

from typing import 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 ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


_UpperCAmelCase	:     int		       = logging.get_logger(__name__)







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    ['''input_features''', '''attention_mask''']




				def __init__(      self   ,     snake_case=80   ,     snake_case=1_6000   ,     snake_case=0.0   ,     snake_case=10   ,     snake_case=25   ,     snake_case="hamming_window"   ,     snake_case=3_27_68.0   ,     snake_case=0.97   ,     snake_case=1.0   ,     snake_case=True   ,     snake_case=True   ,     snake_case=False   ,     **snake_case   ,     ):
											super().__init__(feature_size=snake_case   ,     sampling_rate=snake_case   ,     padding_value=snake_case   ,     **snake_case )

											snake_case_           =					feature_size
											snake_case_           =					sampling_rate
											snake_case_           =					padding_value
											snake_case_           =					hop_length
											snake_case_           =					win_length
											snake_case_           =					frame_signal_scale
											snake_case_           =					preemphasis_coeff
											snake_case_           =					mel_floor
											snake_case_           =					normalize_means
											snake_case_           =					normalize_vars
											snake_case_           =					win_function
											snake_case_           =					return_attention_mask

											snake_case_           =					win_length * sampling_rate // 1000
											snake_case_           =					hop_length * sampling_rate // 1000

											snake_case_           =					optimal_fft_length(self.sample_size )
											snake_case_           =					(self.n_fft // 2) + 1




				def 					a							(      self   ,     snake_case ):
											if self.win_function == "hamming_window":
																		snake_case_           =					window_function(window_length=self.sample_size   ,     name=self.win_function   ,     periodic=snake_case )
											else:
																		snake_case_           =					window_function(window_length=self.sample_size   ,     name=self.win_function )

											snake_case_           =					mel_filter_bank(
											    num_frequency_bins=self.n_freqs   ,     num_mel_filters=self.feature_size   ,     min_frequency=0.0   ,     max_frequency=self.sampling_rate / 2.0   ,     sampling_rate=self.sampling_rate   ,     )

											snake_case_           =					spectrogram(
											    one_waveform * self.frame_signal_scale   ,     window=snake_case   ,     frame_length=self.sample_size   ,     hop_length=self.sample_stride   ,     fft_length=self.n_fft   ,     center=snake_case   ,     preemphasis=self.preemphasis_coeff   ,     mel_filters=snake_case   ,     mel_floor=self.mel_floor   ,     log_mel='log'   ,     )
											return msfc_features.T




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											# make sure we normalize float32 arrays
											if self.normalize_means:
																		snake_case_           =					x[:input_length].mean(axis=0 )
																		snake_case_           =					np.subtract(snake_case   ,     snake_case )
											if self.normalize_vars:
																		snake_case_           =					x[:input_length].std(axis=0 )
																		snake_case_           =					np.divide(snake_case   ,     snake_case )

											if input_length < x.shape[0]:
																		snake_case_           =					padding_value

											# make sure array is in float32
											snake_case_           =					x.astype(np.floataa )

											return x




				def 					a							(      self   ,     snake_case   ,     snake_case = None ):
											snake_case_           =					attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
											return [self._normalize_one(snake_case   ,     snake_case   ,     self.padding_value ) for x, n in zip(snake_case   ,     snake_case )]




				def __call__(      self   ,     snake_case   ,     snake_case = False   ,     snake_case = None   ,     snake_case = False   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     **snake_case   ,     ):

											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 `raw_speech` 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.' )

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

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

											# always return batch
											if not is_batched:
																		snake_case_           =					[raw_speech]

											# extract fbank features
											snake_case_           =					[self._extract_mfsc_features(snake_case ) for one_waveform in raw_speech]

											# convert into correct format for padding
											snake_case_           =					BatchFeature({'input_features': features} )

											snake_case_           =					self.pad(
											    snake_case   ,     padding=snake_case   ,     max_length=snake_case   ,     truncation=snake_case   ,     pad_to_multiple_of=snake_case   ,     return_attention_mask=snake_case   ,     **snake_case   ,     )
											# make sure list is in array format
											snake_case_           =					padded_inputs.get('input_features' )
											if isinstance(input_features[0]   ,     snake_case ):
																		snake_case_           =					[np.asarray(snake_case   ,     dtype=np.floataa ) for feature in input_features]

											snake_case_           =					padded_inputs.get('attention_mask' )
											if attention_mask is not None:
																		snake_case_           =					[np.asarray(snake_case   ,     dtype=np.intaa ) for array in attention_mask]

											if self.normalize_means or self.normalize_vars:
																		snake_case_           =					(
																		    np.array(snake_case   ,     dtype=np.intaa )
																		    if self._get_padding_strategies(snake_case   ,     max_length=snake_case ) is not PaddingStrategy.DO_NOT_PAD
																		    and padding
																		    else None
																		)
																		snake_case_           =					self.normalize(
																		    padded_inputs['input_features']   ,     attention_mask=snake_case )

											if return_tensors is not None:
																		snake_case_           =					padded_inputs.convert_to_tensors(snake_case )

											return padded_inputs







def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							if not nums:  # Makes sure that the list is not empty
														raise ValueError('List is empty'   )

							snake_case_           =					sum(UpperCamelCase__   ) / len(UpperCamelCase__   )  # Calculate the average
							return sum(abs(x - average   ) for x in nums   ) / len(UpperCamelCase__   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




