




from math import ceil
from typing import List, Optional, Union

import numpy as np

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


lowerCamelCase__			:       Tuple         =							logging.get_logger(__name__)





class 	lowerCamelCase_							(				UpperCAmelCase_ ):


 '''simple docstring'''







 lowercase_       =   ["""audio_values""", """audio_mask"""]



 def __init__(							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		Any=2_048    ,    _lowerCAmelCase  :		Optional[Any]=1    ,    _lowerCAmelCase  :		str=[16, 16]    ,    _lowerCAmelCase  :		Optional[Any]=128    ,    _lowerCAmelCase  :		Any=44_100    ,    _lowerCAmelCase  :		Optional[Any]=86    ,    _lowerCAmelCase  :		str=2_048    ,    _lowerCAmelCase  :		Optional[int]=0.0    ,    **_lowerCAmelCase  :		Dict    ,    ):
       super().__init__(
           feature_size=__lowercase    ,    sampling_rate=__lowercase    ,    padding_value=__lowercase    ,    **__lowercase    ,    )

       SCREAMING_SNAKE_CASE_     						=      spectrogram_length
       SCREAMING_SNAKE_CASE_     						=      num_channels
       SCREAMING_SNAKE_CASE_     						=      patch_size
       SCREAMING_SNAKE_CASE_     						=      feature_size // self.patch_size[1]
       SCREAMING_SNAKE_CASE_     						=      n_fft
       SCREAMING_SNAKE_CASE_     						=      sampling_rate // hop_length_to_sampling_rate
       SCREAMING_SNAKE_CASE_     						=      sampling_rate
       SCREAMING_SNAKE_CASE_     						=      padding_value
       SCREAMING_SNAKE_CASE_     						=      mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2    ,    num_mel_filters=__lowercase    ,    min_frequency=0.0    ,    max_frequency=22_050.0    ,    sampling_rate=__lowercase    ,    norm='slaney'    ,    mel_scale='slaney'    ,    ).T



 def 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		List[Any]						):
       SCREAMING_SNAKE_CASE_     						=      spectrogram(
           __lowercase    ,    window_function(self.n_fft    ,    'hann'						)    ,    frame_length=self.n_fft    ,    hop_length=self.hop_length    ,    power=2.0    ,    mel_filters=self.mel_filters.T    ,    log_mel='dB'    ,    db_range=80.0    ,    )
       SCREAMING_SNAKE_CASE_     						=      log_spec[:, :-1]
       SCREAMING_SNAKE_CASE_     						=      log_spec - 20.0
       SCREAMING_SNAKE_CASE_     						=      np.clip(log_spec / 40.0    ,    -2.0    ,    0.0						) + 1.0
       return log_spec




 def __call__(							self  :		List[Any]    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		int = None    ,    _lowerCAmelCase  :		Any = True    ,    _lowerCAmelCase  :		Any = None    ,    _lowerCAmelCase  :		Tuple = False    ,    _lowerCAmelCase  :		str = False    ,    **_lowerCAmelCase  :		int    ,    ):

       if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       'This feature extractor is set to support sampling rate'
                       F" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"
                       F" 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.'						)

       SCREAMING_SNAKE_CASE_     						=      isinstance(__lowercase    ,    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}"						)
       SCREAMING_SNAKE_CASE_     						=      is_batched_numpy or (
           isinstance(__lowercase    ,    (list, tuple)						) and (isinstance(raw_speech[0]    ,    (np.ndarray, tuple, list)						))
       )
       if is_batched:
             SCREAMING_SNAKE_CASE_     						=      [np.asarray([speech]    ,    dtype=np.floataa						).T for speech in raw_speech]
       elif not is_batched and not isinstance(__lowercase    ,    np.ndarray						):
             SCREAMING_SNAKE_CASE_     						=      np.asarray(__lowercase    ,    dtype=np.floataa						)
       elif isinstance(__lowercase    ,    np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
             SCREAMING_SNAKE_CASE_     						=      raw_speech.astype(np.floataa						)
       # always return batch
       if not is_batched:
             SCREAMING_SNAKE_CASE_     						=      [np.asarray([raw_speech]						).T]

       # Convert audio signals to log mel spectrograms, truncate by time axis
       SCREAMING_SNAKE_CASE_     						=      [
           self._np_extract_fbank_features(waveform.squeeze()						).T[: self.spectrogram_length] for waveform in raw_speech
       ]
       if isinstance(audio_features[0]    ,    __lowercase						):
             SCREAMING_SNAKE_CASE_     						=      [np.asarray(__lowercase    ,    dtype=np.floataa						) for feature in audio_features]

       # Create audio attention mask
       SCREAMING_SNAKE_CASE_     						=      max(
           [ceil(feature.shape[0] / self.patch_size[0]						) * self.freq_len for feature in audio_features]						)  # The maximum number of audio patches in a batch
       if return_attention_mask:
             SCREAMING_SNAKE_CASE_     						=      [
                 (ceil(feature.shape[0] / self.patch_size[0]						) * self.freq_len) * [1]
                 + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]						) * self.freq_len) * [0]
                 for feature in audio_features
             ]
             SCREAMING_SNAKE_CASE_     						=      np.array(__lowercase						).astype(np.floataa						)

       # convert into correct format for padding
       SCREAMING_SNAKE_CASE_     						=      max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
       SCREAMING_SNAKE_CASE_     						=      np.ones([len(__lowercase						), 1, max_time_len, self.feature_size]						).astype(np.floataa						)
       SCREAMING_SNAKE_CASE_     						=      padded_audio_features * self.padding_value
       for i in range(len(__lowercase						)						):
             SCREAMING_SNAKE_CASE_     						=      audio_features[i]
             SCREAMING_SNAKE_CASE_     						=      feature

       # return as BatchFeature
       if return_attention_mask:
             SCREAMING_SNAKE_CASE_     						=      {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
       else:
             SCREAMING_SNAKE_CASE_     						=      {'''audio_values''': padded_audio_features}

       SCREAMING_SNAKE_CASE_     						=      BatchFeature(data=__lowercase    ,    tensor_type=__lowercase						)
       return encoded_inputs





def  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			list:
      return [
          txt[:a] + txt[a].upper() + txt[a + 1 :]
          for a in range(len(__UpperCAmelCase							)							)
          if txt[a].isalpha()
      ]


if __name__ == "__main__":
   __import__('doctest').testmod()