'''simple docstring'''

import requests
from bsa import BeautifulSoup



def snake_case_      (					__snake_case  :							str = "AAPL")			->					str:
   lowerCAmelCase_			=      F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
   lowerCAmelCase_			=      BeautifulSoup(requests.get(__snake_case).text	,   '''html.parser''')
   lowerCAmelCase_			=      '''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}''')






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A_     :						Optional[Any]    							={
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A_     :						Dict    							=['''SqueezeBertTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A_     :						Optional[int]    							=[
     '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''SqueezeBertForMaskedLM''',
     '''SqueezeBertForMultipleChoice''',
     '''SqueezeBertForQuestionAnswering''',
     '''SqueezeBertForSequenceClassification''',
     '''SqueezeBertForTokenClassification''',
     '''SqueezeBertModel''',
     '''SqueezeBertModule''',
     '''SqueezeBertPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_squeezebert import (
     SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SqueezeBertConfig,
     SqueezeBertOnnxConfig,
 )
 from .tokenization_squeezebert import SqueezeBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_squeezebert import (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
      SqueezeBertModule,
      SqueezeBertPreTrainedModel,
  )

else:
 import sys

 A_     :						List[str]    							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





