





"""simple docstring"""





from sklearn.neural_network import MLPClassifier

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


SCREAMING_SNAKE_CASE__ =       MLPClassifier(
    solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


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


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Optional[Any]      ):







      '''simple docstring'''




      return list(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()













"""simple docstring"""





def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      lowerCAmelCase  						=	1.0
      for i in range(1							,						r + 1      ):
            ret *= (n + 1 - i) / i
      return round(SCREAMING_SNAKE_CASE      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      return choose(8 + n							,						SCREAMING_SNAKE_CASE      ) + choose(9 + n							,						SCREAMING_SNAKE_CASE      ) - 10


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      return sum(non_bouncy_exact(SCREAMING_SNAKE_CASE      ) for i in range(1							,						n + 1      )      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int = 1_00      ):







      '''simple docstring'''




      return non_bouncy_upto(SCREAMING_SNAKE_CASE      )


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






