






"""simple docstring"""



import os
import sys

_UpperCamelCase		:  Union[str, Any]						=		os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_UpperCamelCase		:  Union[str, Any]						=		[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]
@add_start_docstrings(AutoConfig.__doc__    )
def 						_SCREAMING_SNAKE_CASE (    *__snake_case			:  Optional[Any]		,					**__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  return AutoConfig.from_pretrained(*__snake_case		,					**__snake_case    )
@add_start_docstrings(AutoTokenizer.__doc__    )
def 						_SCREAMING_SNAKE_CASE (    *__snake_case			:  Optional[int]		,					**__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  return AutoTokenizer.from_pretrained(*__snake_case		,					**__snake_case    )
@add_start_docstrings(AutoModel.__doc__    )
def 						_SCREAMING_SNAKE_CASE (    *__snake_case			:  Optional[int]		,					**__snake_case			:  str    ):





  '''simple docstring'''





  return AutoModel.from_pretrained(*__snake_case		,					**__snake_case    )
@add_start_docstrings(AutoModelForCausalLM.__doc__    )
def 						_SCREAMING_SNAKE_CASE (    *__snake_case			:  int		,					**__snake_case			:  Optional[int]    ):





  '''simple docstring'''





  return AutoModelForCausalLM.from_pretrained(*__snake_case		,					**__snake_case    )
@add_start_docstrings(AutoModelForMaskedLM.__doc__    )
def 						_SCREAMING_SNAKE_CASE (    *__snake_case			:  Union[str, Any]		,					**__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  return AutoModelForMaskedLM.from_pretrained(*__snake_case		,					**__snake_case    )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__    )
def 						_SCREAMING_SNAKE_CASE (    *__snake_case			:  Tuple		,					**__snake_case			:  Tuple    ):





  '''simple docstring'''





  return AutoModelForSequenceClassification.from_pretrained(*__snake_case		,					**__snake_case    )



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__    )
def 						_SCREAMING_SNAKE_CASE (    *__snake_case			:  List[Any]		,					**__snake_case			:  Optional[int]    ):





  '''simple docstring'''





  return AutoModelForQuestionAnswering.from_pretrained(*__snake_case		,					**__snake_case    )










"""simple docstring"""



import requests
from bsa import BeautifulSoup
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str = "AAPL"    ):





  '''simple docstring'''





  lowercase							    =       f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
  lowercase							    =       BeautifulSoup(requests.get(__snake_case    ).text		,					'html.parser'    )
  lowercase							    =       'My(6px) Pos(r) smartphone_Mt(6px)'
  return soup.find('div'		,					class_=class_    ).find('span'    ).text


if __name__ == "__main__":
    for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
        print(F'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')


