code: 
  
  
  
  
  
  from sklearn.metrics import accuracy_score
  
  import datasets
  
  
  _lowercase		:     Dict 					="""
  Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
  Accuracy = (TP + TN) / (TP + TN + FP + FN)
   Where:
  TP: True positive
  TN: True negative
  FP: False positive
  FN: False negative
  """
  
  
  _lowercase		:     List[str] 					="""
  Args:
      predictions (`list` of `int`): Predicted labels.
      references (`list` of `int`): Ground truth labels.
      normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
      sample_weight (`list` of `float`): Sample weights Defaults to None.
  
  Returns:
      accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.
  
  Examples:
  
      Example 1-A simple example
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
          >>> print(results)
          {'accuracy': 0.5}
  
      Example 2-The same as Example 1, except with `normalize` set to `False`.
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
          >>> print(results)
          {'accuracy': 3.0}
  
      Example 3-The same as Example 1, except with `sample_weight` set.
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
          >>> print(results)
          {'accuracy': 0.8778625954198473}
  """
  
  
  _lowercase		:     str 					="""
  @article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
  }
  """
  
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,    _KWARGS_DESCRIPTION       )
  class 	UpperCamelCase_     (     datasets.Metric       ):
  
  
  
  					def      __a    (		self    :    Any	):
  							return datasets.MetricInfo(
  							    description=_DESCRIPTION					,			citation=_CITATION					,			inputs_description=_KWARGS_DESCRIPTION					,			features=datasets.Features(
  							        {
  							            'predictions': datasets.Sequence(datasets.Value('int32'	)	),
  							            'references': datasets.Sequence(datasets.Value('int32'	)	),
  							        }
  							        if self.config_name == 'multilabel'
  							        else {
  							            'predictions': datasets.Value('int32'	),
  							            'references': datasets.Value('int32'	),
  							        }	)					,			reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']					,			)
  
  
  
  
  
  					def      __a    (		self    :    Dict					,			lowerCamelCase    :    Optional[int]					,			lowerCamelCase    :    Optional[int]					,			lowerCamelCase    :    Tuple=True					,			lowerCamelCase    :    List[str]=None	):
  							return {
  							    "accuracy": float(
  							        accuracy_score(lowerCamelCase					,			lowerCamelCase					,			normalize=lowerCamelCase					,			sample_weight=lowerCamelCase	)	)
  							}
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		lowerCamelCase_  :    int
  		lowerCamelCase_  :    list[str]   	=			list(lowerCAmelCase__	)
  		lowerCamelCase_	,			lowerCamelCase_  :    List[str]   	=			_vector[:-1], int(_vector[-1]	)
  		lowerCamelCase_  :    list[int]   	=			[int(lowerCAmelCase__	) for digit in __vector]
  
  		vector.reverse()
  		for i, digit in enumerate(lowerCAmelCase__	):
  				if i & 1 == 0:
  						lowerCamelCase_  :    int   	=			digit * 2
  						if doubled > 9:
  								doubled -= 9
  						check_digit += doubled
  				else:
  						check_digit += digit
  
  		return check_digit % 10 == 0
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  		assert is_luhn("""79927398713""")
  		assert not is_luhn("""79927398714""")
  