




"""simple docstring"""




def     a__ (				snake_case__				)						->					bool:
			return number & 1 == 0


if __name__ == "__main__":
					import doctest

					doctest.testmod()









"""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


lowerCAmelCase							:			List[str]     							=							logging.get_logger(__name__)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						["audio_values", "audio_mask"]





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





							"""simple docstring"""
							super().__init__(
							    feature_size=_a		, sampling_rate=_a		, padding_value=_a		, **_a		, )

							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=_a		, min_frequency=0.0		, max_frequency=22_050.0		, sampling_rate=_a		, norm="""slaney"""		, mel_scale="""slaney"""		, ).T





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					spectrogram(
							    _a		, 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		, _a		, _a = None		, _a = True		, _a = None		, _a = False		, _a = False		, **_a		, ):





							"""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(_a		, 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(_a		, (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(_a		, np.ndarray ):
										lowerCamelCase			      =					np.asarray(_a		, dtype=np.floataa )
							elif isinstance(_a		, 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]		, _a ):
										lowerCamelCase			      =					[np.asarray(_a		, 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(_a ).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(_a ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
							lowerCamelCase			      =					padded_audio_features * self.padding_value
							for i in range(len(_a ) ):
										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=_a		, tensor_type=_a )
							return encoded_inputs



