'''simple docstring'''





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


lowercase_     :   Dict 				=				logging.get_logger(__name__)






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      ["audio_values", "audio_mask"]
    def __init__(  self , lowerCAmelCase=2048 , lowerCAmelCase=1 , lowerCAmelCase=[16, 16] , lowerCAmelCase=128 , lowerCAmelCase=44100 , lowerCAmelCase=86 , lowerCAmelCase=2048 , lowerCAmelCase=0.0 , **lowerCAmelCase , )       ->					Any:
     super().__init__(
         feature_size=lowerCAmelCase , sampling_rate=lowerCAmelCase , padding_value=lowerCAmelCase , **lowerCAmelCase , )

     SCREAMING_SNAKE_CASE__:   Optional[int]=      spectrogram_length
     SCREAMING_SNAKE_CASE__:   Optional[int]=      num_channels
     SCREAMING_SNAKE_CASE__:   Any=      patch_size
     SCREAMING_SNAKE_CASE__:   Any=      feature_size // self.patch_size[1]
     SCREAMING_SNAKE_CASE__:   List[str]=      n_fft
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      sampling_rate // hop_length_to_sampling_rate
     SCREAMING_SNAKE_CASE__:   List[str]=      sampling_rate
     SCREAMING_SNAKE_CASE__:   Any=      padding_value
     SCREAMING_SNAKE_CASE__:   Dict=      mel_filter_bank(
         num_frequency_bins=1 + n_fft // 2 , num_mel_filters=lowerCAmelCase , min_frequency=0.0 , max_frequency=22050.0 , sampling_rate=lowerCAmelCase , norm='''slaney''' , mel_scale='''slaney''' , ).T
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					np.ndarray:
     SCREAMING_SNAKE_CASE__:   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.T , log_mel='''dB''' , db_range=80.0 , )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      log_spec[:, :-1]
     SCREAMING_SNAKE_CASE__:   Any=      log_spec - 20.0
     SCREAMING_SNAKE_CASE__:   Any=      np.clip(log_spec / 40.0 , -2.0 , 0.0   ) + 1.0
     return log_spec



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

     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__:   int=      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}'   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      is_batched_numpy or (
         isinstance(lowerCAmelCase , (list, tuple)   ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)   ))
     )
     if is_batched:
      SCREAMING_SNAKE_CASE__:   List[str]=      [np.asarray([speech] , dtype=np.floataa   ).T for speech in raw_speech]
     elif not is_batched and not isinstance(lowerCAmelCase , np.ndarray   ):
      SCREAMING_SNAKE_CASE__:   List[Any]=      np.asarray(lowerCAmelCase , dtype=np.floataa   )
     elif isinstance(lowerCAmelCase , np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
      SCREAMING_SNAKE_CASE__:   str=      raw_speech.astype(np.floataa   )
     # always return batch
     if not is_batched:
      SCREAMING_SNAKE_CASE__:   List[Any]=      [np.asarray([raw_speech]   ).T]

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

     # Create audio attention mask
     SCREAMING_SNAKE_CASE__:   Optional[int]=      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__:   Union[str, Any]=      [
          (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__:   List[Any]=      np.array(lowerCAmelCase   ).astype(np.floataa   )

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

     # return as BatchFeature
     if return_attention_mask:
      SCREAMING_SNAKE_CASE__:   List[Any]=      {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
     else:
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      {'''audio_values''': padded_audio_features}

     SCREAMING_SNAKE_CASE__:   Dict=      BatchFeature(data=lowerCAmelCase , tensor_type=lowerCAmelCase   )
     return encoded_inputs


import re







def 			A__      (	snake_case_ :		str      ):

 if len(re.findall('''[ATCG]'''       ,				snake_case_      )      ) != len(snake_case_      ):
  raise ValueError('''Invalid Strand'''      )

 return dna.translate(dna.maketrans('''ATCG'''       ,				'''TAGC'''      )      )


if __name__ == "__main__":
      import doctest

      doctest.testmod()

