
'''simple docstring'''



from math import ceil
from typing import List, Optional, Union

import numpy as np

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


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Optional[int]										=  ['''audio_values''', '''audio_mask''']




						def __init__(   self       ,  _lowercase=2_048       ,  _lowercase=1       ,  _lowercase=[16, 16]       ,  _lowercase=128       ,  _lowercase=44_100       ,  _lowercase=86       ,  _lowercase=2_048       ,  _lowercase=0.0       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(
												    feature_size=_lowercase       ,  sampling_rate=_lowercase       ,  padding_value=_lowercase       ,  **_lowercase       ,  )

												_lowerCAmelCase    		=					spectrogram_length
												_lowerCAmelCase    		=					num_channels
												_lowerCAmelCase    		=					patch_size
												_lowerCAmelCase    		=					feature_size // self.patch_size[1]
												_lowerCAmelCase    		=					n_fft
												_lowerCAmelCase    		=					sampling_rate // hop_length_to_sampling_rate
												_lowerCAmelCase    		=					sampling_rate
												_lowerCAmelCase    		=					padding_value
												_lowerCAmelCase    		=					mel_filter_bank(
												    num_frequency_bins=1 + n_fft // 2       ,  num_mel_filters=_lowercase       ,  min_frequency=0.0       ,  max_frequency=2_2050.0       ,  sampling_rate=_lowercase       ,  norm="""slaney"""       ,  mel_scale="""slaney"""       ,  ).T




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					spectrogram(
												    _lowercase       ,  window_function(self.n_fft       ,  """hann""" )       ,  frame_length=self.n_fft       ,  hop_length=self.hop_length       ,  power=2.0       ,  mel_filters=self.mel_filters.T       ,  log_mel="""dB"""       ,  db_range=80.0       ,  )
												_lowerCAmelCase    		=					log_spec[:, :-1]
												_lowerCAmelCase    		=					log_spec - 20.0
												_lowerCAmelCase    		=					np.clip(log_spec / 40.0       ,  -2.0       ,  0.0 ) + 1.0
												return log_spec




						def __call__(   self       ,  _lowercase       ,  _lowercase = None       ,  _lowercase = True       ,  _lowercase = None       ,  _lowercase = False       ,  _lowercase = False       ,  **_lowercase       ,  ):

												"""simple docstring"""



												if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    """This feature extractor is set to support sampling rate"""
																								    F' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
																								    F' with {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.""" )

												_lowerCAmelCase    		=					isinstance(_lowercase       ,  np.ndarray ) and len(raw_speech.shape ) > 1
												if is_batched_numpy and len(raw_speech.shape ) > 2:
																		raise ValueError(F'Only mono-channel audio is supported for input to {self}' )
												_lowerCAmelCase    		=					is_batched_numpy or (
												    isinstance(_lowercase       ,  (list, tuple) ) and (isinstance(raw_speech[0]       ,  (np.ndarray, tuple, list) ))
												)
												if is_batched:
																		_lowerCAmelCase    		=					[np.asarray([speech]       ,  dtype=np.floataa ).T for speech in raw_speech]
												elif not is_batched and not isinstance(_lowercase       ,  np.ndarray ):
																		_lowerCAmelCase    		=					np.asarray(_lowercase       ,  dtype=np.floataa )
												elif isinstance(_lowercase       ,  np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
																		_lowerCAmelCase    		=					raw_speech.astype(np.floataa )
												# always return batch
												if not is_batched:
																		_lowerCAmelCase    		=					[np.asarray([raw_speech] ).T]

												# Convert audio signals to log mel spectrograms, truncate by time axis
												_lowerCAmelCase    		=					[
												    self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
												]
												if isinstance(audio_features[0]       ,  _lowercase ):
																		_lowerCAmelCase    		=					[np.asarray(_lowercase       ,  dtype=np.floataa ) for feature in audio_features]

												# Create audio attention mask
												_lowerCAmelCase    		=					max(
												    [ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] )  # The maximum number of audio patches in a batch
												if return_attention_mask:
																		_lowerCAmelCase    		=					[
																		    (ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
																		    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
																		    for feature in audio_features
																		]
																		_lowerCAmelCase    		=					np.array(_lowercase ).astype(np.floataa )

												# convert into correct format for padding
												_lowerCAmelCase    		=					max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
												_lowerCAmelCase    		=					np.ones([len(_lowercase ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
												_lowerCAmelCase    		=					padded_audio_features * self.padding_value
												for i in range(len(_lowercase ) ):
																		_lowerCAmelCase    		=					audio_features[i]
																		_lowerCAmelCase    		=					feature

												# return as BatchFeature
												if return_attention_mask:
																		_lowerCAmelCase    		=					{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
												else:
																		_lowerCAmelCase    		=					{"""audio_values""": padded_audio_features}

												_lowerCAmelCase    		=					BatchFeature(data=_lowercase       ,  tensor_type=_lowercase )
												return encoded_inputs


'''simple docstring'''



from sklearn.metrics import mean_squared_error

import datasets


_lowercase          =						"""\
@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}
}
"""

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


_lowercase          =						"""
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					):






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



												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 ):

												"""simple docstring"""



												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       ,  _lowercase       ,  _lowercase       ,  _lowercase=None       ,  _lowercase="uniform_average"       ,  _lowercase=True ):

												"""simple docstring"""



												_lowerCAmelCase    		=					mean_squared_error(
												    _lowercase       ,  _lowercase       ,  sample_weight=_lowercase       ,  multioutput=_lowercase       ,  squared=_lowercase )

												return {"mse": mse}
