


"""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 PaddingStrategy, TensorType, logging


snake_case							:	Tuple					      =				logging.get_logger(__name__)







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   List[Any]   				=      ['''input_features''', '''attention_mask''']
					def __init__(				self       :Dict      ,__snake_case       :Union[str, Any]=80      ,__snake_case       :int=1_60_00      ,__snake_case       :Any=80      ,__snake_case       :Optional[Any]=0.0      ,__snake_case       :int=True      ,__snake_case       :Union[str, Any]=True      ,__snake_case       :Union[str, Any]=True      ,**__snake_case       :Union[str, Any]      ,) ->   Union[str, Any]:
												super().__init__(feature_size=__snake_case      ,sampling_rate=__snake_case      ,padding_value=__snake_case      ,**__snake_case				)
												a__					=					num_mel_bins
												a__					=					do_ceptral_normalize
												a__					=					normalize_means
												a__					=					normalize_vars
												a__					=					True
					def 			lowerCamelCase__(				self       :str      ,__snake_case       :np.ndarray      ,) ->   np.ndarray:
												a__					=					waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
												a__					=					torch.from_numpy(__snake_case				).unsqueeze(0				)
												a__					=					ta_kaldi.fbank(__snake_case      ,num_mel_bins=self.num_mel_bins      ,sample_frequency=self.sampling_rate				)
												return features.numpy()
					@staticmethod
					def 			lowerCamelCase__(				__snake_case       :np.ndarray      ,__snake_case       :int      ,__snake_case       :Optional[bool] = True      ,__snake_case       :Optional[bool] = True      ,__snake_case       :float = 0.0      ,) ->   np.ndarray:
												# make sure we normalize float32 arrays
												if normalize_means:
																			a__					=					x[:input_length].mean(axis=0				)
																			a__					=					np.subtract(__snake_case      ,__snake_case				)
												if normalize_vars:
																			a__					=					x[:input_length].std(axis=0				)
																			a__					=					np.divide(__snake_case      ,__snake_case				)

												if input_length < x.shape[0]:
																			a__					=					padding_value

												# make sure array is in float32
												a__					=					x.astype(np.floataa				)

												return x
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :List[np.ndarray]      ,__snake_case       :Optional[np.ndarray] = None				) ->   List[np.ndarray]:
												a__					=					attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
												return [
												    self.utterance_cmvn(__snake_case      ,__snake_case      ,self.normalize_means      ,self.normalize_vars      ,self.padding_value				)
												    for x, n in zip(__snake_case      ,__snake_case				)
												]



					def __call__(				self       :Dict      ,__snake_case       :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,__snake_case       :Union[bool, str, PaddingStrategy] = False      ,__snake_case       :Optional[int] = None      ,__snake_case       :bool = False      ,__snake_case       :Optional[int] = None      ,__snake_case       :Optional[Union[str, TensorType]] = None      ,__snake_case       :Optional[int] = None      ,__snake_case       :Optional[bool] = None      ,**__snake_case       :Tuple      ,) ->   BatchFeature:

												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.'				)

												a__					=					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}'				)
												a__					=					is_batched_numpy or (
												    isinstance(__snake_case      ,(list, tuple)				) and (isinstance(raw_speech[0]      ,(np.ndarray, tuple, list)				))
												)

												if is_batched:
																			a__					=					[np.asarray(__snake_case      ,dtype=np.floataa				) for speech in raw_speech]
												elif not is_batched and not isinstance(__snake_case      ,np.ndarray				):
																			a__					=					np.asarray(__snake_case      ,dtype=np.floataa				)
												elif isinstance(__snake_case      ,np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
																			a__					=					raw_speech.astype(np.floataa				)

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

												# extract fbank features
												a__					=					[self._extract_fbank_features(__snake_case				) for waveform in raw_speech]

												# convert into correct format for padding
												a__					=					BatchFeature({'input_features': features}				)

												a__					=					self.pad(
												    __snake_case      ,padding=__snake_case      ,max_length=__snake_case      ,truncation=__snake_case      ,pad_to_multiple_of=__snake_case      ,return_attention_mask=__snake_case      ,**__snake_case      ,)

												# make sure list is in array format
												a__					=					padded_inputs.get('input_features'				)
												if isinstance(input_features[0]      ,__snake_case				):
																			a__					=					[np.asarray(__snake_case      ,dtype=np.floataa				) for feature in input_features]

												a__					=					padded_inputs.get('attention_mask'				)
												if attention_mask is not None:
																			a__					=					[np.asarray(__snake_case      ,dtype=np.intaa				) for array in attention_mask]

												# Utterance-level cepstral mean and variance normalization
												if self.do_ceptral_normalize:
																			a__					=					(
																			    np.array(__snake_case      ,dtype=np.intaa				)
																			    if self._get_padding_strategies(__snake_case      ,max_length=__snake_case				) is not PaddingStrategy.DO_NOT_PAD
																			    else None
																			)
																			a__					=					self.normalize(
																			    padded_inputs['input_features']      ,attention_mask=__snake_case				)

												if return_tensors is not None:
																			a__					=					padded_inputs.convert_to_tensors(__snake_case				)

												return padded_inputs








from __future__ import annotations






def 							__lowercase       (			__lowerCAmelCase							:    list[int] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int       ):
							if (direction == 1 and array[indexa] > array[indexa]) or (
							    direction == 0 and array[indexa] < array[indexa]
							):
														a__				, a__					=					array[indexa], array[indexa]






def 							__lowercase       (			__lowerCAmelCase							:    list[int] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int       ):
							if length > 1:
														a__					=					int(length / 2       )
														for i in range(__lowerCAmelCase ,   low + middle       ):
																					comp_and_swap(__lowerCAmelCase ,   __lowerCAmelCase ,   i + middle ,   __lowerCAmelCase       )
														bitonic_merge(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
														bitonic_merge(__lowerCAmelCase ,   low + middle ,   __lowerCAmelCase ,   __lowerCAmelCase       )






def 							__lowercase       (			__lowerCAmelCase							:    list[int] ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    int       ):
							if length > 1:
														a__					=					int(length / 2       )
														bitonic_sort(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   1       )
														bitonic_sort(__lowerCAmelCase ,   low + middle ,   __lowerCAmelCase ,   0       )
														bitonic_merge(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )


if __name__ == "__main__":
						snake_case							:	int					      =				input('''Enter numbers separated by a comma:\n''').strip()
						snake_case							:	Optional[int]					      =				[int(item.strip()) for item in user_input.split(''',''')]

						bitonic_sort(unsorted, 0, len(unsorted), 1)
						print('''\nSorted array in ascending order is: ''', end='''''')
						print(*unsorted, sep=''', ''')

						bitonic_merge(unsorted, 0, len(unsorted), 0)
						print('''Sorted array in descending order is: ''', end='''''')
						print(*unsorted, sep=''', ''')




