
from sklearn.metrics import mean_absolute_error

import datasets


lowerCamelCase_  :       List[str]					      =     """\
@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}
}
"""

lowerCamelCase_  :       Union[str, Any]					      =     """\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


lowerCamelCase_  :       int					      =     """
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.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

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

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""

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






			'''simple docstring'''

			def 					lowerCAmelCase__		(				self       :				Union[str, 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_absolute_error.html"""
							    ]  ,		)

			def 					lowerCAmelCase__		(				self       :				Optional[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       :				List[Any]  ,		snake_case_       :				str  ,		snake_case_       :				Dict  ,		snake_case_       :				Dict=None  ,		snake_case_       :				Optional[Any]="uniform_average"					):
							UpperCamelCase_:							List[str]		=  mean_absolute_error(snake_case_  ,		snake_case_  ,		sample_weight=snake_case_  ,		multioutput=snake_case_					)

							return {"mae": mae_score}










'''simple docstring'''







import sys

sys.set_int_max_str_digits(0)  # type: ignore






def       lowerCAmelCase__       (				lowerCamelCase     :			int   ):
       _A     :	Optional[Any]      =					[0] * 11
       _A     :	str      =					[0] * 11

       # mark last 9 numbers
       for _ in range(9   ):
              _A     :	int      =					1
              _A     :	Optional[int]      =					number // 10
       # flag
       _A     :	List[Any]      =					True

       # check last 9 numbers for pandigitality

       for x in range(9   ):
              if not check_last[x + 1]:
                     _A     :	Optional[int]      =					False
       if not f:
              return f

       # mark first 9 numbers
       _A     :	Optional[int]      =					int(str(lowerCamelCase   )[:9]   )

       for _ in range(9   ):
              _A     :	Optional[Any]      =					1
              _A     :	str      =					number // 10

       # check first 9 numbers for pandigitality

       for x in range(9   ):
              if not check_front[x + 1]:
                     _A     :	Optional[Any]      =					False
       return f






def       lowerCAmelCase__       (				lowerCamelCase     :			int   ):
       _A     :	Dict      =					[0] * 11

       # mark last 9 numbers
       for _ in range(9   ):
              _A     :	List[str]      =					1
              _A     :	Dict      =					number // 10
       # flag
       _A     :	Optional[int]      =					True

       # check last 9 numbers for pandigitality

       for x in range(9   ):
              if not check_last[x + 1]:
                     _A     :	List[str]      =					False
       return f






def       lowerCAmelCase__       (				):
       _A     :	List[Any]      =					1
       _A     :	Dict      =					1
       _A     :	Dict      =					2
       # temporary Fibonacci numbers

       _A     :	List[Any]      =					1
       _A     :	Union[str, Any]      =					1
       _A     :	List[Any]      =					2
       # temporary Fibonacci numbers mod 1e9

       # mod m=1e9, done for fast optimisation
       _A     :	Tuple      =					[0] * 1000000
       _A     :	Any      =					1000000000

       for x in range(1000000   ):
              _A     :	Tuple      =					(aa + ba) % m
              _A     :	Optional[Any]      =					ba % m
              _A     :	str      =					ca % m
              if checka(lowerCamelCase   ):
                     _A     :	Dict      =					1

       for x in range(1000000   ):
              _A     :	List[str]      =					a + b
              _A     :	Optional[int]      =					b
              _A     :	str      =					c
              # perform check only if in tocheck
              if tocheck[x + 3] and check(lowerCamelCase   ):
                     return x + 3  # first 2 already done
       return -1


if __name__ == "__main__":
   print(f"""{solution() = }""")






