





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


lowerCamelCase_       =		logging.get_logger(__name__)






class  _SCREAMING_SNAKE_CASE(       UpperCAmelCase_  ):
       SCREAMING_SNAKE_CASE_       :				str     						=  ["""input_values""", """attention_mask"""]




       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 1		,SCREAMING_SNAKE_CASE__ = 1_60_00		,SCREAMING_SNAKE_CASE__ = 0.0		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = 80		,SCREAMING_SNAKE_CASE__ = 16		,SCREAMING_SNAKE_CASE__ = 64		,SCREAMING_SNAKE_CASE__ = "hann_window"		,SCREAMING_SNAKE_CASE__ = 1.0		,SCREAMING_SNAKE_CASE__ = 80		,SCREAMING_SNAKE_CASE__ = 76_00		,SCREAMING_SNAKE_CASE__ = 1E-10		,SCREAMING_SNAKE_CASE__ = 2		,SCREAMING_SNAKE_CASE__ = True		,**SCREAMING_SNAKE_CASE__		,)      ->				int:





             """simple docstring"""






             super().__init__(feature_size=__lowercase		,sampling_rate=__lowercase		,padding_value=__lowercase		,**__lowercase							)
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  do_normalize
             __SCREAMING_SNAKE_CASE       :Tuple		    =  return_attention_mask

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  num_mel_bins
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hop_length
             __SCREAMING_SNAKE_CASE       :List[str]		    =  win_length
             __SCREAMING_SNAKE_CASE       :str		    =  win_function
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  frame_signal_scale
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  fmin
             __SCREAMING_SNAKE_CASE       :int		    =  fmax
             __SCREAMING_SNAKE_CASE       :str		    =  mel_floor
             __SCREAMING_SNAKE_CASE       :str		    =  reduction_factor

             __SCREAMING_SNAKE_CASE       :int		    =  win_length * sampling_rate // 10_00
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hop_length * sampling_rate // 10_00
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  optimal_fft_length(self.sample_size							)
             __SCREAMING_SNAKE_CASE       :str		    =  (self.n_fft // 2) + 1

             __SCREAMING_SNAKE_CASE       :str		    =  window_function(window_length=self.sample_size		,name=self.win_function		,periodic=__lowercase							)

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  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'''		,__lowercase		,)
             if reduction_factor != 2.0:
                   warnings.warn(
                       '''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'''		,__lowercase		,)




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





             """simple docstring"""






             if attention_mask is not None:
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  np.array(__lowercase		,np.intaa							)
                   __SCREAMING_SNAKE_CASE       :Any		    =  []

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

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

             return normed_input_values




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,)      ->				np.ndarray:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  spectrogram(
                 __lowercase		,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		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)      ->				BatchFeature:





             """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:
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self._process_audio(
                       __lowercase		,__lowercase		,__lowercase		,__lowercase		,__lowercase		,__lowercase		,__lowercase		,__lowercase		,**__lowercase		,)
             else:
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  None

             if audio_target is not None:
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self._process_audio(
                       __lowercase		,__lowercase		,__lowercase		,__lowercase		,__lowercase		,__lowercase		,__lowercase		,__lowercase		,**__lowercase		,)

                   if inputs is None:
                         return inputs_target
                   else:
                         __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  inputs_target['''input_values''']
                         __SCREAMING_SNAKE_CASE       :str		    =  inputs_target.get('''attention_mask'''							)
                         if decoder_attention_mask is not None:
                               __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  decoder_attention_mask

             return inputs




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)      ->				BatchFeature:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  isinstance(__lowercase		,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}'''							)
             __SCREAMING_SNAKE_CASE       :Dict		    =  is_batched_numpy or (
                 isinstance(__lowercase		,(list, tuple)							) and (isinstance(speech[0]		,(np.ndarray, tuple, list)							))
             )

             if is_batched:
                   __SCREAMING_SNAKE_CASE       :Dict		    =  [np.asarray(__lowercase		,dtype=np.floataa							) for speech in speech]
             elif not is_batched and not isinstance(__lowercase		,np.ndarray							):
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  np.asarray(__lowercase		,dtype=np.floataa							)
             elif isinstance(__lowercase		,np.ndarray							) and speech.dtype is np.dtype(np.floataa							):
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  speech.astype(np.floataa							)

             # always return batch
             if not is_batched:
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  [speech]

             # needed to make pad() work on spectrogram inputs
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.feature_size

             # convert into correct format for padding
             if is_target:
                   __SCREAMING_SNAKE_CASE       :int		    =  [self._extract_mel_features(__lowercase							) for waveform in speech]
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  BatchFeature({'''input_values''': features}							)
                   __SCREAMING_SNAKE_CASE       :str		    =  self.num_mel_bins
             else:
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  BatchFeature({'''input_values''': speech}							)

             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.pad(
                 __lowercase		,padding=__lowercase		,max_length=__lowercase		,truncation=__lowercase		,pad_to_multiple_of=__lowercase		,return_attention_mask=__lowercase		,**__lowercase		,)

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  feature_size_hack

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

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

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

             if return_tensors is not None:
                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  padded_inputs.convert_to_tensors(__lowercase							)

             return padded_inputs




       def    _UpperCamelCase					(						self							)      ->				Dict[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  super().to_dict()

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

             return output






"""simple docstring"""
import math
import unittest



def 					__lowerCamelCase       (							a_					:		int							)      ->				bool:
      assert isinstance(a_			,       a_							) and (
          number >= 0
      ), "'number' must been an int and positive"

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5			,       int(math.sqrt(a_							) + 1							)			,       6							):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True






class  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




       def    _UpperCamelCase					(						self							)      ->				Union[str, Any]:





             """simple docstring"""






             self.assertTrue(is_prime(2							)							)
             self.assertTrue(is_prime(3							)							)
             self.assertTrue(is_prime(5							)							)
             self.assertTrue(is_prime(7							)							)
             self.assertTrue(is_prime(11							)							)
             self.assertTrue(is_prime(13							)							)
             self.assertTrue(is_prime(17							)							)
             self.assertTrue(is_prime(19							)							)
             self.assertTrue(is_prime(23							)							)
             self.assertTrue(is_prime(29							)							)




       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             with self.assertRaises(SCREAMING_SNAKE_CASE__							):
                   is_prime(-19							)
             self.assertFalse(
                 is_prime(0							)		,'''Zero doesn\'t have any positive factors, primes must have exactly two.'''		,)
             self.assertFalse(
                 is_prime(1							)		,'''One only has 1 positive factor, primes must have exactly two.'''		,)
             self.assertFalse(is_prime(2 * 2							)							)
             self.assertFalse(is_prime(2 * 3							)							)
             self.assertFalse(is_prime(3 * 3							)							)
             self.assertFalse(is_prime(3 * 5							)							)
             self.assertFalse(is_prime(3 * 5 * 7							)							)


if __name__ == "__main__":
  unittest.main()