


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     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      ["""input_values""", """attention_mask"""]


     def __init__(	self  ,       __A = 1  ,       __A = 16000  ,       __A = 0.0  ,       __A = False  ,       __A = 80  ,       __A = 16  ,       __A = 64  ,       __A = "hann_window"  ,       __A = 1.0  ,       __A = 80  ,       __A = 7600  ,       __A = 1E-10  ,       __A = 2  ,       __A = True  ,       **__A  ,       ):
        super().__init__(feature_size=__A  ,       sampling_rate=__A  ,       padding_value=__A  ,       **__A    )
        __a					=  do_normalize
        __a					=  return_attention_mask

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

        __a					=  win_length * sampling_rate // 1000
        __a					=  hop_length * sampling_rate // 1000
        __a					=  optimal_fft_length(self.sample_size    )
        __a					=  (self.n_fft // 2) + 1

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

        __a					=  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_  (	__A  ,       __A  ,       __A = 0.0    ):
        if attention_mask is not None:
           __a					=  np.array(__A  ,       np.intaa    )
           __a					=  []

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

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

        return normed_input_values


     def 	snake_case_  (	self  ,       __A  ,       ):
        __a					=  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  ,       __A = None  ,       __A = None  ,       __A = False  ,       __A = None  ,       __A = False  ,       __A = None  ,       __A = None  ,       __A = None  ,       __A = None  ,       **__A  ,       ):
        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:
           __a					=  self._process_audio(
               __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       **__A  ,       )
        else:
           __a					=  None

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

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

        return inputs


     def 	snake_case_  (	self  ,       __A  ,       __A = False  ,       __A = False  ,       __A = None  ,       __A = False  ,       __A = None  ,       __A = None  ,       __A = None  ,       **__A  ,       ):
        __a					=  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}'''    )
        __a					=  is_batched_numpy or (
            isinstance(__A  ,       (list, tuple)    ) and (isinstance(speech[0]  ,       (np.ndarray, tuple, list)    ))
        )

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

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

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

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

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

        __a					=  feature_size_hack

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

        # convert attention_mask to correct format
        __a					=  padded_inputs.get("""attention_mask"""    )
        if attention_mask is not None:
           __a					=  [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:
           __a					=  (
               attention_mask
               if self._get_padding_strategies(__A  ,       max_length=__A    ) is not PaddingStrategy.DO_NOT_PAD
               else None
           )
           __a					=  self.zero_mean_unit_var_norm(
               padded_inputs["""input_values"""]  ,       attention_mask=__A  ,       padding_value=self.padding_value    )

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

        return padded_inputs


     def 	snake_case_  (	self    ):
        __a					=  super().to_dict()

        # Don't serialize these as they are derived from the other properties.
        __a					=  ["""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  a   (lowerCAmelCase__   ):

   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  a   (lowerCAmelCase__ = 10_001   ):

   try:
      __a					=  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."""   )
   __a					=  []
   __a					=  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() = }''')



