






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

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__magic_name__   :  List[str]        =							logging.get_logger(__name__)





class 					A__     (     __snake_case ):

     '''simple docstring'''






     snake_case__					=					["""input_values""", """attention_mask"""]







     def __init__( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    int = 1   ,						_SCREAMING_SNAKE_CASE      :    int = 1_6000   ,						_SCREAMING_SNAKE_CASE      :    float = 0.0   ,						_SCREAMING_SNAKE_CASE      :    bool = False   ,						_SCREAMING_SNAKE_CASE      :    int = 80   ,						_SCREAMING_SNAKE_CASE      :    int = 16   ,						_SCREAMING_SNAKE_CASE      :    int = 64   ,						_SCREAMING_SNAKE_CASE      :    str = "hann_window"   ,						_SCREAMING_SNAKE_CASE      :    float = 1.0   ,						_SCREAMING_SNAKE_CASE      :    float = 80   ,						_SCREAMING_SNAKE_CASE      :    float = 7600   ,						_SCREAMING_SNAKE_CASE      :    float = 1E-1_0   ,						_SCREAMING_SNAKE_CASE      :    int = 2   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						**_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						):






           """simple docstring"""




           super().__init__(feature_size=_SCREAMING_SNAKE_CASE   ,						sampling_rate=_SCREAMING_SNAKE_CASE   ,						padding_value=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  do_normalize
           UpperCamelCase					       =  return_attention_mask

           UpperCamelCase					       =  num_mel_bins
           UpperCamelCase					       =  hop_length
           UpperCamelCase					       =  win_length
           UpperCamelCase					       =  win_function
           UpperCamelCase					       =  frame_signal_scale
           UpperCamelCase					       =  fmin
           UpperCamelCase					       =  fmax
           UpperCamelCase					       =  mel_floor
           UpperCamelCase					       =  reduction_factor

           UpperCamelCase					       =  win_length * sampling_rate // 1000
           UpperCamelCase					       =  hop_length * sampling_rate // 1000
           UpperCamelCase					       =  optimal_fft_length(self.sample_size )
           UpperCamelCase					       =  (self.n_fft // 2) + 1

           UpperCamelCase					       =  window_function(window_length=self.sample_size   ,						name=self.win_function   ,						periodic=_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  mel_filter_bank(
               num_frequency_bins=self.n_freqs   ,						num_mel_filters=self.num_mel_bins   ,						min_frequency=self.fmin   ,						max_frequency=self.fmax   ,						sampling_rate=self.sampling_rate   ,						norm='slaney'   ,						mel_scale='slaney'   ,						)

           if frame_signal_scale != 1.0:
                 warnings.warn(
                     'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'   ,						_SCREAMING_SNAKE_CASE   ,						)
           if reduction_factor != 2.0:
                 warnings.warn(
                     'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'   ,						_SCREAMING_SNAKE_CASE   ,						)







     @staticmethod
     # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
     def   _SCREAMING_SNAKE_CASE     ( _SCREAMING_SNAKE_CASE      :    List[np.ndarray]   ,						_SCREAMING_SNAKE_CASE      :    List[np.ndarray]   ,						_SCREAMING_SNAKE_CASE      :    float = 0.0 ):






           """simple docstring"""




           if attention_mask is not None:
                 UpperCamelCase					       =  np.array(_SCREAMING_SNAKE_CASE   ,						np.intaa )
                 UpperCamelCase					       =  []

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

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

           return normed_input_values







     def   _SCREAMING_SNAKE_CASE     ( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    np.ndarray   ,						):






           """simple docstring"""




           UpperCamelCase					       =  spectrogram(
               _SCREAMING_SNAKE_CASE   ,						window=self.window   ,						frame_length=self.sample_size   ,						hop_length=self.sample_stride   ,						fft_length=self.n_fft   ,						mel_filters=self.mel_filters   ,						mel_floor=self.mel_floor   ,						log_mel='log10'   ,						)
           return log_mel_spec.T







     def __call__( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None   ,						_SCREAMING_SNAKE_CASE      :    Union[bool, str, PaddingStrategy] = False   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] = None   ,						_SCREAMING_SNAKE_CASE      :    bool = False   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[bool] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, TensorType]] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] = None   ,						**_SCREAMING_SNAKE_CASE      :    List[str]   ,						):






           """simple docstring"""




           if audio is None and audio_target is None:
                 raise ValueError('You must provide either `audio` or `audio_target` values.' )

           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 audio 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.' )

           if audio is not None:
                 UpperCamelCase					       =  self._process_audio(
                     _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE   ,						)
           else:
                 UpperCamelCase					       =  None

           if audio_target is not None:
                 UpperCamelCase					       =  self._process_audio(
                     _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE   ,						)

                 if inputs is None:
                       return inputs_target
                 else:
                       UpperCamelCase					       =  inputs_target['input_values']
                       UpperCamelCase					       =  inputs_target.get('attention_mask' )
                       if decoder_attention_mask is not None:
                             UpperCamelCase					       =  decoder_attention_mask

           return inputs







     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,						_SCREAMING_SNAKE_CASE      :    bool = False   ,						_SCREAMING_SNAKE_CASE      :    Union[bool, str, PaddingStrategy] = False   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] = None   ,						_SCREAMING_SNAKE_CASE      :    bool = False   ,						_SCREAMING_SNAKE_CASE      :    Optional[int] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[bool] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, TensorType]] = None   ,						**_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						):






           """simple docstring"""




           UpperCamelCase					       =  isinstance(_SCREAMING_SNAKE_CASE   ,						np.ndarray ) and len(speech.shape ) > 1
           if is_batched_numpy and len(speech.shape ) > 2:
                 raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
           UpperCamelCase					       =  is_batched_numpy or (
               isinstance(_SCREAMING_SNAKE_CASE   ,						(list, tuple) ) and (isinstance(speech[0]   ,						(np.ndarray, tuple, list) ))
           )

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

           # always return batch
           if not is_batched:
                 UpperCamelCase					       =  [speech]

           # needed to make pad() work on spectrogram inputs
           UpperCamelCase					       =  self.feature_size

           # convert into correct format for padding
           if is_target:
                 UpperCamelCase					       =  [self._extract_mel_features(_SCREAMING_SNAKE_CASE ) for waveform in speech]
                 UpperCamelCase					       =  BatchFeature({'input_values': features} )
                 UpperCamelCase					       =  self.num_mel_bins
           else:
                 UpperCamelCase					       =  BatchFeature({'input_values': speech} )

           UpperCamelCase					       =  self.pad(
               _SCREAMING_SNAKE_CASE   ,						padding=_SCREAMING_SNAKE_CASE   ,						max_length=_SCREAMING_SNAKE_CASE   ,						truncation=_SCREAMING_SNAKE_CASE   ,						pad_to_multiple_of=_SCREAMING_SNAKE_CASE   ,						return_attention_mask=_SCREAMING_SNAKE_CASE   ,						**_SCREAMING_SNAKE_CASE   ,						)

           UpperCamelCase					       =  feature_size_hack

           # convert input values to correct format
           UpperCamelCase					       =  padded_inputs['input_values']
           if not isinstance(input_values[0]   ,						np.ndarray ):
                 UpperCamelCase					       =  [np.asarray(_SCREAMING_SNAKE_CASE   ,						dtype=np.floataa ) for array in input_values]
           elif (
               not isinstance(_SCREAMING_SNAKE_CASE   ,						np.ndarray )
               and isinstance(input_values[0]   ,						np.ndarray )
               and input_values[0].dtype is np.dtype(np.floataa )
           ):
                 UpperCamelCase					       =  [array.astype(np.floataa ) for array in input_values]
           elif isinstance(_SCREAMING_SNAKE_CASE   ,						np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
                 UpperCamelCase					       =  input_values.astype(np.floataa )

           # convert attention_mask to correct format
           UpperCamelCase					       =  padded_inputs.get('attention_mask' )
           if attention_mask is not None:
                 UpperCamelCase					       =  [np.asarray(_SCREAMING_SNAKE_CASE   ,						dtype=np.intaa ) for array in attention_mask]

           # zero-mean and unit-variance normalization
           if not is_target and self.do_normalize:
                 UpperCamelCase					       =  (
                     attention_mask
                     if self._get_padding_strategies(_SCREAMING_SNAKE_CASE   ,						max_length=_SCREAMING_SNAKE_CASE ) is not PaddingStrategy.DO_NOT_PAD
                     else None
                 )
                 UpperCamelCase					       =  self.zero_mean_unit_var_norm(
                     padded_inputs['input_values']   ,						attention_mask=_SCREAMING_SNAKE_CASE   ,						padding_value=self.padding_value )

           if return_tensors is not None:
                 UpperCamelCase					       =  padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE )

           return padded_inputs







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  super().to_dict()

           # Don't serialize these as they are derived from the other properties.
           UpperCamelCase					       =  ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
           for name in names:
                 if name in output:
                       del output[name]

           return output











def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if bit_count < 0:
            raise ValueError('The given input must be positive')

      # get the generated string sequence
      UpperCamelCase					       =  gray_code_sequence_string(_UpperCamelCase)
      #
      # convert them to integers
      for i in range(len(_UpperCamelCase)):
            UpperCamelCase					       =  int(sequence[i]       ,							2)

      return sequence
def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if bit_count == 0:
            return ["0"]

      if bit_count == 1:
            return ["0", "1"]

      UpperCamelCase					       =  1 << bit_count  # defines the length of the sequence
      # 1<< n is equivalent to 2^n

      # recursive answer will generate answer for n-1 bits
      UpperCamelCase					       =  gray_code_sequence_string(bit_count - 1)

      UpperCamelCase					       =  []

      # append 0 to first half of the smaller sequence generated
      for i in range(seq_len // 2):
            UpperCamelCase					       =  '0' + smaller_sequence[i]
            sequence.append(_UpperCamelCase)

      # append 1 to second half ... start from the end of the list
      for i in reversed(range(seq_len // 2)):
            UpperCamelCase					       =  '1' + smaller_sequence[i]
            sequence.append(_UpperCamelCase)

      return sequence


if __name__ == "__main__":
       import doctest

       doctest.testmod()



