






"""simple docstring"""



from sklearn.neural_network import MLPClassifier

A_     =				[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
A_     =				[0, 1, 0, 0]


A_     =				MLPClassifier(
    solver='''lbfgs''', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


A_     =				[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
A_     =				clf.predict(test)




def   _lowerCAmelCase		(				UpperCAmelCase__							:       List[Any]   )		->List[Any]:
						return list(UpperCAmelCase__   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()














"""simple docstring"""



from itertools import combinations, product




def   _lowerCAmelCase		(				UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int   )		->bool:
						if xa == ya == 0 or xa == ya == 0:
												return False
						A__    :	List[str]			=	xa * xa + ya * ya
						A__    :	List[Any]			=	xa * xa + ya * ya
						A__    :	Optional[int]			=	(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
						return (
						    a_square + b_square == c_square
						    or a_square + c_square == b_square
						    or b_square + c_square == a_square
						)




def   _lowerCAmelCase		(				UpperCAmelCase__							:       int = 5_0   )		->int:
						return sum(
						    1
						    for pta, pta in combinations(product(range(limit + 1   ),    repeat=2   ),    2   )
						    if is_right(*UpperCAmelCase__,    *UpperCAmelCase__   )   )


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






