"""simple docstring"""






import os



def     a_		(   ):
				UpperCAmelCase__			 =				os.path.dirname(os.path.realpath(lowerCamelCase			)			)
				UpperCAmelCase__			 =				os.path.join(lowerCamelCase   ,     'triangle.txt'			)

				with open(lowerCamelCase			) as in_file:
								UpperCAmelCase__			 =				[[int(lowerCamelCase			) for i in line.split()] for line in in_file]

				while len(lowerCamelCase			) != 1:
								UpperCAmelCase__			 =				triangle.pop()
								UpperCAmelCase__			 =				triangle[-1]
								for j in range(len(lowerCamelCase			) - 1			):
												curr_row[j] += max(last_row[j]   ,     last_row[j + 1]			)
				return triangle[0][0]


if __name__ == "__main__":
						print(solution())




"""simple docstring"""






from sklearn.neural_network import MLPClassifier

lowerCAmelCase__    :    Any			=      [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase__    :    Optional[int]			=      [0, 1, 0, 0]


lowerCAmelCase__    :    Dict			=      MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCAmelCase__    :    Optional[int]			=      [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCAmelCase__    :    Union[str, Any]			=      clf.predict(test)



def     a_		(   lowerCamelCase			):
				return list(lowerCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



