


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


__UpperCamelCase    		=       logging.get_logger(__name__)







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Tuple    =		['input_features', 'attention_mask']




			def __init__(							self      ,						snake_case=80      ,						snake_case=16000      ,						snake_case=80      ,						snake_case=0.0      ,						snake_case=True      ,						snake_case=True      ,						snake_case=True      ,						**snake_case      ,						):

						'''simple docstring'''


						super().__init__(feature_size=snake_case      ,						sampling_rate=snake_case      ,						padding_value=snake_case      ,						**snake_case     )
						UpperCamelCase__					=       num_mel_bins
						UpperCamelCase__					=       do_ceptral_normalize
						UpperCamelCase__					=       normalize_means
						UpperCamelCase__					=       normalize_vars
						UpperCamelCase__					=       True




			def 				snake_case__				(							self      ,						snake_case      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
						UpperCamelCase__					=       torch.from_numpy(snake_case     ).unsqueeze(0     )
						UpperCamelCase__					=       ta_kaldi.fbank(snake_case      ,						num_mel_bins=self.num_mel_bins      ,						sample_frequency=self.sampling_rate     )
						return features.numpy()




			@staticmethod
			def 				snake_case__				(							snake_case      ,						snake_case      ,						snake_case = True      ,						snake_case = True      ,						snake_case = 0.0      ,						):

						'''simple docstring'''


						if normalize_means:
									UpperCamelCase__					=       x[:input_length].mean(axis=0     )
									UpperCamelCase__					=       np.subtract(snake_case      ,						snake_case     )
						if normalize_vars:
									UpperCamelCase__					=       x[:input_length].std(axis=0     )
									UpperCamelCase__					=       np.divide(snake_case      ,						snake_case     )

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

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

						return x




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

						'''simple docstring'''


						UpperCamelCase__					=       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      ,						snake_case      ,						snake_case = False      ,						snake_case = None      ,						snake_case = False      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						**snake_case      ,						):

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

						if is_batched:
									UpperCamelCase__					=       [np.asarray(snake_case      ,						dtype=np.floataa     ) for speech in raw_speech]
						elif not is_batched and not isinstance(snake_case      ,						np.ndarray     ):
									UpperCamelCase__					=       np.asarray(snake_case      ,						dtype=np.floataa     )
						elif isinstance(snake_case      ,						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
						UpperCamelCase__					=       [self._extract_fbank_features(snake_case     ) for waveform in raw_speech]

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

						UpperCamelCase__					=       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
						UpperCamelCase__					=       padded_inputs.get("input_features"     )
						if isinstance(input_features[0]      ,						snake_case     ):
									UpperCamelCase__					=       [np.asarray(snake_case      ,						dtype=np.floataa     ) for feature in input_features]

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

						# Utterance-level cepstral mean and variance normalization
						if self.do_ceptral_normalize:
									UpperCamelCase__					=       (
									    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
									)
									UpperCamelCase__					=       self.normalize(
									    padded_inputs["input_features"]      ,						attention_mask=snake_case     )

						if return_tensors is not None:
									UpperCamelCase__					=       padded_inputs.convert_to_tensors(snake_case     )

						return padded_inputs






from typing import Any







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       data
						UpperCamelCase__					=       None







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       None




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.head
						while temp is not None:
									print(temp.data      ,						end=" "     )
									UpperCamelCase__					=       temp.next
						print()




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       Node(snake_case     )
						UpperCamelCase__					=       self.head
						UpperCamelCase__					=       new_node







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						if node_data_a == node_data_a:
									return
						else:
									UpperCamelCase__					=       self.head
									while node_a is not None and node_a.data != node_data_a:
												UpperCamelCase__					=       node_a.next

									UpperCamelCase__					=       self.head
									while node_a is not None and node_a.data != node_data_a:
												UpperCamelCase__					=       node_a.next

									if node_a is None or node_a is None:
												return

									UpperCamelCase__,      UpperCamelCase__					=       node_a.data, node_a.data


if __name__ == "__main__":
						__UpperCamelCase    		=       LinkedList()
						for i in range(5, 0, -1):
												ll.push(i)

						ll.print_list()

						ll.swap_nodes(1, 4)
						print('After swapping')
						ll.print_list()


