

'''simple docstring'''




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

import numpy as np

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


_a					:			Union[str, Any]							=    logging.get_logger(__name__)



class 							lowercase_						(		_a			):

  '''simple docstring'''





  __lowerCAmelCase       :			Any     	= ['''input_features''']
  def __init__(  self ,			a_=8_0 ,			a_=1_6_0_0_0 ,			a_=1_6_0 ,			a_=3_0 ,			a_=4_0_0 ,			a_=0.0 ,			a_=False ,			**a_ ,			)				->					Union[str, Any]:


    """simple docstring"""
    super().__init__(
        feature_size=__UpperCamelCase ,			sampling_rate=__UpperCamelCase ,			padding_value=__UpperCamelCase ,			return_attention_mask=__UpperCamelCase ,			**__UpperCamelCase ,			)
    UpperCAmelCase    				=						n_fft
    UpperCAmelCase    				=						hop_length
    UpperCAmelCase    				=						chunk_length
    UpperCAmelCase    				=						chunk_length * sampling_rate
    UpperCAmelCase    				=						self.n_samples // hop_length
    UpperCAmelCase    				=						sampling_rate
    UpperCAmelCase    				=						mel_filter_bank(
        num_frequency_bins=1 + n_fft // 2 ,			num_mel_filters=__UpperCamelCase ,			min_frequency=0.0 ,			max_frequency=8_0_0_0.0 ,			sampling_rate=__UpperCamelCase ,			norm='slaney' ,			mel_scale='slaney' ,			)
  def     snake_case_      (  self ,			a_  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						spectrogram(
        __UpperCamelCase ,			window_function(self.n_fft ,			'hann'  ) ,			frame_length=self.n_fft ,			hop_length=self.hop_length ,			power=2.0 ,			mel_filters=self.mel_filters ,			log_mel='log10' ,			)
    UpperCAmelCase    				=						log_spec[:, :-1]
    UpperCAmelCase    				=						np.maximum(__UpperCamelCase ,			log_spec.max() - 8.0  )
    UpperCAmelCase    				=						(log_spec + 4.0) / 4.0
    return log_spec
  @staticmethod
  # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
  def     snake_case_      (  a_ ,			a_ ,			a_ = 0.0  )				->					int:


    """simple docstring"""
    if attention_mask is not None:
      UpperCAmelCase    				=						np.array(__UpperCamelCase ,			np.intaa  )
      UpperCAmelCase    				=						[]

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

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

    return normed_input_values
  def __call__(  self ,			a_ ,			a_ = True ,			a_ = None ,			a_ = None ,			a_ = None ,			a_ = "max_length" ,			a_ = None ,			a_ = None ,			a_ = None ,			**a_ ,			)				->					Dict:


    """simple docstring"""

    if sampling_rate is not None:
      if sampling_rate != self.sampling_rate:
        raise ValueError(
            F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
            F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
            F''' was sampled 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.'  )

    UpperCAmelCase    				=						isinstance(__UpperCamelCase ,			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}'''  )
    UpperCAmelCase    				=						is_batched_numpy or (
        isinstance(__UpperCamelCase ,			(list, tuple)  ) and (isinstance(raw_speech[0] ,			(np.ndarray, tuple, list)  ))
    )

    if is_batched:
      UpperCAmelCase    				=						[np.asarray([speech] ,			dtype=np.floataa  ).T for speech in raw_speech]
    elif not is_batched and not isinstance(__UpperCamelCase ,			np.ndarray  ):
      UpperCAmelCase    				=						np.asarray(__UpperCamelCase ,			dtype=np.floataa  )
    elif isinstance(__UpperCamelCase ,			np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
      UpperCAmelCase    				=						raw_speech.astype(np.floataa  )

    # always return batch
    if not is_batched:
      UpperCAmelCase    				=						[np.asarray([raw_speech]  ).T]

    UpperCAmelCase    				=						BatchFeature({'input_features': raw_speech}  )

    # convert into correct format for padding

    UpperCAmelCase    				=						self.pad(
        __UpperCamelCase ,			padding=__UpperCamelCase ,			max_length=max_length if max_length else self.n_samples ,			truncation=__UpperCamelCase ,			pad_to_multiple_of=__UpperCamelCase ,			return_attention_mask=return_attention_mask or do_normalize ,			)

    # zero-mean and unit-variance normalization
    if do_normalize:
      UpperCAmelCase    				=						self.zero_mean_unit_var_norm(
          padded_inputs['input_features'] ,			attention_mask=padded_inputs['attention_mask'] ,			padding_value=self.padding_value ,			)
      UpperCAmelCase    				=						np.stack(padded_inputs['input_features'] ,			axis=0  )

    # make sure list is in array format
    UpperCAmelCase    				=						padded_inputs.get('input_features'  ).transpose(2 ,			0 ,			1  )

    UpperCAmelCase    				=						[self._np_extract_fbank_features(__UpperCamelCase  ) for waveform in input_features[0]]

    if isinstance(input_features[0] ,			__UpperCamelCase  ):
      UpperCAmelCase    				=						[np.asarray(__UpperCamelCase ,			dtype=np.floataa  ) for feature in input_features]
    else:
      UpperCAmelCase    				=						input_features

    if return_attention_mask:
      # rescale from sample (48000) to feature (3000)
      UpperCAmelCase    				=						padded_inputs['attention_mask'][:, :: self.hop_length]

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

    return padded_inputs





  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						copy.deepcopy(self.__dict__  )
    UpperCAmelCase    				=						self.__class__.__name__
    if "mel_filters" in output:
      del output["mel_filters"]
    return output




from sklearn.metrics import mean_squared_error

import datasets


A		: List[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		: str								=     '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


A		: Any								=     '''
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    a_ (			datasets.Metric    ):






			def    UpperCamelCase_				(      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    UpperCamelCase_				(      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    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase=None				,			__UpperCamelCase="uniform_average"				,			__UpperCamelCase=True  ):
									_lowercase					       =					mean_squared_error(
									    __UpperCamelCase				,			__UpperCamelCase				,			sample_weight=__UpperCamelCase				,			multioutput=__UpperCamelCase				,			squared=__UpperCamelCase  )

									return {"mse": mse}