


def 				__lowerCamelCase						( UpperCAmelCase_						:		List[str]						):




      """simple docstring"""





      return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''')						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      a						:int 							=       credit_card_number
      a						:Optional[int] 							=       0
      a						:Any 							=       len(__snake_case						) - 2
      for i in range(__snake_case ,  -1 ,  -2						):
            #  double the value of every second digit
            a						:Union[str, Any] 							=       int(cc_number[i]						)
            digit *= 2
            # If doubling of a number results in a two digit number
            # i.e greater than 9(e.g., 6 × 2 = 12),
            # then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
            # to get a single digit number.
            if digit > 9:
                  digit %= 10
                  digit += 1
            a						:Dict 							=       cc_number[:i] + str(__snake_case						) + cc_number[i + 1 :]
            total += digit

      # Sum up the remaining digits
      for i in range(len(__snake_case						) - 1 ,  -1 ,  -2						):
            total += int(cc_number[i]						)

      return total % 10 == 0
def 				__lowerCamelCase						( UpperCAmelCase_						:		Tuple						):




      """simple docstring"""





      a						:List[Any] 							=       F'''{credit_card_number} is an invalid credit card number because'''
      if not credit_card_number.isdigit():
            print(F'''{error_message} it has nonnumerical characters.'''						)
            return False

      if not 13 <= len(__snake_case						) <= 16:
            print(F'''{error_message} of its length.'''						)
            return False

      if not validate_initial_digits(__snake_case						):
            print(F'''{error_message} of its first two digits.'''						)
            return False

      if not luhn_validation(__snake_case						):
            print(F'''{error_message} it fails the Luhn check.'''						)
            return False

      print(F'''{credit_card_number} is a valid credit card number.'''						)
      return True


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     validate_credit_card_number('''4111111111111111''')
     validate_credit_card_number('''32323''')







"""simple docstring"""





import datasets


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

_a														=		"""\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

_a														=		"""
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		str:


					"""simple docstring"""





					return (preds == labels).mean()






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



		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''simple docstring'''
							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 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       Dict:






							'''simple docstring'''
							return {"accuracy": simple_accuracy(__a				,		__a)}






