



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











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))






