






'''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


UpperCamelCase     :   Optional[Any]	      =		logging.get_logger(__name__)




class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		_UpperCamelCase						=       ['input_features', 'attention_mask']






		def __init__(   self ,_lowerCAmelCase=80 ,_lowerCAmelCase=1_60_00 ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=10 ,_lowerCAmelCase=25 ,_lowerCAmelCase="hamming_window" ,_lowerCAmelCase=3_2768.0 ,_lowerCAmelCase=0.97 ,_lowerCAmelCase=1.0 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase=False ,**_lowerCAmelCase ,):
								super().__init__(feature_size=_lowerCAmelCase ,sampling_rate=_lowerCAmelCase ,padding_value=_lowerCAmelCase ,**_lowerCAmelCase							)

								lowerCamelCase__								=	feature_size
								lowerCamelCase__								=	sampling_rate
								lowerCamelCase__								=	padding_value
								lowerCamelCase__								=	hop_length
								lowerCamelCase__								=	win_length
								lowerCamelCase__								=	frame_signal_scale
								lowerCamelCase__								=	preemphasis_coeff
								lowerCamelCase__								=	mel_floor
								lowerCamelCase__								=	normalize_means
								lowerCamelCase__								=	normalize_vars
								lowerCamelCase__								=	win_function
								lowerCamelCase__								=	return_attention_mask

								lowerCamelCase__								=	win_length * sampling_rate // 10_00
								lowerCamelCase__								=	hop_length * sampling_rate // 10_00

								lowerCamelCase__								=	optimal_fft_length(self.sample_size							)
								lowerCamelCase__								=	(self.n_fft // 2) + 1






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								if self.win_function == "hamming_window":
														lowerCamelCase__								=	window_function(window_length=self.sample_size ,name=self.win_function ,periodic=_lowerCAmelCase							)
								else:
														lowerCamelCase__								=	window_function(window_length=self.sample_size ,name=self.win_function							)

								lowerCamelCase__								=	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 ,)

								lowerCamelCase__								=	spectrogram(
								    one_waveform * self.frame_signal_scale ,window=_lowerCAmelCase ,frame_length=self.sample_size ,hop_length=self.sample_stride ,fft_length=self.n_fft ,center=_lowerCAmelCase ,preemphasis=self.preemphasis_coeff ,mel_filters=_lowerCAmelCase ,mel_floor=self.mel_floor ,log_mel="""log""" ,)
								return msfc_features.T






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								# make sure we normalize float32 arrays
								if self.normalize_means:
														lowerCamelCase__								=	x[:input_length].mean(axis=0							)
														lowerCamelCase__								=	np.subtract(_lowerCAmelCase ,_lowerCAmelCase							)
								if self.normalize_vars:
														lowerCamelCase__								=	x[:input_length].std(axis=0							)
														lowerCamelCase__								=	np.divide(_lowerCAmelCase ,_lowerCAmelCase							)

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

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

								return x






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase = None							):
								lowerCamelCase__								=	attention_mask.sum(-1							) if attention_mask is not None else [x.shape[0] for x in input_features]
								return [self._normalize_one(_lowerCAmelCase ,_lowerCAmelCase ,self.padding_value							) for x, n in zip(_lowerCAmelCase ,_lowerCAmelCase							)]






		def __call__(   self ,_lowerCAmelCase ,_lowerCAmelCase = False ,_lowerCAmelCase = None ,_lowerCAmelCase = False ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,_lowerCAmelCase = None ,**_lowerCAmelCase ,):

								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."""							)

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

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

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

								# extract fbank features
								lowerCamelCase__								=	[self._extract_mfsc_features(_lowerCAmelCase							) for one_waveform in raw_speech]

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

								lowerCamelCase__								=	self.pad(
								    _lowerCAmelCase ,padding=_lowerCAmelCase ,max_length=_lowerCAmelCase ,truncation=_lowerCAmelCase ,pad_to_multiple_of=_lowerCAmelCase ,return_attention_mask=_lowerCAmelCase ,**_lowerCAmelCase ,)
								# make sure list is in array format
								lowerCamelCase__								=	padded_inputs.get("""input_features"""							)
								if isinstance(input_features[0] ,_lowerCAmelCase							):
														lowerCamelCase__								=	[np.asarray(_lowerCAmelCase ,dtype=np.floataa							) for feature in input_features]

								lowerCamelCase__								=	padded_inputs.get("""attention_mask"""							)
								if attention_mask is not None:
														lowerCamelCase__								=	[np.asarray(_lowerCAmelCase ,dtype=np.intaa							) for array in attention_mask]

								if self.normalize_means or self.normalize_vars:
														lowerCamelCase__								=	(
														    np.array(_lowerCAmelCase ,dtype=np.intaa							)
														    if self._get_padding_strategies(_lowerCAmelCase ,max_length=_lowerCAmelCase							) is not PaddingStrategy.DO_NOT_PAD
														    and padding
														    else None
														)
														lowerCamelCase__								=	self.normalize(
														    padded_inputs["""input_features"""] ,attention_mask=_lowerCAmelCase							)

								if return_tensors is not None:
														lowerCamelCase__								=	padded_inputs.convert_to_tensors(_lowerCAmelCase							)

								return padded_inputs









"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : list				)->		list:






     '''simple docstring'''



     UpperCAmelCase__						:    List[str]							=							False
     while is_sorted is False:  # Until all the indices are traversed keep looping
          UpperCAmelCase__						:    List[Any]							=							True
          for i in range(0			,					len(snake_case				) - 1			,					2				):  # iterating over all even indices
               if input_list[i] > input_list[i + 1]:
                    UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCAmelCase__						:    int							=							False

          for i in range(1			,					len(snake_case				) - 1			,					2				):  # iterating over all odd indices
               if input_list[i] > input_list[i + 1]:
                    UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCAmelCase__						:    str							=							False
     return input_list


if __name__ == "__main__":
 print("""Enter list to be sorted""")
 _lowerCAmelCase	:	List[str]				=      [int(x) for x in input().split()]
 # inputing elements of the list in one line
 _lowerCAmelCase	:	Optional[int]				=      odd_even_sort(input_list)
 print("""The sorted list is""")
 print(sorted_list)


