





'''simple docstring'''
from sklearn.metrics import mean_squared_error

import datasets


a__							:							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}
}
'''

a__							:							Tuple ='''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


a__							:							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      snake_case     (    datasets.Metric       ):




 """simple docstring"""



 def        _lowerCamelCase					(       self       :							Optional[int]	):
        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       :							Union[str, Any]	):
        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       :							Tuple							,	__A       :							List[Any]							,	__A       :							Tuple							,	__A       :							Any=None							,	__A       :							Optional[int]="uniform_average"							,	__A       :							List[Any]=True	):
        __UpperCamelCase       =  mean_squared_error(
            __A							,	__A							,	sample_weight=__A							,	multioutput=__A							,	squared=__A	)

        return {"mse": mse}







'''simple docstring'''
a__							:							Optional[Any] =256
# Modulus to hash a string
a__							:							Dict =1_000_003
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  str     ) ->      bool:



       """simple docstring"""






       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  len(__lowercase     )
       if p_len > t_len:
              return False

       __UpperCamelCase       =  0
       __UpperCamelCase       =  0
       __UpperCamelCase       =  1

       # Calculating the hash of pattern and substring of text
       for i in range(__lowercase     ):
              __UpperCamelCase       =  (ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
              __UpperCamelCase       =  (ord(text[i]     ) + text_hash * alphabet_size) % modulus
              if i == p_len - 1:
                     continue
              __UpperCamelCase       =  (modulus_power * alphabet_size) % modulus

       for i in range(0				,  t_len - p_len + 1     ):
              if text_hash == p_hash and text[i : i + p_len] == pattern:
                     return True
              if i == t_len - p_len:
                     continue
              # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
              __UpperCamelCase       =  (
                  (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
                  + ord(text[i + p_len]     )
              ) % modulus
       return False
def 				lowercase__   (   ) ->      None:



       """simple docstring"""






       __UpperCamelCase       =  'abc1abc12'
       __UpperCamelCase       =  'alskfjaldsabc1abc1abc12k23adsfabcabc'
       __UpperCamelCase       =  'alskfjaldsk23adsfabcabc'
       assert rabin_karp(__lowercase				,  __lowercase     ) and not rabin_karp(__lowercase				,  __lowercase     )

       # Test 2)
       __UpperCamelCase       =  'ABABX'
       __UpperCamelCase       =  'ABABZABABYABABX'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 3)
       __UpperCamelCase       =  'AAAB'
       __UpperCamelCase       =  'ABAAAAAB'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 4)
       __UpperCamelCase       =  'abcdabcy'
       __UpperCamelCase       =  'abcxabcdabxabcdabcdabcy'
       assert rabin_karp(__lowercase				,  __lowercase     )

       # Test 5)
       __UpperCamelCase       =  'Lü'
       __UpperCamelCase       =  'Lüsai'
       assert rabin_karp(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  'Lue'
       assert not rabin_karp(__lowercase				,  __lowercase     )
       print('Success.'     )


if __name__ == "__main__":
    test_rabin_karp()
