


from sklearn.metrics import accuracy_score

import datasets


__UpperCAmelCase			:			str			      =					"\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


__UpperCAmelCase			:			Union[str, Any]			      =					"\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    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.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> 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])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


__UpperCAmelCase			:			List[Any]			      =					"\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,  _KWARGS_DESCRIPTION)
class  UpperCAmelCase_						(				datasets.Metric):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




              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 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=True			,       __SCREAMING_SNAKE_CASE=None    ):




              """simple docstring"""




              return {
                  "accuracy": float(
                      accuracy_score(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       normalize=__SCREAMING_SNAKE_CASE			,       sample_weight=__SCREAMING_SNAKE_CASE    )    )
              }





def   a						(				SCREAMING_SNAKE_CASE_   :		int							):






       """simple docstring"""





       UpperCamelCase     :						Tuple 						=		[True for i in range(n + 1							)]
       UpperCamelCase     :						Tuple 						=		[i - 1 for i in range(n + 1							)]
       UpperCamelCase     :						Optional[Any] 						=		[]
       for i in range(2				,      n + 1							):
              if is_prime[i]:
                     primes.append(SCREAMING_SNAKE_CASE_							)
              for j in range(0				,      len(SCREAMING_SNAKE_CASE_							)							):
                     if i * primes[j] >= n:
                            break
                     UpperCamelCase     :						Any 						=		False

                     if i % primes[j] == 0:
                            UpperCamelCase     :						int 						=		totients[i] * primes[j]
                            break

                     UpperCamelCase     :						Dict 						=		totients[i] * (primes[j] - 1)

       return totients


if __name__ == "__main__":
  import doctest

  doctest.testmod()

