





"""simple docstring"""





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__					=   logging.get_logger(__name__)

class 		lowercase					(					__a					):
      _SCREAMING_SNAKE_CASE					=      ['''input_values''', '''attention_mask''']





      def __init__(       self      ,						lowercase = 1      ,						lowercase = 16_000      ,						lowercase = 0.0      ,						lowercase = False      ,						lowercase = 80      ,						lowercase = 16      ,						lowercase = 64      ,						lowercase = "hann_window"      ,						lowercase = 1.0      ,						lowercase = 80      ,						lowercase = 7_600      ,						lowercase = 1e-10      ,						lowercase = 2      ,						lowercase = True      ,						**lowercase      ,						)		->     Optional[int]:
            super().__init__(feature_size=__a      ,						sampling_rate=__a      ,						padding_value=__a      ,						**__a )
            lowerCAmelCase  						=       do_normalize
            lowerCAmelCase  						=       return_attention_mask

            lowerCAmelCase  						=       num_mel_bins
            lowerCAmelCase  						=       hop_length
            lowerCAmelCase  						=       win_length
            lowerCAmelCase  						=       win_function
            lowerCAmelCase  						=       frame_signal_scale
            lowerCAmelCase  						=       fmin
            lowerCAmelCase  						=       fmax
            lowerCAmelCase  						=       mel_floor
            lowerCAmelCase  						=       reduction_factor

            lowerCAmelCase  						=       win_length * sampling_rate // 1_000
            lowerCAmelCase  						=       hop_length * sampling_rate // 1_000
            lowerCAmelCase  						=       optimal_fft_length(self.sample_size )
            lowerCAmelCase  						=       (self.n_fft // 2) + 1

            lowerCAmelCase  						=       window_function(window_length=self.sample_size      ,						name=self.win_function      ,						periodic=__a )

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





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

            if attention_mask is not None:
                  lowerCAmelCase  						=       np.array(__a      ,						np.intaa )
                  lowerCAmelCase  						=       []

                  for vector, length in zip(__a      ,						attention_mask.sum(-1 ) ):
                        lowerCAmelCase  						=       (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
                        if length < normed_slice.shape[0]:
                              lowerCAmelCase  						=       padding_value

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

            return normed_input_values





      def     _snake_case			(       self      ,						lowercase      ,						)		->     np.ndarray:
            lowerCAmelCase  						=       spectrogram(
                __a      ,						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      ,						lowercase = None      ,						lowercase = None      ,						lowercase = False      ,						lowercase = None      ,						lowercase = False      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						**lowercase      ,						)		->     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  						=       self._process_audio(
                      __a      ,						__a      ,						__a      ,						__a      ,						__a      ,						__a      ,						__a      ,						__a      ,						**__a      ,						)
            else:
                  lowerCAmelCase  						=       None

            if audio_target is not None:
                  lowerCAmelCase  						=       self._process_audio(
                      __a      ,						__a      ,						__a      ,						__a      ,						__a      ,						__a      ,						__a      ,						__a      ,						**__a      ,						)

                  if inputs is None:
                        return inputs_target
                  else:
                        lowerCAmelCase  						=       inputs_target["""input_values"""]
                        lowerCAmelCase  						=       inputs_target.get("""attention_mask""" )
                        if decoder_attention_mask is not None:
                              lowerCAmelCase  						=       decoder_attention_mask

            return inputs





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = False      ,						lowercase = False      ,						lowercase = None      ,						lowercase = False      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						**lowercase      ,						)		->     BatchFeature:
            lowerCAmelCase  						=       isinstance(__a      ,						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  						=       is_batched_numpy or (
                isinstance(__a      ,						(list, tuple) ) and (isinstance(speech[0]      ,						(np.ndarray, tuple, list) ))
            )

            if is_batched:
                  lowerCAmelCase  						=       [np.asarray(__a      ,						dtype=np.floataa ) for speech in speech]
            elif not is_batched and not isinstance(__a      ,						np.ndarray ):
                  lowerCAmelCase  						=       np.asarray(__a      ,						dtype=np.floataa )
            elif isinstance(__a      ,						np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
                  lowerCAmelCase  						=       speech.astype(np.floataa )

            # always return batch
            if not is_batched:
                  lowerCAmelCase  						=       [speech]

            # needed to make pad() work on spectrogram inputs
            lowerCAmelCase  						=       self.feature_size

            # convert into correct format for padding
            if is_target:
                  lowerCAmelCase  						=       [self._extract_mel_features(__a ) for waveform in speech]
                  lowerCAmelCase  						=       BatchFeature({"""input_values""": features} )
                  lowerCAmelCase  						=       self.num_mel_bins
            else:
                  lowerCAmelCase  						=       BatchFeature({"""input_values""": speech} )

            lowerCAmelCase  						=       self.pad(
                __a      ,						padding=__a      ,						max_length=__a      ,						truncation=__a      ,						pad_to_multiple_of=__a      ,						return_attention_mask=__a      ,						**__a      ,						)

            lowerCAmelCase  						=       feature_size_hack

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

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

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

            if return_tensors is not None:
                  lowerCAmelCase  						=       padded_inputs.convert_to_tensors(__a )

            return padded_inputs





      def     _snake_case			(       self )		->     Dict[str, Any]:
            lowerCAmelCase  						=       super().to_dict()

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

            return output














import math
def 					snake_case_						(   lowerCAmelCase_				:  int					):

							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(lowerCAmelCase_					) + 1					)				,				6					):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True
def 					snake_case_						(   lowerCAmelCase_				:  int = 10001					):

							try:
														__lowercase					:							Optional[int]			=   int(lowerCAmelCase_					)
							except (TypeError, ValueError):
														raise TypeError("""Parameter nth must be int or castable to int."""					) from None
							if nth <= 0:
														raise ValueError("""Parameter nth must be greater than or equal to one."""					)
							__lowercase					:							list[int]			=   []
							__lowercase					:							str			=   2
							while len(lowerCAmelCase_					) < nth:
														if is_prime(lowerCAmelCase_					):
																					primes.append(lowerCAmelCase_					)
																					num += 1
														else:
																					num += 1
							return primes[len(lowerCAmelCase_					) - 1]


if __name__ == "__main__":
	print(f'''{solution() = }''')