def   a_						( lowerCamelCase ,      lowerCamelCase = 1_0_0_0	):
				if (not isinstance(lowerCamelCase ,      lowerCamelCase	)) or (digit_position <= 0):
								raise ValueError('Digit position must be a positive integer'	)
				elif (not isinstance(lowerCamelCase ,      lowerCamelCase	)) or (precision < 0):
								raise ValueError('Precision must be a nonnegative integer'	)

				# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
				# accurate
				UpperCAmelCase__					=							(
				    4 * _subsum(lowerCamelCase ,      1 ,      lowerCamelCase	)
				    - 2 * _subsum(lowerCamelCase ,      4 ,      lowerCamelCase	)
				    - _subsum(lowerCamelCase ,      5 ,      lowerCamelCase	)
				    - _subsum(lowerCamelCase ,      6 ,      lowerCamelCase	)
				)

				# return the first hex digit of the fractional part of the result
				return hex(int((sum_result % 1) * 1_6	)	)[2:]



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							0.0
				for sum_index in range(digit_pos_to_extract + precision	):
								UpperCAmelCase__					=							8 * sum_index + denominator_addend
								if sum_index < digit_pos_to_extract:
												# if the exponential term is an integer and we mod it by the denominator
												# before dividing, only the integer part of the sum will change;
												# the fractional part will not
												UpperCAmelCase__					=							pow(
												    1_6 ,      digit_pos_to_extract - 1 - sum_index ,      lowerCamelCase	)
								else:
												UpperCAmelCase__					=							pow(1_6 ,      digit_pos_to_extract - 1 - sum_index	)
								total += exponential_term / denominator
				return total


if __name__ == "__main__":
						import doctest

						doctest.testmod()




"""simple docstring"""






from sklearn.metrics import accuracy_score

import datasets


lowerCAmelCase__  :  List[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'


lowerCAmelCase__  :  List[str]											=     '\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'


lowerCAmelCase__  :  Dict											=     '\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     snake_case	(			datasets.Metric    ):




					"""simple docstring"""

					def 		__lowerCAmelCase						(    self       :     List[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 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     List[str]   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     Tuple=None      ):
									return {
									    "accuracy": float(
									        accuracy_score(lowerCamelCase__   ,lowerCamelCase__   ,normalize=lowerCamelCase__   ,sample_weight=lowerCamelCase__      )      )
									}



