
'''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          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Optional[int]										=  ['''audio_values''', '''audio_mask''']




						def __init__(   self       ,  _lowercase=2_048       ,  _lowercase=1       ,  _lowercase=[16, 16]       ,  _lowercase=128       ,  _lowercase=44_100       ,  _lowercase=86       ,  _lowercase=2_048       ,  _lowercase=0.0       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(
												    feature_size=_lowercase       ,  sampling_rate=_lowercase       ,  padding_value=_lowercase       ,  **_lowercase       ,  )

												_lowerCAmelCase    		=					spectrogram_length
												_lowerCAmelCase    		=					num_channels
												_lowerCAmelCase    		=					patch_size
												_lowerCAmelCase    		=					feature_size // self.patch_size[1]
												_lowerCAmelCase    		=					n_fft
												_lowerCAmelCase    		=					sampling_rate // hop_length_to_sampling_rate
												_lowerCAmelCase    		=					sampling_rate
												_lowerCAmelCase    		=					padding_value
												_lowerCAmelCase    		=					mel_filter_bank(
												    num_frequency_bins=1 + n_fft // 2       ,  num_mel_filters=_lowercase       ,  min_frequency=0.0       ,  max_frequency=2_2050.0       ,  sampling_rate=_lowercase       ,  norm="""slaney"""       ,  mel_scale="""slaney"""       ,  ).T




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					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       ,  )
												_lowerCAmelCase    		=					log_spec[:, :-1]
												_lowerCAmelCase    		=					log_spec - 20.0
												_lowerCAmelCase    		=					np.clip(log_spec / 40.0       ,  -2.0       ,  0.0 ) + 1.0
												return log_spec




						def __call__(   self       ,  _lowercase       ,  _lowercase = None       ,  _lowercase = True       ,  _lowercase = None       ,  _lowercase = False       ,  _lowercase = False       ,  **_lowercase       ,  ):

												"""simple docstring"""



												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.""" )

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

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

												# Create audio attention mask
												_lowerCAmelCase    		=					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:
																		_lowerCAmelCase    		=					[
																		    (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
																		]
																		_lowerCAmelCase    		=					np.array(_lowercase ).astype(np.floataa )

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

												# return as BatchFeature
												if return_attention_mask:
																		_lowerCAmelCase    		=					{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
												else:
																		_lowerCAmelCase    		=					{"""audio_values""": padded_audio_features}

												_lowerCAmelCase    		=					BatchFeature(data=_lowercase       ,  tensor_type=_lowercase )
												return encoded_inputs

'''simple docstring'''






def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			str   ,		__snake_case							:			List[Any]=False				):

     if isinstance(__snake_case   ,		__snake_case				) and isinstance(__snake_case   ,		__snake_case				):
          _A    		=  len(set_a.intersection(__snake_case				)				)

          if alternative_union:
               _A    		=  len(__snake_case				) + len(__snake_case				)
          else:
               _A    		=  len(set_a.union(__snake_case				)				)

          return intersection / union

     if isinstance(__snake_case   ,		(list, tuple)				) and isinstance(__snake_case   ,		(list, tuple)				):
          _A    		=  [element for element in set_a if element in set_b]

          if alternative_union:
               _A    		=  len(__snake_case				) + len(__snake_case				)
               return len(__snake_case				) / union
          else:
               _A    		=  set_a + [element for element in set_b if element not in set_a]
               return len(__snake_case				) / len(__snake_case				)

          return len(__snake_case				) / len(__snake_case				)
     return None


if __name__ == "__main__":
   _UpperCAmelCase    :     Optional[Any]         =       {'''a''', '''b''', '''c''', '''d''', '''e'''}
   _UpperCAmelCase    :     str         =       {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
   print(jaccard_similarity(set_a, set_b))



