'''simple docstring'''






def    _SCREAMING_SNAKE_CASE		(			):

     for a in range(3_0_0							):
          for b in range(a + 1      ,		4_0_0							):
               for c in range(b + 1      ,		5_0_0							):
                    if (a + b + c) == 1_0_0_0 and (a**2) + (b**2) == (c**2):
                         return a * b * c

     return -1





def    _SCREAMING_SNAKE_CASE		(			):

     for a in range(3_0_0							):
          for b in range(4_0_0							):
               _A								=     1_0_0_0 - a - b
               if a < b < c and (a**2) + (b**2) == (c**2):
                    return a * b * c

     return -1





def    _SCREAMING_SNAKE_CASE		(			):
     import timeit

     print(
         timeit.timeit('solution()'      ,		setup='from __main__ import solution'      ,		number=1_0_0_0							)							)
     print(
         timeit.timeit(
             'solution_fast()'      ,		setup='from __main__ import solution_fast'      ,		number=1_0_0_0							)							)


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




'''simple docstring'''






from sklearn.neural_network import MLPClassifier

_UpperCAmelCase						:    str     					=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
_UpperCAmelCase						:    Any     					=					[0, 1, 0, 0]


_UpperCAmelCase						:    List[Any]     					=					MLPClassifier(
    solver='''lbfgs''', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


_UpperCAmelCase						:    Optional[Any]     					=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
_UpperCAmelCase						:    Tuple     					=					clf.predict(test)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]							):
     return list(__snake_case							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



