code: 
  
  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
  
  
  __A   :     List[Any]      =			logging.get_logger(__name__)
  
  
  
  
  
  
  class    __A				(							snake_case__ ):
      lowerCAmelCase_							:		Tuple						=							["input_values", "attention_mask"]
  
  
  
  
  
  
      def __init__(	self			:    str    ,				UpperCAmelCase_			:    Tuple=1    ,				UpperCAmelCase_			:    str=16000    ,				UpperCAmelCase_			:    List[str]=128    ,				UpperCAmelCase_			:    int=1024    ,				UpperCAmelCase_			:    List[str]=0.0    ,				UpperCAmelCase_			:    Dict=True    ,				UpperCAmelCase_			:    Tuple=-4.2_67_73_93    ,				UpperCAmelCase_			:    Union[str, Any]=4.5_68_99_74    ,				UpperCAmelCase_			:    Any=False    ,				**UpperCAmelCase_			:    List[str]    ,				)     ->   List[Any]:
          super().__init__(feature_size=__snake_case    ,				sampling_rate=__snake_case    ,				padding_value=__snake_case    ,				**__snake_case    )
          lowerCAmelCase				:			Any    			=      num_mel_bins
          lowerCAmelCase				:			Tuple    			=      max_length
          lowerCAmelCase				:			Optional[Any]    			=      do_normalize
          lowerCAmelCase				:			List[str]    			=      mean
          lowerCAmelCase				:			Optional[int]    			=      std
          lowerCAmelCase				:			Optional[int]    			=      return_attention_mask
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    List[Any]    ,				)     ->   Tuple:
          lowerCAmelCase				:			Optional[Any]    			=      torch.from_numpy(__snake_case    ).unsqueeze(0    )
          lowerCAmelCase				:			Any    			=      ta_kaldi.fbank(
              __snake_case    ,				htk_compat=__snake_case    ,				sample_frequency=self.sampling_rate    ,				use_energy=__snake_case    ,				window_type='hanning'    ,				num_mel_bins=self.num_mel_bins    ,				dither=0.0    ,				frame_shift=10    ,				)
  
          lowerCAmelCase				:			List[str]    			=      fbank.shape[0]
          lowerCAmelCase				:			Optional[Any]    			=      max_length - n_frames
  
          # pad or truncate, depending on difference
          if difference > 0:
              lowerCAmelCase				:			List[Any]    			=      torch.nn.ZeroPadad((0, 0, 0, difference)    )
              lowerCAmelCase				:			Any    			=      pad_module(__snake_case    )
          elif difference < 0:
              lowerCAmelCase				:			Optional[Any]    			=      fbank[0:max_length, :]
  
          lowerCAmelCase				:			int    			=      fbank.numpy()
  
          return fbank
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ,				UpperCAmelCase_			:    List[str]    )     ->   str:
          return (input_values - (self.mean)) / (self.std * 2)
  
  
  
  
  
  
      def __call__(	self			:    Tuple    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Optional[Any] = None    ,				UpperCAmelCase_			:    Optional[Any] = None    ,				**UpperCAmelCase_			:    Dict    ,				)     ->   Dict:
  
          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				:			Optional[int]    			=      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}"    )
          lowerCAmelCase				:			Tuple    			=      is_batched_numpy or (
              isinstance(__snake_case    ,				(list, tuple)    ) and (isinstance(raw_speech[0]    ,				(np.ndarray, tuple, list)    ))
          )
  
          if is_batched:
              lowerCAmelCase				:			Optional[int]    			=      [np.asarray(__snake_case    ,				dtype=np.floataa    ) for speech in raw_speech]
          elif not is_batched and not isinstance(__snake_case    ,				np.ndarray    ):
              lowerCAmelCase				:			Tuple    			=      np.asarray(__snake_case    ,				dtype=np.floataa    )
          elif isinstance(__snake_case    ,				np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
              lowerCAmelCase				:			Tuple    			=      raw_speech.astype(np.floataa    )
  
          # always return batch
          if not is_batched:
              lowerCAmelCase				:			int    			=      [raw_speech]
  
          # extract fbank features and pad/truncate to max_length
          lowerCAmelCase				:			Optional[Any]    			=      [self._extract_fbank_features(__snake_case    ,				max_length=self.max_length    ) for waveform in raw_speech]
  
          # convert into BatchFeature
          lowerCAmelCase				:			Tuple    			=      BatchFeature({'input_values': features}    )
  
          # make sure list is in array format
          lowerCAmelCase				:			int    			=      padded_inputs.get('input_values'    )
          if isinstance(input_values[0]    ,				__snake_case    ):
              lowerCAmelCase				:			Optional[int]    			=      [np.asarray(__snake_case    ,				dtype=np.floataa    ) for feature in input_values]
  
          # normalization
          if self.do_normalize:
              lowerCAmelCase				:			Any    			=      [self.normalize(__snake_case    ) for feature in input_values]
  
          if return_tensors is not None:
              lowerCAmelCase				:			Union[str, Any]    			=      padded_inputs.convert_to_tensors(__snake_case    )
  
          return padded_inputs
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
  
  
  __A   :     Dict      =			{}
  
  try:
   if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     List[Any]      =			['''BarthezTokenizer''']
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     List[str]      =			['''BarthezTokenizerFast''']
  
  
  if TYPE_CHECKING:
   try:
    if not is_sentencepiece_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_barthez import BarthezTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_barthez_fast import BarthezTokenizerFast
  
  else:
   import sys
  
   __A   :     Tuple      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  