"""simple docstring"""







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


UpperCamelCase_    :    int											=						logging.get_logger(__name__)


class      _lowercase						(				SCREAMING_SNAKE_CASE__					):
				lowerCamelCase__									=    ['input_values', 'attention_mask']

				def __init__(       self    :      Optional[Any]    ,      _lowercase    :      str=1    ,      _lowercase    :      int=1_6_0_0_0    ,      _lowercase    :      Optional[int]=1_2_8    ,      _lowercase    :      Tuple=1_0_2_4    ,      _lowercase    :      Optional[int]=0.0    ,      _lowercase    :      Tuple=True    ,      _lowercase    :      Tuple=-4.267_7393    ,      _lowercase    :      int=4.568_9974    ,      _lowercase    :      Dict=False    ,      **_lowercase    :      int    ,      ):


										'''simple docstring'''
										super().__init__(feature_size=_lowercase    ,      sampling_rate=_lowercase    ,      padding_value=_lowercase    ,      **_lowercase       )
										UpperCAmelCase__   		=						num_mel_bins
										UpperCAmelCase__   		=						max_length
										UpperCAmelCase__   		=						do_normalize
										UpperCAmelCase__   		=						mean
										UpperCAmelCase__   		=						std
										UpperCAmelCase__   		=						return_attention_mask

				def     A	(       self    :      Tuple    ,      _lowercase    :      np.ndarray    ,      _lowercase    :      int    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						torch.from_numpy(_lowercase       ).unsqueeze(0       )
										UpperCAmelCase__   		=						ta_kaldi.fbank(
										    _lowercase    ,      htk_compat=_lowercase    ,      sample_frequency=self.sampling_rate    ,      use_energy=_lowercase    ,      window_type="""hanning"""    ,      num_mel_bins=self.num_mel_bins    ,      dither=0.0    ,      frame_shift=1_0    ,      )

										UpperCAmelCase__   		=						fbank.shape[0]
										UpperCAmelCase__   		=						max_length - n_frames

										# pad or truncate, depending on difference
										if difference > 0:
																UpperCAmelCase__   		=						torch.nn.ZeroPadad((0, 0, 0, difference)       )
																UpperCAmelCase__   		=						pad_module(_lowercase       )
										elif difference < 0:
																UpperCAmelCase__   		=						fbank[0:max_length, :]

										UpperCAmelCase__   		=						fbank.numpy()

										return fbank

				def     A	(       self    :      List[str]    ,      _lowercase    :      np.ndarray       ):


										'''simple docstring'''
										return (input_values - (self.mean)) / (self.std * 2)

				def __call__(       self    :      str    ,      _lowercase    :      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,      _lowercase    :      Optional[int] = None    ,      _lowercase    :      Optional[Union[str, TensorType]] = None    ,      **_lowercase    :      Tuple    ,      ):


										'''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__   		=						isinstance(_lowercase    ,      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(_lowercase    ,      (list, tuple)       ) and (isinstance(raw_speech[0]    ,      (np.ndarray, tuple, list)       ))
										)

										if is_batched:
																UpperCAmelCase__   		=						[np.asarray(_lowercase    ,      dtype=np.floataa       ) for speech in raw_speech]
										elif not is_batched and not isinstance(_lowercase    ,      np.ndarray       ):
																UpperCAmelCase__   		=						np.asarray(_lowercase    ,      dtype=np.floataa       )
										elif isinstance(_lowercase    ,      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__   		=						[raw_speech]

										# extract fbank features and pad/truncate to max_length
										UpperCAmelCase__   		=						[self._extract_fbank_features(_lowercase    ,      max_length=self.max_length       ) for waveform in raw_speech]

										# convert into BatchFeature
										UpperCAmelCase__   		=						BatchFeature({"""input_values""": features}       )

										# make sure list is in array format
										UpperCAmelCase__   		=						padded_inputs.get("""input_values"""       )
										if isinstance(input_values[0]    ,      _lowercase       ):
																UpperCAmelCase__   		=						[np.asarray(_lowercase    ,      dtype=np.floataa       ) for feature in input_values]

										# normalization
										if self.do_normalize:
																UpperCAmelCase__   		=						[self.normalize(_lowercase       ) for feature in input_values]

										if return_tensors is not None:
																UpperCAmelCase__   		=						padded_inputs.convert_to_tensors(_lowercase       )

										return padded_inputs


"""simple docstring"""







def 					a__ (						UpperCAmelCase__    :     List[Any]     )->		Dict:
						UpperCAmelCase__   		=						[7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
						for gap in gaps:
												for i in range(UpperCAmelCase__						,     len(UpperCAmelCase__     )     ):
																		UpperCAmelCase__   		=						collection[i]
																		UpperCAmelCase__   		=						i
																		while j >= gap and collection[j - gap] > insert_value:
																								UpperCAmelCase__   		=						collection[j - gap]
																								j -= gap
																		if j != i:
																								UpperCAmelCase__   		=						insert_value
						return collection


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						UpperCamelCase_    :    Union[str, Any]											=						input("""Enter numbers separated by a comma:\n""").strip()
						UpperCamelCase_    :    int											=						[int(item) for item in user_input.split(""",""")]
						print(shell_sort(unsorted))

