



'''simple docstring'''






def 				snake_case_  (      _lowerCAmelCase						:		int = 1000						)   ->     int:

							'''simple docstring'''





							UpperCAmelCase				: Tuple											=	-1
							UpperCAmelCase				: Optional[Any]											=	0
							for a in range(1     ,   n // 3						):
														# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
														UpperCAmelCase				: int											=	(n * n - 2 * a * n) // (2 * n - 2 * a)
														UpperCAmelCase				: Any											=	n - a - b
														if c * c == (a * a + b * b):
																					UpperCAmelCase				: Optional[int]											=	a * b * c
																					if candidate >= product:
																												UpperCAmelCase				: str											=	candidate
							return product


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










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__:     Union[str, Any]										=    {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:     List[str]										=    [
						    "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LiltForQuestionAnswering",
						    "LiltForSequenceClassification",
						    "LiltForTokenClassification",
						    "LiltModel",
						    "LiltPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lilt import (
												    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LiltForQuestionAnswering,
												    LiltForSequenceClassification,
												    LiltForTokenClassification,
												    LiltModel,
												    LiltPreTrainedModel,
												)

else:
						import sys

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





