





from typing import 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 ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


__UpperCAmelCase       :   Dict				       =							logging.get_logger(__name__)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     ["""input_features""", """attention_mask"""]




       def __init__(  self :    Dict							,				A :    Tuple=80							,				A :    Optional[Any]=16_000							,				A :    str=0.0							,				A :    Optional[int]=10							,				A :    List[Any]=25							,				A :    int="hamming_window"							,				A :    List[Any]=3_2768.0							,				A :    Union[str, Any]=0.97							,				A :    str=1.0							,				A :    Optional[int]=True							,				A :    List[str]=True							,				A :    Dict=False							,				**A :    Dict							,				):
         super().__init__(feature_size=A							,				sampling_rate=A							,				padding_value=A							,				**A		)

         __snake_case:						List[str]   =						feature_size
         __snake_case:						str   =						sampling_rate
         __snake_case:						List[Any]   =						padding_value
         __snake_case:						Union[str, Any]   =						hop_length
         __snake_case:						Optional[int]   =						win_length
         __snake_case:						int   =						frame_signal_scale
         __snake_case:						Optional[Any]   =						preemphasis_coeff
         __snake_case:						Optional[int]   =						mel_floor
         __snake_case:						List[Any]   =						normalize_means
         __snake_case:						List[str]   =						normalize_vars
         __snake_case:						Dict   =						win_function
         __snake_case:						List[str]   =						return_attention_mask

         __snake_case:						List[Any]   =						win_length * sampling_rate // 1_000
         __snake_case:						Optional[int]   =						hop_length * sampling_rate // 1_000

         __snake_case:						List[Any]   =						optimal_fft_length(self.sample_size		)
         __snake_case:						Optional[Any]   =						(self.n_fft // 2) + 1




       def 							UpperCAmelCase__ (  self :    Any							,				A :    np.array		):
         if self.win_function == "hamming_window":
           __snake_case:						int   =						window_function(window_length=self.sample_size							,				name=self.win_function							,				periodic=A		)
         else:
           __snake_case:						Any   =						window_function(window_length=self.sample_size							,				name=self.win_function		)

         __snake_case:						Union[str, Any]   =						mel_filter_bank(
             num_frequency_bins=self.n_freqs							,				num_mel_filters=self.feature_size							,				min_frequency=0.0							,				max_frequency=self.sampling_rate / 2.0							,				sampling_rate=self.sampling_rate							,				)

         __snake_case:						int   =						spectrogram(
             one_waveform * self.frame_signal_scale							,				window=A							,				frame_length=self.sample_size							,				hop_length=self.sample_stride							,				fft_length=self.n_fft							,				center=A							,				preemphasis=self.preemphasis_coeff							,				mel_filters=A							,				mel_floor=self.mel_floor							,				log_mel="""log"""							,				)
         return msfc_features.T




       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    Optional[Any]							,				A :    str							,				A :    List[str]		):
         # make sure we normalize float32 arrays
         if self.normalize_means:
           __snake_case:						Optional[Any]   =						x[:input_length].mean(axis=0		)
           __snake_case:						Dict   =						np.subtract(A							,				A		)
         if self.normalize_vars:
           __snake_case:						Optional[Any]   =						x[:input_length].std(axis=0		)
           __snake_case:						List[str]   =						np.divide(A							,				A		)

         if input_length < x.shape[0]:
           __snake_case:						List[Any]   =						padding_value

         # make sure array is in float32
         __snake_case:						List[str]   =						x.astype(np.floataa		)

         return x




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    List[np.ndarray]							,				A :    Optional[np.ndarray] = None		):
         __snake_case:						Optional[Any]   =						attention_mask.sum(-1		) if attention_mask is not None else [x.shape[0] for x in input_features]
         return [self._normalize_one(A							,				A							,				self.padding_value		) for x, n in zip(A							,				A		)]






       def __call__(  self :    Dict							,				A :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,				A :    Union[bool, str, PaddingStrategy] = False							,				A :    Optional[int] = None							,				A :    bool = False							,				A :    Optional[int] = None							,				A :    Optional[bool] = None							,				A :    Optional[Union[str, TensorType]] = None							,				A :    Optional[int] = None							,				**A :    Optional[Any]							,				):

         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 `raw_speech` 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."""		)

         __snake_case:						int   =						isinstance(A							,				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}'''		)
         __snake_case:						List[Any]   =						is_batched_numpy or (
             isinstance(A							,				(list, tuple)		) and (isinstance(raw_speech[0]							,				(np.ndarray, tuple, list)		))
         )

         if is_batched:
           __snake_case:						Any   =						[np.asarray(A							,				dtype=np.floataa		) for speech in raw_speech]
         elif not is_batched and not isinstance(A							,				np.ndarray		):
           __snake_case:						Tuple   =						np.asarray(A							,				dtype=np.floataa		)
         elif isinstance(A							,				np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
           __snake_case:						Dict   =						raw_speech.astype(np.floataa		)

         # always return batch
         if not is_batched:
           __snake_case:						Dict   =						[raw_speech]

         # extract fbank features
         __snake_case:						Optional[Any]   =						[self._extract_mfsc_features(A		) for one_waveform in raw_speech]

         # convert into correct format for padding
         __snake_case:						int   =						BatchFeature({"""input_features""": features}		)

         __snake_case:						str   =						self.pad(
             A							,				padding=A							,				max_length=A							,				truncation=A							,				pad_to_multiple_of=A							,				return_attention_mask=A							,				**A							,				)
         # make sure list is in array format
         __snake_case:						Union[str, Any]   =						padded_inputs.get("""input_features"""		)
         if isinstance(input_features[0]							,				A		):
           __snake_case:						int   =						[np.asarray(A							,				dtype=np.floataa		) for feature in input_features]

         __snake_case:						Dict   =						padded_inputs.get("""attention_mask"""		)
         if attention_mask is not None:
           __snake_case:						Optional[Any]   =						[np.asarray(A							,				dtype=np.intaa		) for array in attention_mask]

         if self.normalize_means or self.normalize_vars:
           __snake_case:						Dict   =						(
               np.array(A							,				dtype=np.intaa		)
               if self._get_padding_strategies(A							,				max_length=A		) is not PaddingStrategy.DO_NOT_PAD
               and padding
               else None
           )
           __snake_case:						str   =						self.normalize(
               padded_inputs["""input_features"""]							,				attention_mask=A		)

         if return_tensors is not None:
           __snake_case:						Dict   =						padded_inputs.convert_to_tensors(A		)

         return padded_inputs













import math







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    bool:

  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(SCREAMING_SNAKE_CASE__) + 1)					,					6):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def 			A__							(    SCREAMING_SNAKE_CASE__ = 1_0001)   ->    int:

  try:
    __snake_case:						List[str]   =						int(SCREAMING_SNAKE_CASE__)
  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.""")
  __snake_case:						list[int]   =						[]
  __snake_case:						List[str]   =						2
  while len(SCREAMING_SNAKE_CASE__) < nth:
    if is_prime(SCREAMING_SNAKE_CASE__):
      primes.append(SCREAMING_SNAKE_CASE__)
      num += 1
    else:
      num += 1
  return primes[len(SCREAMING_SNAKE_CASE__) - 1]


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






