

'''simple docstring'''



def       snake_case   (	snake_case      : Dict = 50000000			) ->  Any:


    """simple docstring"""


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

    lowerCAmelCase 						=							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:
        lowerCAmelCase 						=							primea * primea
        for primea in primes:
            lowerCAmelCase 						=							primea * primea * primea
            if square + cube >= limit - 16:
                break
            for primea in primes:
                lowerCAmelCase 						=							primea * primea * primea * primea
                lowerCAmelCase 						=							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_torch_available


lowerCAmelCase__			:      str								={
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__			:      Optional[Any]								=[
				    'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'CLIPSegModel',
				    'CLIPSegPreTrainedModel',
				    'CLIPSegTextModel',
				    'CLIPSegVisionModel',
				    'CLIPSegForImageSegmentation',
				]

if TYPE_CHECKING:
				from .configuration_clipseg import (
				    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    CLIPSegConfig,
				    CLIPSegTextConfig,
				    CLIPSegVisionConfig,
				)
				from .processing_clipseg import CLIPSegProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_clipseg import (
								    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CLIPSegForImageSegmentation,
								    CLIPSegModel,
								    CLIPSegPreTrainedModel,
								    CLIPSegTextModel,
								    CLIPSegVisionModel,
								)

else:
				import sys

				lowerCAmelCase__			:      str								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





