





'''simple docstring'''







from typing import List, Optional, Union

import numpy as np

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


A   :  Tuple       		=				logging.get_logger(__name__)


class 				__lowerCamelCase				( A__ ):
    """simple docstring"""
    a									=					['''input_values''', '''attention_mask''']


    def __init__(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							Tuple=1   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=16000   ,							SCREAMING_SNAKE_CASE    :							Dict=0.0   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=False   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=True   ,							**SCREAMING_SNAKE_CASE    :							Optional[int]   ,							):
           super().__init__(feature_size=SCREAMING_SNAKE_CASE   ,							sampling_rate=SCREAMING_SNAKE_CASE   ,							padding_value=SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)
           _A  :			List[str]											=      return_attention_mask
           _A  :			Any											=      do_normalize


    @staticmethod
    def      A					(						SCREAMING_SNAKE_CASE    :							List[np.ndarray]   ,							SCREAMING_SNAKE_CASE    :							List[np.ndarray]   ,							SCREAMING_SNAKE_CASE    :							float = 0.0):






           if attention_mask is not None:
                  _A  :			List[str]											=      np.array(SCREAMING_SNAKE_CASE   ,							np.intaa)
                  _A  :			Optional[Any]											=      []

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

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

           return normed_input_values


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






           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.')

           _A  :			Any											=      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}')
           _A  :			Union[str, Any]											=      is_batched_numpy or (
               isinstance(SCREAMING_SNAKE_CASE   ,							(list, tuple)) and (isinstance(raw_speech[0]   ,							(np.ndarray, tuple, list)))
           )

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

           # convert into correct format for padding
           _A  :			Dict											=      BatchFeature({'input_values': raw_speech})

           _A  :			int											=      self.pad(
               SCREAMING_SNAKE_CASE   ,							padding=SCREAMING_SNAKE_CASE   ,							max_length=SCREAMING_SNAKE_CASE   ,							truncation=SCREAMING_SNAKE_CASE   ,							pad_to_multiple_of=SCREAMING_SNAKE_CASE   ,							return_attention_mask=SCREAMING_SNAKE_CASE   ,							)

           # convert input values to correct format
           _A  :			Optional[int]											=      padded_inputs["input_values"]
           if not isinstance(input_values[0]   ,							np.ndarray):
                  _A  :			Optional[Any]											=      [np.asarray(SCREAMING_SNAKE_CASE   ,							dtype=np.floataa) for array in input_values]
           elif (
               not isinstance(SCREAMING_SNAKE_CASE   ,							np.ndarray)
               and isinstance(input_values[0]   ,							np.ndarray)
               and input_values[0].dtype is np.dtype(np.floataa)
           ):
                  _A  :			List[str]											=      [array.astype(np.floataa) for array in input_values]
           elif isinstance(SCREAMING_SNAKE_CASE   ,							np.ndarray) and input_values.dtype is np.dtype(np.floataa):
                  _A  :			Optional[Any]											=      input_values.astype(np.floataa)

           # convert attention_mask to correct format
           _A  :			Optional[int]											=      padded_inputs.get('attention_mask')
           if attention_mask is not None:
                  _A  :			int											=      [np.asarray(SCREAMING_SNAKE_CASE   ,							dtype=np.intaa) for array in attention_mask]

           # zero-mean and unit-variance normalization
           if self.do_normalize:
                  _A  :			str											=      (
                      attention_mask
                      if self._get_padding_strategies(SCREAMING_SNAKE_CASE   ,							max_length=SCREAMING_SNAKE_CASE) is not PaddingStrategy.DO_NOT_PAD
                      else None
                  )
                  _A  :			int											=      self.zero_mean_unit_var_norm(
                      padded_inputs['input_values']   ,							attention_mask=SCREAMING_SNAKE_CASE   ,							padding_value=self.padding_value)

           if return_tensors is not None:
                  _A  :			Union[str, Any]											=      padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE)

           return padded_inputs










'''simple docstring'''







import math






def    lowerCAmelCase__ (       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    lowerCAmelCase__ (       lowerCamelCase  :    int = 1000						,lowerCamelCase  :    int = 1000     ):
       _A  :			int											=      [0, 0, 0]  # length, a, b
       for a in range((a_limit * -1) + 1						,lowerCamelCase     ):
              for b in range(2						,lowerCamelCase     ):
                     if is_prime(lowerCamelCase     ):
                            _A  :			Dict											=      0
                            _A  :			Tuple											=      0
                            while is_prime((n**2) + (a * n) + b     ):
                                   count += 1
                                   n += 1
                            if count > longest[0]:
                                   _A  :			List[str]											=      [count, a, b]
       _A  :			str											=      longest[1] * longest[2]
       return ans


if __name__ == "__main__":
   print(solution(1000, 1000))






