code: 
  
  
  
  from typing import List, Optional, Union
  
  import numpy as np
  import torch
  import torchaudio.compliance.kaldi as ta_kaldi
  
  from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
  from ...feature_extraction_utils import BatchFeature
  from ...utils import TensorType, logging
  
  
  lowerCAmelCase__	:  int 		=logging.get_logger(__name__)
  
  class      UpperCAmelCase_						(     UpperCamelCase_ ):
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				List[Any]       						=						['''input_values''', '''attention_mask''']
  
  
  
  
  
  
  						def __init__(					self   ,							_A=1   ,							_A=16_000   ,							_A=128   ,							_A=1_024   ,							_A=0.0   ,							_A=True   ,							_A=-4.2_6_7_7_3_9_3   ,							_A=4.5_6_8_9_9_7_4   ,							_A=False   ,							**_A   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													super().__init__(feature_size=_A   ,							sampling_rate=_A   ,							padding_value=_A   ,							**_A	)
  													__SCREAMING_SNAKE_CASE								=      num_mel_bins
  													__SCREAMING_SNAKE_CASE								=      max_length
  													__SCREAMING_SNAKE_CASE								=      do_normalize
  													__SCREAMING_SNAKE_CASE								=      mean
  													__SCREAMING_SNAKE_CASE								=      std
  													__SCREAMING_SNAKE_CASE								=      return_attention_mask
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      torch.from_numpy(_A	).unsqueeze(0	)
  													__SCREAMING_SNAKE_CASE								=      ta_kaldi.fbank(
  													    _A   ,							htk_compat=_A   ,							sample_frequency=self.sampling_rate   ,							use_energy=_A   ,							window_type='hanning'   ,							num_mel_bins=self.num_mel_bins   ,							dither=0.0   ,							frame_shift=10   ,							)
  
  													__SCREAMING_SNAKE_CASE								=      fbank.shape[0]
  													__SCREAMING_SNAKE_CASE								=      max_length - n_frames
  
  													# pad or truncate, depending on difference
  													if difference > 0:
  																				__SCREAMING_SNAKE_CASE								=      torch.nn.ZeroPadad((0, 0, 0, difference)	)
  																				__SCREAMING_SNAKE_CASE								=      pad_module(_A	)
  													elif difference < 0:
  																				__SCREAMING_SNAKE_CASE								=      fbank[0:max_length, :]
  
  													__SCREAMING_SNAKE_CASE								=      fbank.numpy()
  
  													return fbank
  
  
  
  
  
  
  						def  _A			(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													return (input_values - (self.mean)) / (self.std * 2)
  
  
  
  
  
  
  						def __call__(					self   ,							_A   ,							_A = None   ,							_A = None   ,							**_A   ,							):
  
  
  
  
  
  
  
  													'''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.'	)
  
  													__SCREAMING_SNAKE_CASE								=      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}"""	)
  													__SCREAMING_SNAKE_CASE								=      is_batched_numpy or (
  													    isinstance(_A   ,							(list, tuple)	) and (isinstance(raw_speech[0]   ,							(np.ndarray, tuple, list)	))
  													)
  
  													if is_batched:
  																				__SCREAMING_SNAKE_CASE								=      [np.asarray(_A   ,							dtype=np.floataa	) for speech in raw_speech]
  													elif not is_batched and not isinstance(_A   ,							np.ndarray	):
  																				__SCREAMING_SNAKE_CASE								=      np.asarray(_A   ,							dtype=np.floataa	)
  													elif isinstance(_A   ,							np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
  																				__SCREAMING_SNAKE_CASE								=      raw_speech.astype(np.floataa	)
  
  													# always return batch
  													if not is_batched:
  																				__SCREAMING_SNAKE_CASE								=      [raw_speech]
  
  													# extract fbank features and pad/truncate to max_length
  													__SCREAMING_SNAKE_CASE								=      [self._extract_fbank_features(_A   ,							max_length=self.max_length	) for waveform in raw_speech]
  
  													# convert into BatchFeature
  													__SCREAMING_SNAKE_CASE								=      BatchFeature({'input_values': features}	)
  
  													# make sure list is in array format
  													__SCREAMING_SNAKE_CASE								=      padded_inputs.get('input_values'	)
  													if isinstance(input_values[0]   ,							_A	):
  																				__SCREAMING_SNAKE_CASE								=      [np.asarray(_A   ,							dtype=np.floataa	) for feature in input_values]
  
  													# normalization
  													if self.do_normalize:
  																				__SCREAMING_SNAKE_CASE								=      [self.normalize(_A	) for feature in input_values]
  
  													if return_tensors is not None:
  																				__SCREAMING_SNAKE_CASE								=      padded_inputs.convert_to_tensors(_A	)
  
  													return padded_inputs
  
  
  
  
  
  
  
style_context: 
  
  
  
  def 			__lowercase     (				a__     )       ->				list:
  							__SCREAMING_SNAKE_CASE								=      1.3
  							__SCREAMING_SNAKE_CASE								=      len(a__     )
  							__SCREAMING_SNAKE_CASE								=      False
  
  							while not completed:
  														# Update the gap value for a next comb
  														__SCREAMING_SNAKE_CASE								=      int(gap / shrink_factor     )
  														if gap <= 1:
  																					__SCREAMING_SNAKE_CASE								=      True
  
  														__SCREAMING_SNAKE_CASE								=      0
  														while index + gap < len(a__     ):
  																					if data[index] > data[index + gap]:
  																												# Swap values
  																												__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      data[index + gap], data[index]
  																												__SCREAMING_SNAKE_CASE								=      False
  																					index += 1
  
  							return data
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  			lowerCAmelCase__	:  int 		=input('''Enter numbers separated by a comma:\n''').strip()
  			lowerCAmelCase__	:  Dict 		=[int(item) for item in user_input.split(''',''')]
  			print(comb_sort(unsorted))
  
  
  
  
  
  
  