

from sklearn.metrics import mean_squared_error

import datasets


A_	:			str  	=      '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

A_	:			int  	=      '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


A_	:			Optional[Any]  	=      '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'



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


					'''simple docstring'''






					def   __A      (			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   __A      (			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   __A      (			self   ,	A__   ,	A__   ,	A__=None   ,	A__="uniform_average"   ,	A__=True      ):
						A__							:   Optional[Any]					       =		mean_squared_error(
						    _a   ,	_a   ,	sample_weight=_a   ,	multioutput=_a   ,	squared=_a      )

						return {"mse": mse}






from typing import Any

def 						UpperCamelCase (lowercase_:			list )					->				list[Any]:
	if not input_list:
		return []
	A__							:   Any					       =		[input_list.count(lowercase_ ) for value in input_list]
	A__							:   List[Any]					       =		max(lowercase_ )  # Gets the maximum count in the input list.
	# Gets values of modes
	return sorted({input_list[i] for i, value in enumerate(lowercase_ ) if value == y} )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



