





from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowercase_       =		logging.get_logger(__name__)
class 				SCREAMING_SNAKE_CASE   (lowerCAmelCase_      ):
   _UpperCamelCase    :		List[Any] 					=       ['''input_values''', '''attention_mask''']


   def __init__(    self			:				Optional[int]			,     a			:				str=1			,     a			:				Optional[Any]=16_000			,     a			:				Optional[Any]=128			,     a			:				Union[str, Any]=1_024			,     a			:				Dict=0.0			,     a			:				int=True			,     a			:				int=-4.2677393			,     a			:				List[str]=4.5689974			,     a			:				List[Any]=False			,     **a			:				Any			,     )->   Tuple:





     """simple docstring"""







     super().__init__(feature_size=_SCREAMING_SNAKE_CASE			,     sampling_rate=_SCREAMING_SNAKE_CASE			,     padding_value=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE       )
     lowercase__				=  num_mel_bins
     lowercase__				=  max_length
     lowercase__				=  do_normalize
     lowercase__				=  mean
     lowercase__				=  std
     lowercase__				=  return_attention_mask


   def        SCREAMING_SNAKE_CASE_							(    self			:				Optional[int]			,     a			:				np.ndarray			,     a			:				int			,     )->   np.ndarray:





     """simple docstring"""







     lowercase__				=  torch.from_numpy(_SCREAMING_SNAKE_CASE       ).unsqueeze(0       )
     lowercase__				=  ta_kaldi.fbank(
         _SCREAMING_SNAKE_CASE			,     htk_compat=_SCREAMING_SNAKE_CASE			,     sample_frequency=self.sampling_rate			,     use_energy=_SCREAMING_SNAKE_CASE			,     window_type='hanning'			,     num_mel_bins=self.num_mel_bins			,     dither=0.0			,     frame_shift=10			,     )

     lowercase__				=  fbank.shape[0]
     lowercase__				=  max_length - n_frames

     # pad or truncate, depending on difference
     if difference > 0:
       lowercase__				=  torch.nn.ZeroPadad((0, 0, 0, difference)       )
       lowercase__				=  pad_module(_SCREAMING_SNAKE_CASE       )
     elif difference < 0:
       lowercase__				=  fbank[0:max_length, :]

     lowercase__				=  fbank.numpy()

     return fbank


   def        SCREAMING_SNAKE_CASE_							(    self			:				List[Any]			,     a			:				np.ndarray       )->   np.ndarray:





     """simple docstring"""







     return (input_values - (self.mean)) / (self.std * 2)


   def __call__(    self			:				List[str]			,     a			:				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,     a			:				Optional[int] = None			,     a			:				Optional[Union[str, TensorType]] = None			,     **a			:				Optional[int]			,     )->   BatchFeature:





     """simple docstring"""







     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.'       )

     lowercase__				=  isinstance(_SCREAMING_SNAKE_CASE			,     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}"""       )
     lowercase__				=  is_batched_numpy or (
         isinstance(_SCREAMING_SNAKE_CASE			,     (list, tuple)       ) and (isinstance(raw_speech[0]			,     (np.ndarray, tuple, list)       ))
     )

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

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

     # extract fbank features and pad/truncate to max_length
     lowercase__				=  [self._extract_fbank_features(_SCREAMING_SNAKE_CASE			,     max_length=self.max_length       ) for waveform in raw_speech]

     # convert into BatchFeature
     lowercase__				=  BatchFeature({'input_values': features}       )

     # make sure list is in array format
     lowercase__				=  padded_inputs.get('input_values'       )
     if isinstance(input_values[0]			,     _SCREAMING_SNAKE_CASE       ):
       lowercase__				=  [np.asarray(_SCREAMING_SNAKE_CASE			,     dtype=np.floataa       ) for feature in input_values]

     # normalization
     if self.do_normalize:
       lowercase__				=  [self.normalize(_SCREAMING_SNAKE_CASE       ) for feature in input_values]

     if return_tensors is not None:
       lowercase__				=  padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE       )

     return padded_inputs









import math




def        lowerCAmelCase_       (							UpperCamelCase_     )   ->						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(UpperCamelCase_     ) + 1     )    ,						6     ):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True




def        lowerCAmelCase_       (							UpperCamelCase_ = 600851475143     )   ->						int:

       try:
              UpperCamelCase_      				=     int(UpperCamelCase_     )
       except (TypeError, ValueError):
              raise TypeError("Parameter n must be int or castable to int."     )
       if n <= 0:
              raise ValueError("Parameter n must be greater than or equal to one."     )
       UpperCamelCase_      				=     0
       if is_prime(UpperCamelCase_     ):
              return n
       while n % 2 == 0:
              n //= 2
       if is_prime(UpperCamelCase_     ):
              return n
       for i in range(3    ,						int(math.sqrt(UpperCamelCase_     )     ) + 1    ,						2     ):
              if n % i == 0:
                     if is_prime(n // i     ):
                            UpperCamelCase_      				=     n // i
                            break
                     elif is_prime(UpperCamelCase_     ):
                            UpperCamelCase_      				=     i
       return max_number


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

