

def 			UpperCAmelCase__				(			_A      : int = 50_00_00_00							):





  '''simple docstring'''







  a__  =set()
  a__  =int((limit - 24) ** (1 / 2)							)

  a__  =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	,      _A							)							)							)

  for primea in primes:
    a__  =primea * primea
    for primea in primes:
      a__  =primea * primea * primea
      if square + cube >= limit - 16:
        break
      for primea in primes:
        a__  =primea * primea * primea * primea
        a__  =square + cube + tetr
        if total >= limit:
          break
        ret.add(_A							)

  return len(_A							)


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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase           =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''CanineForMultipleChoice''',
           '''CanineForQuestionAnswering''',
           '''CanineForSequenceClassification''',
           '''CanineForTokenClassification''',
           '''CanineLayer''',
           '''CanineModel''',
           '''CaninePreTrainedModel''',
           '''load_tf_weights_in_canine''',
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
