





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


__A							:       str         =  logging.get_logger(__name__)

class       A_			(a_     ):
	UpperCAmelCase__   =     ['''audio_values''', '''audio_mask''']
	def __init__(       self				,   _A=2_0_4_8				,   _A=1				,   _A=[1_6, 1_6]				,   _A=1_2_8				,   _A=4_4_1_0_0				,   _A=8_6				,   _A=2_0_4_8				,   _A=0.0				,   **_A				,   ):






				'''simple docstring'''







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

				UpperCAmelCase  						=							spectrogram_length
				UpperCAmelCase  						=							num_channels
				UpperCAmelCase  						=							patch_size
				UpperCAmelCase  						=							feature_size // self.patch_size[1]
				UpperCAmelCase  						=							n_fft
				UpperCAmelCase  						=							sampling_rate // hop_length_to_sampling_rate
				UpperCAmelCase  						=							sampling_rate
				UpperCAmelCase  						=							padding_value
				UpperCAmelCase  						=							mel_filter_bank(
				    num_frequency_bins=1 + n_fft // 2				,   num_mel_filters=UpperCamelCase_				,   min_frequency=0.0				,   max_frequency=2_2_0_5_0.0				,   sampling_rate=UpperCamelCase_				,   norm='''slaney'''				,   mel_scale='''slaney'''				,   ).T
	def 	_lowercase							(       self				,   _A						):






				'''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.T				,   log_mel='''dB'''				,   db_range=8_0.0				,   )
				UpperCAmelCase  						=							log_spec[:, :-1]
				UpperCAmelCase  						=							log_spec - 2_0.0
				UpperCAmelCase  						=							np.clip(log_spec / 4_0.0				,   -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.'''						)

				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]

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

				# Create audio attention mask
				UpperCAmelCase  						=							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:
							UpperCAmelCase  						=							[
							    (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
							]
							UpperCAmelCase  						=							np.array(UpperCamelCase_						).astype(np.floataa						)

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

				# return as BatchFeature
				if return_attention_mask:
							UpperCAmelCase  						=							{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
				else:
							UpperCAmelCase  						=							{'''audio_values''': padded_audio_features}

				UpperCAmelCase  						=							BatchFeature(data=UpperCamelCase_				,   tensor_type=UpperCamelCase_						)
				return encoded_inputs










'''simple docstring'''




def 						__lowerCamelCase			(	UpperCAmelCase_ = 10_00							)   ->int:
	return sum(e for e in range(3					,				UpperCAmelCase_							) if e % 3 == 0 or e % 5 == 0							)


if __name__ == "__main__":
			print(f"""{solution() = }""")



