



'''simple docstring'''




import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


a     :    Optional[int]      						=					logging.get_logger(__name__)

class     UpperCamelCase_				(							snake_case__    ):
	lowercase    =				["""input_values""", """attention_mask"""]





	def __init__(						self	,	A = 1	,	A = 16000	,	A = 0.0	,	A = False	,	A = 80	,	A = 16	,	A = 64	,	A = "hann_window"	,	A = 1.0	,	A = 80	,	A = 7600	,	A = 1e-10	,	A = 2	,	A = True	,	**A	,	)    ->    str:
								super().__init__(feature_size=UpperCAmelCase_	,	sampling_rate=UpperCAmelCase_	,	padding_value=UpperCAmelCase_	,	**UpperCAmelCase_					)
								UpperCAmelCase  :	str							  =	do_normalize
								UpperCAmelCase  :	List[Any]							  =	return_attention_mask

								UpperCAmelCase  :	List[Any]							  =	num_mel_bins
								UpperCAmelCase  :	Union[str, Any]							  =	hop_length
								UpperCAmelCase  :	List[Any]							  =	win_length
								UpperCAmelCase  :	Dict							  =	win_function
								UpperCAmelCase  :	Any							  =	frame_signal_scale
								UpperCAmelCase  :	int							  =	fmin
								UpperCAmelCase  :	str							  =	fmax
								UpperCAmelCase  :	List[str]							  =	mel_floor
								UpperCAmelCase  :	Optional[Any]							  =	reduction_factor

								UpperCAmelCase  :	Union[str, Any]							  =	win_length * sampling_rate // 1000
								UpperCAmelCase  :	Any							  =	hop_length * sampling_rate // 1000
								UpperCAmelCase  :	Optional[Any]							  =	optimal_fft_length(self.sample_size					)
								UpperCAmelCase  :	str							  =	(self.n_fft // 2) + 1

								UpperCAmelCase  :	int							  =	window_function(window_length=self.sample_size	,	name=self.win_function	,	periodic=UpperCAmelCase_					)

								UpperCAmelCase  :	Dict							  =	mel_filter_bank(
								    num_frequency_bins=self.n_freqs	,	num_mel_filters=self.num_mel_bins	,	min_frequency=self.fmin	,	max_frequency=self.fmax	,	sampling_rate=self.sampling_rate	,	norm="""slaney"""	,	mel_scale="""slaney"""	,	)

								if frame_signal_scale != 1.0:
															warnings.warn(
															    """The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"""	,	UpperCAmelCase_	,	)
								if reduction_factor != 2.0:
															warnings.warn(
															    """The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"""	,	UpperCAmelCase_	,	)





	@staticmethod
	# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
	def 				_lowercase(						A	,	A	,	A = 0.0					)    ->    List[np.ndarray]:



								if attention_mask is not None:
															UpperCAmelCase  :	List[Any]							  =	np.array(UpperCAmelCase_	,	np.intaa					)
															UpperCAmelCase  :	List[Any]							  =	[]

															for vector, length in zip(UpperCAmelCase_	,	attention_mask.sum(-1					)					):
																						UpperCAmelCase  :	str							  =	(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7					)
																						if length < normed_slice.shape[0]:
																													UpperCAmelCase  :	List[str]							  =	padding_value

																						normed_input_values.append(UpperCAmelCase_					)
								else:
															UpperCAmelCase  :	List[Any]							  =	[(x - x.mean()) / np.sqrt(x.var() + 1e-7					) for x in input_values]

								return normed_input_values





	def 				_lowercase(						self	,	A	,	)    ->    np.ndarray:
								UpperCAmelCase  :	List[Any]							  =	spectrogram(
								    UpperCAmelCase_	,	window=self.window	,	frame_length=self.sample_size	,	hop_length=self.sample_stride	,	fft_length=self.n_fft	,	mel_filters=self.mel_filters	,	mel_floor=self.mel_floor	,	log_mel="""log10"""	,	)
								return log_mel_spec.T





	def __call__(						self	,	A = None	,	A = None	,	A = False	,	A = None	,	A = False	,	A = None	,	A = None	,	A = None	,	A = None	,	**A	,	)    ->    BatchFeature:



								if audio is None and audio_target is None:
															raise ValueError("""You must provide either `audio` or `audio_target` values."""					)

								if sampling_rate is not None:
															if sampling_rate != self.sampling_rate:
																						raise ValueError(
																						    f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
																						    f''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
																						    f''' {self.sampling_rate} and not {sampling_rate}.'''					)
								else:
															logger.warning(
															    """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
															    """Failing to do so can result in silent errors that might be hard to debug."""					)

								if audio is not None:
															UpperCAmelCase  :	List[Any]							  =	self._process_audio(
															    UpperCAmelCase_	,	UpperCAmelCase_	,	UpperCAmelCase_	,	UpperCAmelCase_	,	UpperCAmelCase_	,	UpperCAmelCase_	,	UpperCAmelCase_	,	UpperCAmelCase_	,	**UpperCAmelCase_	,	)
								else:
															UpperCAmelCase  :	Tuple							  =	None

								if audio_target is not None:
															UpperCAmelCase  :	Union[str, Any]							  =	self._process_audio(
															    UpperCAmelCase_	,	UpperCAmelCase_	,	UpperCAmelCase_	,	UpperCAmelCase_	,	UpperCAmelCase_	,	UpperCAmelCase_	,	UpperCAmelCase_	,	UpperCAmelCase_	,	**UpperCAmelCase_	,	)

															if inputs is None:
																						return inputs_target
															else:
																						UpperCAmelCase  :	Union[str, Any]							  =	inputs_target["""input_values"""]
																						UpperCAmelCase  :	List[Any]							  =	inputs_target.get("""attention_mask"""					)
																						if decoder_attention_mask is not None:
																													UpperCAmelCase  :	Optional[int]							  =	decoder_attention_mask

								return inputs





	def 				_lowercase(						self	,	A	,	A = False	,	A = False	,	A = None	,	A = False	,	A = None	,	A = None	,	A = None	,	**A	,	)    ->    BatchFeature:
								UpperCAmelCase  :	str							  =	isinstance(UpperCAmelCase_	,	np.ndarray					) and len(speech.shape					) > 1
								if is_batched_numpy and len(speech.shape					) > 2:
															raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''					)
								UpperCAmelCase  :	Tuple							  =	is_batched_numpy or (
								    isinstance(UpperCAmelCase_	,	(list, tuple)					) and (isinstance(speech[0]	,	(np.ndarray, tuple, list)					))
								)

								if is_batched:
															UpperCAmelCase  :	Optional[int]							  =	[np.asarray(UpperCAmelCase_	,	dtype=np.floataa					) for speech in speech]
								elif not is_batched and not isinstance(UpperCAmelCase_	,	np.ndarray					):
															UpperCAmelCase  :	Tuple							  =	np.asarray(UpperCAmelCase_	,	dtype=np.floataa					)
								elif isinstance(UpperCAmelCase_	,	np.ndarray					) and speech.dtype is np.dtype(np.floataa					):
															UpperCAmelCase  :	List[str]							  =	speech.astype(np.floataa					)

								# always return batch
								if not is_batched:
															UpperCAmelCase  :	List[str]							  =	[speech]

								# needed to make pad() work on spectrogram inputs
								UpperCAmelCase  :	Dict							  =	self.feature_size

								# convert into correct format for padding
								if is_target:
															UpperCAmelCase  :	Any							  =	[self._extract_mel_features(UpperCAmelCase_					) for waveform in speech]
															UpperCAmelCase  :	Any							  =	BatchFeature({"""input_values""": features}					)
															UpperCAmelCase  :	Any							  =	self.num_mel_bins
								else:
															UpperCAmelCase  :	Optional[Any]							  =	BatchFeature({"""input_values""": speech}					)

								UpperCAmelCase  :	int							  =	self.pad(
								    UpperCAmelCase_	,	padding=UpperCAmelCase_	,	max_length=UpperCAmelCase_	,	truncation=UpperCAmelCase_	,	pad_to_multiple_of=UpperCAmelCase_	,	return_attention_mask=UpperCAmelCase_	,	**UpperCAmelCase_	,	)

								UpperCAmelCase  :	Tuple							  =	feature_size_hack

								# convert input values to correct format
								UpperCAmelCase  :	int							  =	padded_inputs["""input_values"""]
								if not isinstance(input_values[0]	,	np.ndarray					):
															UpperCAmelCase  :	Tuple							  =	[np.asarray(UpperCAmelCase_	,	dtype=np.floataa					) for array in input_values]
								elif (
								    not isinstance(UpperCAmelCase_	,	np.ndarray					)
								    and isinstance(input_values[0]	,	np.ndarray					)
								    and input_values[0].dtype is np.dtype(np.floataa					)
								):
															UpperCAmelCase  :	List[Any]							  =	[array.astype(np.floataa					) for array in input_values]
								elif isinstance(UpperCAmelCase_	,	np.ndarray					) and input_values.dtype is np.dtype(np.floataa					):
															UpperCAmelCase  :	Optional[Any]							  =	input_values.astype(np.floataa					)

								# convert attention_mask to correct format
								UpperCAmelCase  :	Tuple							  =	padded_inputs.get("""attention_mask"""					)
								if attention_mask is not None:
															UpperCAmelCase  :	int							  =	[np.asarray(UpperCAmelCase_	,	dtype=np.intaa					) for array in attention_mask]

								# zero-mean and unit-variance normalization
								if not is_target and self.do_normalize:
															UpperCAmelCase  :	Optional[int]							  =	(
															    attention_mask
															    if self._get_padding_strategies(UpperCAmelCase_	,	max_length=UpperCAmelCase_					) is not PaddingStrategy.DO_NOT_PAD
															    else None
															)
															UpperCAmelCase  :	int							  =	self.zero_mean_unit_var_norm(
															    padded_inputs["""input_values"""]	,	attention_mask=UpperCAmelCase_	,	padding_value=self.padding_value					)

								if return_tensors is not None:
															UpperCAmelCase  :	Dict							  =	padded_inputs.convert_to_tensors(UpperCAmelCase_					)

								return padded_inputs






	def 				_lowercase(						self					)    ->    Dict[str, Any]:
								UpperCAmelCase  :	Tuple							  =	super().to_dict()

								# Don't serialize these as they are derived from the other properties.
								UpperCAmelCase  :	Dict							  =	["""window""", """mel_filters""", """sample_size""", """sample_stride""", """n_fft""", """n_freqs"""]
								for name in names:
															if name in output:
																						del output[name]

								return output






'''simple docstring'''




from sklearn.metrics import mean_squared_error

import datasets


a     :    Union[str, Any]      						=					"""\
@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}
}
"""

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


a     :    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.

    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     UpperCamelCase_				(							datasets.Metric    ):





	def 				_lowercase(						self					)    ->    List[str]:
								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 				_lowercase(						self					)    ->    List[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 				_lowercase(						self	,	A	,	A	,	A=None	,	A="uniform_average"	,	A=True					)    ->    List[Any]:
								UpperCAmelCase  :	List[Any]							  =	mean_squared_error(
								    A	,	A	,	sample_weight=A	,	multioutput=A	,	squared=A					)

								return {"mse": mse}

