






'''simple docstring'''





from sklearn.metrics import mean_squared_error

import datasets


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


_UpperCamelCase		:		List[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):




					def 						A	(   self   :						Any	)  ->				List[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_squared_error.html'''
								    ]							,      )




					def 						A	(   self   :						List[Any]	)  ->				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 						A	(   self   :						int							,      _A   :						List[str]							,      _A   :						Optional[Any]							,      _A   :						Union[str, Any]=None							,      _A   :						Dict="uniform_average"							,      _A   :						str=True	)  ->				List[str]:
								UpperCAmelCase_	:    Dict	  =    mean_squared_error(
								    _a							,      _a							,      sample_weight=_a							,      multioutput=_a							,      squared=_a	)

								return {"mse": mse}




"""simple docstring"""




from __future__ import annotations

from functools import lru_cache
from math import ceil

_A			:       Optional[Any]   		=       1_00

_A			:       Optional[int]   		=       set(range(3, NUM_PRIMES, 2))
primes.add(2)
_A			:       int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
   if prime not in primes:
      continue
   primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))


@lru_cache(maxsize=100  )
def   __magic_name__	(      __snake_case				:       int  ) ->				set[int]:
       if number_to_partition < 0:
              return set()
       elif number_to_partition == 0:
              return {1}

       lowercase    :				set[int]       		=							set()
       lowercase    :				int
       lowercase    :				int

       for prime in primes:
              if prime > number_to_partition:
                     continue
              for sub in partition(number_to_partition - prime  ):
                     ret.add(sub * prime  )

       return ret


def   __magic_name__	(      __snake_case				:       int = 5000  ) ->				int | None:
       for number_to_partition in range(1     , __snake_case  ):
              if len(partition(__snake_case  )  ) > number_unique_partitions:
                     return number_to_partition
       return None


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


