

from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np





class 	snake_case_   (		__A					):







			'''simple docstring'''

			def 				snake_case__(     self :				int							,   _UpperCamelCase :				float							)      ->float:
									return 0.0






def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	min([-20, np.min(fft_results[1 : samplerate // 2 - 1]       )]       )
						snake_case_   =	max([20, np.max(fft_results[1 : samplerate // 2 - 1]       )]       )
						return lowest, highest


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	512
						snake_case_   =	[1] + [0] * (size - 1)
						snake_case_   =	[filter_type.process(SCREAMING_SNAKE_CASE__       ) for item in inputs]

						snake_case_   =	[0] * (samplerate - size)  # zero-padding
						outputs += filler
						snake_case_   =	np.abs(np.fft.fft(SCREAMING_SNAKE_CASE__       )       )
						snake_case_   =	20 * np.logaa(SCREAMING_SNAKE_CASE__       )

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24  ,		samplerate / 2 - 1       )
						plt.xlabel('''Frequency (Hz)'''       )
						plt.xscale('''log'''       )

						# Display within reasonable bounds
						snake_case_   =	get_bounds(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
						plt.ylim(max([-80, bounds[0]]       )  ,		min([80, bounds[1]]       )       )
						plt.ylabel('''Gain (dB)'''       )

						plt.plot(SCREAMING_SNAKE_CASE__       )
						plt.show()


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	512
						snake_case_   =	[1] + [0] * (size - 1)
						snake_case_   =	[filter_type.process(SCREAMING_SNAKE_CASE__       ) for item in inputs]

						snake_case_   =	[0] * (samplerate - size)  # zero-padding
						outputs += filler
						snake_case_   =	np.angle(np.fft.fft(SCREAMING_SNAKE_CASE__       )       )

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24  ,		samplerate / 2 - 1       )
						plt.xlabel('''Frequency (Hz)'''       )
						plt.xscale('''log'''       )

						plt.ylim(-2 * pi  ,		2 * pi       )
						plt.ylabel('''Phase shift (Radians)'''       )
						plt.plot(np.unwrap(SCREAMING_SNAKE_CASE__  ,		-2 * pi       )       )
						plt.show()


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}