


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()


