






from sklearn.metrics import mean_squared_error

import datasets


lowercase  :							Optional[int]			  =    '''\
@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}
}
'''

lowercase  :							List[Any]			  =    '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


lowercase  :							Union[str, Any]			  =    '''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {\'mse\': 0.6123724356957945}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mse\': array([0.41666667, 1. ])}
'''




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


						'''simple docstring'''




						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								return datasets.MetricInfo(
								    description=_DESCRIPTION		,				citation=_CITATION		,				inputs_description=_KWARGS_DESCRIPTION		,				features=datasets.Features(self._get_feature_types()				)		,				reference_urls=[
								        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
								    ]		,				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								if self.config_name == "multilist":
										return {
										    "predictions": datasets.Sequence(datasets.Value("float"				)				),
										    "references": datasets.Sequence(datasets.Value("float"				)				),
										}
								else:
										return {
										    "predictions": datasets.Value("float"				),
										    "references": datasets.Value("float"				),
										}






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE="uniform_average"		,				_SCREAMING_SNAKE_CASE=True				) ->				Union[str, Any]:
								snake_case_				:							Optional[int]						 =       mean_squared_error(
								    __lowercase		,				__lowercase		,				sample_weight=__lowercase		,				multioutput=__lowercase		,				squared=__lowercase				)

								return {"mse": mse}








import math

def      lowerCAmelCase__	(    _a       :			int    ):

		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5  ,      int(math.sqrt(_a    ) + 1    )  ,      6    ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True

def      lowerCAmelCase__	(    _a       :			float = 0.1    ):
		snake_case_				:							int						 =       3
		snake_case_				:							Optional[Any]						 =       3

		while primes / (2 * j - 1) >= ratio:
				for i in range(j * j + j + 1  ,      (j + 2) * (j + 2)  ,      j + 1    ):
						primes += is_prime(_a    )
				j += 2
		return j


if __name__ == "__main__":
							import doctest

							doctest.testmod()
