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
  
  
  SCREAMING_SNAKE_CASE : int          =    logging.get_logger(__name__)
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							Any         = ["""input_values""", """attention_mask"""]
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_=1				,    UpperCamelCase_=16000				,    UpperCamelCase_=128				,    UpperCamelCase_=1024				,    UpperCamelCase_=0.0				,    UpperCamelCase_=True				,    UpperCamelCase_=-4.267_7393				,    UpperCamelCase_=4.568_9974				,    UpperCamelCase_=False				,    **UpperCamelCase_				,    )		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								super().__init__(feature_size=UpperCamelCase_				,    sampling_rate=UpperCamelCase_				,    padding_value=UpperCamelCase_				,    **UpperCamelCase_							)
  								a_       :   List[str]			=	num_mel_bins
  								a_       :   Union[str, Any]			=	max_length
  								a_       :   str			=	do_normalize
  								a_       :   Optional[int]			=	mean
  								a_       :   int			=	std
  								a_       :   Dict			=	return_attention_mask
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    )		->			np.ndarray:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   str			=	torch.from_numpy(UpperCamelCase_							).unsqueeze(0							)
  								a_       :   Dict			=	ta_kaldi.fbank(
  								    UpperCamelCase_				,    htk_compat=UpperCamelCase_				,    sample_frequency=self.sampling_rate				,    use_energy=UpperCamelCase_				,    window_type="""hanning"""				,    num_mel_bins=self.num_mel_bins				,    dither=0.0				,    frame_shift=10				,    )
  
  								a_       :   List[Any]			=	fbank.shape[0]
  								a_       :   int			=	max_length - n_frames
  
  								# pad or truncate, depending on difference
  								if difference > 0:
  															a_       :   str			=	torch.nn.ZeroPadad((0, 0, 0, difference)							)
  															a_       :   str			=	pad_module(UpperCamelCase_							)
  								elif difference < 0:
  															a_       :   Optional[int]			=	fbank[0:max_length, :]
  
  								a_       :   Union[str, Any]			=	fbank.numpy()
  
  								return fbank
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			np.ndarray:
  								"""simple docstring"""
  
  
  
  
  
  
  								return (input_values - (self.mean)) / (self.std * 2)
  
  
  
  
  
  	def __call__(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None				,    UpperCamelCase_ = None				,    **UpperCamelCase_				,    )		->			BatchFeature:
  								"""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."""							)
  
  								a_       :   Tuple			=	isinstance(UpperCamelCase_				,    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_       :   int			=	is_batched_numpy or (
  								    isinstance(UpperCamelCase_				,    (list, tuple)							) and (isinstance(raw_speech[0]				,    (np.ndarray, tuple, list)							))
  								)
  
  								if is_batched:
  															a_       :   Union[str, Any]			=	[np.asarray(UpperCamelCase_				,    dtype=np.floataa							) for speech in raw_speech]
  								elif not is_batched and not isinstance(UpperCamelCase_				,    np.ndarray							):
  															a_       :   int			=	np.asarray(UpperCamelCase_				,    dtype=np.floataa							)
  								elif isinstance(UpperCamelCase_				,    np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
  															a_       :   Union[str, Any]			=	raw_speech.astype(np.floataa							)
  
  								# always return batch
  								if not is_batched:
  															a_       :   List[str]			=	[raw_speech]
  
  								# extract fbank features and pad/truncate to max_length
  								a_       :   Tuple			=	[self._extract_fbank_features(UpperCamelCase_				,    max_length=self.max_length							) for waveform in raw_speech]
  
  								# convert into BatchFeature
  								a_       :   List[Any]			=	BatchFeature({"""input_values""": features}							)
  
  								# make sure list is in array format
  								a_       :   int			=	padded_inputs.get("""input_values"""							)
  								if isinstance(input_values[0]				,    UpperCamelCase_							):
  															a_       :   Union[str, Any]			=	[np.asarray(UpperCamelCase_				,    dtype=np.floataa							) for feature in input_values]
  
  								# normalization
  								if self.do_normalize:
  															a_       :   Dict			=	[self.normalize(UpperCamelCase_							) for feature in input_values]
  
  								if return_tensors is not None:
  															a_       :   Optional[int]			=	padded_inputs.convert_to_tensors(UpperCamelCase_							)
  
  								return padded_inputs
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  SCREAMING_SNAKE_CASE : Optional[Any]          =    {
      "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
  }
  
  try:
  	if not is_sentencepiece_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Optional[int]          =    ["CamembertTokenizer"]
  
  try:
  	if not is_tokenizers_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Any          =    ["CamembertTokenizerFast"]
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Union[str, Any]          =    [
  	    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  	    "CamembertForCausalLM",
  	    "CamembertForMaskedLM",
  	    "CamembertForMultipleChoice",
  	    "CamembertForQuestionAnswering",
  	    "CamembertForSequenceClassification",
  	    "CamembertForTokenClassification",
  	    "CamembertModel",
  	    "CamembertPreTrainedModel",
  	]
  
  try:
  	if not is_tf_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : List[Any]          =    [
  	    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  	    "TFCamembertForCausalLM",
  	    "TFCamembertForMaskedLM",
  	    "TFCamembertForMultipleChoice",
  	    "TFCamembertForQuestionAnswering",
  	    "TFCamembertForSequenceClassification",
  	    "TFCamembertForTokenClassification",
  	    "TFCamembertModel",
  	    "TFCamembertPreTrainedModel",
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig
  
  	try:
  		if not is_sentencepiece_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert import CamembertTokenizer
  
  	try:
  		if not is_tokenizers_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert_fast import CamembertTokenizerFast
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_camembert import (
  		    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    CamembertForCausalLM,
  		    CamembertForMaskedLM,
  		    CamembertForMultipleChoice,
  		    CamembertForQuestionAnswering,
  		    CamembertForSequenceClassification,
  		    CamembertForTokenClassification,
  		    CamembertModel,
  		    CamembertPreTrainedModel,
  		)
  
  	try:
  		if not is_tf_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_tf_camembert import (
  		    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    TFCamembertForCausalLM,
  		    TFCamembertForMaskedLM,
  		    TFCamembertForMultipleChoice,
  		    TFCamembertForQuestionAnswering,
  		    TFCamembertForSequenceClassification,
  		    TFCamembertForTokenClassification,
  		    TFCamembertModel,
  		    TFCamembertPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	SCREAMING_SNAKE_CASE : Any          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  