



'''simple docstring'''




def        lowerCAmelCase_     (			_lowerCamelCase:     str ,   _lowerCamelCase:     str      ):
		__SCREAMING_SNAKE_CASE :	Dict					 =	len(_lowerCamelCase      ) + 1
		__SCREAMING_SNAKE_CASE :	Any					 =	len(_lowerCamelCase      ) + 1

		# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
		# length i of input_string matches with prefix string of length j of
		# given pattern.
		# "dp" stands for dynamic programming.
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	[[0 for i in range(_lowerCamelCase      )] for j in range(_lowerCamelCase      )]

		# since string of zero length match pattern of zero length
		__SCREAMING_SNAKE_CASE :	str					 =	1

		# since pattern of zero length will never match with string of non-zero length
		for i in range(1 ,   _lowerCamelCase      ):
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	0

		# since string of zero length will match with pattern where there
		# is at least one * alternatively
		for j in range(1 ,   _lowerCamelCase      ):
				__SCREAMING_SNAKE_CASE :	List[str]					 =	dp[0][j - 2] if pattern[j - 1] == """*""" else 0

		# now using bottom-up approach to find for all remaining lengths
		for i in range(1 ,   _lowerCamelCase      ):
				for j in range(1 ,   _lowerCamelCase      ):
						if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
								__SCREAMING_SNAKE_CASE :	Tuple					 =	dp[i - 1][j - 1]

						elif pattern[j - 1] == "*":
								if dp[i][j - 2] == 1:
										__SCREAMING_SNAKE_CASE :	Any					 =	1
								elif pattern[j - 2] in (input_string[i - 1], "."):
										__SCREAMING_SNAKE_CASE :	Optional[int]					 =	dp[i - 1][j]
								else:
										__SCREAMING_SNAKE_CASE :	Optional[int]					 =	0
						else:
								__SCREAMING_SNAKE_CASE :	List[Any]					 =	0

		return bool(dp[-1][-1]      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						# inputing the strings
						# input_string = input("input a string :")
						# pattern = input("input a pattern :")

						UpperCamelCase__ :			Tuple												=				'''aab'''
						UpperCamelCase__ :			str												=				'''c*a*b'''

						# using function to check whether given string matches the given pattern
						if match_pattern(input_string, pattern):
												print(f"{input_string} matches the given pattern {pattern}")
						else:
												print(f"{input_string} does not match with the given pattern {pattern}")




'''simple docstring'''




from sklearn.metrics import mean_squared_error

import datasets


UpperCamelCase__ :			Optional[Any]												=				'''\
@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}
}
'''

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


UpperCamelCase__ :			Dict												=				'''
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 UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					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 UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					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 UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				List[Any]=None							,  lowerCAmelCase__  :				Optional[int]="uniform_average"							,  lowerCAmelCase__  :				int=True		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	mean_squared_error(
					    lowerCAmelCase__							,  lowerCAmelCase__							,  sample_weight=lowerCAmelCase__							,  multioutput=lowerCAmelCase__							,  squared=lowerCAmelCase__		)

					return {"mse": mse}