




from __future__ import annotations






def    snake_case   (				A     )				->				set[int]:




	'''simple docstring'''


	lowerCamelCase_				:						List[str]				  =				{1}
	lowerCamelCase_				:						Tuple				  =				[1]
	for _ in range(2		,  depth + 1     ):
		lowerCamelCase_				:						Optional[Any]				  =				[*previous_coefficients, 0]
		lowerCamelCase_				:						List[str]				  =				[0, *previous_coefficients]
		lowerCamelCase_				:						Dict				  =				[]
		for x, y in zip(A		,  A     ):
			coefficients.add(x + y     )
			previous_coefficients.append(x + y     )
	return coefficients






def    snake_case   (				A     )				->				set[int]:




	'''simple docstring'''


	lowerCamelCase_				:						int				  =				set()
	for number in unique_coefficients:
		lowerCamelCase_				:						List[str]				  =				2
		lowerCamelCase_				:						Any				  =				number
		while divisor**2 <= copy_number:
			lowerCamelCase_				:						int				  =				0
			while copy_number % divisor == 0:
				copy_number //= divisor
				multiplicity += 1
			if multiplicity >= 2:
				non_squarefrees.add(A     )
				break
			divisor += 1

	return unique_coefficients.difference(A     )






def    snake_case   (				A = 51     )				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						Optional[Any]				  =				get_pascal_triangle_unique_coefficients(A     )
	lowerCamelCase_				:						Tuple				  =				get_squarefrees(A     )
	return sum(A     )


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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_			:Union[str, Any]	    =		{'''configuration_nat''': ['''NAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NatConfig''']}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_			:Optional[Any]	    =		[
							    '''NAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''NatForImageClassification''',
							    '''NatModel''',
							    '''NatPreTrainedModel''',
							    '''NatBackbone''',
							]

if TYPE_CHECKING:
							from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nat import (
														    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NatBackbone,
														    NatForImageClassification,
														    NatModel,
														    NatPreTrainedModel,
														)

else:
							import sys

							A_			:Tuple	    =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

