

"""simple docstring"""
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


_A							=  logging.get_logger(__name__)

class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  ['input_features', 'attention_mask']



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


        """simple docstring"""

        super().__init__(feature_size=_lowerCamelCase							,       sampling_rate=_lowerCamelCase							,       padding_value=_lowerCamelCase							,       **_lowerCamelCase	)
        UpperCAmelCase__			:							int							=    num_mel_bins
        UpperCAmelCase__			:							Optional[Any]							=    do_ceptral_normalize
        UpperCAmelCase__			:							Any							=    normalize_means
        UpperCAmelCase__			:							int							=    normalize_vars
        UpperCAmelCase__			:							Any							=    True



  def  _a      (self							,       _lowerCamelCase							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[Any]							=    waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
        UpperCAmelCase__			:							Dict							=    torch.from_numpy(_lowerCamelCase	).unsqueeze(0	)
        UpperCAmelCase__			:							Union[str, Any]							=    ta_kaldi.fbank(_lowerCamelCase							,       num_mel_bins=self.num_mel_bins							,       sample_frequency=self.sampling_rate	)
        return features.numpy()



  @staticmethod
  def  _a      (_lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase = True							,       _lowerCamelCase = True							,       _lowerCamelCase = 0.0							,       ):


        """simple docstring"""

        if normalize_means:
              UpperCAmelCase__			:							Optional[Any]							=    x[:input_length].mean(axis=0	)
              UpperCAmelCase__			:							List[Any]							=    np.subtract(_lowerCamelCase							,       _lowerCamelCase	)
        if normalize_vars:
              UpperCAmelCase__			:							Optional[int]							=    x[:input_length].std(axis=0	)
              UpperCAmelCase__			:							Union[str, Any]							=    np.divide(_lowerCamelCase							,       _lowerCamelCase	)

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

        # make sure array is in float32
        UpperCAmelCase__			:							str							=    x.astype(np.floataa	)

        return x



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    attention_mask.sum(-1	) if attention_mask is not None else [x.shape[0] for x in input_features]
        return [
            self.utterance_cmvn(_lowerCamelCase							,       _lowerCamelCase							,       self.normalize_means							,       self.normalize_vars							,       self.padding_value	)
            for x, n in zip(_lowerCamelCase							,       _lowerCamelCase	)
        ]




  def __call__(self							,       _lowerCamelCase							,       _lowerCamelCase = False							,       _lowerCamelCase = None							,       _lowerCamelCase = False							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       **_lowerCamelCase							,       ):


        """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__			:							Tuple							=    isinstance(_lowerCamelCase							,       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__			:							int							=    is_batched_numpy or (
            isinstance(_lowerCamelCase							,       (list, tuple)	) and (isinstance(raw_speech[0]							,       (np.ndarray, tuple, list)	))
        )

        if is_batched:
              UpperCAmelCase__			:							Union[str, Any]							=    [np.asarray(_lowerCamelCase							,       dtype=np.floataa	) for speech in raw_speech]
        elif not is_batched and not isinstance(_lowerCamelCase							,       np.ndarray	):
              UpperCAmelCase__			:							Optional[Any]							=    np.asarray(_lowerCamelCase							,       dtype=np.floataa	)
        elif isinstance(_lowerCamelCase							,       np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
              UpperCAmelCase__			:							List[str]							=    raw_speech.astype(np.floataa	)

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

        # extract fbank features
        UpperCAmelCase__			:							Any							=    [self._extract_fbank_features(_lowerCamelCase	) for waveform in raw_speech]

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

        UpperCAmelCase__			:							List[str]							=    self.pad(
            _lowerCamelCase							,       padding=_lowerCamelCase							,       max_length=_lowerCamelCase							,       truncation=_lowerCamelCase							,       pad_to_multiple_of=_lowerCamelCase							,       return_attention_mask=_lowerCamelCase							,       **_lowerCamelCase							,       )

        # make sure list is in array format
        UpperCAmelCase__			:							str							=    padded_inputs.get("""input_features"""	)
        if isinstance(input_features[0]							,       _lowerCamelCase	):
              UpperCAmelCase__			:							Optional[Any]							=    [np.asarray(_lowerCamelCase							,       dtype=np.floataa	) for feature in input_features]

        UpperCAmelCase__			:							Optional[Any]							=    padded_inputs.get("""attention_mask"""	)
        if attention_mask is not None:
              UpperCAmelCase__			:							Tuple							=    [np.asarray(_lowerCamelCase							,       dtype=np.intaa	) for array in attention_mask]

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

        if return_tensors is not None:
              UpperCAmelCase__			:							int							=    padded_inputs.convert_to_tensors(_lowerCamelCase	)

        return padded_inputs




"""simple docstring"""
def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      if a < 0 or b < 0:
            raise ValueError("""the value of both inputs must be positive"""    )

      UpperCAmelCase__			:							Dict							=    str(bin(lowerCAmelCase    )    )[2:]  # remove the leading "0b"
      UpperCAmelCase__			:							Any							=    str(bin(lowerCAmelCase    )    )[2:]  # remove the leading "0b"

      UpperCAmelCase__			:							Optional[Any]							=    max(len(lowerCAmelCase    )			,	len(lowerCAmelCase    )    )

      return "0b" + "".join(
          str(int(char_a != char_b    )    )
          for char_a, char_b in zip(a_binary.zfill(lowerCAmelCase    )			,	b_binary.zfill(lowerCAmelCase    )    )    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

