





from sklearn.metrics import accuracy_score

import datasets


__UpperCamelCase:  List[Any]   =   """
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
"""


__UpperCamelCase:  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}
"""


__UpperCamelCase:  List[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 			__lowerCAmelCase				(					datasets.Metric			):


	'''simple docstring'''





	def       snake_case__(	self:	str ):
			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       snake_case__(	self:	List[Any],				lowerCamelCase_:	str,				lowerCamelCase_:	Tuple,				lowerCamelCase_:	Union[str, Any]=True,				lowerCamelCase_:	Optional[int]=None ):
			return {
			    "accuracy": float(
			        accuracy_score(lowerCamelCase_,				lowerCamelCase_,				normalize=lowerCamelCase_,				sample_weight=lowerCamelCase_ ) )
			}









def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   list )      ->       list:




		'''simple docstring'''
		lowercase__				:			Any            =		len(_lowercase )
		for cycle_start in range(0      ,						array_len - 1 ):
				lowercase__				:			List[str]            =		array[cycle_start]

				lowercase__				:			str            =		cycle_start
				for i in range(cycle_start + 1      ,						_lowercase ):
						if array[i] < item:
								pos += 1

				if pos == cycle_start:
						continue

				while item == array[pos]:
						pos += 1

				lowercase__      ,							lowercase__				:			Optional[Any]            =		item, array[pos]
				while pos != cycle_start:
						lowercase__				:			str            =		cycle_start
						for i in range(cycle_start + 1      ,						_lowercase ):
								if array[i] < item:
										pos += 1

						while item == array[pos]:
								pos += 1

						lowercase__      ,							lowercase__				:			Tuple            =		item, array[pos]

		return array


if __name__ == "__main__":
			assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
			assert cycle_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]


