
"""simple docstring"""




from sklearn.metrics import mean_squared_error

import datasets


a		:       str     						=       '\\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'

a		:       Dict     						=       '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


a		:       int     						=       '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'







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



     def 			lowercase__  (							self   )		->     Optional[Any]:







         """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 			lowercase__  (							self   )		->     Union[str, 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 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=None     ,				__SCREAMING_SNAKE_CASE="uniform_average"     ,				__SCREAMING_SNAKE_CASE=True   )		->     Dict:







         """simple docstring"""



         a__		=			mean_squared_error(
             __SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				sample_weight=__SCREAMING_SNAKE_CASE     ,				multioutput=__SCREAMING_SNAKE_CASE     ,				squared=__SCREAMING_SNAKE_CASE   )

         return {"mse": mse}




"""simple docstring"""




def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:

    def count_of_possible_combinations(UpperCamelCase     :		int     ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        return sum(count_of_possible_combinations(target - item     ) for item in array     )

    return count_of_possible_combinations(UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:

    def count_of_possible_combinations_with_dp_array(
        UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]     ) -> int:
        if target < 0:
            return 0
        if target == 0:
            return 1
        if dp_array[target] != -1:
            return dp_array[target]
        a__		=			sum(
            count_of_possible_combinations_with_dp_array(target - item							,		UpperCamelCase     )
            for item in array     )
        a__		=			answer
        return answer

    a__		=			[-1] * (target + 1)
    return count_of_possible_combinations_with_dp_array(UpperCamelCase							,		UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		list[int]							,		UpperCamelCase     :		int     )     ->						int:
    a__		=			[0] * (target + 1)
    a__		=			1

    for i in range(1							,		target + 1     ):
        for j in range(UpperCamelCase     ):
            if i - array[j] >= 0:
                dp_array[i] += dp_array[i - array[j]]

    return dp_array[target]


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    a		:       Optional[Any]     						=       3
    a		:       List[str]     						=       5
    a		:       Dict     						=       [1, 2, 5]
    print(combination_sum_iv(n, array, target))


