

def    __lowerCamelCase      (					UpperCamelCase__ = 50000000 ):







							'''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  ,				UpperCamelCase__ ) ) )

							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(UpperCamelCase__ )

							return len(UpperCamelCase__ )


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







from typing import TYPE_CHECKING

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


_UpperCAmelCase						:  Tuple									=			{
    """configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
    """tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  Tuple									=			[
					    """M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """M2M100ForConditionalGeneration""",
					    """M2M100Model""",
					    """M2M100PreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
					from .tokenization_mam_aaa import MaMaaaTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mam_aaa import (
										    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaMaaaForConditionalGeneration,
										    MaMaaaModel,
										    MaMaaaPreTrainedModel,
										)


else:
					import sys

					_UpperCAmelCase						:  Any									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




