


'''simple docstring'''






import datasets


__A   :    Tuple											= "\\n@InProceedings{conneau2018xnli,\n  author = \"Conneau, Alexis\n                 and Rinott, Ruty\n                 and Lample, Guillaume\n                 and Williams, Adina\n                 and Bowman, Samuel R.\n                 and Schwenk, Holger\n                 and Stoyanov, Veselin\",\n  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",\n  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods\n               in Natural Language Processing\",\n  year = \"2018\",\n  publisher = \"Association for Computational Linguistics\",\n  location = \"Brussels, Belgium\",\n}\n"

__A   :    Union[str, Any]											= "\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n"

__A   :    int											= "\nComputes XNLI score which is just simple accuracy.\nArgs:\n    predictions: Predicted labels.\n    references: Ground truth labels.\nReturns:\n    'accuracy': accuracy\nExamples:\n\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> xnli_metric = datasets.load_metric(\"xnli\")\n    >>> results = xnli_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n"







def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  List[Any]   ):
  '''simple docstring'''





  return (preds == labels).mean()






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,_KWARGS_DESCRIPTION)
class   __snake_case			(  datasets.Metric):







    """simple docstring"""



    def __lowercase  (  self       :    List[Any]						)  ->    List[str]:
      return datasets.MetricInfo(
          description=_DESCRIPTION       ,		citation=_CITATION       ,		inputs_description=_KWARGS_DESCRIPTION       ,		features=datasets.Features(
              {
                  """predictions""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32"""						),
                  """references""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32"""						),
              }						)       ,		codebase_urls=[]       ,		reference_urls=[]       ,		format="""numpy"""       ,		)





    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    Union[str, Any]       ,		lowerCamelCase       :    Any						)  ->    int:
      return {"accuracy": simple_accuracy(lowerCamelCase       ,		lowerCamelCase						)}







'''simple docstring'''






import requests
from bsa import BeautifulSoup







def 							UpperCamelCase_     (       A__   :  str = "AAPL"   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
  lowerCAmelCase_     : Optional[Any]     =  BeautifulSoup(requests.get(A__   ).text				,       """html.parser"""   )
  lowerCAmelCase_     : Union[str, Any]     =  """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}''')



