
import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


lowerCamelCase      =logging.get_logger(__name__)




class 				_lowerCamelCase (      lowerCAmelCase__ ):





					"""simple docstring"""







					def __init__(							self						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   None:




											"""simple docstring"""







											warnings.warn(
											    '''The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
											    ''' use FlavaImageProcessor instead.'''						,						_lowerCamelCase						,						)
											super().__init__(*_lowerCamelCase						,						**_lowerCamelCase  )




def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ = 5_0_0_0_0_0_0_0		):
      UpperCamelCase__    :   Any					=				set()
      UpperCamelCase__    :   Any					=				int((limit - 2_4) ** (1 / 2)		)

      UpperCamelCase__    :   Dict					=				set(range(3 ,		prime_square_limit + 1 ,		2		)		)
      primes.add(2		)
      for p in range(3 ,		prime_square_limit + 1 ,		2		):
            if p not in primes:
                  continue
            primes.difference_update(set(range(p * p ,		prime_square_limit + 1 ,		UpperCamelCase__		)		)		)

      for primea in primes:
            UpperCamelCase__    :   Dict					=				primea * primea
            for primea in primes:
                  UpperCamelCase__    :   str					=				primea * primea * primea
                  if square + cube >= limit - 1_6:
                        break
                  for primea in primes:
                        UpperCamelCase__    :   Dict					=				primea * primea * primea * primea
                        UpperCamelCase__    :   Tuple					=				square + cube + tetr
                        if total >= limit:
                              break
                        ret.add(UpperCamelCase__		)

      return len(UpperCamelCase__		)


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


