





'''simple docstring'''







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


snake_case_     :       int						=  logging.get_logger(__name__)


class 					__a      (__SCREAMING_SNAKE_CASE     ):
							__a					:   Any									=       ['input_features', 'attention_mask']

							def __init__(							self							:     Union[str, Any]       ,     __magic_name__							:     Dict=80       ,     __magic_name__							:     Union[str, Any]=1_60_00       ,     __magic_name__							:     Any=0.0       ,     __magic_name__							:     int=10       ,     __magic_name__							:     Tuple=25       ,     __magic_name__							:     Union[str, Any]="hamming_window"       ,     __magic_name__							:     List[str]=3_27_68.0       ,     __magic_name__							:     Dict=0.9_7       ,     __magic_name__							:     Dict=1.0       ,     __magic_name__							:     Dict=True       ,     __magic_name__							:     Union[str, Any]=True       ,     __magic_name__							:     List[str]=False       ,     **__magic_name__							:     Tuple       ,     )					->       Tuple:







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

													UpperCAmelCase_			:      int     						=  feature_size
													UpperCAmelCase_			:      Any     						=  sampling_rate
													UpperCAmelCase_			:      Optional[Any]     						=  padding_value
													UpperCAmelCase_			:      List[str]     						=  hop_length
													UpperCAmelCase_			:      List[str]     						=  win_length
													UpperCAmelCase_			:      List[Any]     						=  frame_signal_scale
													UpperCAmelCase_			:      Optional[Any]     						=  preemphasis_coeff
													UpperCAmelCase_			:      Optional[int]     						=  mel_floor
													UpperCAmelCase_			:      Any     						=  normalize_means
													UpperCAmelCase_			:      Dict     						=  normalize_vars
													UpperCAmelCase_			:      List[Any]     						=  win_function
													UpperCAmelCase_			:      List[str]     						=  return_attention_mask

													UpperCAmelCase_			:      Union[str, Any]     						=  win_length * sampling_rate // 10_00
													UpperCAmelCase_			:      List[str]     						=  hop_length * sampling_rate // 10_00

													UpperCAmelCase_			:      Tuple     						=  optimal_fft_length(self.sample_size   )
													UpperCAmelCase_			:      Dict     						=  (self.n_fft // 2) + 1

							def     UpperCAmelCase__	(							self							:     List[Any]       ,     __magic_name__							:     Optional[int]   )					->       Any:







													"""simple docstring"""
													if self.win_function == "hamming_window":
																			UpperCAmelCase_			:      str     						=  window_function(window_length=self.sample_size       ,     name=self.win_function       ,     periodic=_a   )
													else:
																			UpperCAmelCase_			:      Tuple     						=  window_function(window_length=self.sample_size       ,     name=self.win_function   )

													UpperCAmelCase_			:      Optional[Any]     						=  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       ,     )

													UpperCAmelCase_			:      List[Any]     						=  spectrogram(
													    one_waveform * self.frame_signal_scale       ,     window=_a       ,     frame_length=self.sample_size       ,     hop_length=self.sample_stride       ,     fft_length=self.n_fft       ,     center=_a       ,     preemphasis=self.preemphasis_coeff       ,     mel_filters=_a       ,     mel_floor=self.mel_floor       ,     log_mel='''log'''       ,     )
													return msfc_features.T

							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     Dict   )					->       Dict:







													"""simple docstring"""
													# make sure we normalize float32 arrays
													if self.normalize_means:
																			UpperCAmelCase_			:      Optional[Any]     						=  x[:input_length].mean(axis=0   )
																			UpperCAmelCase_			:      Union[str, Any]     						=  np.subtract(_a       ,     _a   )
													if self.normalize_vars:
																			UpperCAmelCase_			:      Union[str, Any]     						=  x[:input_length].std(axis=0   )
																			UpperCAmelCase_			:      str     						=  np.divide(_a       ,     _a   )

													if input_length < x.shape[0]:
																			UpperCAmelCase_			:      Optional[int]     						=  padding_value

													# make sure array is in float32
													UpperCAmelCase_			:      str     						=  x.astype(np.floataa   )

													return x

							def     UpperCAmelCase__	(							self							:     Optional[Any]       ,     __magic_name__							:     List[Any]       ,     __magic_name__							:     Optional[int] = None   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  attention_mask.sum(-1   ) if attention_mask is not None else [x.shape[0] for x in input_features]
													return [self._normalize_one(_a       ,     _a       ,     self.padding_value   ) for x, n in zip(_a       ,     _a   )]



							def __call__(							self							:     Optional[int]       ,     __magic_name__							:     Optional[Any]       ,     __magic_name__							:     List[str] = False       ,     __magic_name__							:     Dict = None       ,     __magic_name__							:     Optional[int] = False       ,     __magic_name__							:     Optional[int] = None       ,     __magic_name__							:     str = None       ,     __magic_name__							:     List[str] = None       ,     __magic_name__							:     List[Any] = None       ,     **__magic_name__							:     List[str]       ,     )					->       List[Any]:







													"""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} 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.'''   )

													UpperCAmelCase_			:      Optional[Any]     						=  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}"""   )
													UpperCAmelCase_			:      Optional[int]     						=  is_batched_numpy or (
													    isinstance(_a       ,     (list, tuple)   ) and (isinstance(raw_speech[0]       ,     (np.ndarray, tuple, list)   ))
													)

													if is_batched:
																			UpperCAmelCase_			:      Dict     						=  [np.asarray(_a       ,     dtype=np.floataa   ) for speech in raw_speech]
													elif not is_batched and not isinstance(_a       ,     np.ndarray   ):
																			UpperCAmelCase_			:      Union[str, Any]     						=  np.asarray(_a       ,     dtype=np.floataa   )
													elif isinstance(_a       ,     np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
																			UpperCAmelCase_			:      Tuple     						=  raw_speech.astype(np.floataa   )

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

													# extract fbank features
													UpperCAmelCase_			:      Dict     						=  [self._extract_mfsc_features(_a   ) for one_waveform in raw_speech]

													# convert into correct format for padding
													UpperCAmelCase_			:      List[str]     						=  BatchFeature({'''input_features''': features}   )

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

													UpperCAmelCase_			:      List[Any]     						=  padded_inputs.get('''attention_mask'''   )
													if attention_mask is not None:
																			UpperCAmelCase_			:      List[str]     						=  [np.asarray(_a       ,     dtype=np.intaa   ) for array in attention_mask]

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

													if return_tensors is not None:
																			UpperCAmelCase_			:      Optional[int]     						=  padded_inputs.convert_to_tensors(_a   )

													return padded_inputs












'''simple docstring'''







def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int   ) ->	str:
						if number > 0:
												raise ValueError('''input must be a negative integer'''   )
						UpperCAmelCase_			:      Union[str, Any]     						=  len(bin(SCREAMING_SNAKE_CASE__   )[3:]   )
						UpperCAmelCase_			:      Union[str, Any]     						=  bin(abs(SCREAMING_SNAKE_CASE__   ) - (1 << binary_number_length)   )[3:]
						UpperCAmelCase_			:      Optional[Any]     						=  (
						    (
						        '''1'''
						        + '''0''' * (binary_number_length - len(SCREAMING_SNAKE_CASE__   ))
						        + twos_complement_number
						    )
						    if number < 0
						    else '''0'''
						)
						return "0b" + twos_complement_number


if __name__ == "__main__":
					import doctest

					doctest.testmod()





