

from sklearn.metrics import mean_squared_error

import datasets


snake_case_        =			'''\
@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}
}
'''

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


snake_case_        =			'''
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 				SCREAMING_SNAKE_CASE__ (datasets.Metric					):





				def 				snake_case_    (				self):
									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 				snake_case_    (				self):
									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 				snake_case_    (				self						,       a						,       a						,       a=None						,       a="uniform_average"						,       a=True):
									lowercase__   :       List[Any]				=	mean_squared_error(
									    a						,       a						,       sample_weight=a						,       multioutput=a						,       squared=a)

									return {"mse": mse}




from __future__ import annotations


class 				SCREAMING_SNAKE_CASE__ :





    def __init__(				self						,       a						,       a):
         lowercase__				,			lowercase__   :       Dict				=	text, pattern
         lowercase__				,			lowercase__   :       Any				=	len(a), len(a)





    def 				snake_case_    (				self						,       a):

         for i in range(self.patLen - 1						,       -1						,       -1):
              if char == self.pattern[i]:
                   return i
         return -1





    def 				snake_case_    (				self						,       a):

         for i in range(self.patLen - 1						,       -1						,       -1):
              if self.pattern[i] != self.text[current_pos + i]:
                   return current_pos + i
         return -1





    def 				snake_case_    (				self):
         # searches pattern in text and returns index positions
         lowercase__   :       Any				=	[]
         for i in range(self.textLen - self.patLen + 1):
              lowercase__   :       Optional[Any]				=	self.mismatch_in_text(a)
              if mismatch_index == -1:
                   positions.append(a)
              else:
                   lowercase__   :       Optional[int]				=	self.match_in_pattern(self.text[mismatch_index])
                   lowercase__   :       Optional[Any]				=	(
                       mismatch_index - match_index
                   )  # shifting index lgtm [py/multiple-definition]
         return positions



snake_case_        =			'''ABAABA'''
snake_case_        =			'''AB'''
snake_case_        =			BoyerMooreSearch(text, pattern)
snake_case_        =			bms.bad_character_heuristic()

if len(positions) == 0:
       print('''No match found''')
else:
       print('''Pattern found in following positions: ''')
       print(positions)

