




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


SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   logging.get_logger(__name__)




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Optional[Any]	     =      ["""input_values""", """attention_mask"""]







    def __init__(    self     ,  UpperCamelCase__ = 1     ,  UpperCamelCase__ = 1_6000     ,  UpperCamelCase__ = 0.0     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = 80     ,  UpperCamelCase__ = 16     ,  UpperCamelCase__ = 64     ,  UpperCamelCase__ = "hann_window"     ,  UpperCamelCase__ = 1.0     ,  UpperCamelCase__ = 80     ,  UpperCamelCase__ = 7600     ,  UpperCamelCase__ = 1e-10     ,  UpperCamelCase__ = 2     ,  UpperCamelCase__ = True     ,  **UpperCamelCase__     ,  )     ->      Dict:
           super().__init__(feature_size=UpperCamelCase__     ,  sampling_rate=UpperCamelCase__     ,  padding_value=UpperCamelCase__     ,  **UpperCamelCase__     )
           lowerCamelCase   :      Any			 =					do_normalize
           lowerCamelCase   :      Tuple			 =					return_attention_mask

           lowerCamelCase   :      Optional[Any]			 =					num_mel_bins
           lowerCamelCase   :      Optional[int]			 =					hop_length
           lowerCamelCase   :      Dict			 =					win_length
           lowerCamelCase   :      Any			 =					win_function
           lowerCamelCase   :      Any			 =					frame_signal_scale
           lowerCamelCase   :      int			 =					fmin
           lowerCamelCase   :      int			 =					fmax
           lowerCamelCase   :      Optional[int]			 =					mel_floor
           lowerCamelCase   :      Any			 =					reduction_factor

           lowerCamelCase   :      Tuple			 =					win_length * sampling_rate // 1000
           lowerCamelCase   :      int			 =					hop_length * sampling_rate // 1000
           lowerCamelCase   :      int			 =					optimal_fft_length(self.sample_size     )
           lowerCamelCase   :      List[str]			 =					(self.n_fft // 2) + 1

           lowerCamelCase   :      List[str]			 =					window_function(window_length=self.sample_size     ,  name=self.win_function     ,  periodic=UpperCamelCase__     )

           lowerCamelCase   :      int			 =					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"     ,  UpperCamelCase__     ,  )
           if reduction_factor != 2.0:
                  warnings.warn(
                      "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"     ,  UpperCamelCase__     ,  )







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

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

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

           return normed_input_values







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  )     ->      np.ndarray:
           lowerCamelCase   :      Optional[int]			 =					spectrogram(
               UpperCamelCase__     ,  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     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = None     ,  **UpperCamelCase__     ,  )     ->      BatchFeature:
           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:
                  lowerCamelCase   :      Dict			 =					self._process_audio(
                      UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     ,  )
           else:
                  lowerCamelCase   :      Dict			 =					None

           if audio_target is not None:
                  lowerCamelCase   :      Optional[int]			 =					self._process_audio(
                      UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     ,  )

                  if inputs is None:
                         return inputs_target
                  else:
                         lowerCamelCase   :      Optional[Any]			 =					inputs_target["input_values"]
                         lowerCamelCase   :      List[str]			 =					inputs_target.get("attention_mask"     )
                         if decoder_attention_mask is not None:
                                lowerCamelCase   :      Dict			 =					decoder_attention_mask

           return inputs







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = None     ,  **UpperCamelCase__     ,  )     ->      BatchFeature:
           lowerCamelCase   :      Dict			 =					isinstance(UpperCamelCase__     ,  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}'''     )
           lowerCamelCase   :      Optional[int]			 =					is_batched_numpy or (
               isinstance(UpperCamelCase__     ,  (list, tuple)     ) and (isinstance(speech[0]     ,  (np.ndarray, tuple, list)     ))
           )

           if is_batched:
                  lowerCamelCase   :      Dict			 =					[np.asarray(UpperCamelCase__     ,  dtype=np.floataa     ) for speech in speech]
           elif not is_batched and not isinstance(UpperCamelCase__     ,  np.ndarray     ):
                  lowerCamelCase   :      Optional[int]			 =					np.asarray(UpperCamelCase__     ,  dtype=np.floataa     )
           elif isinstance(UpperCamelCase__     ,  np.ndarray     ) and speech.dtype is np.dtype(np.floataa     ):
                  lowerCamelCase   :      str			 =					speech.astype(np.floataa     )

           # always return batch
           if not is_batched:
                  lowerCamelCase   :      List[Any]			 =					[speech]

           # needed to make pad() work on spectrogram inputs
           lowerCamelCase   :      Any			 =					self.feature_size

           # convert into correct format for padding
           if is_target:
                  lowerCamelCase   :      List[Any]			 =					[self._extract_mel_features(UpperCamelCase__     ) for waveform in speech]
                  lowerCamelCase   :      Union[str, Any]			 =					BatchFeature({"input_values": features}     )
                  lowerCamelCase   :      Any			 =					self.num_mel_bins
           else:
                  lowerCamelCase   :      List[str]			 =					BatchFeature({"input_values": speech}     )

           lowerCamelCase   :      Tuple			 =					self.pad(
               UpperCamelCase__     ,  padding=UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  pad_to_multiple_of=UpperCamelCase__     ,  return_attention_mask=UpperCamelCase__     ,  **UpperCamelCase__     ,  )

           lowerCamelCase   :      Optional[int]			 =					feature_size_hack

           # convert input values to correct format
           lowerCamelCase   :      Optional[Any]			 =					padded_inputs["input_values"]
           if not isinstance(input_values[0]     ,  np.ndarray     ):
                  lowerCamelCase   :      Any			 =					[np.asarray(UpperCamelCase__     ,  dtype=np.floataa     ) for array in input_values]
           elif (
               not isinstance(UpperCamelCase__     ,  np.ndarray     )
               and isinstance(input_values[0]     ,  np.ndarray     )
               and input_values[0].dtype is np.dtype(np.floataa     )
           ):
                  lowerCamelCase   :      Any			 =					[array.astype(np.floataa     ) for array in input_values]
           elif isinstance(UpperCamelCase__     ,  np.ndarray     ) and input_values.dtype is np.dtype(np.floataa     ):
                  lowerCamelCase   :      int			 =					input_values.astype(np.floataa     )

           # convert attention_mask to correct format
           lowerCamelCase   :      Any			 =					padded_inputs.get("attention_mask"     )
           if attention_mask is not None:
                  lowerCamelCase   :      Dict			 =					[np.asarray(UpperCamelCase__     ,  dtype=np.intaa     ) for array in attention_mask]

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

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

           return padded_inputs







    def     _lowercase     (    self     )     ->      Dict[str, Any]:
           lowerCamelCase   :      Optional[int]			 =					super().to_dict()

           # Don't serialize these as they are derived from the other properties.
           lowerCamelCase   :      Dict			 =					["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        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__=False      ):

						if isinstance(lowercase__    ,       lowercase__      ) and isinstance(lowercase__    ,       lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     List[str]					=		len(set_a.intersection(lowercase__      )      )

												if alternative_union:
																		__SCREAMING_SNAKE_CASE						:     int					=		len(lowercase__      ) + len(lowercase__      )
												else:
																		__SCREAMING_SNAKE_CASE						:     int					=		len(set_a.union(lowercase__      )      )

												return intersection / union

						if isinstance(lowercase__    ,       (list, tuple)      ) and isinstance(lowercase__    ,       (list, tuple)      ):
												__SCREAMING_SNAKE_CASE						:     Dict					=		[element for element in set_a if element in set_b]

												if alternative_union:
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		len(lowercase__      ) + len(lowercase__      )
																		return len(lowercase__      ) / union
												else:
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		set_a + [element for element in set_b if element not in set_a]
																		return len(lowercase__      ) / len(lowercase__      )

												return len(lowercase__      ) / len(lowercase__      )
						return None


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