






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


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





class 	snake_case__ (							A__				):
	lowercase__						:					Tuple											=   ['''audio_values''', '''audio_mask''']
	def __init__(    self      ,       lowerCAmelCase__=20_48      ,       lowerCAmelCase__=1      ,       lowerCAmelCase__=[16, 16]      ,       lowerCAmelCase__=1_28      ,       lowerCAmelCase__=4_41_00      ,       lowerCAmelCase__=86      ,       lowerCAmelCase__=20_48      ,       lowerCAmelCase__=0.0      ,       **lowerCAmelCase__      ,       )							->				str:
							super().__init__(
							    feature_size=__snake_case      ,       sampling_rate=__snake_case      ,       padding_value=__snake_case      ,       **__snake_case      ,       )

							__magic_name__      :       Tuple       						= spectrogram_length
							__magic_name__      :       Tuple       						= num_channels
							__magic_name__      :       Dict       						= patch_size
							__magic_name__      :       Optional[Any]       						= feature_size // self.patch_size[1]
							__magic_name__      :       Tuple       						= n_fft
							__magic_name__      :       Optional[int]       						= sampling_rate // hop_length_to_sampling_rate
							__magic_name__      :       Tuple       						= sampling_rate
							__magic_name__      :       Dict       						= padding_value
							__magic_name__      :       str       						= mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2      ,       num_mel_filters=__snake_case      ,       min_frequency=0.0      ,       max_frequency=2_20_50.0      ,       sampling_rate=__snake_case      ,       norm="""slaney"""      ,       mel_scale="""slaney"""      ,       ).T
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				np.ndarray:
							__magic_name__      :       Union[str, Any]       						= spectrogram(
							    __snake_case      ,       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      ,       )
							__magic_name__      :       int       						= log_spec[:, :-1]
							__magic_name__      :       Union[str, Any]       						= log_spec - 2_0.0
							__magic_name__      :       Tuple       						= np.clip(log_spec / 4_0.0      ,       -2.0      ,       0.0			) + 1.0
							return log_spec







	def __call__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = True      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = False      ,       **lowerCAmelCase__      ,       )							->				BatchFeature:

							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."""			)

							__magic_name__      :       List[str]       						= 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}'			)
							__magic_name__      :       Optional[Any]       						= is_batched_numpy or (
							    isinstance(__snake_case      ,       (list, tuple)			) and (isinstance(raw_speech[0]      ,       (np.ndarray, tuple, list)			))
							)
							if is_batched:
													__magic_name__      :       List[str]       						= [np.asarray([speech]      ,       dtype=np.floataa			).T for speech in raw_speech]
							elif not is_batched and not isinstance(__snake_case      ,       np.ndarray			):
													__magic_name__      :       Optional[Any]       						= np.asarray(__snake_case      ,       dtype=np.floataa			)
							elif isinstance(__snake_case      ,       np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
													__magic_name__      :       List[str]       						= raw_speech.astype(np.floataa			)
							# always return batch
							if not is_batched:
													__magic_name__      :       Optional[int]       						= [np.asarray([raw_speech]			).T]

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

							# Create audio attention mask
							__magic_name__      :       Dict       						= 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:
													__magic_name__      :       str       						= [
													    (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
													]
													__magic_name__      :       Optional[int]       						= np.array(__snake_case			).astype(np.floataa			)

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

							# return as BatchFeature
							if return_attention_mask:
													__magic_name__      :       Any       						= {"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
							else:
													__magic_name__      :       Optional[Any]       						= {"""audio_values""": padded_audio_features}

							__magic_name__      :       Union[str, Any]       						= BatchFeature(data=__snake_case      ,       tensor_type=__snake_case			)
							return encoded_inputs














def    UpperCamelCase						(	_A = 1,   _A = 1000			):






						"""simple docstring"""







						__magic_name__      :       Optional[int]       						= 1
						__magic_name__      :       Dict       						= 0

						for divide_by_number in range(_A,   digit + 1			):
												__magic_name__      :       list[int]       						= []
												__magic_name__      :       Any       						= numerator
												for _ in range(1,   digit + 1			):
																		if now_divide in has_been_divided:
																								if longest_list_length < len(_A			):
																														__magic_name__      :       int       						= len(_A			)
																														__magic_name__      :       Dict       						= divide_by_number
																		else:
																								has_been_divided.append(_A			)
																								__magic_name__      :       Optional[int]       						= now_divide * 10 % divide_by_number

						return the_digit


# Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()
