


'''simple docstring'''






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
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   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   ['input_features']

    def __init__(  self       :    Tuple       ,		lowerCamelCase       :    Dict=80       ,		lowerCamelCase       :    Dict=1_60_00       ,		lowerCamelCase       :    int=1_60       ,		lowerCamelCase       :    str=30       ,		lowerCamelCase       :    Tuple=4_00       ,		lowerCamelCase       :    List[str]=0.0       ,		lowerCamelCase       :    List[str]=False       ,		**lowerCamelCase       :    Tuple       ,		)  ->    Dict:
      super().__init__(
          feature_size=lowerCamelCase       ,		sampling_rate=lowerCamelCase       ,		padding_value=lowerCamelCase       ,		return_attention_mask=lowerCamelCase       ,		**lowerCamelCase       ,		)
      lowerCAmelCase_     : Tuple     =  n_fft
      lowerCAmelCase_     : Any     =  hop_length
      lowerCAmelCase_     : Tuple     =  chunk_length
      lowerCAmelCase_     : Union[str, Any]     =  chunk_length * sampling_rate
      lowerCAmelCase_     : Union[str, Any]     =  self.n_samples // hop_length
      lowerCAmelCase_     : int     =  sampling_rate
      lowerCAmelCase_     : Optional[int]     =  mel_filter_bank(
          num_frequency_bins=1 + n_fft // 2       ,		num_mel_filters=lowerCamelCase       ,		min_frequency=0.0       ,		max_frequency=8_000.0       ,		sampling_rate=lowerCamelCase       ,		norm="""slaney"""       ,		mel_scale="""slaney"""       ,		)

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    np.array						)  ->    np.ndarray:
      lowerCAmelCase_     : str     =  spectrogram(
          lowerCamelCase       ,		window_function(self.n_fft       ,		"""hann"""						)       ,		frame_length=self.n_fft       ,		hop_length=self.hop_length       ,		power=2.0       ,		mel_filters=self.mel_filters       ,		log_mel="""log10"""       ,		)
      lowerCAmelCase_     : Optional[int]     =  log_spec[:, :-1]
      lowerCAmelCase_     : int     =  np.maximum(lowerCamelCase       ,		log_spec.max() - 8.0						)
      lowerCAmelCase_     : Tuple     =  (log_spec + 4.0) / 4.0
      return log_spec

    @staticmethod
    # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
    def __lowercase  (  lowerCamelCase       :    List[np.ndarray]       ,		lowerCamelCase       :    List[np.ndarray]       ,		lowerCamelCase       :    float = 0.0						)  ->    List[np.ndarray]:
      if attention_mask is not None:
        lowerCAmelCase_     : Tuple     =  np.array(lowerCamelCase       ,		np.intaa						)
        lowerCAmelCase_     : List[Any]     =  []

        for vector, length in zip(lowerCamelCase       ,		attention_mask.sum(-1						)						):
          lowerCAmelCase_     : Optional[Any]     =  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7						)
          if length < normed_slice.shape[0]:
            lowerCAmelCase_     : Optional[Any]     =  padding_value

          normed_input_values.append(lowerCamelCase						)
      else:
        lowerCAmelCase_     : int     =  [(x - x.mean()) / np.sqrt(x.var() + 1E-7						) for x in input_values]

      return normed_input_values

    def __call__(  self       :    Any       ,		lowerCamelCase       :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,		lowerCamelCase       :    bool = True       ,		lowerCamelCase       :    Optional[int] = None       ,		lowerCamelCase       :    Optional[Union[str, TensorType]] = None       ,		lowerCamelCase       :    Optional[bool] = None       ,		lowerCamelCase       :    Optional[str] = "max_length"       ,		lowerCamelCase       :    Optional[int] = None       ,		lowerCamelCase       :    Optional[int] = None       ,		lowerCamelCase       :    Optional[bool] = None       ,		**lowerCamelCase       :    Any       ,		)  ->    BatchFeature:

      if sampling_rate is not None:
        if sampling_rate != self.sampling_rate:
          raise ValueError(
              F'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
              F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
              F' was sampled with {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[Any]     =  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}'						)
      lowerCAmelCase_     : Tuple     =  is_batched_numpy or (
          isinstance(lowerCamelCase       ,		(list, tuple)						) and (isinstance(raw_speech[0]       ,		(np.ndarray, tuple, list)						))
      )

      if is_batched:
        lowerCAmelCase_     : Tuple     =  [np.asarray([speech]       ,		dtype=np.floataa						).T for speech in raw_speech]
      elif not is_batched and not isinstance(lowerCamelCase       ,		np.ndarray						):
        lowerCAmelCase_     : Any     =  np.asarray(lowerCamelCase       ,		dtype=np.floataa						)
      elif isinstance(lowerCamelCase       ,		np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
        lowerCAmelCase_     : Optional[Any]     =  raw_speech.astype(np.floataa						)

      # always return batch
      if not is_batched:
        lowerCAmelCase_     : int     =  [np.asarray([raw_speech]						).T]

      lowerCAmelCase_     : List[Any]     =  BatchFeature({"""input_features""": raw_speech}						)

      # convert into correct format for padding

      lowerCAmelCase_     : Tuple     =  self.pad(
          lowerCamelCase       ,		padding=lowerCamelCase       ,		max_length=max_length if max_length else self.n_samples       ,		truncation=lowerCamelCase       ,		pad_to_multiple_of=lowerCamelCase       ,		return_attention_mask=return_attention_mask or do_normalize       ,		)

      # zero-mean and unit-variance normalization
      if do_normalize:
        lowerCAmelCase_     : str     =  self.zero_mean_unit_var_norm(
            padded_inputs["""input_features"""]       ,		attention_mask=padded_inputs["""attention_mask"""]       ,		padding_value=self.padding_value       ,		)
        lowerCAmelCase_     : List[Any]     =  np.stack(padded_inputs["""input_features"""]       ,		axis=0						)

      # make sure list is in array format
      lowerCAmelCase_     : Any     =  padded_inputs.get("""input_features"""						).transpose(2       ,		0       ,		1						)

      lowerCAmelCase_     : Any     =  [self._np_extract_fbank_features(lowerCamelCase						) for waveform in input_features[0]]

      if isinstance(input_features[0]       ,		lowerCamelCase						):
        lowerCAmelCase_     : List[str]     =  [np.asarray(lowerCamelCase       ,		dtype=np.floataa						) for feature in input_features]
      else:
        lowerCAmelCase_     : Optional[int]     =  input_features

      if return_attention_mask:
        # rescale from sample (48000) to feature (3000)
        lowerCAmelCase_     : int     =  padded_inputs["""attention_mask"""][:, :: self.hop_length]

      if return_tensors is not None:
        lowerCAmelCase_     : Tuple     =  padded_inputs.convert_to_tensors(lowerCamelCase						)

      return padded_inputs





    def __lowercase  (  self       :    Union[str, Any]						)  ->    Dict[str, Any]:
      lowerCAmelCase_     : Optional[Any]     =  copy.deepcopy(self.__dict__						)
      lowerCAmelCase_     : Tuple     =  self.__class__.__name__
      if "mel_filters" in output:
        del output["mel_filters"]
      return output







'''simple docstring'''






def 							UpperCamelCase_     (       A__   :  list[list[int | float]]   ):
  '''simple docstring'''





  lowerCAmelCase_     : List[str]     =  len(A__   )
  lowerCAmelCase_     : Union[str, Any]     =  len(matrix[0]   )
  lowerCAmelCase_     : Optional[int]     =  min(A__				,       A__   )

  for row in range(A__   ):
    # Check if diagonal element is not zero
    if matrix[row][row] != 0:
      # Eliminate all the elements below the diagonal
      for col in range(row + 1				,       A__   ):
        lowerCAmelCase_     : int     =  matrix[col][row] / matrix[row][row]
        for i in range(A__				,       A__   ):
          matrix[col][i] -= multiplier * matrix[row][i]
    else:
      # Find a non-zero diagonal element to swap rows
      lowerCAmelCase_     : int     =  True
      for i in range(row + 1				,       A__   ):
        if matrix[i][row] != 0:
          lowerCAmelCase_,				lowerCAmelCase_     : List[Any]     =  matrix[i], matrix[row]
          lowerCAmelCase_     : Dict     =  False
          break
      if reduce:
        rank -= 1
        for i in range(A__   ):
          lowerCAmelCase_     : List[Any]     =  matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
      row -= 1

  return rank


if __name__ == "__main__":
  import doctest

  doctest.testmod()



