=ddim_steps, eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-        if unconditional_guidance_scale > 1.0:
-            uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label)
-            # TODO explore better "unconditional" choices for the other keys
-            # maybe guide away from empty text label and highest noise level and maximally degraded zx?
-            uc = dict()
-            for k in c:
-                if k == "c_crossattn":
-                    assert isinstance(c[k], list) and len(c[k]) == 1
-                    uc[k] = [uc_tmp]
-                elif k == "c_adm":  # todo: only run with text-based guidance?
-                    assert isinstance(c[k], torch.Tensor)
-                    #uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level
-                    uc[k] = c[k]
-                elif isinstance(c[k], list):
-                    uc[k] = [c[k][i] for i in range(len(c[k]))]
-                else:
-                    uc[k] = c[k]
-
-            with ema_scope("Sampling with classifier-free guidance"):
-                samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
-                                                 ddim_steps=ddim_steps, eta=ddim_eta,
-                                                 unconditional_guidance_scale=unconditional_guidance_scale,
-                                                 unconditional_conditioning=uc,
-                                                 )
-                x_samples_cfg = self.decode_first_stage(samples_cfg)
-                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
-
-        if plot_progressive_rows:
-            with ema_scope("Plotting Progressives"):
-                img, progressives = self.progressive_denoising(c,
-                                                               shape=(self.channels, self.image_size, self.image_size),
-                                                               batch_size=N)
-            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
-            log["progressive_row"] = prog_row
-
-        return log
-
-
-class LatentFinetuneDiffusion(LatentDiffusion):
-    """
-         Basis for different finetunas, such as inpainting or depth2image
-         To disable finetuning mode, set finetune_keys to None
-    """
-
-    def __init__(self,
-                 concat_keys: tuple,
-                 finetune_keys=("model.diffusion_model.input_blocks.0.0.weight",
-                                "model_ema.diffusion_modelinput_blocks00weight"
-                                ),
-                 keep_finetune_dims=4,
-                 # if model was trained without concat mode before and we would like to keep these channels
-                 c_concat_log_start=None,  # to log reconstruction of c_concat codes
-                 c_concat_log_end=None,
-                 *args, **kwargs
-                 ):
-        ckpt_path = kwargs.pop("ckpt_path", None)
-        ignore_keys = kwargs.pop("ignore_keys", list())
-        super().__init__(*args, **kwargs)
-        self.finetune_keys = finetune_keys
-        self.concat_keys = concat_keys
-        self.keep_dims = keep_finetune_dims
-        self.c_concat_log_start = c_concat_log_start
-        self.c_concat_log_end = c_concat_log_end
-        if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint'
-        if exists(ckpt_path):
-            self.init_from_ckpt(ckpt_path, ignore_keys)
-
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
-        keys = list(sd.keys())
-        for k in keys:
-            for ik in ignore_keys:
-                if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
-                    del sd[k]
-
-            # make it explicit, finetune by including extra input channels
-            if exists(self.finetune_keys) and k in self.finetune_keys:
-                new_entry = None
-                for name, param in self.named_parameters():
-                    if name in self.finetune_keys:
-                        print(
-                            f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only")
-                        new_entry = torch.zeros_like(param)  # zero init
-                assert exists(new_entry), 'did not find matching parameter to modify'
-                new_entry[:, :self.keep_dims, ...] = sd[k]
-                sd[k] = new_entry
-
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
-        if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-        if len(unexpected) > 0:
-            print(f"Unexpected Keys: {unexpected}")
-
-    @torch.no_grad()
-    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
-                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
-                   plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
-                   use_ema_scope=True,
-                   **kwargs):
-        ema_scope = self.ema_scope if use_ema_scope else nullcontext
-        use_ddim = ddim_steps is not None
-
-        log = dict()
-        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True)
-        c_cat, c = c["c_concat"][0], c["c_crossattn"][0]
-        N = min(x.shape[0], N)
-        n_row = min(x.shape[0], n_row)
-        log["inputs"] = x
-        log["reconstruction"] = xrec
-        if self.model.conditioning_key is not None:
-            if hasattr(self.cond_stage_model, "decode"):
-                xc = self.cond_stage_model.decode(c)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ["caption", "txt"]:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
-                log["conditioning"] = xc
-            elif self.cond_stage_key in ['class_label', 'cls']:
-                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
-                log['conditioning'] = xc
-            elif isimage(xc):
-                log["conditioning"] = xc
-            if ismap(xc):
-                log["original_conditioning"] = self.to_rgb(xc)
-
-        if not (self.c_concat_log_start is None and self.c_concat_log_end is None):
-            log["c_concat_decoded"] = self.decode_first_stage(c_cat[:, self.c_concat_log_start:self.c_concat_log_end])
-
-        if plot_diffusion_rows:
-            # get diffusion row
-            diffusion_row = list()
-            z_start = z[:n_row]
-            for t in range(self.num_timesteps):
-                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
-                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
-                    t = t.to(self.device).long()
-                    noise = torch.randn_like(z_start)
-                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
-                    diffusion_row.append(self.decode_first_stage(z_noisy))
-
-            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
-            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
-            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
-            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
-            log["diffusion_row"] = diffusion_grid
-
-        if sample:
-            # get denoise row
-            with ema_scope("Sampling"):
-                samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
-                                                         batch_size=N, ddim=use_ddim,
-                                                         ddim_steps=ddim_steps, eta=ddim_eta)
-                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
-            x_samples = self.decode_first_stage(samples)
-            log["samples"] = x_samples
-            if plot_denoise_rows:
-                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
-                log["denoise_row"] = denoise_grid
-
-        if unconditional_guidance_scale > 1.0:
-            uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label)
-            uc_cat = c_cat
-            uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
-            with ema_scope("Sampling with classifier-free guidance"):
-                samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
-                                                 batch_size=N, ddim=use_ddim,
-                                                 ddim_steps=ddim_steps, eta=ddim_eta,
-                                                 unconditional_guidance_scale=unconditional_guidance_scale,
-                                                 unconditional_conditioning=uc_full,
-                                                 )
-                x_samples_cfg = self.decode_first_stage(samples_cfg)
-                log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
-
-        return log
-
-
-class LatentInpaintDiffusion(LatentFinetuneDiffusion):
-    """
-    can either run as pure inpainting model (only concat mode) or with mixed conditionings,
-    e.g. mask as concat and text via cross-attn.
-    To disable finetuning mode, set finetune_keys to None
-     """
-
-    def __init__(self,
-                 concat_keys=("mask", "masked_image"),
-                 masked_image_key="masked_image",
-                 *args, **kwargs
-                 ):
-        super().__init__(concat_keys, *args, **kwargs)
-        self.masked_image_key = masked_image_key
-        assert self.masked_image_key in concat_keys
-
-    @torch.no_grad()
-    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
-        # note: restricted to non-trainable encoders currently
-        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting'
-        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
-                                              force_c_encode=True, return_original_cond=True, bs=bs)
-
-        assert exists(self.concat_keys)
-        c_cat = list()
-        for ck in self.concat_keys:
-            cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
-            if bs is not None:
-                cc = cc[:bs]
-                cc = cc.to(self.device)
-            bchw = z.shape
-            if ck != self.masked_image_key:
-                cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
-            else:
-                cc = self.get_first_stage_encoding(self.encode_first_stage(cc))
-            c_cat.append(cc)
-        c_cat = torch.cat(c_cat, dim=1)
-        all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
-        if return_first_stage_outputs:
-            return z, all_conds, x, xrec, xc
-        return z, all_conds
-
-    @torch.no_grad()
-    def log_images(self, *args, **kwargs):
-        log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs)
-        log["masked_image"] = rearrange(args[0]["masked_image"],
-                                        'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
-        return log
-
-
-class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion):
-    """
-    condition on monocular depth estimation
-    """
-
-    def __init__(self, depth_stage_config, concat_keys=("midas_in",), *args, **kwargs):
-        super().__init__(concat_keys=concat_keys, *args, **kwargs)
-        self.depth_model = instantiate_from_config(depth_stage_config)
-        self.depth_stage_key = concat_keys[0]
-
-    @torch.no_grad()
-    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
-        # note: restricted to non-trainable encoders currently
-        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for depth2img'
-        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
-                                              force_c_encode=True, return_original_cond=True, bs=bs)
-
-        assert exists(self.concat_keys)
-        assert len(self.concat_keys) == 1
-        c_cat = list()
-        for ck in self.concat_keys:
-            cc = batch[ck]
-            if bs is not None:
-                cc = cc[:bs]
-                cc = cc.to(self.device)
-            cc = self.depth_model(cc)
-            cc = torch.nn.functional.interpolate(
-                cc,
-                size=z.shape[2:],
-                mode="bicubic",
-                align_corners=False,
-            )
-            # TODO: think about this. ideally rescale by some global values
-            depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3],
-                                                                                           keepdim=True)
-            cc = 2. * (cc - depth_min) / (depth_max - depth_min + 0.001) - 1.
-            c_cat.append(cc)
-        c_cat = torch.cat(c_cat, dim=1)
-        all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
-        if return_first_stage_outputs:
-            return z, all_conds, x, xrec, xc
-        return z, all_conds
-
-    @torch.no_grad()
-    def log_images(self, *args, **kwargs):
-        log = super().log_images(*args, **kwargs)
-        depth = self.depth_model(args[0][self.depth_stage_key])
-        depth_min, depth_max = torch.amin(depth, dim=[1, 2, 3], keepdim=True), \
-                               torch.amax(depth, dim=[1, 2, 3], keepdim=True)
-        log["depth"] = 2. * (depth - depth_min) / (depth_max - depth_min) - 1.
-        return log
-
-
-class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion):
-    """
-        condition on low-res image (and optionally on some spatial noise augmentation)
-    """
-    def __init__(self, concat_keys=("lr",), reshuffle_patch_size=None,
-                 low_scale_config=None, low_scale_key=None, *args, **kwargs):
-        super().__init__(concat_keys=concat_keys, *args, **kwargs)
-        self.reshuffle_patch_size = reshuffle_patch_size
-        self.low_scale_model = None
-        if low_scale_config is not None:
-            print("Initializing a low-scale model")
-            assert exists(low_scale_key)
-            self.instantiate_low_stage(low_scale_config)
-            self.low_scale_key = low_scale_key
-
-    def instantiate_low_stage(self, config):
-        model = instantiate_from_config(config)
-        self.low_scale_model = model.eval()
-        self.low_scale_model.train = disabled_train
-        for param in self.low_scale_model.parameters():
-            param.requires_grad = False
-
-    @torch.no_grad()
-    def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
-        # note: restricted to non-trainable encoders currently
-        assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for upscaling-ft'
-        z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
-                                              force_c_encode=True, return_original_cond=True, bs=bs)
-
-        assert exists(self.concat_keys)
-        assert len(self.concat_keys) == 1
-        # optionally make spatial noise_level here
-        c_cat = list()
-        noise_level = None
-        for ck in self.concat_keys:
-            cc = batch[ck]
-            cc = rearrange(cc, 'b h w c -> b c h w')
-            if exists(self.reshuffle_patch_size):
-                assert isinstance(self.reshuffle_patch_size, int)
-                cc = rearrange(cc, 'b c (p1 h) (p2 w) -> b (p1 p2 c) h w',
-                               p1=self.reshuffle_patch_size, p2=self.reshuffle_patch_size)
-            if bs is not None:
-                cc = cc[:bs]
-                cc = cc.to(self.device)
-            if exists(self.low_scale_model) and ck == self.low_scale_key:
-                cc, noise_level = self.low_scale_model(cc)
-            c_cat.append(cc)
-        c_cat = torch.cat(c_cat, dim=1)
-        if exists(noise_level):
-            all_conds = {"c_concat": [c_cat], "c_crossattn": [c], "c_adm": noise_level}
-        else:
-            all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
-        if return_first_stage_outputs:
-            return z, all_conds, x, xrec, xc
-        return z, all_conds
-
-    @torch.no_grad()
-    def log_images(self, *args, **kwargs):
-        log = super().log_images(*args, **kwargs)
-        log["lr"] = rearrange(args[0]["lr"], 'b h w c -> b c h w')
-        return log
diff --git a/spaces/vumichien/canvas_controlnet/annotator/midas/midas/midas_net.py b/spaces/vumichien/canvas_controlnet/annotator/midas/midas/midas_net.py
deleted file mode 100644
index 8a954977800b0a0f48807e80fa63041910e33c1f..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/midas/midas/midas_net.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
-
-
-class MidasNet(BaseModel):
-    """Network for monocular depth estimation.
-    """
-
-    def __init__(self, path=None, features=256, non_negative=True):
-        """Init.
-
-        Args:
-            path (str, optional): Path to saved model. Defaults to None.
-            features (int, optional): Number of features. Defaults to 256.
-            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
-        """
-        print("Loading weights: ", path)
-
-        super(MidasNet, self).__init__()
-
-        use_pretrained = False if path is None else True
-
-        self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
-
-        self.scratch.refinenet4 = FeatureFusionBlock(features)
-        self.scratch.refinenet3 = FeatureFusionBlock(features)
-        self.scratch.refinenet2 = FeatureFusionBlock(features)
-        self.scratch.refinenet1 = FeatureFusionBlock(features)
-
-        self.scratch.output_conv = nn.Sequential(
-            nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
-            Interpolate(scale_factor=2, mode="bilinear"),
-            nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
-            nn.ReLU(True),
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-        )
-
-        if path:
-            self.load(path)
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input data (image)
-
-        Returns:
-            tensor: depth
-        """
-
-        layer_1 = self.pretrained.layer1(x)
-        layer_2 = self.pretrained.layer2(layer_1)
-        layer_3 = self.pretrained.layer3(layer_2)
-        layer_4 = self.pretrained.layer4(layer_3)
-
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
-        out = self.scratch.output_conv(path_1)
-
-        return torch.squeeze(out, dim=1)
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/visualization/optflow.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/visualization/optflow.py
deleted file mode 100644
index c3870c700f7c946177ee5d536ce3f6c814a77ce7..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/visualization/optflow.py
+++ /dev/null
@@ -1,112 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from __future__ import division
-
-import numpy as np
-
-from annotator.uniformer.mmcv.image import rgb2bgr
-from annotator.uniformer.mmcv.video import flowread
-from .image import imshow
-
-
-def flowshow(flow, win_name='', wait_time=0):
-    """Show optical flow.
-
-    Args:
-        flow (ndarray or str): The optical flow to be displayed.
-        win_name (str): The window name.
-        wait_time (int): Value of waitKey param.
-    """
-    flow = flowread(flow)
-    flow_img = flow2rgb(flow)
-    imshow(rgb2bgr(flow_img), win_name, wait_time)
-
-
-def flow2rgb(flow, color_wheel=None, unknown_thr=1e6):
-    """Convert flow map to RGB image.
-
-    Args:
-        flow (ndarray): Array of optical flow.
-        color_wheel (ndarray or None): Color wheel used to map flow field to
-            RGB colorspace. Default color wheel will be used if not specified.
-        unknown_thr (str): Values above this threshold will be marked as
-            unknown and thus ignored.
-
-    Returns:
-        ndarray: RGB image that can be visualized.
-    """
-    assert flow.ndim == 3 and flow.shape[-1] == 2
-    if color_wheel is None:
-        color_wheel = make_color_wheel()
-    assert color_wheel.ndim == 2 and color_wheel.shape[1] == 3
-    num_bins = color_wheel.shape[0]
-
-    dx = flow[:, :, 0].copy()
-    dy = flow[:, :, 1].copy()
-
-    ignore_inds = (
-        np.isnan(dx) | np.isnan(dy) | (np.abs(dx) > unknown_thr) |
-        (np.abs(dy) > unknown_thr))
-    dx[ignore_inds] = 0
-    dy[ignore_inds] = 0
-
-    rad = np.sqrt(dx**2 + dy**2)
-    if np.any(rad > np.finfo(float).eps):
-        max_rad = np.max(rad)
-        dx /= max_rad
-        dy /= max_rad
-
-    rad = np.sqrt(dx**2 + dy**2)
-    angle = np.arctan2(-dy, -dx) / np.pi
-
-    bin_real = (angle + 1) / 2 * (num_bins - 1)
-    bin_left = np.floor(bin_real).astype(int)
-    bin_right = (bin_left + 1) % num_bins
-    w = (bin_real - bin_left.astype(np.float32))[..., None]
-    flow_img = (1 -
-                w) * color_wheel[bin_left, :] + w * color_wheel[bin_right, :]
-    small_ind = rad <= 1
-    flow_img[small_ind] = 1 - rad[small_ind, None] * (1 - flow_img[small_ind])
-    flow_img[np.logical_not(small_ind)] *= 0.75
-
-    flow_img[ignore_inds, :] = 0
-
-    return flow_img
-
-
-def make_color_wheel(bins=None):
-    """Build a color wheel.
-
-    Args:
-        bins(list or tuple, optional): Specify the number of bins for each
-            color range, corresponding to six ranges: red -> yellow,
-            yellow -> green, green -> cyan, cyan -> blue, blue -> magenta,
-            magenta -> red. [15, 6, 4, 11, 13, 6] is used for default
-            (see Middlebury).
-
-    Returns:
-        ndarray: Color wheel of shape (total_bins, 3).
-    """
-    if bins is None:
-        bins = [15, 6, 4, 11, 13, 6]
-    assert len(bins) == 6
-
-    RY, YG, GC, CB, BM, MR = tuple(bins)
-
-    ry = [1, np.arange(RY) / RY, 0]
-    yg = [1 - np.arange(YG) / YG, 1, 0]
-    gc = [0, 1, np.arange(GC) / GC]
-    cb = [0, 1 - np.arange(CB) / CB, 1]
-    bm = [np.arange(BM) / BM, 0, 1]
-    mr = [1, 0, 1 - np.arange(MR) / MR]
-
-    num_bins = RY + YG + GC + CB + BM + MR
-
-    color_wheel = np.zeros((3, num_bins), dtype=np.float32)
-
-    col = 0
-    for i, color in enumerate([ry, yg, gc, cb, bm, mr]):
-        for j in range(3):
-            color_wheel[j, col:col + bins[i]] = color[j]
-        col += bins[i]
-
-    return color_wheel.T
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/core/seg/sampler/ohem_pixel_sampler.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/core/seg/sampler/ohem_pixel_sampler.py
deleted file mode 100644
index 88bb10d44026ba9f21756eaea9e550841cd59b9f..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/core/seg/sampler/ohem_pixel_sampler.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import torch
-import torch.nn.functional as F
-
-from ..builder import PIXEL_SAMPLERS
-from .base_pixel_sampler import BasePixelSampler
-
-
-@PIXEL_SAMPLERS.register_module()
-class OHEMPixelSampler(BasePixelSampler):
-    """Online Hard Example Mining Sampler for segmentation.
-
-    Args:
-        context (nn.Module): The context of sampler, subclass of
-            :obj:`BaseDecodeHead`.
-        thresh (float, optional): The threshold for hard example selection.
-            Below which, are prediction with low confidence. If not
-            specified, the hard examples will be pixels of top ``min_kept``
-            loss. Default: None.
-        min_kept (int, optional): The minimum number of predictions to keep.
-            Default: 100000.
-    """
-
-    def __init__(self, context, thresh=None, min_kept=100000):
-        super(OHEMPixelSampler, self).__init__()
-        self.context = context
-        assert min_kept > 1
-        self.thresh = thresh
-        self.min_kept = min_kept
-
-    def sample(self, seg_logit, seg_label):
-        """Sample pixels that have high loss or with low prediction confidence.
-
-        Args:
-            seg_logit (torch.Tensor): segmentation logits, shape (N, C, H, W)
-            seg_label (torch.Tensor): segmentation label, shape (N, 1, H, W)
-
-        Returns:
-            torch.Tensor: segmentation weight, shape (N, H, W)
-        """
-        with torch.no_grad():
-            assert seg_logit.shape[2:] == seg_label.shape[2:]
-            assert seg_label.shape[1] == 1
-            seg_label = seg_label.squeeze(1).long()
-            batch_kept = self.min_kept * seg_label.size(0)
-            valid_mask = seg_label != self.context.ignore_index
-            seg_weight = seg_logit.new_zeros(size=seg_label.size())
-            valid_seg_weight = seg_weight[valid_mask]
-            if self.thresh is not None:
-                seg_prob = F.softmax(seg_logit, dim=1)
-
-                tmp_seg_label = seg_label.clone().unsqueeze(1)
-                tmp_seg_label[tmp_seg_label == self.context.ignore_index] = 0
-                seg_prob = seg_prob.gather(1, tmp_seg_label).squeeze(1)
-                sort_prob, sort_indices = seg_prob[valid_mask].sort()
-
-                if sort_prob.numel() > 0:
-                    min_threshold = sort_prob[min(batch_kept,
-                                                  sort_prob.numel() - 1)]
-                else:
-                    min_threshold = 0.0
-                threshold = max(min_threshold, self.thresh)
-                valid_seg_weight[seg_prob[valid_mask] < threshold] = 1.
-            else:
-                losses = self.context.loss_decode(
-                    seg_logit,
-                    seg_label,
-                    weight=None,
-                    ignore_index=self.context.ignore_index,
-                    reduction_override='none')
-                # faster than topk according to https://github.com/pytorch/pytorch/issues/22812  # noqa
-                _, sort_indices = losses[valid_mask].sort(descending=True)
-                valid_seg_weight[sort_indices[:batch_kept]] = 1.
-
-            seg_weight[valid_mask] = valid_seg_weight
-
-            return seg_weight
diff --git a/spaces/wanghuoto/gogoai/src/components/chat-header.tsx b/spaces/wanghuoto/gogoai/src/components/chat-header.tsx
deleted file mode 100644
index c6664b8dee61179f844d45c5bd650518fc2cb4c2..0000000000000000000000000000000000000000
--- a/spaces/wanghuoto/gogoai/src/components/chat-header.tsx
+++ /dev/null
@@ -1,12 +0,0 @@
-import LogoIcon from '@/assets/images/logo.svg'
-import Image from 'next/image'
-
-export function ChatHeader() {
-  return (
-    <div className="flex flex-col items-center justify-center">
-      <Image alt="logo" src={LogoIcon} width={60}/>
-      <div className="mt-8 text-4xl font-bold">欢迎使用新必应</div>
-      <div className="mt-4 mb-8 text-lg">由 AI 支持的网页版 Copilot</div>
-    </div>
-  )
-}
diff --git a/spaces/wangrongsheng/ChatImprovement/check_proxy.py b/spaces/wangrongsheng/ChatImprovement/check_proxy.py
deleted file mode 100644
index d6263ad981272b0a798bf278a9e83b99e6928711..0000000000000000000000000000000000000000
--- a/spaces/wangrongsheng/ChatImprovement/check_proxy.py
+++ /dev/null
@@ -1,22 +0,0 @@
-
-def check_proxy(proxies):
-    import requests
-    proxies_https = proxies['https'] if proxies is not None else '无'
-    try:
-        response = requests.get("https://ipapi.co/json/", proxies=proxies, timeout=4)
-        data = response.json()
-        print(f'查询代理的地理位置，返回的结果是{data}')
-        country = data['country_name']
-        result = f"代理配置 {proxies_https}, 代理所在地：{country}"
-        print(result)
-        return result
-    except:
-        result = f"代理配置 {proxies_https}, 代理所在地查询超时，代理可能无效"
-        print(result)
-        return result
-
-
-if __name__ == '__main__':
-    try: from config_private import proxies # 放自己的秘密如API和代理网址 os.path.exists('config_private.py')
-    except: from config import proxies
-    check_proxy(proxies)
\ No newline at end of file
diff --git a/spaces/wffcyrus/MetaGPT-v1/tests/metagpt/memory/__init__.py b/spaces/wffcyrus/MetaGPT-v1/tests/metagpt/memory/__init__.py
deleted file mode 100644
index 2bcf8efd09712339308e72659e84450d3fa829fd..0000000000000000000000000000000000000000
--- a/spaces/wffcyrus/MetaGPT-v1/tests/metagpt/memory/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# @Desc   :
diff --git a/spaces/whgwd2023/bingo/src/components/chat-image.tsx b/spaces/whgwd2023/bingo/src/components/chat-image.tsx
deleted file mode 100644
index c914febed448c3a65a4392fc4b90e7d574e6b791..0000000000000000000000000000000000000000
--- a/spaces/whgwd2023/bingo/src/components/chat-image.tsx
+++ /dev/null
@@ -1,178 +0,0 @@
-import {
-  useEffect,
-  useState,
-  useCallback,
-  ChangeEvent,
-  ClipboardEvent,
-  MouseEventHandler,
-  useRef,
-  KeyboardEvent
-} from "react"
-import Image from 'next/image'
-import PasteIcon from '@/assets/images/paste.svg'
-import UploadIcon from '@/assets/images/upload.svg'
-import CameraIcon from '@/assets/images/camera.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { cn } from '@/lib/utils'
-import { toast } from "react-hot-toast"
-
-interface ChatImageProps extends Pick<ReturnType<typeof useBing>, 'uploadImage'> {}
-
-const preventDefault: MouseEventHandler<HTMLDivElement> = (event) => {
-  event.nativeEvent.stopImmediatePropagation()
-}
-
-const toBase64 = (file: File): Promise<string> => new Promise((resolve, reject) => {
-  const reader = new FileReader()
-  reader.readAsDataURL(file)
-  reader.onload = () => resolve(reader.result as string)
-  reader.onerror = reject
-})
-
-export function ChatImage({ children, uploadImage }: React.PropsWithChildren<ChatImageProps>) {
-  const videoRef = useRef<HTMLVideoElement>(null)
-  const canvasRef = useRef<HTMLCanvasElement>(null)
-  const mediaStream = useRef<MediaStream>()
-  const [panel, setPanel] = useState('none')
-  const [inputUrl, setInputUrl] = useState('')
-
-  const upload = useCallback((url: string) => {
-    if (url) {
-      uploadImage(url)
-    }
-    setPanel('none')
-  }, [panel])
-
-  const onUpload = useCallback(async (event: ChangeEvent<HTMLInputElement>) => {
-    const file = event.target.files?.[0]
-    if (file) {
-      const fileDataUrl = await toBase64(file)
-      if (fileDataUrl) {
-        upload(fileDataUrl)
-      }
-    }
-  }, [])
-
-  const onPaste = useCallback((event: ClipboardEvent<HTMLInputElement>) => {
-    const pasteUrl = event.clipboardData.getData('text') ?? ''
-    upload(pasteUrl)
-  }, [])
-
-  const onEnter = useCallback((event: KeyboardEvent<HTMLInputElement>) => {
-    // @ts-ignore
-    event.preventDefault()
-    // @ts-ignore
-    event.stopPropagation()
-    if (/^https?:\/\/.+/.test(inputUrl)) {
-      upload(inputUrl)
-    } else {
-      toast.error('请输入有效的图片链接')
-    }
-  }, [inputUrl])
-
-  const openVideo: MouseEventHandler<HTMLButtonElement> = async (event) => {
-    event.stopPropagation()
-    setPanel('camera-mode')
-  }
-
-  const onCapture = () => {
-    if (canvasRef.current && videoRef.current) {
-      const canvas = canvasRef.current
-      canvas.width = videoRef.current!.videoWidth
-      canvas.height = videoRef.current!.videoHeight
-      canvas.getContext('2d')?.drawImage(videoRef.current, 0, 0, canvas.width, canvas.height)
-      const cameraUrl = canvas.toDataURL('image/jpeg')
-      upload(cameraUrl)
-    }
-  }
-
-  useEffect(() => {
-    const handleBlur = () => {
-      if (panel !== 'none') {
-        setPanel('none')
-      }
-    }
-    document.addEventListener('click', handleBlur)
-    return () => {
-      document.removeEventListener('click', handleBlur)
-    }
-  }, [panel])
-
-  useEffect(() => {
-    if (panel === 'camera-mode') {
-      navigator.mediaDevices.getUserMedia({ video: true, audio: false })
-      .then(videoStream => {
-        mediaStream.current = videoStream
-        if (videoRef.current) {
-          videoRef.current.srcObject = videoStream
-        }
-      })
-    } else {
-      if (mediaStream.current) {
-        mediaStream.current.getTracks().forEach(function(track) {
-          track.stop()
-        })
-        mediaStream.current = undefined
-      }
-    }
-  }, [panel])
-
-  return (
-    <div className="visual-search-container">
-      <div onClick={() => panel === 'none' ? setPanel('normal') : setPanel('none')}>{children}</div>
-      <div className={cn('visual-search', panel)} onClick={preventDefault}>
-        <div className="normal-content">
-          <div className="header">
-            <h4>添加图像</h4>
-          </div>
-          <div className="paste">
-            <Image alt="paste" src={PasteIcon} width={24} />
-            <input
-              className="paste-input"
-              id="sb_imgpst"
-              type="text"
-              name="image"
-              placeholder="粘贴图像 URL"
-              aria-label="粘贴图像 URL"
-              onPaste={onPaste}
-              onChange={(event) => setInputUrl(event.target.value.trim())}
-              onKeyDownCapture={event => {
-                if (event.key === 'Enter') {
-                  onEnter(event)
-                }
-              }}
-              onClickCapture={(e) => e.stopPropagation()}
-            />
-          </div>
-          <div className="buttons">
-            <button type="button" aria-label="从此设备上传">
-              <input
-                id="vs_fileinput"
-                className="fileinput"
-                type="file"
-                accept="image/gif, image/jpeg, image/png, image/webp"
-                onChange={onUpload}
-              />
-              <Image alt="uplaod" src={UploadIcon} width={20} />
-              从此设备上传
-            </button>
-            <button type="button" aria-label="拍照" onClick={openVideo}>
-              <Image alt="camera" src={CameraIcon} width={20} />
-              拍照
-            </button>
-          </div>
-        </div>
-        {panel === 'camera-mode' && <div className="cam-content">
-          <div className="webvideo-container">
-            <video className="webvideo" autoPlay muted playsInline ref={videoRef} />
-            <canvas className="webcanvas" ref={canvasRef} />
-          </div>
-          <div className="cambtn" role="button" aria-label="拍照" onClick={onCapture}>
-            <div className="cam-btn-circle-large"></div>
-            <div className="cam-btn-circle-small"></div>
-          </div>
-        </div>}
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/whgwd2023/bingo/src/components/ui/codeblock.tsx b/spaces/whgwd2023/bingo/src/components/ui/codeblock.tsx
deleted file mode 100644
index aabda4e3b59f4e36b6ab79feb19d8d18b70e881b..0000000000000000000000000000000000000000
--- a/spaces/whgwd2023/bingo/src/components/ui/codeblock.tsx
+++ /dev/null
@@ -1,142 +0,0 @@
-'use client'
-
-import { FC, memo } from 'react'
-import { Prism as SyntaxHighlighter } from 'react-syntax-highlighter'
-import { coldarkDark } from 'react-syntax-highlighter/dist/cjs/styles/prism'
-
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-import { IconCheck, IconCopy, IconDownload } from '@/components/ui/icons'
-import { Button } from '@/components/ui/button'
-
-interface Props {
-  language: string
-  value: string
-}
-
-interface languageMap {
-  [key: string]: string | undefined
-}
-
-export const programmingLanguages: languageMap = {
-  javascript: '.js',
-  python: '.py',
-  java: '.java',
-  c: '.c',
-  cpp: '.cpp',
-  'c++': '.cpp',
-  'c#': '.cs',
-  ruby: '.rb',
-  php: '.php',
-  swift: '.swift',
-  'objective-c': '.m',
-  kotlin: '.kt',
-  typescript: '.ts',
-  go: '.go',
-  perl: '.pl',
-  rust: '.rs',
-  scala: '.scala',
-  haskell: '.hs',
-  lua: '.lua',
-  shell: '.sh',
-  sql: '.sql',
-  html: '.html',
-  css: '.css'
-  // add more file extensions here, make sure the key is same as language prop in CodeBlock.tsx component
-}
-
-export const generateRandomString = (length: number, lowercase = false) => {
-  const chars = 'ABCDEFGHJKLMNPQRSTUVWXY3456789' // excluding similar looking characters like Z, 2, I, 1, O, 0
-  let result = ''
-  for (let i = 0; i < length; i++) {
-    result += chars.charAt(Math.floor(Math.random() * chars.length))
-  }
-  return lowercase ? result.toLowerCase() : result
-}
-
-const CodeBlock: FC<Props> = memo(({ language, value }) => {
-  const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
-
-  const downloadAsFile = () => {
-    if (typeof window === 'undefined') {
-      return
-    }
-    const fileExtension = programmingLanguages[language] || '.file'
-    const suggestedFileName = `file-${generateRandomString(
-      3,
-      true
-    )}${fileExtension}`
-    const fileName = window.prompt('Enter file name' || '', suggestedFileName)
-
-    if (!fileName) {
-      // User pressed cancel on prompt.
-      return
-    }
-
-    const blob = new Blob([value], { type: 'text/plain' })
-    const url = URL.createObjectURL(blob)
-    const link = document.createElement('a')
-    link.download = fileName
-    link.href = url
-    link.style.display = 'none'
-    document.body.appendChild(link)
-    link.click()
-    document.body.removeChild(link)
-    URL.revokeObjectURL(url)
-  }
-
-  const onCopy = () => {
-    if (isCopied) return
-    copyToClipboard(value)
-  }
-
-  return (
-    <div className="codeblock relative w-full bg-zinc-950 font-sans">
-      <div className="flex w-full items-center justify-between bg-zinc-800 px-6 py-2 pr-4 text-zinc-100">
-        <span className="text-xs lowercase">{language}</span>
-        <div className="flex items-center space-x-1">
-          <Button
-            variant="ghost"
-            className="hover:bg-zinc-800 focus-visible:ring-1 focus-visible:ring-slate-700 focus-visible:ring-offset-0"
-            onClick={downloadAsFile}
-            size="icon"
-          >
-            <IconDownload />
-            <span className="sr-only">Download</span>
-          </Button>
-          <Button
-            variant="ghost"
-            size="icon"
-            className="text-xs hover:bg-zinc-800 focus-visible:ring-1 focus-visible:ring-slate-700 focus-visible:ring-offset-0"
-            onClick={onCopy}
-          >
-            {isCopied ? <IconCheck /> : <IconCopy />}
-            <span className="sr-only">Copy code</span>
-          </Button>
-        </div>
-      </div>
-      <SyntaxHighlighter
-        language={language}
-        style={coldarkDark}
-        PreTag="div"
-        showLineNumbers
-        customStyle={{
-          margin: 0,
-          width: '100%',
-          background: 'transparent',
-          padding: '1.5rem 1rem'
-        }}
-        codeTagProps={{
-          style: {
-            fontSize: '0.9rem',
-            fontFamily: 'var(--font-mono)'
-          }
-        }}
-      >
-        {value}
-      </SyntaxHighlighter>
-    </div>
-  )
-})
-CodeBlock.displayName = 'CodeBlock'
-
-export { CodeBlock }
diff --git a/spaces/wz758727829/ChuanhuChatGPT/presets.py b/spaces/wz758727829/ChuanhuChatGPT/presets.py
deleted file mode 100644
index 935b9b8d9250838ef06af8e3fbe0979162bfa394..0000000000000000000000000000000000000000
--- a/spaces/wz758727829/ChuanhuChatGPT/presets.py
+++ /dev/null
@@ -1,87 +0,0 @@
-# -*- coding:utf-8 -*-
-
-# ChatGPT 设置
-initial_prompt = "You are a helpful assistant."
-API_URL = "https://api.openai.com/v1/chat/completions"
-HISTORY_DIR = "history"
-TEMPLATES_DIR = "templates"
-
-# 错误信息
-standard_error_msg = "☹️发生了错误："  # 错误信息的标准前缀
-error_retrieve_prompt = "请检查网络连接，或者API-Key是否有效。"  # 获取对话时发生错误
-connection_timeout_prompt = "连接超时，无法获取对话。"  # 连接超时
-read_timeout_prompt = "读取超时，无法获取对话。"  # 读取超时
-proxy_error_prompt = "代理错误，无法获取对话。"  # 代理错误
-ssl_error_prompt = "SSL错误，无法获取对话。"  # SSL 错误
-no_apikey_msg = "API key长度不是51位，请检查是否输入正确。"  # API key 长度不足 51 位
-
-max_token_streaming = 3500  # 流式对话时的最大 token 数
-timeout_streaming = 30  # 流式对话时的超时时间
-max_token_all = 3500  # 非流式对话时的最大 token 数
-timeout_all = 200  # 非流式对话时的超时时间
-enable_streaming_option = True  # 是否启用选择选择是否实时显示回答的勾选框
-HIDE_MY_KEY = False  # 如果你想在UI中隐藏你的 API 密钥，将此值设置为 True
-
-SIM_K = 5
-INDEX_QUERY_TEMPRATURE = 1.0
-
-title = """<h1 align="left" style="min-width:200px; margin-top:0;">川虎ChatGPT 🚀</h1>"""
-description = """\
-<div align="center" style="margin:16px 0">
-
-由Bilibili [土川虎虎虎](https://space.bilibili.com/29125536) 和 [明昭MZhao](https://space.bilibili.com/24807452)开发
-
-访问川虎ChatGPT的 [GitHub项目](https://github.com/GaiZhenbiao/ChuanhuChatGPT) 下载最新版脚本
-
-此App使用 `gpt-3.5-turbo` 大语言模型
-</div>
-"""
-
-summarize_prompt = "你是谁？我们刚才聊了什么？"  # 总结对话时的 prompt
-
-MODELS = [
-    "gpt-3.5-turbo",
-    "gpt-3.5-turbo-0301",
-    "gpt-4",
-    "gpt-4-0314",
-    "gpt-4-32k",
-    "gpt-4-32k-0314",
-]  # 可选的模型
-
-
-WEBSEARCH_PTOMPT_TEMPLATE = """\
-Web search results:
-
-{web_results}
-Current date: {current_date}
-
-Instructions: Using the provided web search results, write a comprehensive reply to the given query. Make sure to cite results using [[number](URL)] notation after the reference. If the provided search results refer to multiple subjects with the same name, write separate answers for each subject.
-Query: {query}
-Reply in 中文"""
-
-PROMPT_TEMPLATE = """\
-Context information is below.
----------------------
-{context_str}
----------------------
-Current date: {current_date}.
-Using the provided context information, write a comprehensive reply to the given query.
-Make sure to cite results using [number] notation after the reference.
-If the provided context information refer to multiple subjects with the same name, write separate answers for each subject.
-Use prior knowledge only if the given context didn't provide enough information.
-Answer the question: {query_str}
-Reply in 中文
-"""
-
-REFINE_TEMPLATE = """\
-The original question is as follows: {query_str}
-We have provided an existing answer: {existing_answer}
-We have the opportunity to refine the existing answer
-(only if needed) with some more context below.
-------------
-{context_msg}
-------------
-Given the new context, refine the original answer to better
-Answer in the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch.
-If the context isn't useful, return the original answer.
-"""
diff --git a/spaces/xc9/VITS-Umamusume-voice-synthesizer/text/cleaners.py b/spaces/xc9/VITS-Umamusume-voice-synthesizer/text/cleaners.py
deleted file mode 100644
index c80e113b2b81a66134800dbdaa29c7d96a0152a7..0000000000000000000000000000000000000000
--- a/spaces/xc9/VITS-Umamusume-voice-synthesizer/text/cleaners.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import re
-
-
-def japanese_cleaners(text):
-    from text.japanese import japanese_to_romaji_with_accent
-    text = japanese_to_romaji_with_accent(text)
-    text = re.sub(r'([A-Za-z])$', r'\1.', text)
-    return text
-
-
-def japanese_cleaners2(text):
-    return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…')
-
-
-def korean_cleaners(text):
-    '''Pipeline for Korean text'''
-    from text.korean import latin_to_hangul, number_to_hangul, divide_hangul
-    text = latin_to_hangul(text)
-    text = number_to_hangul(text)
-    text = divide_hangul(text)
-    text = re.sub(r'([\u3131-\u3163])$', r'\1.', text)
-    return text
-
-
-def chinese_cleaners(text):
-    '''Pipeline for Chinese text'''
-    from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = re.sub(r'([ˉˊˇˋ˙])$', r'\1。', text)
-    return text
-
-
-def zh_ja_mixture_cleaners(text):
-    from text.mandarin import chinese_to_romaji
-    from text.japanese import japanese_to_romaji_with_accent
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_romaji(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_romaji_with_accent(
-        x.group(1)).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…')+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def sanskrit_cleaners(text):
-    text = text.replace('॥', '।').replace('ॐ', 'ओम्')
-    if text[-1] != '।':
-        text += ' ।'
-    return text
-
-
-def cjks_cleaners(text):
-    from text.mandarin import chinese_to_lazy_ipa
-    from text.japanese import japanese_to_ipa
-    from text.korean import korean_to_lazy_ipa
-    from text.sanskrit import devanagari_to_ipa
-    from text.english import english_to_lazy_ipa
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_lazy_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_lazy_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[SA\](.*?)\[SA\]',
-                  lambda x: devanagari_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_lazy_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cjke_cleaners(text):
-    from text.mandarin import chinese_to_lazy_ipa
-    from text.japanese import japanese_to_ipa
-    from text.korean import korean_to_ipa
-    from text.english import english_to_ipa2
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]', lambda x: chinese_to_lazy_ipa(x.group(1)).replace(
-        'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn')+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_ipa(x.group(1)).replace('ʧ', 'tʃ').replace(
-        'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz')+' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]', lambda x: english_to_ipa2(x.group(1)).replace('ɑ', 'a').replace(
-        'ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u')+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def cjke_cleaners2(text):
-    from text.mandarin import chinese_to_ipa
-    from text.japanese import japanese_to_ipa2
-    from text.korean import korean_to_ipa
-    from text.english import english_to_ipa2
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\[KO\](.*?)\[KO\]',
-                  lambda x: korean_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def thai_cleaners(text):
-    from text.thai import num_to_thai, latin_to_thai
-    text = num_to_thai(text)
-    text = latin_to_thai(text)
-    return text
-
-
-def shanghainese_cleaners(text):
-    from text.shanghainese import shanghainese_to_ipa
-    text = shanghainese_to_ipa(text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
-
-
-def chinese_dialect_cleaners(text):
-    from text.mandarin import chinese_to_ipa2
-    from text.japanese import japanese_to_ipa3
-    from text.shanghainese import shanghainese_to_ipa
-    from text.cantonese import cantonese_to_ipa
-    from text.english import english_to_lazy_ipa2
-    from text.ngu_dialect import ngu_dialect_to_ipa
-    text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                  lambda x: chinese_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\[JA\](.*?)\[JA\]',
-                  lambda x: japanese_to_ipa3(x.group(1)).replace('Q', 'ʔ')+' ', text)
-    text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: shanghainese_to_ipa(x.group(1)).replace('1', '˥˧').replace('5',
-                  '˧˧˦').replace('6', '˩˩˧').replace('7', '˥').replace('8', '˩˨').replace('ᴀ', 'ɐ').replace('ᴇ', 'e')+' ', text)
-    text = re.sub(r'\[GD\](.*?)\[GD\]',
-                  lambda x: cantonese_to_ipa(x.group(1))+' ', text)
-    text = re.sub(r'\[EN\](.*?)\[EN\]',
-                  lambda x: english_to_lazy_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\[([A-Z]{2})\](.*?)\[\1\]', lambda x: ngu_dialect_to_ipa(x.group(2), x.group(
-        1)).replace('ʣ', 'dz').replace('ʥ', 'dʑ').replace('ʦ', 'ts').replace('ʨ', 'tɕ')+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
diff --git a/spaces/xfys/yolov5_tracking/val_utils/tests/test_mot17.py b/spaces/xfys/yolov5_tracking/val_utils/tests/test_mot17.py
deleted file mode 100644
index 92b9375428a63ab9e8eb8985de3baa16f01ae1f0..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/val_utils/tests/test_mot17.py
+++ /dev/null
@@ -1,76 +0,0 @@
-""" Test to ensure that the code is working correctly.
-Runs all metrics on 14 trackers for the MOT Challenge MOT17 benchmark.
-"""
-
-
-import sys
-import os
-import numpy as np
-from multiprocessing import freeze_support
-
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-import trackeval  # noqa: E402
-
-# Fixes multiprocessing on windows, does nothing otherwise
-if __name__ == '__main__':
-    freeze_support()
-
-eval_config = {'USE_PARALLEL': False,
-               'NUM_PARALLEL_CORES': 8,
-               }
-evaluator = trackeval.Evaluator(eval_config)
-metrics_list = [trackeval.metrics.HOTA(), trackeval.metrics.CLEAR(), trackeval.metrics.Identity()]
-test_data_loc = os.path.join(os.path.dirname(__file__), '..', 'data', 'tests', 'mot_challenge', 'MOT17-train')
-trackers = [
-    'DPMOT',
-    'GNNMatch',
-    'IA',
-    'ISE_MOT17R',
-    'Lif_T',
-    'Lif_TsimInt',
-    'LPC_MOT',
-    'MAT',
-    'MIFTv2',
-    'MPNTrack',
-    'SSAT',
-    'TracktorCorr',
-    'Tracktorv2',
-    'UnsupTrack',
-]
-
-for tracker in trackers:
-    # Run code on tracker
-    dataset_config = {'TRACKERS_TO_EVAL': [tracker],
-                      'BENCHMARK': 'MOT17'}
-    dataset_list = [trackeval.datasets.MotChallenge2DBox(dataset_config)]
-    raw_results, messages = evaluator.evaluate(dataset_list, metrics_list)
-
-    results = {seq: raw_results['MotChallenge2DBox'][tracker][seq]['pedestrian'] for seq in
-               raw_results['MotChallenge2DBox'][tracker].keys()}
-    current_metrics_list = metrics_list + [trackeval.metrics.Count()]
-    metric_names = trackeval.utils.validate_metrics_list(current_metrics_list)
-
-    # Load expected results:
-    test_data = trackeval.utils.load_detail(os.path.join(test_data_loc, tracker, 'pedestrian_detailed.csv'))
-    assert len(test_data.keys()) == 22, len(test_data.keys())
-
-    # Do checks
-    for seq in test_data.keys():
-        assert len(test_data[seq].keys()) > 250, len(test_data[seq].keys())
-
-        details = []
-        for metric, metric_name in zip(current_metrics_list, metric_names):
-            table_res = {seq_key: seq_value[metric_name] for seq_key, seq_value in results.items()}
-            details.append(metric.detailed_results(table_res))
-        res_fields = sum([list(s['COMBINED_SEQ'].keys()) for s in details], [])
-        res_values = sum([list(s[seq].values()) for s in details], [])
-        res_dict = dict(zip(res_fields, res_values))
-
-        for field in test_data[seq].keys():
-            if not np.isclose(res_dict[field], test_data[seq][field]):
-                print(tracker, seq, res_dict[field], test_data[seq][field], field)
-                raise AssertionError
-
-    print('Tracker %s tests passed' % tracker)
-print('All tests passed')
-
diff --git a/spaces/xfys/yolov5_tracking/yolov5/models/experimental.py b/spaces/xfys/yolov5_tracking/yolov5/models/experimental.py
deleted file mode 100644
index b554401b133438a7d38a927c6fdd263503e78c35..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/yolov5/models/experimental.py
+++ /dev/null
@@ -1,111 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-"""
-Experimental modules
-"""
-import math
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-from yolov5.utils.downloads import attempt_download
-
-
-class Sum(nn.Module):
-    # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
-    def __init__(self, n, weight=False):  # n: number of inputs
-        super().__init__()
-        self.weight = weight  # apply weights boolean
-        self.iter = range(n - 1)  # iter object
-        if weight:
-            self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True)  # layer weights
-
-    def forward(self, x):
-        y = x[0]  # no weight
-        if self.weight:
-            w = torch.sigmoid(self.w) * 2
-            for i in self.iter:
-                y = y + x[i + 1] * w[i]
-        else:
-            for i in self.iter:
-                y = y + x[i + 1]
-        return y
-
-
-class MixConv2d(nn.Module):
-    # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595
-    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):  # ch_in, ch_out, kernel, stride, ch_strategy
-        super().__init__()
-        n = len(k)  # number of convolutions
-        if equal_ch:  # equal c_ per group
-            i = torch.linspace(0, n - 1E-6, c2).floor()  # c2 indices
-            c_ = [(i == g).sum() for g in range(n)]  # intermediate channels
-        else:  # equal weight.numel() per group
-            b = [c2] + [0] * n
-            a = np.eye(n + 1, n, k=-1)
-            a -= np.roll(a, 1, axis=1)
-            a *= np.array(k) ** 2
-            a[0] = 1
-            c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b
-
-        self.m = nn.ModuleList([
-            nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
-        self.bn = nn.BatchNorm2d(c2)
-        self.act = nn.SiLU()
-
-    def forward(self, x):
-        return self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
-
-
-class Ensemble(nn.ModuleList):
-    # Ensemble of models
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x, augment=False, profile=False, visualize=False):
-        y = [module(x, augment, profile, visualize)[0] for module in self]
-        # y = torch.stack(y).max(0)[0]  # max ensemble
-        # y = torch.stack(y).mean(0)  # mean ensemble
-        y = torch.cat(y, 1)  # nms ensemble
-        return y, None  # inference, train output
-
-
-def attempt_load(weights, device=None, inplace=True, fuse=True):
-    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
-    from models.yolo import Detect, Model
-
-    model = Ensemble()
-    for w in weights if isinstance(weights, list) else [weights]:
-        ckpt = torch.load(attempt_download(w), map_location='cpu')  # load
-        ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float()  # FP32 model
-
-        # Model compatibility updates
-        if not hasattr(ckpt, 'stride'):
-            ckpt.stride = torch.tensor([32.])
-        if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)):
-            ckpt.names = dict(enumerate(ckpt.names))  # convert to dict
-
-        model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval())  # model in eval mode
-
-    # Module compatibility updates
-    for m in model.modules():
-        t = type(m)
-        if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
-            m.inplace = inplace  # torch 1.7.0 compatibility
-            if t is Detect and not isinstance(m.anchor_grid, list):
-                delattr(m, 'anchor_grid')
-                setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
-        elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'):
-            m.recompute_scale_factor = None  # torch 1.11.0 compatibility
-
-    # Return model
-    if len(model) == 1:
-        return model[-1]
-
-    # Return detection ensemble
-    print(f'Ensemble created with {weights}\n')
-    for k in 'names', 'nc', 'yaml':
-        setattr(model, k, getattr(model[0], k))
-    model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride  # max stride
-    assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}'
-    return model
diff --git a/spaces/xiaorong/fork2-so-vits/README.md b/spaces/xiaorong/fork2-so-vits/README.md
deleted file mode 100644
index 2e44ec5507a21c84647346865c876ce2b48db560..0000000000000000000000000000000000000000
--- a/spaces/xiaorong/fork2-so-vits/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Vits Models
-emoji: 🏃
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: sayashi/vits-models
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/xinyu1205/recognize-anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py b/spaces/xinyu1205/recognize-anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py
deleted file mode 100644
index 10c0920c1a217af5bb3e1b13077568035ab3b7b5..0000000000000000000000000000000000000000
--- a/spaces/xinyu1205/recognize-anything/GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-DETR Transformer class.
-
-Copy-paste from torch.nn.Transformer with modifications:
-    * positional encodings are passed in MHattention
-    * extra LN at the end of encoder is removed
-    * decoder returns a stack of activations from all decoding layers
-"""
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-from torch import Tensor, nn
-
-from .utils import (
-    MLP,
-    _get_activation_fn,
-    _get_clones,
-    gen_encoder_output_proposals,
-    gen_sineembed_for_position,
-    sigmoid_focal_loss,
-)
-
-
-class TextTransformer(nn.Module):
-    def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1):
-        super().__init__()
-        self.num_layers = num_layers
-        self.d_model = d_model
-        self.nheads = nheads
-        self.dim_feedforward = dim_feedforward
-        self.norm = None
-
-        single_encoder_layer = TransformerEncoderLayer(
-            d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout
-        )
-        self.layers = _get_clones(single_encoder_layer, num_layers)
-
-    def forward(self, memory_text: torch.Tensor, text_attention_mask: torch.Tensor):
-        """
-
-        Args:
-            text_attention_mask: bs, num_token
-            memory_text: bs, num_token, d_model
-
-        Raises:
-            RuntimeError: _description_
-
-        Returns:
-            output: bs, num_token, d_model
-        """
-
-        output = memory_text.transpose(0, 1)
-
-        for layer in self.layers:
-            output = layer(output, src_key_padding_mask=text_attention_mask)
-
-        if self.norm is not None:
-            output = self.norm(output)
-
-        return output.transpose(0, 1)
-
-
-class TransformerEncoderLayer(nn.Module):
-    def __init__(
-        self,
-        d_model,
-        nhead,
-        dim_feedforward=2048,
-        dropout=0.1,
-        activation="relu",
-        normalize_before=False,
-    ):
-        super().__init__()
-        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
-        # Implementation of Feedforward model
-        self.linear1 = nn.Linear(d_model, dim_feedforward)
-        self.dropout = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(dim_feedforward, d_model)
-
-        self.norm1 = nn.LayerNorm(d_model)
-        self.norm2 = nn.LayerNorm(d_model)
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(dropout)
-
-        self.activation = _get_activation_fn(activation)
-        self.normalize_before = normalize_before
-        self.nhead = nhead
-
-    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
-        return tensor if pos is None else tensor + pos
-
-    def forward(
-        self,
-        src,
-        src_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-        pos: Optional[Tensor] = None,
-    ):
-        # repeat attn mask
-        if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]:
-            # bs, num_q, num_k
-            src_mask = src_mask.repeat(self.nhead, 1, 1)
-
-        q = k = self.with_pos_embed(src, pos)
-
-        src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0]
-
-        # src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
-        src = src + self.dropout1(src2)
-        src = self.norm1(src)
-        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
-        src = src + self.dropout2(src2)
-        src = self.norm2(src)
-        return src
diff --git a/spaces/xswu/HPSv2/src/training/imagenet_zeroshot_data.py b/spaces/xswu/HPSv2/src/training/imagenet_zeroshot_data.py
deleted file mode 100644
index 27abd8bf24ebe077a73e8496576d949d8bb16f69..0000000000000000000000000000000000000000
--- a/spaces/xswu/HPSv2/src/training/imagenet_zeroshot_data.py
+++ /dev/null
@@ -1,254 +0,0 @@
-
-
-imagenet_classnames = ["tench", "goldfish", "great white shark", "tiger shark", "hammerhead shark", "electric ray",
-                        "stingray", "rooster", "hen", "ostrich", "brambling", "goldfinch", "house finch", "junco",
-                        "indigo bunting", "American robin", "bulbul", "jay", "magpie", "chickadee", "American dipper",
-                        "kite (bird of prey)", "bald eagle", "vulture", "great grey owl", "fire salamander",
-                        "smooth newt", "newt", "spotted salamander", "axolotl", "American bullfrog", "tree frog",
-                        "tailed frog", "loggerhead sea turtle", "leatherback sea turtle", "mud turtle", "terrapin",
-                        "box turtle", "banded gecko", "green iguana", "Carolina anole",
-                        "desert grassland whiptail lizard", "agama", "frilled-necked lizard", "alligator lizard",
-                        "Gila monster", "European green lizard", "chameleon", "Komodo dragon", "Nile crocodile",
-                        "American alligator", "triceratops", "worm snake", "ring-necked snake",
-                        "eastern hog-nosed snake", "smooth green snake", "kingsnake", "garter snake", "water snake",
-                        "vine snake", "night snake", "boa constrictor", "African rock python", "Indian cobra",
-                        "green mamba", "sea snake", "Saharan horned viper", "eastern diamondback rattlesnake",
-                        "sidewinder rattlesnake", "trilobite", "harvestman", "scorpion", "yellow garden spider",
-                        "barn spider", "European garden spider", "southern black widow", "tarantula", "wolf spider",
-                        "tick", "centipede", "black grouse", "ptarmigan", "ruffed grouse", "prairie grouse", "peafowl",
-                        "quail", "partridge", "african grey parrot", "macaw", "sulphur-crested cockatoo", "lorikeet",
-                        "coucal", "bee eater", "hornbill", "hummingbird", "jacamar", "toucan", "duck",
-                        "red-breasted merganser", "goose", "black swan", "tusker", "echidna", "platypus", "wallaby",
-                        "koala", "wombat", "jellyfish", "sea anemone", "brain coral", "flatworm", "nematode", "conch",
-                        "snail", "slug", "sea slug", "chiton", "chambered nautilus", "Dungeness crab", "rock crab",
-                        "fiddler crab", "red king crab", "American lobster", "spiny lobster", "crayfish", "hermit crab",
-                        "isopod", "white stork", "black stork", "spoonbill", "flamingo", "little blue heron",
-                        "great egret", "bittern bird", "crane bird", "limpkin", "common gallinule", "American coot",
-                        "bustard", "ruddy turnstone", "dunlin", "common redshank", "dowitcher", "oystercatcher",
-                        "pelican", "king penguin", "albatross", "grey whale", "killer whale", "dugong", "sea lion",
-                        "Chihuahua", "Japanese Chin", "Maltese", "Pekingese", "Shih Tzu", "King Charles Spaniel",
-                        "Papillon", "toy terrier", "Rhodesian Ridgeback", "Afghan Hound", "Basset Hound", "Beagle",
-                        "Bloodhound", "Bluetick Coonhound", "Black and Tan Coonhound", "Treeing Walker Coonhound",
-                        "English foxhound", "Redbone Coonhound", "borzoi", "Irish Wolfhound", "Italian Greyhound",
-                        "Whippet", "Ibizan Hound", "Norwegian Elkhound", "Otterhound", "Saluki", "Scottish Deerhound",
-                        "Weimaraner", "Staffordshire Bull Terrier", "American Staffordshire Terrier",
-                        "Bedlington Terrier", "Border Terrier", "Kerry Blue Terrier", "Irish Terrier",
-                        "Norfolk Terrier", "Norwich Terrier", "Yorkshire Terrier", "Wire Fox Terrier",
-                        "Lakeland Terrier", "Sealyham Terrier", "Airedale Terrier", "Cairn Terrier",
-                        "Australian Terrier", "Dandie Dinmont Terrier", "Boston Terrier", "Miniature Schnauzer",
-                        "Giant Schnauzer", "Standard Schnauzer", "Scottish Terrier", "Tibetan Terrier",
-                        "Australian Silky Terrier", "Soft-coated Wheaten Terrier", "West Highland White Terrier",
-                        "Lhasa Apso", "Flat-Coated Retriever", "Curly-coated Retriever", "Golden Retriever",
-                        "Labrador Retriever", "Chesapeake Bay Retriever", "German Shorthaired Pointer", "Vizsla",
-                        "English Setter", "Irish Setter", "Gordon Setter", "Brittany dog", "Clumber Spaniel",
-                        "English Springer Spaniel", "Welsh Springer Spaniel", "Cocker Spaniel", "Sussex Spaniel",
-                        "Irish Water Spaniel", "Kuvasz", "Schipperke", "Groenendael dog", "Malinois", "Briard",
-                        "Australian Kelpie", "Komondor", "Old English Sheepdog", "Shetland Sheepdog", "collie",
-                        "Border Collie", "Bouvier des Flandres dog", "Rottweiler", "German Shepherd Dog", "Dobermann",
-                        "Miniature Pinscher", "Greater Swiss Mountain Dog", "Bernese Mountain Dog",
-                        "Appenzeller Sennenhund", "Entlebucher Sennenhund", "Boxer", "Bullmastiff", "Tibetan Mastiff",
-                        "French Bulldog", "Great Dane", "St. Bernard", "husky", "Alaskan Malamute", "Siberian Husky",
-                        "Dalmatian", "Affenpinscher", "Basenji", "pug", "Leonberger", "Newfoundland dog",
-                        "Great Pyrenees dog", "Samoyed", "Pomeranian", "Chow Chow", "Keeshond", "brussels griffon",
-                        "Pembroke Welsh Corgi", "Cardigan Welsh Corgi", "Toy Poodle", "Miniature Poodle",
-                        "Standard Poodle", "Mexican hairless dog (xoloitzcuintli)", "grey wolf", "Alaskan tundra wolf",
-                        "red wolf or maned wolf", "coyote", "dingo", "dhole", "African wild dog", "hyena", "red fox",
-                        "kit fox", "Arctic fox", "grey fox", "tabby cat", "tiger cat", "Persian cat", "Siamese cat",
-                        "Egyptian Mau", "cougar", "lynx", "leopard", "snow leopard", "jaguar", "lion", "tiger",
-                        "cheetah", "brown bear", "American black bear", "polar bear", "sloth bear", "mongoose",
-                        "meerkat", "tiger beetle", "ladybug", "ground beetle", "longhorn beetle", "leaf beetle",
-                        "dung beetle", "rhinoceros beetle", "weevil", "fly", "bee", "ant", "grasshopper",
-                        "cricket insect", "stick insect", "cockroach", "praying mantis", "cicada", "leafhopper",
-                        "lacewing", "dragonfly", "damselfly", "red admiral butterfly", "ringlet butterfly",
-                        "monarch butterfly", "small white butterfly", "sulphur butterfly", "gossamer-winged butterfly",
-                        "starfish", "sea urchin", "sea cucumber", "cottontail rabbit", "hare", "Angora rabbit",
-                        "hamster", "porcupine", "fox squirrel", "marmot", "beaver", "guinea pig", "common sorrel horse",
-                        "zebra", "pig", "wild boar", "warthog", "hippopotamus", "ox", "water buffalo", "bison",
-                        "ram (adult male sheep)", "bighorn sheep", "Alpine ibex", "hartebeest", "impala (antelope)",
-                        "gazelle", "arabian camel", "llama", "weasel", "mink", "European polecat",
-                        "black-footed ferret", "otter", "skunk", "badger", "armadillo", "three-toed sloth", "orangutan",
-                        "gorilla", "chimpanzee", "gibbon", "siamang", "guenon", "patas monkey", "baboon", "macaque",
-                        "langur", "black-and-white colobus", "proboscis monkey", "marmoset", "white-headed capuchin",
-                        "howler monkey", "titi monkey", "Geoffroy's spider monkey", "common squirrel monkey",
-                        "ring-tailed lemur", "indri", "Asian elephant", "African bush elephant", "red panda",
-                        "giant panda", "snoek fish", "eel", "silver salmon", "rock beauty fish", "clownfish",
-                        "sturgeon", "gar fish", "lionfish", "pufferfish", "abacus", "abaya", "academic gown",
-                        "accordion", "acoustic guitar", "aircraft carrier", "airliner", "airship", "altar", "ambulance",
-                        "amphibious vehicle", "analog clock", "apiary", "apron", "trash can", "assault rifle",
-                        "backpack", "bakery", "balance beam", "balloon", "ballpoint pen", "Band-Aid", "banjo",
-                        "baluster / handrail", "barbell", "barber chair", "barbershop", "barn", "barometer", "barrel",
-                        "wheelbarrow", "baseball", "basketball", "bassinet", "bassoon", "swimming cap", "bath towel",
-                        "bathtub", "station wagon", "lighthouse", "beaker", "military hat (bearskin or shako)",
-                        "beer bottle", "beer glass", "bell tower", "baby bib", "tandem bicycle", "bikini",
-                        "ring binder", "binoculars", "birdhouse", "boathouse", "bobsleigh", "bolo tie", "poke bonnet",
-                        "bookcase", "bookstore", "bottle cap", "hunting bow", "bow tie", "brass memorial plaque", "bra",
-                        "breakwater", "breastplate", "broom", "bucket", "buckle", "bulletproof vest",
-                        "high-speed train", "butcher shop", "taxicab", "cauldron", "candle", "cannon", "canoe",
-                        "can opener", "cardigan", "car mirror", "carousel", "tool kit", "cardboard box / carton",
-                        "car wheel", "automated teller machine", "cassette", "cassette player", "castle", "catamaran",
-                        "CD player", "cello", "mobile phone", "chain", "chain-link fence", "chain mail", "chainsaw",
-                        "storage chest", "chiffonier", "bell or wind chime", "china cabinet", "Christmas stocking",
-                        "church", "movie theater", "cleaver", "cliff dwelling", "cloak", "clogs", "cocktail shaker",
-                        "coffee mug", "coffeemaker", "spiral or coil", "combination lock", "computer keyboard",
-                        "candy store", "container ship", "convertible", "corkscrew", "cornet", "cowboy boot",
-                        "cowboy hat", "cradle", "construction crane", "crash helmet", "crate", "infant bed",
-                        "Crock Pot", "croquet ball", "crutch", "cuirass", "dam", "desk", "desktop computer",
-                        "rotary dial telephone", "diaper", "digital clock", "digital watch", "dining table",
-                        "dishcloth", "dishwasher", "disc brake", "dock", "dog sled", "dome", "doormat", "drilling rig",
-                        "drum", "drumstick", "dumbbell", "Dutch oven", "electric fan", "electric guitar",
-                        "electric locomotive", "entertainment center", "envelope", "espresso machine", "face powder",
-                        "feather boa", "filing cabinet", "fireboat", "fire truck", "fire screen", "flagpole", "flute",
-                        "folding chair", "football helmet", "forklift", "fountain", "fountain pen", "four-poster bed",
-                        "freight car", "French horn", "frying pan", "fur coat", "garbage truck",
-                        "gas mask or respirator", "gas pump", "goblet", "go-kart", "golf ball", "golf cart", "gondola",
-                        "gong", "gown", "grand piano", "greenhouse", "radiator grille", "grocery store", "guillotine",
-                        "hair clip", "hair spray", "half-track", "hammer", "hamper", "hair dryer", "hand-held computer",
-                        "handkerchief", "hard disk drive", "harmonica", "harp", "combine harvester", "hatchet",
-                        "holster", "home theater", "honeycomb", "hook", "hoop skirt", "gymnastic horizontal bar",
-                        "horse-drawn vehicle", "hourglass", "iPod", "clothes iron", "carved pumpkin", "jeans", "jeep",
-                        "T-shirt", "jigsaw puzzle", "rickshaw", "joystick", "kimono", "knee pad", "knot", "lab coat",
-                        "ladle", "lampshade", "laptop computer", "lawn mower", "lens cap", "letter opener", "library",
-                        "lifeboat", "lighter", "limousine", "ocean liner", "lipstick", "slip-on shoe", "lotion",
-                        "music speaker", "loupe magnifying glass", "sawmill", "magnetic compass", "messenger bag",
-                        "mailbox", "tights", "one-piece bathing suit", "manhole cover", "maraca", "marimba", "mask",
-                        "matchstick", "maypole", "maze", "measuring cup", "medicine cabinet", "megalith", "microphone",
-                        "microwave oven", "military uniform", "milk can", "minibus", "miniskirt", "minivan", "missile",
-                        "mitten", "mixing bowl", "mobile home", "ford model t", "modem", "monastery", "monitor",
-                        "moped", "mortar and pestle", "graduation cap", "mosque", "mosquito net", "vespa",
-                        "mountain bike", "tent", "computer mouse", "mousetrap", "moving van", "muzzle", "metal nail",
-                        "neck brace", "necklace", "baby pacifier", "notebook computer", "obelisk", "oboe", "ocarina",
-                        "odometer", "oil filter", "pipe organ", "oscilloscope", "overskirt", "bullock cart",
-                        "oxygen mask", "product packet / packaging", "paddle", "paddle wheel", "padlock", "paintbrush",
-                        "pajamas", "palace", "pan flute", "paper towel", "parachute", "parallel bars", "park bench",
-                        "parking meter", "railroad car", "patio", "payphone", "pedestal", "pencil case",
-                        "pencil sharpener", "perfume", "Petri dish", "photocopier", "plectrum", "Pickelhaube",
-                        "picket fence", "pickup truck", "pier", "piggy bank", "pill bottle", "pillow", "ping-pong ball",
-                        "pinwheel", "pirate ship", "drink pitcher", "block plane", "planetarium", "plastic bag",
-                        "plate rack", "farm plow", "plunger", "Polaroid camera", "pole", "police van", "poncho",
-                        "pool table", "soda bottle", "plant pot", "potter's wheel", "power drill", "prayer rug",
-                        "printer", "prison", "missile", "projector", "hockey puck", "punching bag", "purse", "quill",
-                        "quilt", "race car", "racket", "radiator", "radio", "radio telescope", "rain barrel",
-                        "recreational vehicle", "fishing casting reel", "reflex camera", "refrigerator",
-                        "remote control", "restaurant", "revolver", "rifle", "rocking chair", "rotisserie", "eraser",
-                        "rugby ball", "ruler measuring stick", "sneaker", "safe", "safety pin", "salt shaker", "sandal",
-                        "sarong", "saxophone", "scabbard", "weighing scale", "school bus", "schooner", "scoreboard",
-                        "CRT monitor", "screw", "screwdriver", "seat belt", "sewing machine", "shield", "shoe store",
-                        "shoji screen / room divider", "shopping basket", "shopping cart", "shovel", "shower cap",
-                        "shower curtain", "ski", "balaclava ski mask", "sleeping bag", "slide rule", "sliding door",
-                        "slot machine", "snorkel", "snowmobile", "snowplow", "soap dispenser", "soccer ball", "sock",
-                        "solar thermal collector", "sombrero", "soup bowl", "keyboard space bar", "space heater",
-                        "space shuttle", "spatula", "motorboat", "spider web", "spindle", "sports car", "spotlight",
-                        "stage", "steam locomotive", "through arch bridge", "steel drum", "stethoscope", "scarf",
-                        "stone wall", "stopwatch", "stove", "strainer", "tram", "stretcher", "couch", "stupa",
-                        "submarine", "suit", "sundial", "sunglasses", "sunglasses", "sunscreen", "suspension bridge",
-                        "mop", "sweatshirt", "swim trunks / shorts", "swing", "electrical switch", "syringe",
-                        "table lamp", "tank", "tape player", "teapot", "teddy bear", "television", "tennis ball",
-                        "thatched roof", "front curtain", "thimble", "threshing machine", "throne", "tile roof",
-                        "toaster", "tobacco shop", "toilet seat", "torch", "totem pole", "tow truck", "toy store",
-                        "tractor", "semi-trailer truck", "tray", "trench coat", "tricycle", "trimaran", "tripod",
-                        "triumphal arch", "trolleybus", "trombone", "hot tub", "turnstile", "typewriter keyboard",
-                        "umbrella", "unicycle", "upright piano", "vacuum cleaner", "vase", "vaulted or arched ceiling",
-                        "velvet fabric", "vending machine", "vestment", "viaduct", "violin", "volleyball",
-                        "waffle iron", "wall clock", "wallet", "wardrobe", "military aircraft", "sink",
-                        "washing machine", "water bottle", "water jug", "water tower", "whiskey jug", "whistle",
-                        "hair wig", "window screen", "window shade", "Windsor tie", "wine bottle", "airplane wing",
-                        "wok", "wooden spoon", "wool", "split-rail fence", "shipwreck", "sailboat", "yurt", "website",
-                        "comic book", "crossword", "traffic or street sign", "traffic light", "dust jacket", "menu",
-                        "plate", "guacamole", "consomme", "hot pot", "trifle", "ice cream", "popsicle", "baguette",
-                        "bagel", "pretzel", "cheeseburger", "hot dog", "mashed potatoes", "cabbage", "broccoli",
-                        "cauliflower", "zucchini", "spaghetti squash", "acorn squash", "butternut squash", "cucumber",
-                        "artichoke", "bell pepper", "cardoon", "mushroom", "Granny Smith apple", "strawberry", "orange",
-                        "lemon", "fig", "pineapple", "banana", "jackfruit", "cherimoya (custard apple)", "pomegranate",
-                        "hay", "carbonara", "chocolate syrup", "dough", "meatloaf", "pizza", "pot pie", "burrito",
-                        "red wine", "espresso", "tea cup", "eggnog", "mountain", "bubble", "cliff", "coral reef",
-                        "geyser", "lakeshore", "promontory", "sandbar", "beach", "valley", "volcano", "baseball player",
-                        "bridegroom", "scuba diver", "rapeseed", "daisy", "yellow lady's slipper", "corn", "acorn",
-                        "rose hip", "horse chestnut seed", "coral fungus", "agaric", "gyromitra", "stinkhorn mushroom",
-                        "earth star fungus", "hen of the woods mushroom", "bolete", "corn cob", "toilet paper"]
-
-
-
-
-
-openai_imagenet_template = [
-    lambda c: f'a bad photo of a {c}.',
-    lambda c: f'a photo of many {c}.',
-    lambda c: f'a sculpture of a {c}.',
-    lambda c: f'a photo of the hard to see {c}.',
-    lambda c: f'a low resolution photo of the {c}.',
-    lambda c: f'a rendering of a {c}.',
-    lambda c: f'graffiti of a {c}.',
-    lambda c: f'a bad photo of the {c}.',
-    lambda c: f'a cropped photo of the {c}.',
-    lambda c: f'a tattoo of a {c}.',
-    lambda c: f'the embroidered {c}.',
-    lambda c: f'a photo of a hard to see {c}.',
-    lambda c: f'a bright photo of a {c}.',
-    lambda c: f'a photo of a clean {c}.',
-    lambda c: f'a photo of a dirty {c}.',
-    lambda c: f'a dark photo of the {c}.',
-    lambda c: f'a drawing of a {c}.',
-    lambda c: f'a photo of my {c}.',
-    lambda c: f'the plastic {c}.',
-    lambda c: f'a photo of the cool {c}.',
-    lambda c: f'a close-up photo of a {c}.',
-    lambda c: f'a black and white photo of the {c}.',
-    lambda c: f'a painting of the {c}.',
-    lambda c: f'a painting of a {c}.',
-    lambda c: f'a pixelated photo of the {c}.',
-    lambda c: f'a sculpture of the {c}.',
-    lambda c: f'a bright photo of the {c}.',
-    lambda c: f'a cropped photo of a {c}.',
-    lambda c: f'a plastic {c}.',
-    lambda c: f'a photo of the dirty {c}.',
-    lambda c: f'a jpeg corrupted photo of a {c}.',
-    lambda c: f'a blurry photo of the {c}.',
-    lambda c: f'a photo of the {c}.',
-    lambda c: f'a good photo of the {c}.',
-    lambda c: f'a rendering of the {c}.',
-    lambda c: f'a {c} in a video game.',
-    lambda c: f'a photo of one {c}.',
-    lambda c: f'a doodle of a {c}.',
-    lambda c: f'a close-up photo of the {c}.',
-    lambda c: f'a photo of a {c}.',
-    lambda c: f'the origami {c}.',
-    lambda c: f'the {c} in a video game.',
-    lambda c: f'a sketch of a {c}.',
-    lambda c: f'a doodle of the {c}.',
-    lambda c: f'a origami {c}.',
-    lambda c: f'a low resolution photo of a {c}.',
-    lambda c: f'the toy {c}.',
-    lambda c: f'a rendition of the {c}.',
-    lambda c: f'a photo of the clean {c}.',
-    lambda c: f'a photo of a large {c}.',
-    lambda c: f'a rendition of a {c}.',
-    lambda c: f'a photo of a nice {c}.',
-    lambda c: f'a photo of a weird {c}.',
-    lambda c: f'a blurry photo of a {c}.',
-    lambda c: f'a cartoon {c}.',
-    lambda c: f'art of a {c}.',
-    lambda c: f'a sketch of the {c}.',
-    lambda c: f'a embroidered {c}.',
-    lambda c: f'a pixelated photo of a {c}.',
-    lambda c: f'itap of the {c}.',
-    lambda c: f'a jpeg corrupted photo of the {c}.',
-    lambda c: f'a good photo of a {c}.',
-    lambda c: f'a plushie {c}.',
-    lambda c: f'a photo of the nice {c}.',
-    lambda c: f'a photo of the small {c}.',
-    lambda c: f'a photo of the weird {c}.',
-    lambda c: f'the cartoon {c}.',
-    lambda c: f'art of the {c}.',
-    lambda c: f'a drawing of the {c}.',
-    lambda c: f'a photo of the large {c}.',
-    lambda c: f'a black and white photo of a {c}.',
-    lambda c: f'the plushie {c}.',
-    lambda c: f'a dark photo of a {c}.',
-    lambda c: f'itap of a {c}.',
-    lambda c: f'graffiti of the {c}.',
-    lambda c: f'a toy {c}.',
-    lambda c: f'itap of my {c}.',
-    lambda c: f'a photo of a cool {c}.',
-    lambda c: f'a photo of a small {c}.',
-    lambda c: f'a tattoo of the {c}.',
-]
diff --git "a/spaces/xwsm/gpt/crazy_functions/\344\270\213\350\275\275arxiv\350\256\272\346\226\207\347\277\273\350\257\221\346\221\230\350\246\201.py" "b/spaces/xwsm/gpt/crazy_functions/\344\270\213\350\275\275arxiv\350\256\272\346\226\207\347\277\273\350\257\221\346\221\230\350\246\201.py"
deleted file mode 100644
index 3da831fd07e361a532777c83bb02cff265b94abd..0000000000000000000000000000000000000000
--- "a/spaces/xwsm/gpt/crazy_functions/\344\270\213\350\275\275arxiv\350\256\272\346\226\207\347\277\273\350\257\221\346\221\230\350\246\201.py"
+++ /dev/null
@@ -1,194 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file, get_conf
-import re, requests, unicodedata, os
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-def download_arxiv_(url_pdf):
-    if 'arxiv.org' not in url_pdf:
-        if ('.' in url_pdf) and ('/' not in url_pdf):
-            new_url = 'https://arxiv.org/abs/'+url_pdf
-            print('下载编号：', url_pdf, '自动定位：', new_url)
-            # download_arxiv_(new_url)
-            return download_arxiv_(new_url)
-        else:
-            print('不能识别的URL！')
-            return None
-    if 'abs' in url_pdf:
-        url_pdf = url_pdf.replace('abs', 'pdf')
-        url_pdf = url_pdf + '.pdf'
-
-    url_abs = url_pdf.replace('.pdf', '').replace('pdf', 'abs')
-    title, other_info = get_name(_url_=url_abs)
-
-    paper_id = title.split()[0]  # '[1712.00559]'
-    if '2' in other_info['year']:
-        title = other_info['year'] + ' ' + title
-
-    known_conf = ['NeurIPS', 'NIPS', 'Nature', 'Science', 'ICLR', 'AAAI']
-    for k in known_conf:
-        if k in other_info['comment']:
-            title = k + ' ' + title
-
-    download_dir = './gpt_log/arxiv/'
-    os.makedirs(download_dir, exist_ok=True)
-
-    title_str = title.replace('?', '？')\
-        .replace(':', '：')\
-        .replace('\"', '“')\
-        .replace('\n', '')\
-        .replace('  ', ' ')\
-        .replace('  ', ' ')
-
-    requests_pdf_url = url_pdf
-    file_path = download_dir+title_str
-    # if os.path.exists(file_path):
-    #     print('返回缓存文件')
-    #     return './gpt_log/arxiv/'+title_str
-
-    print('下载中')
-    proxies, = get_conf('proxies')
-    r = requests.get(requests_pdf_url, proxies=proxies)
-    with open(file_path, 'wb+') as f:
-        f.write(r.content)
-    print('下载完成')
-
-    # print('输出下载命令：','aria2c -o \"%s\" %s'%(title_str,url_pdf))
-    # subprocess.call('aria2c --all-proxy=\"172.18.116.150:11084\" -o \"%s\" %s'%(download_dir+title_str,url_pdf), shell=True)
-
-    x = "%s  %s %s.bib" % (paper_id, other_info['year'], other_info['authors'])
-    x = x.replace('?', '？')\
-        .replace(':', '：')\
-        .replace('\"', '“')\
-        .replace('\n', '')\
-        .replace('  ', ' ')\
-        .replace('  ', ' ')
-    return './gpt_log/arxiv/'+title_str, other_info
-
-
-def get_name(_url_):
-    import os
-    from bs4 import BeautifulSoup
-    print('正在获取文献名！')
-    print(_url_)
-
-    # arxiv_recall = {}
-    # if os.path.exists('./arxiv_recall.pkl'):
-    #     with open('./arxiv_recall.pkl', 'rb') as f:
-    #         arxiv_recall = pickle.load(f)
-
-    # if _url_ in arxiv_recall:
-    #     print('在缓存中')
-    #     return arxiv_recall[_url_]
-
-    proxies, = get_conf('proxies')
-    res = requests.get(_url_, proxies=proxies)
-
-    bs = BeautifulSoup(res.text, 'html.parser')
-    other_details = {}
-
-    # get year
-    try:
-        year = bs.find_all(class_='dateline')[0].text
-        year = re.search(r'(\d{4})', year, re.M | re.I).group(1)
-        other_details['year'] = year
-        abstract = bs.find_all(class_='abstract mathjax')[0].text
-        other_details['abstract'] = abstract
-    except:
-        other_details['year'] = ''
-        print('年份获取失败')
-
-    # get author
-    try:
-        authors = bs.find_all(class_='authors')[0].text
-        authors = authors.split('Authors:')[1]
-        other_details['authors'] = authors
-    except:
-        other_details['authors'] = ''
-        print('authors获取失败')
-
-    # get comment
-    try:
-        comment = bs.find_all(class_='metatable')[0].text
-        real_comment = None
-        for item in comment.replace('\n', ' ').split('   '):
-            if 'Comments' in item:
-                real_comment = item
-        if real_comment is not None:
-            other_details['comment'] = real_comment
-        else:
-            other_details['comment'] = ''
-    except:
-        other_details['comment'] = ''
-        print('年份获取失败')
-
-    title_str = BeautifulSoup(
-        res.text, 'html.parser').find('title').contents[0]
-    print('获取成功：', title_str)
-    # arxiv_recall[_url_] = (title_str+'.pdf', other_details)
-    # with open('./arxiv_recall.pkl', 'wb') as f:
-    #     pickle.dump(arxiv_recall, f)
-
-    return title_str+'.pdf', other_details
-
-
-
-@CatchException
-def 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-
-    CRAZY_FUNCTION_INFO = "下载arxiv论文并翻译摘要，函数插件作者[binary-husky]。正在提取摘要并下载PDF文档……"
-    import glob
-    import os
-
-    # 基本信息：功能、贡献者
-    chatbot.append(["函数插件功能？", CRAZY_FUNCTION_INFO])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import pdfminer, bs4
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pdfminer beautifulsoup4```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 提取摘要，下载PDF文档
-    try:
-        pdf_path, info = download_arxiv_(txt)
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"下载pdf文件未成功")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    
-    # 翻译摘要等
-    i_say =            f"请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。材料如下：{str(info)}"
-    i_say_show_user =  f'请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。论文：{pdf_path}'
-    chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    msg = '正常'
-    # ** gpt request **
-    # 单线，获取文章meta信息
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=i_say,
-        inputs_show_user=i_say_show_user,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot, history=[],
-        sys_prompt="Your job is to collect information from materials and translate to Chinese。",
-    )
-
-    chatbot[-1] = (i_say_show_user, gpt_say)
-    history.append(i_say_show_user); history.append(gpt_say)
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-    # 写入文件
-    import shutil
-    # 重置文件的创建时间
-    shutil.copyfile(pdf_path, f'./gpt_log/{os.path.basename(pdf_path)}'); os.remove(pdf_path)
-    res = write_results_to_file(history)
-    chatbot.append(("完成了吗？", res + "\n\nPDF文件也已经下载"))
-    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
diff --git a/spaces/yderre-aubay/midi-player-demo/public/style.css b/spaces/yderre-aubay/midi-player-demo/public/style.css
deleted file mode 100644
index e3b66d96a6f6c7be6f37f02ca371f5e0a42bbbac..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/public/style.css
+++ /dev/null
@@ -1,255 +0,0 @@
-:root {
-  --themeColor: #5a4edf;
-  --themeColor2: #4c41cc;
-}
-body {
-  margin: 0;
-  font-family: "Inter", -apple-system, BlinkMacSystemFont, Helvetica Neue,
-    sans-serif;
-  line-height: 1.5;
-  -webkit-font-smoothing: antialiased;
-  background: hsl(245deg 5% 16%);
-  color: white;
-}
-a {
-  color: var(--themeColor);
-}
-.content {
-  max-width: 60rem;
-  width: 100%;
-  margin: 0 auto;
-}
-header {
-  background: var(--themeColor2);
-}
-header .content {
-  padding: 1rem 0;
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-}
-section .content {
-  padding: 5rem 0;
-}
-#title {
-  display: flex;
-}
-#title img {
-  height: 1.7rem;
-}
-#title:hover {
-  opacity: 0.7;
-}
-#hero {
-  background: var(--themeColor);
-  color: white;
-  overflow: hidden;
-}
-#hero .content {
-  display: flex;
-}
-#hero .text h1 {
-  font-weight: 800;
-  font-size: 3.2rem;
-  line-height: 1;
-  margin: 0 0 1rem 0;
-}
-#hero .image {
-  width: 35%;
-  padding-left: 2rem;
-}
-#hero .image img {
-  width: 34rem;
-  border-radius: 0.5rem;
-  box-shadow: 0 1rem 8rem #0000004f;
-}
-#hero .text .description {
-  font-size: 1.4rem;
-  line-height: 1;
-}
-#launch-button {
-  background: white;
-  padding: 0.8rem 2rem;
-  border-radius: 9999px;
-  text-decoration: none;
-  color: var(--themeColor);
-  font-weight: bold;
-  display: inline-block;
-  margin: 2rem 0 0 0;
-  font-size: 1.2rem;
-  border: 2px solid transparent;
-  transition: border-color 0.2s ease, background 0.2s ease, color 0.2s ease;
-  box-shadow: 0 1rem 7rem #0000004f;
-}
-#launch-button:hover {
-  border-color: white;
-  background: transparent;
-  color: white;
-}
-p.platform {
-  opacity: 0.8;
-  font-size: 0.9rem;
-}
-p.issue {
-  display: inline-block;
-  color: #ffb200;
-  background: #ffd4001f;
-  border: 1px solid #ffb200;
-  padding: 0.5em;
-  font-size: 0.8rem;
-  border-radius: 0.5rem;
-  margin: 0;
-}
-h3 {
-  font-size: 1.5rem;
-}
-.sponsor-intro {
-  width: 40em;
-  line-height: 1.9;
-  margin-bottom: 1.5em;
-  max-width: 100%;
-}
-section#features ul {
-  line-height: 2;
-}
-
-#features .content {
-  display: flex;
-}
-
-#features .content .left {
-  flex-grow: 1;
-  display: flex;
-  flex-direction: column;
-}
-
-#features .content .right {
-  flex-shrink: 0;
-  min-width: 342px;
-}
-
-#features .feature {
-  background: #ffffff0d;
-  padding: 2rem;
-  border-radius: 1rem;
-  margin-right: 2rem;
-  margin-bottom: 2rem;
-  box-shadow: 0 1rem 3rem 0 #0000000e;
-}
-
-#features .feature .icon {
-  display: inline-flex;
-  width: 4rem;
-  border-radius: 2rem;
-  height: 4rem;
-  overflow: hidden;
-  background: white;
-  align-items: center;
-  justify-content: center;
-}
-
-#features .feature .title {
-  font-size: 1.1rem;
-  font-weight: bold;
-  margin-top: 1rem;
-  margin-bottom: 1rem;
-}
-
-#features .feature .description {
-  opacity: 0.5;
-}
-
-#features .feature .icon img {
-  width: 2rem;
-}
-
-section#sponsor {
-  background: #1f1f23;
-}
-#github-link {
-  display: flex;
-}
-#github-link svg {
-  fill: white;
-}
-
-@media screen and (max-width: 896px) {
-  .content {
-    width: 90%;
-  }
-
-  #hero .content {
-    flex-direction: column;
-  }
-
-  #hero .text h1 {
-    font-size: 2.5rem;
-  }
-
-  #hero .image {
-    padding: 0;
-    width: 100%;
-  }
-
-  #hero .image img {
-    margin-top: 3rem;
-    width: 100%;
-  }
-
-  #features .content {
-    flex-direction: column-reverse;
-  }
-
-  #features .feature {
-    margin-right: 0;
-  }
-}
-
-a.external-link {
-  font-size: 0.9rem;
-  display: inline-flex;
-  border: #3b3b44 2px solid;
-  border-radius: 0.3em;
-  padding: 0.5em 1em;
-  color: white;
-  text-decoration: none;
-  align-items: center;
-}
-
-a.external-link img {
-  margin-right: 0.7em;
-}
-
-a.sponsor-button {
-  display: inline-flex;
-  align-items: center;
-  font-weight: 600;
-  background: #bb5d9029;
-  border-radius: 0.3em;
-  padding: 1em 1.5em;
-  color: white;
-  text-decoration: none;
-  border: 2px solid transparent;
-  transition: 0.2s ease;
-}
-
-a.sponsor-button:hover {
-  border-color: #bb5d90;
-}
-
-a.sponsor-button img {
-  margin-right: 0.7em;
-  width: 1.2em;
-}
-
-.support-row {
-  display: flex;
-  align-items: center;
-  margin-bottom: 1rem;
-}
-
-.support-row span {
-  padding-left: 1em;
-  font-size: 0.9rem;
-  opacity: 0.6;
-}
diff --git a/spaces/yefengzi/vits-models/app.py b/spaces/yefengzi/vits-models/app.py
deleted file mode 100644
index a8afe878524bebfd8565de75b015d2ae32e06397..0000000000000000000000000000000000000000
--- a/spaces/yefengzi/vits-models/app.py
+++ /dev/null
@@ -1,263 +0,0 @@
-# coding=utf-8
-import os
-import re
-import argparse
-import utils
-import commons
-import json
-import torch
-import gradio as gr
-from models import SynthesizerTrn
-from text import text_to_sequence, _clean_text
-from torch import no_grad, LongTensor
-import gradio.processing_utils as gr_processing_utils
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
-
-hps_ms = utils.get_hparams_from_file(r'config/config.json')
-
-audio_postprocess_ori = gr.Audio.postprocess
-
-def audio_postprocess(self, y):
-    data = audio_postprocess_ori(self, y)
-    if data is None:
-        return None
-    return gr_processing_utils.encode_url_or_file_to_base64(data["name"])
-
-
-gr.Audio.postprocess = audio_postprocess
-
-def get_text(text, hps, is_symbol):
-    text_norm, clean_text = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = LongTensor(text_norm)
-    return text_norm, clean_text
-
-def create_tts_fn(net_g_ms, speaker_id):
-    def tts_fn(text, language, noise_scale, noise_scale_w, length_scale, is_symbol):
-        text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
-        if limitation:
-            text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
-            max_len = 100
-            if text_len > max_len:
-                return "Error: Text is too long", None
-        if not is_symbol:
-            if language == 0:
-                text = f"[ZH]{text}[ZH]"
-            elif language == 1:
-                text = f"[JA]{text}[JA]"
-            else:
-                text = f"{text}"
-        stn_tst, clean_text = get_text(text, hps_ms, is_symbol)
-        with no_grad():
-            x_tst = stn_tst.unsqueeze(0).to(device)
-            x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
-            sid = LongTensor([speaker_id]).to(device)
-            audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
-                                   length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
-
-        return "Success", (22050, audio)
-    return tts_fn
-
-def create_to_symbol_fn(hps):
-    def to_symbol_fn(is_symbol_input, input_text, temp_text, temp_lang):
-        if temp_lang == 'Chinese':
-            clean_text = f'[ZH]{input_text}[ZH]'
-        elif temp_lang == "Japanese":
-            clean_text = f'[JA]{input_text}[JA]'
-        else:
-            clean_text = input_text
-        return (_clean_text(clean_text, hps.data.text_cleaners), input_text) if is_symbol_input else (temp_text, temp_text)
-
-    return to_symbol_fn
-def change_lang(language):
-    if language == 0:
-        return 0.6, 0.668, 1.2, "Chinese"
-    elif language == 1:
-        return 0.6, 0.668, 1, "Japanese"
-    else:
-        return 0.6, 0.668, 1, "Mix"
-
-download_audio_js = """
-() =>{{
-    let root = document.querySelector("body > gradio-app");
-    if (root.shadowRoot != null)
-        root = root.shadowRoot;
-    let audio = root.querySelector("#tts-audio-{audio_id}").querySelector("audio");
-    let text = root.querySelector("#input-text-{audio_id}").querySelector("textarea");
-    if (audio == undefined)
-        return;
-    text = text.value;
-    if (text == undefined)
-        text = Math.floor(Math.random()*100000000);
-    audio = audio.src;
-    let oA = document.createElement("a");
-    oA.download = text.substr(0, 20)+'.wav';
-    oA.href = audio;
-    document.body.appendChild(oA);
-    oA.click();
-    oA.remove();
-}}
-"""
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
-    args = parser.parse_args()
-    device = torch.device(args.device)
-    
-    models = []
-    with open("pretrained_models/info.json", "r", encoding="utf-8") as f:
-        models_info = json.load(f)
-    for i, info in models_info.items():
-        sid = info['sid']
-        name_en = info['name_en']
-        name_zh = info['name_zh']
-        title = info['title']
-        cover = f"pretrained_models/{i}/{info['cover']}"
-        example = info['example']
-        language = info['language']
-        net_g_ms = SynthesizerTrn(
-            len(hps_ms.symbols),
-            hps_ms.data.filter_length // 2 + 1,
-            hps_ms.train.segment_size // hps_ms.data.hop_length,
-            n_speakers=hps_ms.data.n_speakers if info['type'] == "multi" else 0,
-            **hps_ms.model)
-        utils.load_checkpoint(f'pretrained_models/{i}/{i}.pth', net_g_ms, None)
-        _ = net_g_ms.eval().to(device)
-        models.append((sid, name_en, name_zh, title, cover, example, language, net_g_ms, create_tts_fn(net_g_ms, sid), create_to_symbol_fn(hps_ms)))
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> vits-models\n"
-            "## <center> Please do not generate content that could infringe upon the rights or cause harm to individuals or organizations.\n"
-            "## <center> ·请不要生成会对个人以及组织造成侵害的内容\n"
-            "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=sayashi.vits-models)\n\n"
-            "[Open In Colab]"
-            "(https://colab.research.google.com/drive/10QOk9NPgoKZUXkIhhuVaZ7SYra1MPMKH?usp=share_link)"
-            " without queue and length limitation.(无需等待队列，并且没有长度限制)\n\n"
-            "[Finetune your own model](https://github.com/SayaSS/vits-finetuning)"
-        )
-
-        with gr.Tabs():
-            with gr.TabItem("EN"):
-                for (sid, name_en, name_zh, title, cover, example, language, net_g_ms, tts_fn, to_symbol_fn) in models:
-                    with gr.TabItem(name_en):
-                        with gr.Row():
-                            gr.Markdown(
-                                '<div align="center">'
-                                f'<a><strong>{title}</strong></a>'
-                                f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else ""
-                                '</div>'
-                            )
-                        with gr.Row():
-                            with gr.Column():
-                                input_text = gr.Textbox(label="Text (100 words limitation)", lines=5, value=example, elem_id=f"input-text-en-{name_en.replace(' ','')}")
-                                lang = gr.Dropdown(label="Language", choices=["Chinese", "Japanese", "Mix（wrap the Chinese text with [ZH][ZH], wrap the Japanese text with [JA][JA]）"],
-                                            type="index", value=language)
-                                temp_lang = gr.Variable(value=language)
-                                with gr.Accordion(label="Advanced Options", open=False):
-                                    temp_text_var = gr.Variable()
-                                    symbol_input = gr.Checkbox(value=False, label="Symbol input")
-                                    symbol_list = gr.Dataset(label="Symbol list", components=[input_text],
-                                                             samples=[[x] for x in hps_ms.symbols])
-                                    symbol_list_json = gr.Json(value=hps_ms.symbols, visible=False)
-                                btn = gr.Button(value="Generate", variant="primary")
-                                with gr.Row():
-                                    ns = gr.Slider(label="noise_scale", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
-                                    nsw = gr.Slider(label="noise_scale_w", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
-                                    ls = gr.Slider(label="length_scale", minimum=0.1, maximum=2.0, step=0.1, value=1.2 if language=="Chinese" else 1, interactive=True)
-                            with gr.Column():
-                                o1 = gr.Textbox(label="Output Message")
-                                o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio-en-{name_en.replace(' ','')}")
-                                download = gr.Button("Download Audio")
-                            btn.click(tts_fn, inputs=[input_text, lang,  ns, nsw, ls, symbol_input], outputs=[o1, o2])
-                            download.click(None, [], [], _js=download_audio_js.format(audio_id=f"en-{name_en.replace(' ', '')}"))
-                            lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls, temp_lang])
-                            symbol_input.change(
-                                to_symbol_fn,
-                                [symbol_input, input_text, temp_text_var, temp_lang],
-                                [input_text, temp_text_var]
-                            )
-                            symbol_list.click(None, [symbol_list, symbol_list_json], [input_text],
-                                              _js=f"""
-                            (i,symbols) => {{
-                                let root = document.querySelector("body > gradio-app");
-                                if (root.shadowRoot != null)
-                                    root = root.shadowRoot;
-                                let text_input = root.querySelector("#input-text-en-{name_en.replace(' ', '')}").querySelector("textarea");
-                                let startPos = text_input.selectionStart;
-                                let endPos = text_input.selectionEnd;
-                                let oldTxt = text_input.value;
-                                let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
-                                text_input.value = result;
-                                let x = window.scrollX, y = window.scrollY;
-                                text_input.focus();
-                                text_input.selectionStart = startPos + symbols[i].length;
-                                text_input.selectionEnd = startPos + symbols[i].length;
-                                text_input.blur();
-                                window.scrollTo(x, y);
-                                return text_input.value;
-                            }}""")
-            with gr.TabItem("中文"):
-                for (sid, name_en, name_zh, title, cover, example, language,  net_g_ms, tts_fn, to_symbol_fn) in models:
-                    with gr.TabItem(name_zh):
-                        with gr.Row():
-                            gr.Markdown(
-                                '<div align="center">'
-                                f'<a><strong>{title}</strong></a>'
-                                f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else ""
-                                '</div>'
-                            )
-                        with gr.Row():
-                            with gr.Column():
-                                input_text = gr.Textbox(label="文本 (100字上限)", lines=5, value=example, elem_id=f"input-text-zh-{name_zh}")
-                                lang = gr.Dropdown(label="语言", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"],
-                                            type="index", value="中文"if language == "Chinese" else "日语")
-                                temp_lang = gr.Variable(value=language)
-                                with gr.Accordion(label="高级选项", open=False):
-                                    temp_text_var = gr.Variable()
-                                    symbol_input = gr.Checkbox(value=False, label="符号输入")
-                                    symbol_list = gr.Dataset(label="符号列表", components=[input_text],
-                                                             samples=[[x] for x in hps_ms.symbols])
-                                    symbol_list_json = gr.Json(value=hps_ms.symbols, visible=False)
-                                btn = gr.Button(value="生成", variant="primary")
-                                with gr.Row():
-                                    ns = gr.Slider(label="控制感情变化程度", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
-                                    nsw = gr.Slider(label="控制音素发音长度", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
-                                    ls = gr.Slider(label="控制整体语速", minimum=0.1, maximum=2.0, step=0.1, value=1.2 if language=="Chinese" else 1, interactive=True)
-                            with gr.Column():
-                                o1 = gr.Textbox(label="输出信息")
-                                o2 = gr.Audio(label="输出音频", elem_id=f"tts-audio-zh-{name_zh}")
-                                download = gr.Button("下载音频")
-                            btn.click(tts_fn, inputs=[input_text, lang,  ns, nsw, ls, symbol_input], outputs=[o1, o2])
-                            download.click(None, [], [], _js=download_audio_js.format(audio_id=f"zh-{name_zh}"))
-                            lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
-                            symbol_input.change(
-                                to_symbol_fn,
-                                [symbol_input, input_text, temp_text_var, temp_lang],
-                                [input_text, temp_text_var]
-                            )
-                            symbol_list.click(None, [symbol_list, symbol_list_json], [input_text],
-                                              _js=f"""
-                            (i,symbols) => {{
-                                let root = document.querySelector("body > gradio-app");
-                                if (root.shadowRoot != null)
-                                    root = root.shadowRoot;
-                                let text_input = root.querySelector("#input-text-zh-{name_zh}").querySelector("textarea");
-                                let startPos = text_input.selectionStart;
-                                let endPos = text_input.selectionEnd;
-                                let oldTxt = text_input.value;
-                                let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
-                                text_input.value = result;
-                                let x = window.scrollX, y = window.scrollY;
-                                text_input.focus();
-                                text_input.selectionStart = startPos + symbols[i].length;
-                                text_input.selectionEnd = startPos + symbols[i].length;
-                                text_input.blur();
-                                window.scrollTo(x, y);
-                                return text_input.value;
-                            }}""")
-    app.queue(concurrency_count=1).launch(show_api=False, share=args.share)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/flaubert/tokenization_flaubert.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/flaubert/tokenization_flaubert.py
deleted file mode 100644
index b1b34cc0f78da7d99e7185e2f4839c719b5a2d41..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/flaubert/tokenization_flaubert.py
+++ /dev/null
@@ -1,609 +0,0 @@
-# coding=utf-8
-# Copyright 2019-present CNRS, Facebook Inc. and the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Tokenization classes for Flaubert."""
-
-
-import json
-import os
-import re
-import unicodedata
-from typing import List, Optional, Tuple
-
-from ...tokenization_utils import PreTrainedTokenizer
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {
-    "vocab_file": "vocab.json",
-    "merges_file": "merges.txt",
-}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "flaubert/flaubert_small_cased": (
-            "https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/vocab.json"
-        ),
-        "flaubert/flaubert_base_uncased": (
-            "https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/vocab.json"
-        ),
-        "flaubert/flaubert_base_cased": "https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/vocab.json",
-        "flaubert/flaubert_large_cased": (
-            "https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/vocab.json"
-        ),
-    },
-    "merges_file": {
-        "flaubert/flaubert_small_cased": (
-            "https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/merges.txt"
-        ),
-        "flaubert/flaubert_base_uncased": (
-            "https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/merges.txt"
-        ),
-        "flaubert/flaubert_base_cased": "https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/merges.txt",
-        "flaubert/flaubert_large_cased": (
-            "https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/merges.txt"
-        ),
-    },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "flaubert/flaubert_small_cased": 512,
-    "flaubert/flaubert_base_uncased": 512,
-    "flaubert/flaubert_base_cased": 512,
-    "flaubert/flaubert_large_cased": 512,
-}
-
-PRETRAINED_INIT_CONFIGURATION = {
-    "flaubert/flaubert_small_cased": {"do_lowercase": False},
-    "flaubert/flaubert_base_uncased": {"do_lowercase": True},
-    "flaubert/flaubert_base_cased": {"do_lowercase": False},
-    "flaubert/flaubert_large_cased": {"do_lowercase": False},
-}
-
-
-def convert_to_unicode(text):
-    """
-    Converts `text` to Unicode (if it's not already), assuming UTF-8 input.
-    """
-
-    def ensure_text(s, encoding="utf-8", errors="strict"):
-        if isinstance(s, bytes):
-            return s.decode(encoding, errors)
-        elif isinstance(s, str):
-            return s
-        else:
-            raise TypeError(f"not expecting type '{type(s)}'")
-
-    return ensure_text(text, encoding="utf-8", errors="ignore")
-
-
-# Copied from transformers.models.xlm.tokenization_xlm.get_pairs
-def get_pairs(word):
-    """
-    Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length
-    strings)
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-# Copied from transformers.models.xlm.tokenization_xlm.replace_unicode_punct
-def replace_unicode_punct(text):
-    """
-    Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/replace-unicode-punctuation.perl
-    """
-    text = text.replace("，", ",")
-    text = re.sub(r"。\s*", ". ", text)
-    text = text.replace("、", ",")
-    text = text.replace("”", '"')
-    text = text.replace("“", '"')
-    text = text.replace("∶", ":")
-    text = text.replace("：", ":")
-    text = text.replace("？", "?")
-    text = text.replace("《", '"')
-    text = text.replace("》", '"')
-    text = text.replace("）", ")")
-    text = text.replace("！", "!")
-    text = text.replace("（", "(")
-    text = text.replace("；", ";")
-    text = text.replace("１", "1")
-    text = text.replace("」", '"')
-    text = text.replace("「", '"')
-    text = text.replace("０", "0")
-    text = text.replace("３", "3")
-    text = text.replace("２", "2")
-    text = text.replace("５", "5")
-    text = text.replace("６", "6")
-    text = text.replace("９", "9")
-    text = text.replace("７", "7")
-    text = text.replace("８", "8")
-    text = text.replace("４", "4")
-    text = re.sub(r"．\s*", ". ", text)
-    text = text.replace("～", "~")
-    text = text.replace("’", "'")
-    text = text.replace("…", "...")
-    text = text.replace("━", "-")
-    text = text.replace("〈", "<")
-    text = text.replace("〉", ">")
-    text = text.replace("【", "[")
-    text = text.replace("】", "]")
-    text = text.replace("％", "%")
-    return text
-
-
-# Copied from transformers.models.xlm.tokenization_xlm.remove_non_printing_char
-def remove_non_printing_char(text):
-    """
-    Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/remove-non-printing-char.perl
-    """
-    output = []
-    for char in text:
-        cat = unicodedata.category(char)
-        if cat.startswith("C"):
-            continue
-        output.append(char)
-    return "".join(output)
-
-
-class FlaubertTokenizer(PreTrainedTokenizer):
-    """
-    Construct a Flaubert tokenizer. Based on Byte-Pair Encoding. The tokenization process is the following:
-
-    - Moses preprocessing and tokenization.
-    - Normalizing all inputs text.
-    - The arguments `special_tokens` and the function `set_special_tokens`, can be used to add additional symbols (like
-      "__classify__") to a vocabulary.
-    - The argument `do_lowercase` controls lower casing (automatically set for pretrained vocabularies).
-
-    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
-    this superclass for more information regarding those methods.
-
-    Args:
-        vocab_file (`str`):
-            Vocabulary file.
-        merges_file (`str`):
-            Merges file.
-        do_lowercase (`bool`, *optional*, defaults to `False`):
-            Controls lower casing.
-        unk_token (`str`, *optional*, defaults to `"<unk>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        bos_token (`str`, *optional*, defaults to `"<s>"`):
-            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
-
-            <Tip>
-
-            When building a sequence using special tokens, this is not the token that is used for the beginning of
-            sequence. The token used is the `cls_token`.
-
-            </Tip>
-
-        sep_token (`str`, *optional*, defaults to `"</s>"`):
-            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
-            sequence classification or for a text and a question for question answering. It is also used as the last
-            token of a sequence built with special tokens.
-        pad_token (`str`, *optional*, defaults to `"<pad>"`):
-            The token used for padding, for example when batching sequences of different lengths.
-        cls_token (`str`, *optional*, defaults to `"</s>"`):
-            The classifier token which is used when doing sequence classification (classification of the whole sequence
-            instead of per-token classification). It is the first token of the sequence when built with special tokens.
-        mask_token (`str`, *optional*, defaults to `"<special1>"`):
-            The token used for masking values. This is the token used when training this model with masked language
-            modeling. This is the token which the model will try to predict.
-        additional_special_tokens (`List[str]`, *optional*, defaults to `['<special0>', '<special1>', '<special2>', '<special3>', '<special4>', '<special5>', '<special6>', '<special7>', '<special8>', '<special9>']`):
-            List of additional special tokens.
-        lang2id (`Dict[str, int]`, *optional*):
-            Dictionary mapping languages string identifiers to their IDs.
-        id2lang (`Dict[int, str]`, *optional*):
-            Dictionary mapping language IDs to their string identifiers.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-
-    def __init__(
-        self,
-        vocab_file,
-        merges_file,
-        do_lowercase=False,
-        unk_token="<unk>",
-        bos_token="<s>",
-        sep_token="</s>",
-        pad_token="<pad>",
-        cls_token="</s>",
-        mask_token="<special1>",
-        additional_special_tokens=[
-            "<special0>",
-            "<special1>",
-            "<special2>",
-            "<special3>",
-            "<special4>",
-            "<special5>",
-            "<special6>",
-            "<special7>",
-            "<special8>",
-            "<special9>",
-        ],
-        lang2id=None,
-        id2lang=None,
-        **kwargs,
-    ):
-        do_lowercase_and_remove_accent = kwargs.pop("do_lowercase_and_remove_accent", None)
-        if do_lowercase_and_remove_accent is not None:
-            logger.warning(
-                "`do_lowercase_and_remove_accent` is passed as a keyword argument, but this won't do anything."
-                " `FlaubertTokenizer` will always set it to `False`."
-            )
-        # always `False`
-        self.do_lowercase_and_remove_accent = False
-
-        self.do_lowercase = do_lowercase
-
-        try:
-            import sacremoses
-        except ImportError:
-            raise ImportError(
-                "You need to install sacremoses to use FlaubertTokenizer. "
-                "See https://pypi.org/project/sacremoses/ for installation."
-            )
-
-        self.sm = sacremoses
-
-        # cache of sm.MosesPunctNormalizer instance
-        self.cache_moses_punct_normalizer = {}
-        # cache of sm.MosesTokenizer instance
-        self.cache_moses_tokenizer = {}
-        self.lang_with_custom_tokenizer = {"zh", "th", "ja"}
-        self.lang2id = lang2id
-        self.id2lang = id2lang
-        if lang2id is not None and id2lang is not None:
-            assert len(lang2id) == len(id2lang)
-
-        self.ja_word_tokenizer = None
-        self.zh_word_tokenizer = None
-
-        with open(vocab_file, encoding="utf-8") as vocab_handle:
-            self.encoder = json.load(vocab_handle)
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        with open(merges_file, encoding="utf-8") as merges_handle:
-            merges = merges_handle.read().split("\n")[:-1]
-        merges = [tuple(merge.split()[:2]) for merge in merges]
-        self.bpe_ranks = dict(zip(merges, range(len(merges))))
-        self.cache = {}
-
-        super().__init__(
-            unk_token=unk_token,
-            bos_token=bos_token,
-            sep_token=sep_token,
-            pad_token=pad_token,
-            cls_token=cls_token,
-            mask_token=mask_token,
-            additional_special_tokens=additional_special_tokens,
-            lang2id=lang2id,
-            id2lang=id2lang,
-            **kwargs,
-        )
-
-    @property
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.do_lower_case
-    def do_lower_case(self):
-        return self.do_lowercase_and_remove_accent
-
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_punct_norm
-    def moses_punct_norm(self, text, lang):
-        if lang not in self.cache_moses_punct_normalizer:
-            punct_normalizer = self.sm.MosesPunctNormalizer(lang=lang)
-            self.cache_moses_punct_normalizer[lang] = punct_normalizer
-        else:
-            punct_normalizer = self.cache_moses_punct_normalizer[lang]
-        return punct_normalizer.normalize(text)
-
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_tokenize
-    def moses_tokenize(self, text, lang):
-        if lang not in self.cache_moses_tokenizer:
-            moses_tokenizer = self.sm.MosesTokenizer(lang=lang)
-            self.cache_moses_tokenizer[lang] = moses_tokenizer
-        else:
-            moses_tokenizer = self.cache_moses_tokenizer[lang]
-        return moses_tokenizer.tokenize(text, return_str=False, escape=False)
-
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_pipeline
-    def moses_pipeline(self, text, lang):
-        text = replace_unicode_punct(text)
-        text = self.moses_punct_norm(text, lang)
-        text = remove_non_printing_char(text)
-        return text
-
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.ja_tokenize
-    def ja_tokenize(self, text):
-        if self.ja_word_tokenizer is None:
-            try:
-                import Mykytea
-
-                self.ja_word_tokenizer = Mykytea.Mykytea(
-                    f"-model {os.path.expanduser('~')}/local/share/kytea/model.bin"
-                )
-            except (AttributeError, ImportError):
-                logger.error(
-                    "Make sure you install KyTea (https://github.com/neubig/kytea) and it's python wrapper"
-                    " (https://github.com/chezou/Mykytea-python) with the following steps"
-                )
-                logger.error("1. git clone git@github.com:neubig/kytea.git && cd kytea")
-                logger.error("2. autoreconf -i")
-                logger.error("3. ./configure --prefix=$HOME/local")
-                logger.error("4. make && make install")
-                logger.error("5. pip install kytea")
-                raise
-        return list(self.ja_word_tokenizer.getWS(text))
-
-    @property
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.vocab_size
-    def vocab_size(self):
-        return len(self.encoder)
-
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.get_vocab
-    def get_vocab(self):
-        return dict(self.encoder, **self.added_tokens_encoder)
-
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.bpe
-    def bpe(self, token):
-        word = tuple(token[:-1]) + (token[-1] + "</w>",)
-        if token in self.cache:
-            return self.cache[token]
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token + "</w>"
-
-        while True:
-            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                except ValueError:
-                    new_word.extend(word[i:])
-                    break
-                else:
-                    new_word.extend(word[i:j])
-                    i = j
-
-                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
-                    new_word.append(first + second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = " ".join(word)
-        if word == "\n  </w>":
-            word = "\n</w>"
-        self.cache[token] = word
-        return word
-
-    def preprocess_text(self, text):
-        text = text.replace("``", '"').replace("''", '"')
-        text = convert_to_unicode(text)
-        text = unicodedata.normalize("NFC", text)
-
-        if self.do_lowercase:
-            text = text.lower()
-
-        return text
-
-    def _tokenize(self, text, bypass_tokenizer=False):
-        """
-        Tokenize a string given language code using Moses.
-
-        Details of tokenization:
-
-            - [sacremoses](https://github.com/alvations/sacremoses): port of Moses
-            - Install with `pip install sacremoses`
-
-        Args:
-            - bypass_tokenizer: Allow users to preprocess and tokenize the sentences externally (default = False)
-              (bool). If True, we only apply BPE.
-
-        Returns:
-            List of tokens.
-        """
-        lang = "fr"
-        if lang and self.lang2id and lang not in self.lang2id:
-            logger.error(
-                "Supplied language code not found in lang2id mapping. Please check that your language is supported by"
-                " the loaded pretrained model."
-            )
-
-        if bypass_tokenizer:
-            text = text.split()
-        else:
-            text = self.preprocess_text(text)
-            text = self.moses_pipeline(text, lang=lang)
-            text = self.moses_tokenize(text, lang=lang)
-
-        split_tokens = []
-        for token in text:
-            if token:
-                split_tokens.extend(list(self.bpe(token).split(" ")))
-
-        return split_tokens
-
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer._convert_token_to_id
-    def _convert_token_to_id(self, token):
-        """Converts a token (str) in an id using the vocab."""
-        return self.encoder.get(token, self.encoder.get(self.unk_token))
-
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer._convert_id_to_token
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        return self.decoder.get(index, self.unk_token)
-
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.convert_tokens_to_string
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (string) in a single string."""
-        out_string = "".join(tokens).replace("</w>", " ").strip()
-        return out_string
-
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.build_inputs_with_special_tokens
-    def build_inputs_with_special_tokens(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. An XLM sequence has the following format:
-
-        - single sequence: `<s> X </s>`
-        - pair of sequences: `<s> A </s> B </s>`
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs to which the special tokens will be added.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
-
-        """
-        bos = [self.bos_token_id]
-        sep = [self.sep_token_id]
-
-        if token_ids_1 is None:
-            return bos + token_ids_0 + sep
-        return bos + token_ids_0 + sep + token_ids_1 + sep
-
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.get_special_tokens_mask
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieve 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` method.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-
-        if already_has_special_tokens:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
-            )
-
-        if token_ids_1 is not None:
-            return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
-        return [1] + ([0] * len(token_ids_0)) + [1]
-
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.create_token_type_ids_from_sequences
-    def create_token_type_ids_from_sequences(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLM sequence
-        pair mask has the following format:
-
-        ```
-        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
-        | first sequence    | second sequence |
-        ```
-
-        If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
-        """
-        sep = [self.sep_token_id]
-        cls = [self.cls_token_id]
-        if token_ids_1 is None:
-            return len(cls + token_ids_0 + sep) * [0]
-        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
-
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.save_vocabulary
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-        vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-        merge_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
-        )
-
-        with open(vocab_file, "w", encoding="utf-8") as f:
-            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
-
-        index = 0
-        with open(merge_file, "w", encoding="utf-8") as writer:
-            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
-                if index != token_index:
-                    logger.warning(
-                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
-                        " Please check that the tokenizer is not corrupted!"
-                    )
-                    index = token_index
-                writer.write(" ".join(bpe_tokens) + "\n")
-                index += 1
-
-        return vocab_file, merge_file
-
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.__getstate__
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        state["sm"] = None
-        return state
-
-    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.__setstate__
-    def __setstate__(self, d):
-        self.__dict__ = d
-
-        try:
-            import sacremoses
-        except ImportError:
-            raise ImportError(
-                "You need to install sacremoses to use XLMTokenizer. "
-                "See https://pypi.org/project/sacremoses/ for installation."
-            )
-
-        self.sm = sacremoses
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/jukebox/configuration_jukebox.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/jukebox/configuration_jukebox.py
deleted file mode 100644
index d4a8f0a0072cfcce8e73c9a1343d06d83a249c96..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/jukebox/configuration_jukebox.py
+++ /dev/null
@@ -1,614 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The OpenAI Team Authors and 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.
-""" Jukebox configuration"""
-
-import os
-from typing import List, Union
-
-from ...configuration_utils import PretrainedConfig
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "openai/jukebox-5b-lyrics": "https://huggingface.co/openai/jukebox-5b-lyrics/blob/main/config.json",
-    "openai/jukebox-1b-lyrics": "https://huggingface.co/openai/jukebox-1b-lyrics/blob/main/config.json",
-}
-
-_LARGE_ATTENTION = [
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "cross_attention",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "cross_attention",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "cross_attention",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "cross_attention",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "cross_attention",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "cross_attention",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "block_attn",
-    "transpose_block_attn",
-    "prev_block_attn",
-    "cross_attention",
-]
-_RawColumnPreviousRowAttention = ["block_attn", "transpose_block_attn", "prev_block_attn"]
-_FullDenseAttention = ["dense_attention"]
-_PrimePrimeDenseAttention = ["prime_attn", "prime_attn", "dense_attn"]
-
-
-def full_dense_attention(layer):
-    return _FullDenseAttention[0]
-
-
-def raw_column_previous_row_attention(layer):
-    return _RawColumnPreviousRowAttention[layer % 3]
-
-
-def large_separated_enc_dec_w_lyrics(layer):
-    return _LARGE_ATTENTION[layer % 79]
-
-
-def enc_dec_with_lyrics(layer):
-    if layer % 16 == 15:
-        return _PrimePrimeDenseAttention[layer % 3]
-    return _RawColumnPreviousRowAttention[layer % 3]
-
-
-ATTENTION_PATTERNS = {
-    "full_dense_attention": full_dense_attention,
-    "raw_column_previous_row_attention": raw_column_previous_row_attention,  # Alternate row, column and previous row attn
-    "large_separated_enc_dec_w_lyrics": large_separated_enc_dec_w_lyrics,  # Used by large separated_enc_dec model with lyrics
-    "enc_dec_with_lyrics": enc_dec_with_lyrics,  # Used by encoder_decoder model with lyrics
-}
-
-
-class JukeboxPriorConfig(PretrainedConfig):
-    """
-        This is the configuration class to store the configuration of a [`JukeboxPrior`]. It is used to instantiate a
-        `JukeboxPrior` according to the specified arguments, defining the model architecture. Instantiating a
-        configuration with the defaults will yield a similar configuration to that of the top level prior from the
-        [openai/jukebox-1b-lyrics](https://huggingface.co/openai/jukebox
-    -1b-lyrics) architecture.
-
-        Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-        documentation from [`PretrainedConfig`] for more information.
-
-
-
-    Args:
-        act_fn (`str`, *optional*, defaults to `"quick_gelu"`):
-            Activation function.
-        alignment_head (`int`, *optional*, defaults to 2):
-            Head that is responsible of the alignment between lyrics and music. Only used to compute the lyric to audio
-            alignment
-        alignment_layer (`int`, *optional*, defaults to 68):
-            Index of the layer that is responsible of the alignment between lyrics and music. Only used to compute the
-            lyric to audio alignment
-        attention_multiplier (`float`, *optional*, defaults to 0.25):
-            Multiplier coefficient used to define the hidden dimension of the attention layers. 0.25 means that
-            0.25*width of the model will be used.
-        attention_pattern (`str`, *optional*, defaults to `"enc_dec_with_lyrics"`):
-            Which attention pattern to use for the decoder/
-        attn_dropout (`int`, *optional*, defaults to 0):
-            Dropout probability for the post-attention layer dropout in the decoder.
-        attn_res_scale (`bool`, *optional*, defaults to `False`):
-            Whether or not to scale the residuals in the attention conditioner block.
-        blocks (`int`, *optional*, defaults to 64):
-            Number of blocks used in the `block_attn`. A sequence of length seq_len is factored as `[blocks, seq_len //
-            blocks]` in the `JukeboxAttention` layer.
-        conv_res_scale (`int`, *optional*):
-            Whether or not to scale the residuals in the conditioner block. Since the top level prior does not have a
-            conditioner, the default value is to None and should not be modified.
-        num_layers (`int`, *optional*, defaults to 72):
-            Number of layers of the transformer architecture.
-        emb_dropout (`int`, *optional*, defaults to 0):
-            Embedding dropout used in the lyric decoder.
-        encoder_config (`JukeboxPriorConfig`, *optional*) :
-            Configuration of the encoder which models the prior on the lyrics.
-        encoder_loss_fraction (`float`, *optional*, defaults to 0.4):
-            Multiplication factor used in front of the lyric encoder loss.
-        hidden_size (`int`, *optional*, defaults to 2048):
-            Hidden dimension of the attention layers.
-        init_scale (`float`, *optional*, defaults to 0.2):
-            Initialization scales for the prior modules.
-        is_encoder_decoder (`bool`, *optional*, defaults to `True`):
-            Whether or not the prior is an encoder-decoder model. In case it is not, and `nb_relevant_lyric_tokens` is
-            greater than 0, the `encoder` args should be specified for the lyric encoding.
-        mask (`bool`, *optional*, defaults to `False`):
-            Whether or not to mask the previous positions in the attention.
-        max_duration (`int`, *optional*, defaults to 600):
-            Maximum supported duration of the generated song in seconds.
-        max_nb_genres (`int`, *optional*, defaults to 1):
-            Maximum number of genres that can be used to condition the model.
-        merged_decoder (`bool`, *optional*, defaults to `True`):
-            Whether or not the decoder and the encoder inputs are merged. This is used for the separated
-            encoder-decoder architecture
-        metadata_conditioning (`bool`, *optional*, defaults to `True)`:
-            Whether or not to condition on the artist and genre metadata.
-        metadata_dims (`List[int]`, *optional*, defaults to `[604, 7898]`):
-            Number of genres and the number of artists that were used to train the embedding layers of the prior
-            models.
-        min_duration (`int`, *optional*, defaults to 0):
-            Minimum duration of the generated audio on which the model was trained.
-        mlp_multiplier (`float`, *optional*, defaults to 1.0):
-            Multiplier coefficient used to define the hidden dimension of the MLP layers. 0.25 means that 0.25*width of
-            the model will be used.
-        music_vocab_size (`int`, *optional*, defaults to 2048):
-            Number of different music tokens. Should be similar to the `JukeboxVQVAEConfig.nb_discrete_codes`.
-        n_ctx (`int`, *optional*, defaults to 6144):
-            Number of context tokens for each prior. The context tokens are the music tokens that are attended to when
-            generating music tokens.
-        n_heads (`int`, *optional*, defaults to 2):
-                Number of attention heads.
-        nb_relevant_lyric_tokens (`int`, *optional*, defaults to 384):
-            Number of lyric tokens that are used when sampling a single window of length `n_ctx`
-        res_conv_depth (`int`, *optional*, defaults to 3):
-            Depth of the `JukeboxDecoderConvBock` used to upsample the previously sampled audio in the
-            `JukeboxMusicTokenConditioner`.
-        res_conv_width (`int`, *optional*, defaults to 128):
-            Width of the `JukeboxDecoderConvBock` used to upsample the previously sampled audio in the
-            `JukeboxMusicTokenConditioner`.
-        res_convolution_multiplier (`int`, *optional*, defaults to 1):
-            Multiplier used to scale the `hidden_dim` of the `JukeboxResConv1DBlock`.
-        res_dilation_cycle (`int`, *optional*):
-            Dilation cycle used to define the `JukeboxMusicTokenConditioner`. Usually similar to the ones used in the
-            corresponding level of the VQVAE. The first prior does not use it as it is not conditioned on upper level
-            tokens.
-        res_dilation_growth_rate (`int`, *optional*, defaults to 1):
-            Dilation grow rate used between each convolutionnal block of the `JukeboxMusicTokenConditioner`
-        res_downs_t (`List[int]`, *optional*, defaults to `[3, 2, 2]`):
-            Downsampling rates used in the audio conditioning network
-        res_strides_t (`List[int]`, *optional*, defaults to `[2, 2, 2]`):
-            Striding used in the audio conditioning network
-        resid_dropout (`int`, *optional*, defaults to 0):
-            Residual dropout used in the attention pattern.
-        sampling_rate (`int`, *optional*, defaults to 44100):
-            Sampling rate used for training.
-        spread (`int`, *optional*):
-            Spread used in the `summary_spread_attention` pattern
-        timing_dims (`int`, *optional*, defaults to 64):
-            Dimension of the timing embedding.
-        zero_out (`bool`, *optional*, defaults to `False`):
-            Whether or not to zero out convolution weights when initializing.
-    """
-
-    model_type = "jukebox_prior"
-    attribute_map = {
-        "max_position_embeddings": "n_positions",
-        "num_attention_heads": "n_head",
-    }
-
-    def __init__(
-        self,
-        act_fn="quick_gelu",
-        level=0,
-        alignment_head=2,
-        alignment_layer=68,
-        attention_multiplier=0.25,
-        attention_pattern="enc_dec_with_lyrics",
-        attn_dropout=0,
-        attn_res_scale=False,
-        blocks=64,
-        conv_res_scale=None,
-        num_layers=72,
-        emb_dropout=0,
-        encoder_config=None,
-        encoder_loss_fraction=0.4,
-        hidden_size=2048,
-        init_scale=0.2,
-        is_encoder_decoder=True,
-        lyric_vocab_size=80,
-        mask=False,
-        max_duration=600,
-        max_nb_genres=1,
-        merged_decoder=True,
-        metadata_conditioning=True,
-        metadata_dims=[604, 7898],
-        min_duration=0,
-        mlp_multiplier=1.0,
-        music_vocab_size=2048,
-        n_ctx=6144,
-        n_heads=2,
-        nb_relevant_lyric_tokens=384,
-        res_conv_depth=3,
-        res_conv_width=128,
-        res_convolution_multiplier=1,
-        res_dilation_cycle=None,
-        res_dilation_growth_rate=1,
-        res_downs_t=[3, 2, 2],
-        res_strides_t=[2, 2, 2],
-        resid_dropout=0,
-        sampling_rate=44100,
-        spread=None,
-        timing_dims=64,
-        zero_out=False,
-        **kwargs,
-    ):
-        self.act_fn = act_fn
-        self.alignment_head = alignment_head
-        self.alignment_layer = alignment_layer
-        self.attention_multiplier = attention_multiplier
-        self.attention_pattern = attention_pattern
-        self.attn_dropout = attn_dropout
-        self.attn_res_scale = attn_res_scale
-        self.blocks = blocks
-        self.conv_res_scale = conv_res_scale
-        self.num_layers = num_layers
-        self.emb_dropout = emb_dropout
-        self.music_vocab_size = music_vocab_size
-        if encoder_config is not None:
-            self.encoder_config = JukeboxPriorConfig(**encoder_config)
-        else:
-            self.encoder_config = None
-        self.encoder_loss_fraction = encoder_loss_fraction
-        self.init_scale = init_scale
-        self.is_encoder_decoder = is_encoder_decoder
-        self.lyric_vocab_size = lyric_vocab_size
-        self.level = level
-        self.mask = mask
-        self.max_duration = max_duration
-        self.max_nb_genres = max_nb_genres
-        self.merged_decoder = merged_decoder
-        self.metadata_conditioning = metadata_conditioning
-        self.metadata_dims = metadata_dims
-        self.min_duration = min_duration
-        self.mlp_multiplier = mlp_multiplier
-        self.n_ctx = n_ctx
-        self.n_heads = n_heads
-        self.nb_relevant_lyric_tokens = nb_relevant_lyric_tokens
-        self.res_conv_depth = res_conv_depth
-        self.res_conv_width = res_conv_width
-        self.res_convolution_multiplier = res_convolution_multiplier
-        self.res_dilation_cycle = res_dilation_cycle
-        self.res_dilation_growth_rate = res_dilation_growth_rate
-        self.res_downs_t = res_downs_t
-        self.res_strides_t = res_strides_t
-        self.resid_dropout = resid_dropout
-        self.sampling_rate = sampling_rate
-        self.spread = spread
-        self.timing_dims = timing_dims
-        self.hidden_size = hidden_size
-        self.zero_out = zero_out
-
-    @classmethod
-    def from_pretrained(
-        cls, pretrained_model_name_or_path: Union[str, os.PathLike], level=0, **kwargs
-    ) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
-        # get the prior config dict if we are loading from JukeboxConfig
-        if config_dict.get("model_type") == "jukebox":
-            config_dict = config_dict[f"prior_{level}"]
-
-        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
-
-        return cls.from_dict(config_dict, **kwargs)
-
-
-class JukeboxVQVAEConfig(PretrainedConfig):
-    """
-    This is the configuration class to store the configuration of a [`JukeboxVQVAE`]. It is used to instantiate a
-    `JukeboxVQVAE` according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of the VQVAE from
-    [openai/jukebox-1b-lyrics](https://huggingface.co/openai/jukebox-1b-lyrics) architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        act_fn (`str`, *optional*, defaults to `"relu"`):
-            Activation function of the model.
-        nb_discrete_codes (`int`, *optional*, defaults to 2048):
-            Number of codes of the VQVAE.
-        commit (`float`, *optional*, defaults to 0.02):
-            Commit loss multiplier.
-        conv_input_shape (`int`, *optional*, defaults to 1):
-            Number of audio channels.
-        conv_res_scale (`bool`, *optional*, defaults to `False`):
-            Whether or not to scale the residuals of the `JukeboxResConv1DBlock`.
-        embed_dim (`int`, *optional*, defaults to 64):
-            Embedding dimension of the codebook vectors.
-        hop_fraction (`List[int]`, *optional*, defaults to `[0.125, 0.5, 0.5]`):
-            Fraction of non-intersecting window used when continuing the sampling process.
-        levels (`int`, *optional*, defaults to 3):
-            Number of hierarchical levels that used in the VQVAE.
-        lmu (`float`, *optional*, defaults to 0.99):
-            Used in the codebook update, exponential moving average coefficient. For more detail refer to Appendix A.1
-            of the original [VQVAE paper](https://arxiv.org/pdf/1711.00937v2.pdf)
-        multipliers (`List[int]`, *optional*, defaults to `[2, 1, 1]`):
-            Depth and width multipliers used for each level. Used on the `res_conv_width` and `res_conv_depth`
-        res_conv_depth (`int`, *optional*, defaults to 4):
-            Depth of the encoder and decoder block. If no `multipliers` are used, this is the same for each level.
-        res_conv_width (`int`, *optional*, defaults to 32):
-            Width of the encoder and decoder block. If no `multipliers` are used, this is the same for each level.
-        res_convolution_multiplier (`int`, *optional*, defaults to 1):
-            Scaling factor of the hidden dimension used in the `JukeboxResConv1DBlock`.
-        res_dilation_cycle (`int`, *optional*):
-            Dilation cycle value used in the `JukeboxResnet`. If an int is used, each new Conv1 block will have a depth
-            reduced by a power of `res_dilation_cycle`.
-        res_dilation_growth_rate (`int`, *optional*, defaults to 3):
-            Resnet dilation growth rate used in the VQVAE (dilation_growth_rate ** depth)
-        res_downs_t (`List[int]`, *optional*, defaults to `[3, 2, 2]`):
-            Downsampling rate for each level of the hierarchical VQ-VAE.
-        res_strides_t (`List[int]`, *optional*, defaults to `[2, 2, 2]`):
-            Stride used for each level of the hierarchical VQ-VAE.
-        sample_length (`int`, *optional*, defaults to 1058304):
-            Provides the max input shape of the VQVAE. Is used to compute the input shape of each level.
-        init_scale (`float`, *optional*, defaults to 0.2):
-            Initialization scale.
-        zero_out (`bool`, *optional*, defaults to `False`):
-            Whether or not to zero out convolution weights when initializing.
-    """
-
-    model_type = "jukebox_vqvae"
-
-    def __init__(
-        self,
-        act_fn="relu",
-        nb_discrete_codes=2048,
-        commit=0.02,
-        conv_input_shape=1,
-        conv_res_scale=False,
-        embed_dim=64,
-        hop_fraction=[0.125, 0.5, 0.5],
-        levels=3,
-        lmu=0.99,
-        multipliers=[2, 1, 1],
-        res_conv_depth=4,
-        res_conv_width=32,
-        res_convolution_multiplier=1,
-        res_dilation_cycle=None,
-        res_dilation_growth_rate=3,
-        res_downs_t=[3, 2, 2],
-        res_strides_t=[2, 2, 2],
-        sample_length=1058304,
-        init_scale=0.2,
-        zero_out=False,
-        **kwargs,
-    ):
-        self.hop_fraction = hop_fraction
-        self.conv_input_shape = conv_input_shape
-        self.sample_length = sample_length
-
-        # VQVAE parameters (all used)
-        self.levels = levels
-        self.embed_dim = embed_dim
-        self.nb_discrete_codes = nb_discrete_codes
-        self.res_conv_width = res_conv_width
-        self.res_conv_depth = res_conv_depth
-        self.res_convolution_multiplier = res_convolution_multiplier
-        self.res_dilation_growth_rate = res_dilation_growth_rate
-        self.res_dilation_cycle = res_dilation_cycle
-        self.multipliers = multipliers
-        self.res_downs_t = res_downs_t
-        self.res_strides_t = res_strides_t
-        self.lmu = lmu
-        self.commit = commit
-        self.conv_res_scale = conv_res_scale
-        self.act_fn = act_fn
-        self.init_scale = init_scale
-        self.zero_out = zero_out
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
-        # get the text config dict if we are loading from CLIPConfig
-        if config_dict.get("model_type") == "jukebox":
-            config_dict = config_dict["vqvae_config"]
-
-        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
-
-        return cls.from_dict(config_dict, **kwargs)
-
-
-class JukeboxConfig(PretrainedConfig):
-    """
-    This is the configuration class to store the configuration of a [`JukeboxModel`].
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information. Instantiating a configuration with the defaults will
-    yield a similar configuration to that of
-    [openai/jukebox-1b-lyrics](https://huggingface.co/openai/jukebox-1b-lyrics) architecture.
-
-
-    The downsampling and stride are used to determine downsampling of the input sequence. For example, downsampling =
-    (5,3), and strides = (2, 2) will downsample the audio by 2^5 = 32 to get the first level of codes, and 2**8 = 256
-    to get the second level codes. This is mostly true for training the top level prior and the upsamplers.
-
-    Args:
-        vqvae_config (`JukeboxVQVAEConfig`, *optional*):
-            Configuration for the `JukeboxVQVAE` model.
-        prior_config_list (`List[JukeboxPriorConfig]`, *optional*):
-            List of the configs for each of the `JukeboxPrior` of the model. The original architecture uses 3 priors.
-        nb_priors (`int`, *optional*, defaults to 3):
-            Number of prior models that will sequentially sample tokens. Each prior is conditional auto regressive
-            (decoder) model, apart from the top prior, which can include a lyric encoder. The available models were
-            trained using a top prior and 2 upsampler priors.
-        sampling_rate (`int`, *optional*, defaults to 44100):
-            Sampling rate of the raw audio.
-        timing_dims (`int`, *optional*, defaults to 64):
-            Dimensions of the JukeboxRangeEmbedding layer which is equivalent to traditional positional embedding
-            layer. The timing embedding layer converts the absolute and relative position in the currently sampled
-            audio to a tensor of length `timing_dims` that will be added to the music tokens.
-        min_duration (`int`, *optional*, defaults to 0):
-            Minimum duration of the audios to generate
-        max_duration (`float`, *optional*, defaults to 600.0):
-            Maximum duration of the audios to generate
-        max_nb_genres (`int`, *optional*, defaults to 5):
-            Maximum number of genres that can be used to condition a single sample.
-        metadata_conditioning (`bool`, *optional*, defaults to `True`):
-            Whether or not to use metadata conditioning, corresponding to the artist, the genre and the min/maximum
-            duration.
-
-    Example:
-
-    ```python
-    >>> from transformers import JukeboxModel, JukeboxConfig
-
-    >>> # Initializing a Jukebox configuration
-    >>> configuration = JukeboxConfig()
-
-    >>> # Initializing a model from the configuration
-    >>> model = JukeboxModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```
-    """
-
-    model_type = "jukebox"
-
-    def __init__(
-        self,
-        vqvae_config=None,
-        prior_config_list=None,
-        nb_priors=3,
-        sampling_rate=44100,
-        timing_dims=64,
-        min_duration=0,
-        max_duration=600.0,
-        max_nb_genres=5,
-        metadata_conditioning=True,
-        **kwargs,
-    ):
-        if vqvae_config is None:
-            vqvae_config = {}
-            logger.info("vqvae_config is None. initializing the JukeboxVQVAE with default values.")
-
-        self.vqvae_config = JukeboxVQVAEConfig(**vqvae_config)
-        if prior_config_list is not None:
-            self.prior_configs = [JukeboxPriorConfig(**prior_config) for prior_config in prior_config_list]
-        else:
-            self.prior_configs = []
-            for prior_idx in range(nb_priors):
-                prior_config = kwargs.pop(f"prior_{prior_idx}", None)
-                if prior_config is None:
-                    prior_config = {}
-                    logger.info(
-                        f"prior_{prior_idx}'s  config is None. Initializing the JukeboxPriorConfig list with default"
-                        " values."
-                    )
-                self.prior_configs.append(JukeboxPriorConfig(**prior_config))
-
-        self.hop_fraction = self.vqvae_config.hop_fraction
-
-        self.nb_priors = nb_priors
-
-        # Metadata conditioning
-        self.max_nb_genres = max_nb_genres
-        self.sampling_rate = sampling_rate
-        self.timing_dims = timing_dims
-        self.min_duration = min_duration
-        self.max_duration = max_duration
-        self.metadata_conditioning = metadata_conditioning
-
-        super().__init__(**kwargs)
-
-    @classmethod
-    def from_configs(cls, prior_configs: List[JukeboxPriorConfig], vqvae_config: JukeboxVQVAEConfig, **kwargs):
-        r"""
-        Instantiate a [`JukeboxConfig`] (or a derived class) from clip text model configuration and clip vision model
-        configuration.
-
-        Returns:
-            [`JukeboxConfig`]: An instance of a configuration object
-        """
-        prior_config_list = [config.to_dict() for config in prior_configs]
-        return cls(prior_config_list=prior_config_list, vqvae_config_dict=vqvae_config.to_dict(), **kwargs)
-
-    def to_dict(self):
-        # Override the default to_dict to apply to_dict to the list of prior configs.
-        result = super().to_dict()
-        result["prior_config_list"] = [config.to_dict() for config in result.pop("prior_configs")]
-        return result
diff --git a/spaces/ykilcher/apes/torch_utils/ops/upfirdn2d.h b/spaces/ykilcher/apes/torch_utils/ops/upfirdn2d.h
deleted file mode 100644
index c9e2032bcac9d2abde7a75eea4d812da348afadd..0000000000000000000000000000000000000000
--- a/spaces/ykilcher/apes/torch_utils/ops/upfirdn2d.h
+++ /dev/null
@@ -1,59 +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 <cuda_runtime.h>
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct upfirdn2d_kernel_params
-{
-    const void*     x;
-    const float*    f;
-    void*           y;
-
-    int2            up;
-    int2            down;
-    int2            pad0;
-    int             flip;
-    float           gain;
-
-    int4            inSize;         // [width, height, channel, batch]
-    int4            inStride;
-    int2            filterSize;     // [width, height]
-    int2            filterStride;
-    int4            outSize;        // [width, height, channel, batch]
-    int4            outStride;
-    int             sizeMinor;
-    int             sizeMajor;
-
-    int             loopMinor;
-    int             loopMajor;
-    int             loopX;
-    int             launchMinor;
-    int             launchMajor;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel specialization.
-
-struct upfirdn2d_kernel_spec
-{
-    void*   kernel;
-    int     tileOutW;
-    int     tileOutH;
-    int     loopMinor;
-    int     loopX;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
-
-//------------------------------------------------------------------------
diff --git a/spaces/yl12053/so-vits-4.1-Grass-Wonder/README.md b/spaces/yl12053/so-vits-4.1-Grass-Wonder/README.md
deleted file mode 100644
index a55a8e52f9f8569fb08a44c069d8df2c90ddd332..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Grass-Wonder/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: So Vits 4.1 Grass Wonder
-emoji: 🐨
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-python_version: 3.8.10
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/diffusion/data_loaders.py b/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/diffusion/data_loaders.py
deleted file mode 100644
index 87d7865a2e3624f6467c19999bf163b1d06d1b5a..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/diffusion/data_loaders.py
+++ /dev/null
@@ -1,288 +0,0 @@
-import os
-import random
-import re
-import numpy as np
-import librosa
-import torch
-import random
-from utils import repeat_expand_2d
-from tqdm import tqdm
-from torch.utils.data import Dataset
-
-def traverse_dir(
-        root_dir,
-        extensions,
-        amount=None,
-        str_include=None,
-        str_exclude=None,
-        is_pure=False,
-        is_sort=False,
-        is_ext=True):
-
-    file_list = []
-    cnt = 0
-    for root, _, files in os.walk(root_dir):
-        for file in files:
-            if any([file.endswith(f".{ext}") for ext in extensions]):
-                # path
-                mix_path = os.path.join(root, file)
-                pure_path = mix_path[len(root_dir)+1:] if is_pure else mix_path
-
-                # amount
-                if (amount is not None) and (cnt == amount):
-                    if is_sort:
-                        file_list.sort()
-                    return file_list
-                
-                # check string
-                if (str_include is not None) and (str_include not in pure_path):
-                    continue
-                if (str_exclude is not None) and (str_exclude in pure_path):
-                    continue
-                
-                if not is_ext:
-                    ext = pure_path.split('.')[-1]
-                    pure_path = pure_path[:-(len(ext)+1)]
-                file_list.append(pure_path)
-                cnt += 1
-    if is_sort:
-        file_list.sort()
-    return file_list
-
-
-def get_data_loaders(args, whole_audio=False):
-    data_train = AudioDataset(
-        filelists = args.data.training_files,
-        waveform_sec=args.data.duration,
-        hop_size=args.data.block_size,
-        sample_rate=args.data.sampling_rate,
-        load_all_data=args.train.cache_all_data,
-        whole_audio=whole_audio,
-        extensions=args.data.extensions,
-        n_spk=args.model.n_spk,
-        spk=args.spk,
-        device=args.train.cache_device,
-        fp16=args.train.cache_fp16,
-        unit_interpolate_mode = args.data.unit_interpolate_mode,
-        use_aug=True)
-    loader_train = torch.utils.data.DataLoader(
-        data_train ,
-        batch_size=args.train.batch_size if not whole_audio else 1,
-        shuffle=True,
-        num_workers=args.train.num_workers if args.train.cache_device=='cpu' else 0,
-        persistent_workers=(args.train.num_workers > 0) if args.train.cache_device=='cpu' else False,
-        pin_memory=True if args.train.cache_device=='cpu' else False
-    )
-    data_valid = AudioDataset(
-        filelists = args.data.validation_files,
-        waveform_sec=args.data.duration,
-        hop_size=args.data.block_size,
-        sample_rate=args.data.sampling_rate,
-        load_all_data=args.train.cache_all_data,
-        whole_audio=True,
-        spk=args.spk,
-        extensions=args.data.extensions,
-        unit_interpolate_mode = args.data.unit_interpolate_mode,
-        n_spk=args.model.n_spk)
-    loader_valid = torch.utils.data.DataLoader(
-        data_valid,
-        batch_size=1,
-        shuffle=False,
-        num_workers=0,
-        pin_memory=True
-    )
-    return loader_train, loader_valid 
-
-
-class AudioDataset(Dataset):
-    def __init__(
-        self,
-        filelists,
-        waveform_sec,
-        hop_size,
-        sample_rate,
-        spk,
-        load_all_data=True,
-        whole_audio=False,
-        extensions=['wav'],
-        n_spk=1,
-        device='cpu',
-        fp16=False,
-        use_aug=False,
-        unit_interpolate_mode = 'left'
-    ):
-        super().__init__()
-        
-        self.waveform_sec = waveform_sec
-        self.sample_rate = sample_rate
-        self.hop_size = hop_size
-        self.filelists = filelists
-        self.whole_audio = whole_audio
-        self.use_aug = use_aug
-        self.data_buffer={}
-        self.pitch_aug_dict = {}
-        self.unit_interpolate_mode = unit_interpolate_mode
-        # np.load(os.path.join(self.path_root, 'pitch_aug_dict.npy'), allow_pickle=True).item()
-        if load_all_data:
-            print('Load all the data filelists:', filelists)
-        else:
-            print('Load the f0, volume data filelists:', filelists)
-        with open(filelists,"r") as f:
-            self.paths = f.read().splitlines()
-        for name_ext in tqdm(self.paths, total=len(self.paths)):
-            name = os.path.splitext(name_ext)[0]
-            path_audio = name_ext
-            duration = librosa.get_duration(filename = path_audio, sr = self.sample_rate)
-            
-            path_f0 = name_ext + ".f0.npy"
-            f0,_ = np.load(path_f0,allow_pickle=True)
-            f0 = torch.from_numpy(np.array(f0,dtype=float)).float().unsqueeze(-1).to(device)
-                
-            path_volume = name_ext + ".vol.npy"
-            volume = np.load(path_volume)
-            volume = torch.from_numpy(volume).float().unsqueeze(-1).to(device)
-            
-            path_augvol = name_ext + ".aug_vol.npy"
-            aug_vol = np.load(path_augvol)
-            aug_vol = torch.from_numpy(aug_vol).float().unsqueeze(-1).to(device)
-                        
-            if n_spk is not None and n_spk > 1:
-                spk_name = name_ext.split("/")[-2]
-                spk_id = spk[spk_name] if spk_name in spk else 0
-                if spk_id < 0 or spk_id >= n_spk:
-                    raise ValueError(' [x] Muiti-speaker traing error : spk_id must be a positive integer from 0 to n_spk-1 ')
-            else:
-                spk_id = 0
-            spk_id = torch.LongTensor(np.array([spk_id])).to(device)
-
-            if load_all_data:
-                '''
-                audio, sr = librosa.load(path_audio, sr=self.sample_rate)
-                if len(audio.shape) > 1:
-                    audio = librosa.to_mono(audio)
-                audio = torch.from_numpy(audio).to(device)
-                '''
-                path_mel = name_ext + ".mel.npy"
-                mel = np.load(path_mel)
-                mel = torch.from_numpy(mel).to(device)
-                
-                path_augmel = name_ext + ".aug_mel.npy"
-                aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
-                aug_mel = np.array(aug_mel,dtype=float)
-                aug_mel = torch.from_numpy(aug_mel).to(device)
-                self.pitch_aug_dict[name_ext] = keyshift
-
-                path_units = name_ext + ".soft.pt"
-                units = torch.load(path_units).to(device)
-                units = units[0]  
-                units = repeat_expand_2d(units,f0.size(0),unit_interpolate_mode).transpose(0,1)
-                
-                if fp16:
-                    mel = mel.half()
-                    aug_mel = aug_mel.half()
-                    units = units.half()
-                    
-                self.data_buffer[name_ext] = {
-                        'duration': duration,
-                        'mel': mel,
-                        'aug_mel': aug_mel,
-                        'units': units,
-                        'f0': f0,
-                        'volume': volume,
-                        'aug_vol': aug_vol,
-                        'spk_id': spk_id
-                        }
-            else:
-                path_augmel = name_ext + ".aug_mel.npy"               
-                aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
-                self.pitch_aug_dict[name_ext] = keyshift
-                self.data_buffer[name_ext] = {
-                        'duration': duration,
-                        'f0': f0,
-                        'volume': volume,
-                        'aug_vol': aug_vol,
-                        'spk_id': spk_id
-                        }
-           
-
-    def __getitem__(self, file_idx):
-        name_ext = self.paths[file_idx]
-        data_buffer = self.data_buffer[name_ext]
-        # check duration. if too short, then skip
-        if data_buffer['duration'] < (self.waveform_sec + 0.1):
-            return self.__getitem__( (file_idx + 1) % len(self.paths))
-            
-        # get item
-        return self.get_data(name_ext, data_buffer)
-
-    def get_data(self, name_ext, data_buffer):
-        name = os.path.splitext(name_ext)[0]
-        frame_resolution = self.hop_size / self.sample_rate
-        duration = data_buffer['duration']
-        waveform_sec = duration if self.whole_audio else self.waveform_sec
-        
-        # load audio
-        idx_from = 0 if self.whole_audio else random.uniform(0, duration - waveform_sec - 0.1)
-        start_frame = int(idx_from / frame_resolution)
-        units_frame_len = int(waveform_sec / frame_resolution)
-        aug_flag = random.choice([True, False]) and self.use_aug
-        '''
-        audio = data_buffer.get('audio')
-        if audio is None:
-            path_audio = os.path.join(self.path_root, 'audio', name) + '.wav'
-            audio, sr = librosa.load(
-                    path_audio, 
-                    sr = self.sample_rate, 
-                    offset = start_frame * frame_resolution,
-                    duration = waveform_sec)
-            if len(audio.shape) > 1:
-                audio = librosa.to_mono(audio)
-            # clip audio into N seconds
-            audio = audio[ : audio.shape[-1] // self.hop_size * self.hop_size]       
-            audio = torch.from_numpy(audio).float()
-        else:
-            audio = audio[start_frame * self.hop_size : (start_frame + units_frame_len) * self.hop_size]
-        '''
-        # load mel
-        mel_key = 'aug_mel' if aug_flag else 'mel'
-        mel = data_buffer.get(mel_key)
-        if mel is None:
-            mel = name_ext + ".mel.npy"
-            mel = np.load(mel)
-            mel = mel[start_frame : start_frame + units_frame_len]
-            mel = torch.from_numpy(mel).float() 
-        else:
-            mel = mel[start_frame : start_frame + units_frame_len]
-            
-        # load f0
-        f0 = data_buffer.get('f0')
-        aug_shift = 0
-        if aug_flag:
-            aug_shift = self.pitch_aug_dict[name_ext]
-        f0_frames = 2 ** (aug_shift / 12) * f0[start_frame : start_frame + units_frame_len]
-        
-        # load units
-        units = data_buffer.get('units')
-        if units is None:
-            path_units = name_ext + ".soft.pt"
-            units = torch.load(path_units)
-            units = units[0]  
-            units = repeat_expand_2d(units,f0.size(0),self.unit_interpolate_mode).transpose(0,1)
-            
-        units = units[start_frame : start_frame + units_frame_len]
-
-        # load volume
-        vol_key = 'aug_vol' if aug_flag else 'volume'
-        volume = data_buffer.get(vol_key)
-        volume_frames = volume[start_frame : start_frame + units_frame_len]
-        
-        # load spk_id
-        spk_id = data_buffer.get('spk_id')
-        
-        # load shift
-        aug_shift = torch.from_numpy(np.array([[aug_shift]])).float()
-        
-        return dict(mel=mel, f0=f0_frames, volume=volume_frames, units=units, spk_id=spk_id, aug_shift=aug_shift, name=name, name_ext=name_ext)
-
-    def __len__(self):
-        return len(self.paths)
\ No newline at end of file
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/docs/_static/css/custom.css b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/docs/_static/css/custom.css
deleted file mode 100644
index 6c511764cf4c1d55a227619a98e5ba6578619ad7..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/docs/_static/css/custom.css
+++ /dev/null
@@ -1,30 +0,0 @@
-/*
- * Copyright (c) Facebook, Inc. and its affiliates.
- * some extra css to make markdown look similar between github/sphinx
- */
-
-/*
- * Below is for install.md:
- */
-.rst-content code {
-  white-space: pre;
-  border: 0px;
-}
-
-.rst-content th {
-  border: 1px solid #e1e4e5;
-}
-
-.rst-content th p {
-  /* otherwise will be default 24px for regular paragraph */
-  margin-bottom: 0px;
-}
-
-.rst-content .line-block {
-  /* otherwise will be 24px */
-  margin-bottom: 0px;
-}
-
-div.section > details {
-  padding-bottom: 1em;
-}
diff --git a/spaces/ysharma/ChatGLM-6b_Gradio_Streaming/README.md b/spaces/ysharma/ChatGLM-6b_Gradio_Streaming/README.md
deleted file mode 100644
index 4f7196bc8716fb75fb3fc46355a7d8470f919467..0000000000000000000000000000000000000000
--- a/spaces/ysharma/ChatGLM-6b_Gradio_Streaming/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ChatGLM6b_Gradio_Streaming
-emoji: 🏃
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.23.0
-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/yufiofficial/MusicGenQ/audiocraft/modules/transformer.py b/spaces/yufiofficial/MusicGenQ/audiocraft/modules/transformer.py
deleted file mode 100644
index e69cca829d774d0b8b36c0de9b7924373da81b43..0000000000000000000000000000000000000000
--- a/spaces/yufiofficial/MusicGenQ/audiocraft/modules/transformer.py
+++ /dev/null
@@ -1,747 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Transformer model, with streaming support, xformer attention support
-and easy causal attention with a potentially finite receptive field.
-
-See `StreamingTransformer` for more information.
-
-Unlike regular PyTorch Transformer, we make the hard choice that batches are first.
-"""
-
-import typing as tp
-
-from einops import rearrange
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from torch.utils.checkpoint import checkpoint as torch_checkpoint
-from xformers import ops
-
-from .rope import RotaryEmbedding
-from .streaming import StreamingModule
-
-_efficient_attention_backend: str = 'torch'
-
-
-def set_efficient_attention_backend(backend: str = 'torch'):
-    # Using torch by default, it seems a bit faster on older P100 GPUs (~20% faster).
-    global _efficient_attention_backend
-    assert _efficient_attention_backend in ['xformers', 'torch']
-    _efficient_attention_backend = backend
-
-
-def _get_attention_time_dimension() -> int:
-    if _efficient_attention_backend == 'torch':
-        return 2
-    else:
-        return 1
-
-
-def _is_profiled() -> bool:
-    # Return true if we are currently running with a xformers profiler activated.
-    try:
-        from xformers.profiler import profiler
-    except ImportError:
-        return False
-    return profiler._Profiler._CURRENT_PROFILER is not None
-
-
-def create_norm_fn(norm_type: str, dim: int, **kwargs) -> nn.Module:
-    """Create normalization module for transformer encoder layer.
-
-    Args:
-        norm_type (str): Normalization method.
-        dim (int): Dimension of the normalized layer.
-        **kwargs (dict): Additional parameters for normalization layer.
-    Returns:
-        nn.Module: Normalization module.
-    """
-    if norm_type == 'layer_norm':
-        return nn.LayerNorm(dim, eps=1e-5, **kwargs)
-    else:
-        raise ValueError(f"Unknown norm type: {norm_type}")
-
-
-def create_sin_embedding(positions: torch.Tensor, dim: int, max_period: float = 10000,
-                         dtype: torch.dtype = torch.float32) -> torch.Tensor:
-    """Create sinusoidal positional embedding, with shape `[B, T, C]`.
-
-    Args:
-        positions (torch.Tensor): LongTensor of positions.
-        dim (int): Dimension of the embedding.
-        max_period (float): Maximum period of the cosine/sine functions.
-        dtype (torch.dtype or str): dtype to use to generate the embedding.
-    Returns:
-        torch.Tensor: Sinusoidal positional embedding.
-    """
-    # We aim for BTC format
-    assert dim % 2 == 0
-    half_dim = dim // 2
-    positions = positions.to(dtype)
-    adim = torch.arange(half_dim, device=positions.device, dtype=dtype).view(1, 1, -1)
-    max_period_tensor = torch.full([], max_period, device=positions.device, dtype=dtype)  # avoid sync point
-    phase = positions / (max_period_tensor ** (adim / (half_dim - 1)))
-    return torch.cat([torch.cos(phase), torch.sin(phase)], dim=-1)
-
-
-def expand_repeated_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """torch.repeat_interleave(x, dim=2, repeats=n_rep) from xlformers"""
-    if n_rep == 1:
-        return x
-    if _efficient_attention_backend == 'torch':
-        bs, n_kv_heads, slen, head_dim = x.shape
-        return (
-            x[:, :, None, :, :]
-            .expand(bs, n_kv_heads, n_rep, slen, head_dim)
-            .reshape(bs, n_kv_heads * n_rep, slen, head_dim)
-        )
-    else:
-        bs, slen, n_kv_heads, head_dim = x.shape
-        return (
-            x[:, :, :, None, :]
-            .expand(bs, slen, n_kv_heads, n_rep, head_dim)
-            .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
-        )
-
-
-class LayerScale(nn.Module):
-    """Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).
-    This rescales diagonaly the residual outputs close to 0, with a learnt scale.
-
-    Args:
-        channels (int): Number of channels.
-        init (float): Initial scale.
-        channel_last (bool): If True, expect `[*, C]` shaped tensors, otherwise, `[*, C, T]`.
-        device (torch.device or None): Device on which to initialize the module.
-        dtype (torch.dtype or None): dtype to use to initialize the module.
-    """
-    def __init__(self, channels: int, init: float = 1e-4, channel_last: bool = True,
-                 device=None, dtype=None):
-        super().__init__()
-        self.channel_last = channel_last
-        self.scale = nn.Parameter(
-            torch.full((channels,), init,
-                       requires_grad=True, device=device, dtype=dtype))
-
-    def forward(self, x: torch.Tensor):
-        if self.channel_last:
-            return self.scale * x
-        else:
-            return self.scale[:, None] * x
-
-
-class StreamingMultiheadAttention(StreamingModule):
-    """Similar to `nn.MultiheadAttention` but with support for streaming, causal evaluation.
-
-    Args:
-        embed_dim (int): Dimension to project to.
-        num_heads (int): Number of heads.
-        dropout (float): Dropout level.
-        bias (bool): Use bias in projections.
-        causal (bool): Causal mask applied automatically.
-        past_context (int or None): Receptive field for the causal mask, infinite if None.
-        custom (bool): Use custom MHA implementation, for testing / benchmarking.
-        memory_efficient (bool): Use xformers based memory efficient attention.
-        attention_as_float32 (bool): Perform the attention as float32
-            (especially important with memory_efficient as autocast won't do this automatically).
-        rope (`RotaryEmbedding` or None): Rope embedding to use.
-        cross_attention: Should be true when used as a cross attention.
-            All keys and values must be available at once, streaming is only for the queries.
-            Cannot be used with `causal` or `rope` (as it wouldn't make sens to
-            intepret the time steps in the keys relative to those in the queries).
-        safe_streaming (bool): Bug fix, will go away with xformers update.
-        qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product.
-        kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads).
-            This will lead to faster decoding time on A100 or other GPUs with tensorcore.
-        device (torch.device or None): Sevice on which to initialize.
-        dtype (torch.dtype or None): dtype to use.
-    """
-    def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.0, bias: bool = True,
-                 causal: bool = False, past_context: tp.Optional[int] = None, custom: bool = False,
-                 memory_efficient: bool = False, attention_as_float32: bool = False,
-                 rope: tp.Optional[RotaryEmbedding] = None, cross_attention: bool = False,
-                 safe_streaming: bool = True, qk_layer_norm: bool = False, kv_repeat: int = 1,
-                 device=None, dtype=None):
-        super().__init__()
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        if past_context is not None:
-            assert causal
-
-        self.embed_dim = embed_dim
-        self.causal = causal
-        self.past_context = past_context
-        self.memory_efficient = memory_efficient
-        self.attention_as_float32 = attention_as_float32
-        self.rope = rope
-        self.cross_attention = cross_attention
-        self.safe_streaming = safe_streaming
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.kv_repeat = kv_repeat
-        if cross_attention:
-            assert not causal, "Causal cannot work with cross attention."
-            assert rope is None, "Rope cannot work with cross attention."
-
-        if memory_efficient:
-            _verify_xformers_memory_efficient_compat()
-
-        self.custom = _is_custom(custom, memory_efficient)
-        if self.custom:
-            out_dim = embed_dim
-            assert num_heads % kv_repeat == 0
-            assert not cross_attention or kv_repeat == 1
-            num_kv = num_heads // kv_repeat
-            kv_dim = (embed_dim // num_heads) * num_kv
-            out_dim += 2 * kv_dim
-            in_proj = nn.Linear(embed_dim, out_dim, bias=bias, **factory_kwargs)
-            # We try to follow the default PyTorch MHA convention, to easily compare results.
-            self.in_proj_weight = in_proj.weight
-            self.in_proj_bias = in_proj.bias
-            if bias:
-                self.in_proj_bias.data.zero_()  # Following Pytorch convention
-            self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias, **factory_kwargs)
-            if bias:
-                self.out_proj.bias.data.zero_()
-        else:
-            assert not qk_layer_norm
-            assert kv_repeat == 1
-            self.mha = nn.MultiheadAttention(
-                embed_dim, num_heads, dropout=dropout, bias=bias, batch_first=True,
-                **factory_kwargs)
-        self.qk_layer_norm = qk_layer_norm
-        if qk_layer_norm:
-            assert self.custom
-            assert kv_repeat == 1
-            ln_dim = embed_dim
-            self.q_layer_norm = nn.LayerNorm(ln_dim)
-            self.k_layer_norm = nn.LayerNorm(ln_dim)
-
-    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
-        if not self.custom:
-            # Support compat with regular MHA
-            keys = [n for n, _ in self.mha.named_parameters()]
-            for key in keys:
-                if prefix + key in state_dict:
-                    state_dict[prefix + "mha." + key] = state_dict.pop(prefix + key)
-        super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
-
-    def _get_mask(self, current_steps: int, device: torch.device, dtype: torch.dtype):
-        # Return a causal mask, accounting for potentially stored past keys/values
-        # We actually return a bias for the attention score, as this has the same
-        # convention both in the builtin MHA in Pytorch, and Xformers functions.
-        time_dim = _get_attention_time_dimension()
-        if self.memory_efficient:
-            from xformers.ops import LowerTriangularMask
-            if current_steps == 1:
-                # If we only have one step, then we do not need a mask.
-                return None
-            elif 'past_keys' in self._streaming_state:
-                raise RuntimeError('Not supported at the moment')
-            else:
-                # Then we can safely use a lower triangular mask
-                return LowerTriangularMask()
-        if self._streaming_state:
-            past_keys = self._streaming_state['past_keys']
-            past_steps = past_keys.shape[time_dim]
-        else:
-            past_steps = 0
-
-        queries_pos = torch.arange(
-            past_steps, current_steps + past_steps, device=device).view(-1, 1)
-        keys_pos = torch.arange(past_steps + current_steps, device=device).view(1, -1)
-        delta = queries_pos - keys_pos
-        valid = delta >= 0
-        if self.past_context is not None:
-            valid &= (delta <= self.past_context)
-        return torch.where(
-            valid,
-            torch.zeros([], device=device, dtype=dtype),
-            torch.full([], float('-inf'), device=device, dtype=dtype))
-
-    def _complete_kv(self, k, v):
-        time_dim = _get_attention_time_dimension()
-        if self.cross_attention:
-            # With cross attention we assume all keys and values
-            # are already available, and streaming is with respect
-            # to the queries only.
-            return k, v
-        # Complete the key/value pair using the streaming state.
-        if self._streaming_state:
-            pk = self._streaming_state['past_keys']
-            nk = torch.cat([pk, k], dim=time_dim)
-            if v is k:
-                nv = nk
-            else:
-                pv = self._streaming_state['past_values']
-                nv = torch.cat([pv, v], dim=time_dim)
-        else:
-            nk = k
-            nv = v
-
-        assert nk.shape[time_dim] == nv.shape[time_dim]
-        offset = 0
-        if self.past_context is not None:
-            offset = max(0, nk.shape[time_dim] - self.past_context)
-        if self._is_streaming:
-            self._streaming_state['past_keys'] = nk[:, offset:]
-            if v is not k:
-                self._streaming_state['past_values'] = nv[:, offset:]
-            if 'offset' in self._streaming_state:
-                self._streaming_state['offset'] += offset
-            else:
-                self._streaming_state['offset'] = torch.tensor(0)
-        return nk, nv
-
-    def _apply_rope(self, query: torch.Tensor, key: torch.Tensor):
-        # TODO: fix and verify layout.
-        assert _efficient_attention_backend == 'xformers', 'Rope not supported with torch attn.'
-        # Apply rope embeddings to query and key tensors.
-        assert self.rope is not None
-        if 'past_keys' in self._streaming_state:
-            past_keys_offset = self._streaming_state['past_keys'].shape[1]
-        else:
-            past_keys_offset = 0
-        if 'offset' in self._streaming_state:
-            past_context_offset = int(self._streaming_state['offset'].item())
-        else:
-            past_context_offset = 0
-        streaming_offset = past_context_offset + past_keys_offset
-        return self.rope.rotate_qk(query, key, start=streaming_offset)
-
-    def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor,
-                key_padding_mask=None, need_weights=False, attn_mask=None,
-                average_attn_weights=True, is_causal=False):
-        assert attn_mask is None
-        assert not is_causal, ("new param added in torch 2.0.1 not supported, "
-                               "use the causal args in the constructor.")
-
-        time_dim = _get_attention_time_dimension()
-        if time_dim == 2:
-            layout = "b h t d"
-        else:
-            layout = "b t h d"
-        dtype = query.dtype
-        if self._is_streaming:
-            assert self.causal or self.cross_attention, \
-                "Streaming only available for causal or cross attention"
-
-        if self.causal:
-            # At the moment we specialize only for the self-attention case.
-            assert query.shape[1] == key.shape[1], "Causal only for same length query / key / value"
-            assert value.shape[1] == key.shape[1], "Causal only for same length query / key / value"
-            attn_mask = self._get_mask(query.shape[1], query.device, query.dtype)
-
-        if self.custom:
-            # custom implementation
-            assert need_weights is False
-            assert key_padding_mask is None
-            if self.cross_attention:
-                # Different queries, keys, values, we have to spit manually the weights
-                # before applying the linear.
-                dim = self.in_proj_weight.shape[0] // 3
-                if self.in_proj_bias is None:
-                    bias_q, bias_k, bias_v = None, None, None
-                else:
-                    bias_q = self.in_proj_bias[:dim]
-                    bias_k = self.in_proj_bias[dim: 2 * dim]
-                    bias_v = self.in_proj_bias[2 * dim:]
-                q = nn.functional.linear(query, self.in_proj_weight[:dim], bias_q)
-                # todo: when streaming, we could actually save k, v and check the shape actually match.
-                k = nn.functional.linear(key, self.in_proj_weight[dim: 2 * dim], bias_k)
-                v = nn.functional.linear(value, self.in_proj_weight[2 * dim:], bias_v)
-                if self.qk_layer_norm is True:
-                    q = self.q_layer_norm(q)
-                    k = self.k_layer_norm(k)
-                q, k, v = [rearrange(x, f"b t (h d) -> {layout}", h=self.num_heads) for x in [q, k, v]]
-            else:
-                if not _is_profiled():
-                    # profiling breaks that propertysomehow.
-                    assert query is key, "specialized implementation"
-                    assert value is key, "specialized implementation"
-                projected = nn.functional.linear(query, self.in_proj_weight, self.in_proj_bias)
-                if self.kv_repeat == 1:
-                    if time_dim == 2:
-                        bound_layout = "b h p t d"
-                    else:
-                        bound_layout = "b t p h d"
-                    packed = rearrange(projected, f"b t (p h d) -> {bound_layout}", p=3, h=self.num_heads)
-                    q, k, v = ops.unbind(packed, dim=2)
-                else:
-                    embed_dim = self.embed_dim
-                    per_head_dim = (embed_dim // self.num_heads)
-                    kv_heads = self.num_heads // self.kv_repeat
-                    q = projected[:, :, :embed_dim]
-                    start = embed_dim
-                    end = start + per_head_dim * kv_heads
-                    k = projected[:, :, start: end]
-                    v = projected[:, :, end:]
-                    q = rearrange(q, f"b t (h d) -> {layout}", h=self.num_heads)
-                    k = rearrange(k, f"b t (h d) -> {layout}", h=kv_heads)
-                    v = rearrange(v, f"b t (h d) -> {layout}", h=kv_heads)
-
-                if self.qk_layer_norm is True:
-                    assert self.kv_repeat == 1
-                    q, k = [rearrange(x, f"{layout} -> b t (h d)") for x in [q, k]]
-                    q = self.q_layer_norm(q)
-                    k = self.k_layer_norm(k)
-                    q, k = [rearrange(x, f"b t (h d) -> {layout}", h=self.num_heads) for x in [q, k]]
-                if self.rope:
-                    q, k = self._apply_rope(q, k)
-                k, v = self._complete_kv(k, v)
-                if self.kv_repeat > 1:
-                    k = expand_repeated_kv(k, self.kv_repeat)
-                    v = expand_repeated_kv(v, self.kv_repeat)
-            if self.attention_as_float32:
-                q, k, v = [x.float() for x in [q, k, v]]
-            if self.memory_efficient:
-                p = self.dropout if self.training else 0
-                if _efficient_attention_backend == 'torch':
-                    x = torch.nn.functional.scaled_dot_product_attention(
-                        q, k, v, is_causal=attn_mask is not None, dropout_p=p)
-                else:
-                    x = ops.memory_efficient_attention(q, k, v, attn_mask, p=p)
-            else:
-                # We include the dot product as float32, for consistency
-                # with the other implementations that include that step
-                # as part of the attention. Note that when using `autocast`,
-                # the einsums would be done as bfloat16, but the softmax
-                # would be done as bfloat16, so `attention_as_float32` will
-                # extend a bit the range of operations done in float32,
-                # although this should make no difference.
-                q = q / q.shape[-1] ** 0.5
-                key_layout = layout.replace('t', 'k')
-                query_layout = layout
-                if self._is_streaming and self.safe_streaming and q.device.type == 'cuda':
-                    with torch.autocast(device_type=q.device.type, dtype=torch.float32):
-                        pre_w = torch.einsum(f"{query_layout},{key_layout}-> b h t k", q, k)
-                else:
-                    pre_w = torch.einsum(f"{query_layout},{key_layout}-> b h t k", q, k)
-                if attn_mask is not None:
-                    pre_w = pre_w + attn_mask
-                w = torch.softmax(pre_w, dim=-1)
-                w = F.dropout(w, self.dropout, training=self.training).to(v)
-                # Key and value have the same format.
-                x = torch.einsum(f"b h t k, {key_layout} -> {layout}", w, v)
-            x = x.to(dtype)
-            x = rearrange(x, f"{layout} -> b t (h d)", h=self.num_heads)
-            x = self.out_proj(x)
-        else:
-            key, value = self._complete_kv(key, value)
-            if self.attention_as_float32:
-                query, key, value = [x.float() for x in [query, key, value]]
-            x, _ = self.mha(
-                query, key, value, key_padding_mask,
-                need_weights, attn_mask, average_attn_weights)
-            x = x.to(dtype)
-
-        return x, None
-
-
-class StreamingTransformerLayer(nn.TransformerEncoderLayer):
-    """TransformerLayer with Streaming / Causal support.
-    This also integrates cross_attention, when passing `cross_attention=True`,
-    rather than having two separate classes like in PyTorch.
-
-    Args:
-        d_model (int): Dimension of the data.
-        num_heads (int): Number of heads.
-        dim_feedforward (int): Intermediate dimension of FF module.
-        dropout (float): Dropout both for MHA and FF.
-        bias_ff (bool): Use bias for FF.
-        bias_attn (bool): Use bias for MHA.
-        causal (bool): Causal mask applied automatically.
-        past_context (int or None): Receptive field for the causal mask, infinite if None.
-        custom (bool): Use custom MHA implementation, for testing / benchmarking.
-        memory_efficient (bool): Use xformers based memory efficient attention.
-        attention_as_float32 (bool): Perform the attention as float32
-            (especially important with memory_efficient as autocast won't do this automatically).
-        qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product in attention.
-        qk_layer_norm_cross (bool): Same for the cross attention.
-        cross_attention (bool): If True, expect to get secondary input for cross-attention.
-            Cross attention will use the default MHA, as it typically won't require
-            special treatment.
-        layer_scale (float or None): If not None, LayerScale will be used with
-            the given value as initial scale.
-        rope (`RotaryEmbedding` or None): Rope embedding to use.
-        attention_dropout (float or None): If not None, separate the value of the dimension dropout
-            in FFN and of the attention dropout.
-        kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads).
-            This will lead to faster decoding time on A100 or other GPUs with tensorcore.
-        device (torch.device or None): Device on which to initialize.
-        dtype (torch.dtype or None): dtype to use.
-        **kwargs: See `nn.TransformerEncoderLayer`.
-    """
-    def __init__(self, d_model: int, num_heads: int, dim_feedforward: int = 2048, dropout: float = 0.1,
-                 bias_ff: bool = True, bias_attn: bool = True, causal: bool = False,
-                 past_context: tp.Optional[int] = None, custom: bool = False,
-                 memory_efficient: bool = False, attention_as_float32: bool = False,
-                 qk_layer_norm: bool = False, qk_layer_norm_cross: bool = False,
-                 cross_attention: bool = False, layer_scale: tp.Optional[float] = None,
-                 rope: tp.Optional[RotaryEmbedding] = None, attention_dropout: tp.Optional[float] = None,
-                 kv_repeat: int = 1, norm: str = 'layer_norm', device=None, dtype=None, **kwargs):
-        super().__init__(d_model, num_heads, dim_feedforward, dropout,
-                         device=device, dtype=dtype, batch_first=True, **kwargs)
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        # Redefine self_attn to our streaming multi-head attention
-        attn_kwargs: tp.Dict[str, tp.Any] = {
-            'embed_dim': d_model,
-            'num_heads': num_heads,
-            'dropout': dropout if attention_dropout is None else attention_dropout,
-            'bias': bias_attn,
-            'custom': custom,
-            'memory_efficient': memory_efficient,
-            'attention_as_float32': attention_as_float32,
-        }
-        self.self_attn: StreamingMultiheadAttention = StreamingMultiheadAttention(
-            causal=causal, past_context=past_context, rope=rope, qk_layer_norm=qk_layer_norm,
-            kv_repeat=kv_repeat, **attn_kwargs, **factory_kwargs)  # type: ignore
-        # Redefine feedforward layers to expose bias parameter
-        self.linear1 = nn.Linear(d_model, dim_feedforward, bias=bias_ff, **factory_kwargs)
-        self.linear2 = nn.Linear(dim_feedforward, d_model, bias=bias_ff, **factory_kwargs)
-
-        self.layer_scale_1: nn.Module
-        self.layer_scale_2: nn.Module
-        if layer_scale is None:
-            self.layer_scale_1 = nn.Identity()
-            self.layer_scale_2 = nn.Identity()
-        else:
-            self.layer_scale_1 = LayerScale(d_model, layer_scale, **factory_kwargs)
-            self.layer_scale_2 = LayerScale(d_model, layer_scale, **factory_kwargs)
-
-        self.cross_attention: tp.Optional[nn.Module] = None
-        if cross_attention:
-            self.cross_attention = StreamingMultiheadAttention(
-                cross_attention=True, qk_layer_norm=qk_layer_norm_cross,
-                **attn_kwargs, **factory_kwargs)
-            # Norm and dropout
-            self.dropout_cross = nn.Dropout(dropout)
-            # eps value matching that used in PyTorch reference implementation.
-            self.norm_cross = nn.LayerNorm(d_model, eps=1e-5, **factory_kwargs)
-            self.layer_scale_cross: nn.Module
-            if layer_scale is None:
-                self.layer_scale_cross = nn.Identity()
-            else:
-                self.layer_scale_cross = LayerScale(d_model, layer_scale, **factory_kwargs)
-        self.norm1 = create_norm_fn(norm, d_model, **factory_kwargs)  # type: ignore
-        self.norm2 = create_norm_fn(norm, d_model, **factory_kwargs)  # type: ignore
-
-    def _cross_attention_block(self, src: torch.Tensor,
-                               cross_attention_src: torch.Tensor) -> torch.Tensor:
-        assert self.cross_attention is not None
-        # queries are from src, keys and values from cross_attention_src.
-        x = self.cross_attention(
-            src, cross_attention_src, cross_attention_src, need_weights=False)[0]
-        return self.dropout_cross(x)  # type: ignore
-
-    def forward(self, src: torch.Tensor, src_mask: tp.Optional[torch.Tensor] = None,  # type: ignore
-                src_key_padding_mask: tp.Optional[torch.Tensor] = None,
-                cross_attention_src: tp.Optional[torch.Tensor] = None):
-        if self.cross_attention is None:
-            assert cross_attention_src is None
-        else:
-            assert cross_attention_src is not None
-        x = src
-        if self.norm_first:
-            x = x + self.layer_scale_1(
-                self._sa_block(self.norm1(x), src_mask, src_key_padding_mask))
-            if cross_attention_src is not None:
-                x = x + self.layer_scale_cross(
-                    self._cross_attention_block(
-                        self.norm_cross(x), cross_attention_src))
-            x = x + self.layer_scale_2(self._ff_block(self.norm2(x)))
-        else:
-            x = self.norm1(x + self.layer_scale_1(
-                self._sa_block(x, src_mask, src_key_padding_mask)))
-            if cross_attention_src is not None:
-                x = self.norm_cross(
-                    x + self.layer_scale_cross(
-                        self._cross_attention_block(src, cross_attention_src)))
-            x = self.norm2(x + self.layer_scale_2(self._ff_block(x)))
-        return x
-
-
-class StreamingTransformer(StreamingModule):
-    """Transformer with Streaming / Causal support.
-
-    Args:
-        d_model (int): Dimension of the data.
-        num_heads (int): Number of heads.
-        dim_feedforward (int): Intermediate dimension of FF module.
-        dropout (float): Dropout both for MHA and FF.
-        bias_ff (bool): Use bias for FF.
-        bias_attn (bool): Use bias for MHA.
-        causal (bool): Causal mask applied automatically.
-        past_context (int or None): Receptive field for the causal mask, infinite if None.
-        custom (bool): Use custom MHA implementation, for testing / benchmarking.
-        memory_efficient (bool): Use xformers based memory efficient attention.
-        attention_as_float32 (bool): Perform the attention as float32
-            (especially important with memory_efficient as autocast won't do this automatically).
-        cross_attention (bool): If True, expect to get secondary input for cross-attention.
-        layer_scale (float or None): If not None, LayerScale will be used
-            with the given value as initial scale.
-        positional_embedding (str): Positional embedding strategy (sin, rope, or sin_rope).
-        max_period (float): Maximum period of the time embedding.
-        positional_scale (float): Scale of positional embedding, set to 0 to deactivate.
-        xpos (bool): Apply xpos exponential decay to positional embedding (rope only).
-        lr (float or None): learning rate override through the `make_optim_group` API.
-        weight_decay (float or None): Weight_decay override through the `make_optim_group` API.
-        layer_class: (subclass of `StreamingTransformerLayer): class to use
-            to initialize the layers, allowing further customization outside of Audiocraft.
-        checkpointing (str): Checkpointing strategy to reduce memory usage.
-            No checkpointing if set to 'none'. Per layer checkpointing using PyTorch
-            if set to 'torch' (entire layer checkpointed, i.e. linears are evaluated twice,
-            minimal memory usage, but maximal runtime). Finally, `xformers_default` provide
-            a policy for opting-out some operations of the checkpointing like
-            linear layers and attention, providing a middle ground between speed and memory.
-        device (torch.device or None): Device on which to initialize.
-        dtype (torch.dtype or None): dtype to use.
-        **kwargs: See `nn.TransformerEncoderLayer`.
-    """
-    def __init__(self, d_model: int, num_heads: int, num_layers: int, dim_feedforward: int = 2048,
-                 dropout: float = 0.1, bias_ff: bool = True, bias_attn: bool = True,
-                 causal: bool = False, past_context: tp.Optional[int] = None,
-                 custom: bool = False, memory_efficient: bool = False, attention_as_float32: bool = False,
-                 cross_attention: bool = False, layer_scale: tp.Optional[float] = None,
-                 positional_embedding: str = 'sin', max_period: float = 10_000, positional_scale: float = 1.,
-                 xpos: bool = False, lr: tp.Optional[float] = None, weight_decay: tp.Optional[float] = None,
-                 layer_class: tp.Type[StreamingTransformerLayer] = StreamingTransformerLayer,
-                 checkpointing: str = 'none', device=None, dtype=None, **kwargs):
-        super().__init__()
-        assert d_model % num_heads == 0
-
-        self.positional_embedding = positional_embedding
-        self.max_period = max_period
-        self.positional_scale = positional_scale
-        self.weight_decay = weight_decay
-        self.lr = lr
-
-        assert positional_embedding in ['sin', 'rope', 'sin_rope']
-        self.rope: tp.Optional[RotaryEmbedding] = None
-        if self.positional_embedding in ['rope', 'sin_rope']:
-            assert _is_custom(custom, memory_efficient)
-            self.rope = RotaryEmbedding(d_model // num_heads, max_period=max_period,
-                                        xpos=xpos, scale=positional_scale, device=device)
-
-        self.checkpointing = checkpointing
-
-        assert checkpointing in ['none', 'torch', 'xformers_default', 'xformers_mm']
-        if self.checkpointing.startswith('xformers'):
-            _verify_xformers_internal_compat()
-
-        self.layers = nn.ModuleList()
-        for idx in range(num_layers):
-            self.layers.append(
-                layer_class(
-                    d_model=d_model, num_heads=num_heads, dim_feedforward=dim_feedforward,
-                    dropout=dropout, bias_ff=bias_ff, bias_attn=bias_attn,
-                    causal=causal, past_context=past_context, custom=custom,
-                    memory_efficient=memory_efficient, attention_as_float32=attention_as_float32,
-                    cross_attention=cross_attention, layer_scale=layer_scale, rope=self.rope,
-                    device=device, dtype=dtype, **kwargs))
-
-        if self.checkpointing != 'none':
-            for layer in self.layers:
-                # see audiocraft/optim/fsdp.py, magic signal to indicate this requires fixing the
-                # backward hook inside of FSDP...
-                layer._magma_checkpointed = True  # type: ignore
-                assert layer.layer_drop == 0., "Need further checking"  # type: ignore
-
-    def _apply_layer(self, layer, *args, **kwargs):
-        method = self.checkpointing
-        if method == 'none':
-            return layer(*args, **kwargs)
-        elif method == 'torch':
-            return torch_checkpoint(layer, *args, use_reentrant=False, **kwargs)
-        elif method.startswith('xformers'):
-            from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy
-            if method == 'xformers_default':
-                # those operations will be saved, and not recomputed.
-                # According to Francisco we can get smarter policies but this is a good start.
-                allow_list = [
-                    "xformers.efficient_attention_forward_cutlass.default",
-                    "xformers_flash.flash_fwd.default",
-                    "aten.addmm.default",
-                    "aten.mm.default",
-                ]
-            elif method == 'xformers_mm':
-                # those operations will be saved, and not recomputed.
-                # According to Francisco we can get smarter policies but this is a good start.
-                allow_list = [
-                    "aten.addmm.default",
-                    "aten.mm.default",
-                ]
-            else:
-                raise ValueError(f"xformers checkpointing xformers policy {method} is not known.")
-            policy_fn = _get_default_policy(allow_list)
-            return checkpoint(layer, *args, policy_fn=policy_fn, **kwargs)
-        else:
-            raise ValueError(f"Checkpointing method {method} is unknown.")
-
-    def forward(self, x: torch.Tensor, *args, **kwargs):
-        B, T, C = x.shape
-
-        if 'offsets' in self._streaming_state:
-            offsets = self._streaming_state['offsets']
-        else:
-            offsets = torch.zeros(B, dtype=torch.long, device=x.device)
-
-        if self.positional_embedding in ['sin', 'sin_rope']:
-            positions = torch.arange(T, device=x.device).view(1, -1, 1)
-            positions = positions + offsets.view(-1, 1, 1)
-            pos_emb = create_sin_embedding(positions, C, max_period=self.max_period, dtype=x.dtype)
-            x = x + self.positional_scale * pos_emb
-
-        for layer in self.layers:
-            x = self._apply_layer(layer, x, *args, **kwargs)
-
-        if self._is_streaming:
-            self._streaming_state['offsets'] = offsets + T
-
-        return x
-
-    def make_optim_group(self):
-        group = {"params": list(self.parameters())}
-        if self.lr is not None:
-            group["lr"] = self.lr
-        if self.weight_decay is not None:
-            group["weight_decay"] = self.weight_decay
-        return group
-
-
-# special attention attention related function
-
-def _verify_xformers_memory_efficient_compat():
-    try:
-        from xformers.ops import memory_efficient_attention, LowerTriangularMask  # noqa
-    except ImportError:
-        raise ImportError(
-            "xformers is not installed. Please install it and try again.\n"
-            "To install on AWS and Azure, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n"
-            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n"
-            "To install on FAIR Cluster, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n"
-            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n")
-
-
-def _verify_xformers_internal_compat():
-    try:
-        from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy  # noqa
-    except ImportError:
-        raise ImportError(
-            "Francisco's fairinternal xformers is not installed. Please install it and try again.\n"
-            "To install on AWS and Azure, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n"
-            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n"
-            "To install on FAIR Cluster, run \n"
-            "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n"
-            "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n")
-
-
-def _is_custom(custom: bool, memory_efficient: bool):
-    return custom or memory_efficient
diff --git a/spaces/zhaoys/wfms-kuiwenc/src/components/ui/icons.tsx b/spaces/zhaoys/wfms-kuiwenc/src/components/ui/icons.tsx
deleted file mode 100644
index 742b489b50437c5b64c86082f2ebc712eeb6a2b0..0000000000000000000000000000000000000000
--- a/spaces/zhaoys/wfms-kuiwenc/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-${id}-2`}
-          x1="11.7555"
-          y1="4.8"
-          x2="11.7376"
-          y2="9.50002"
-          gradientUnits="userSpaceOnUse"
-        >
-          <stop stopColor={inverted ? 'white' : 'black'} />
-          <stop
-            offset={1}
-            stopColor={inverted ? 'white' : 'black'}
-            stopOpacity={0}
-          />
-        </linearGradient>
-      </defs>
-      <path
-        d="M1 16L2.58314 11.2506C1.83084 9.74642 1.63835 8.02363 2.04013 6.39052C2.4419 4.75741 3.41171 3.32057 4.776 2.33712C6.1403 1.35367 7.81003 0.887808 9.4864 1.02289C11.1628 1.15798 12.7364 1.8852 13.9256 3.07442C15.1148 4.26363 15.842 5.83723 15.9771 7.5136C16.1122 9.18997 15.6463 10.8597 14.6629 12.224C13.6794 13.5883 12.2426 14.5581 10.6095 14.9599C8.97637 15.3616 7.25358 15.1692 5.74942 14.4169L1 16Z"
-        fill={inverted ? 'black' : 'white'}
-        stroke={inverted ? 'black' : 'white'}
-        strokeWidth={2}
-        strokeLinecap="round"
-        strokeLinejoin="round"
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-      <mask
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-        style={{ maskType: 'alpha' }}
-        maskUnits="userSpaceOnUse"
-        x={1}
-        y={0}
-        width={16}
-        height={16}
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-        <circle cx={9} cy={8} r={8} fill={inverted ? 'black' : 'white'} />
-      </mask>
-      <g mask="url(#mask0_91_2047)">
-        <circle cx={9} cy={8} r={8} fill={inverted ? 'black' : 'white'} />
-        <path
-          d="M14.2896 14.0018L7.146 4.8H5.80005V11.1973H6.87681V6.16743L13.4444 14.6529C13.7407 14.4545 14.0231 14.2369 14.2896 14.0018Z"
-          fill={`url(#gradient-${id}-1)`}
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-          x="11.2222"
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-      </g>
-    </svg>
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-}
-
-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
-}