

from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

_lowerCamelCase				:   int					   =							TypeVar("T")





class 					__UpperCAmelCase     (       Generic[T]						):
   UpperCamelCase        =     42  # Cache store of keys
   UpperCamelCase        =     42  # References of the keys in cache
   UpperCamelCase        =     1_0  # Maximum capacity of cache



   def __init__(							self							:     Dict,		__A							:     int						):
     UpperCAmelCase       :							int  = deque()
     UpperCAmelCase       :							Any  = set()
     if not n:
       UpperCAmelCase       :							Any  = sys.maxsize
     elif n < 0:
       raise ValueError('''n should be an integer greater than 0.'''						)
     else:
       UpperCAmelCase       :							Union[str, Any]  = n



   def        __magic_name__    (							self							:     Dict,		__A							:     T						):
     if x not in self.key_reference:
       if len(self.dq_store						) == LRUCache._MAX_CAPACITY:
         UpperCAmelCase       :							Optional[Any]  = self.dq_store.pop()
         self.key_reference.remove(_UpperCamelCase						)
     else:
       self.dq_store.remove(_UpperCamelCase						)

     self.dq_store.appendleft(_UpperCamelCase						)
     self.key_reference.add(_UpperCamelCase						)



   def        __magic_name__    (							self							:     List[Any]						):
     for k in self.dq_store:
       print(_UpperCamelCase						)





   def __repr__(							self							:     Optional[Any]						):
     return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store						)}'''


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     _lowerCamelCase				:   Optional[int]					   =							LRUCache(4)
     lru_cache.refer("A")
     lru_cache.refer(2)
     lru_cache.refer(3)
     lru_cache.refer("A")
     lru_cache.refer(4)
     lru_cache.refer(5)
     lru_cache.display()

     print(lru_cache)
     assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"








from sklearn.metrics import mean_squared_error

import datasets


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


lowerCAmelCase_       	=	'''
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 				snake_case__(     self :				Optional[int]							)      ->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 				snake_case__(     self :				List[Any]							)      ->Optional[int]:
									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 :				int							,   _UpperCamelCase :				int							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Any=None							,   _UpperCamelCase :				Optional[int]="uniform_average"							,   _UpperCamelCase :				Tuple=True							)      ->Tuple:
									snake_case_   =	mean_squared_error(
									    _UpperCamelCase							,   _UpperCamelCase							,   sample_weight=_UpperCamelCase							,   multioutput=_UpperCamelCase							,   squared=_UpperCamelCase							)

									return {"mse": mse}