




'''simple docstring'''

def 		__magic_name__    (	__UpperCAmelCase = 5000_0000	)		->					Dict:

					'''simple docstring'''

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

					snake_case_									=				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:
										snake_case_									=				primea * primea
										for primea in primes:
															snake_case_									=				primea * primea * primea
															if square + cube >= limit - 16:
																				break
															for primea in primes:
																				snake_case_									=				primea * primea * primea * primea
																				snake_case_									=				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__)
