


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__   (       A__):
     SCREAMING_SNAKE_CASE__      			=			['''input_features''', '''attention_mask''']





     def __init__(self		,     UpperCAmelCase=8_0		,     UpperCAmelCase=1_6_0_0_0		,     UpperCAmelCase=8_0		,     UpperCAmelCase=0.0		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     **UpperCAmelCase		,     )     ->   Optional[int]:
      super().__init__(feature_size=_a		,     sampling_rate=_a		,     padding_value=_a		,     **_a       )
      _lowercase							      =num_mel_bins
      _lowercase							      =do_ceptral_normalize
      _lowercase							      =normalize_means
      _lowercase							      =normalize_vars
      _lowercase							      =True





     def      __A					(self		,     UpperCAmelCase		,     )     ->   np.ndarray:
      _lowercase							      =waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
      _lowercase							      =torch.from_numpy(_a       ).unsqueeze(0       )
      _lowercase							      =ta_kaldi.fbank(_a		,     num_mel_bins=self.num_mel_bins		,     sample_frequency=self.sampling_rate       )
      return features.numpy()





     @staticmethod
     def      __A					(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase = True		,     UpperCAmelCase = True		,     UpperCAmelCase = 0.0		,     )     ->   np.ndarray:






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

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

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

      return x





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   List[np.ndarray]:
      _lowercase							      =attention_mask.sum(-1       ) if attention_mask is not None else [x.shape[0] for x in input_features]
      return [
          self.utterance_cmvn(_a		,     _a		,     self.normalize_means		,     self.normalize_vars		,     self.padding_value       )
          for x, n in zip(_a		,     _a       )
      ]





     def __call__(self		,     UpperCAmelCase		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     **UpperCAmelCase		,     )     ->   BatchFeature:






      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.'''       )

      _lowercase							      =isinstance(_a		,     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}"       )
      _lowercase							      =is_batched_numpy or (
          isinstance(_a		,     (list, tuple)       ) and (isinstance(raw_speech[0]		,     (np.ndarray, tuple, list)       ))
      )

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

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

      # extract fbank features
      _lowercase							      =[self._extract_fbank_features(_a       ) for waveform in raw_speech]

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

      _lowercase							      =self.pad(
          _a		,     padding=_a		,     max_length=_a		,     truncation=_a		,     pad_to_multiple_of=_a		,     return_attention_mask=_a		,     **_a		,     )

      # make sure list is in array format
      _lowercase							      =padded_inputs.get('''input_features'''       )
      if isinstance(input_features[0]		,     _a       ):
       _lowercase							      =[np.asarray(_a		,     dtype=np.floataa       ) for feature in input_features]

      _lowercase							      =padded_inputs.get('''attention_mask'''       )
      if attention_mask is not None:
       _lowercase							      =[np.asarray(_a		,     dtype=np.intaa       ) for array in attention_mask]

      # Utterance-level cepstral mean and variance normalization
      if self.do_ceptral_normalize:
       _lowercase							      =(
           np.array(_a		,     dtype=np.intaa       )
           if self._get_padding_strategies(_a		,     max_length=_a       ) is not PaddingStrategy.DO_NOT_PAD
           else None
       )
       _lowercase							      =self.normalize(
           padded_inputs['''input_features''']		,     attention_mask=_a       )

      if return_tensors is not None:
       _lowercase							      =padded_inputs.convert_to_tensors(_a       )

      return padded_inputs




'''simple docstring'''
lowerCamelCase :      Any					 =     "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowerCamelCase :      int					 =     [{"type": "code", "content": INSTALL_CONTENT}]
lowerCamelCase :      str					 =     {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}
