




'''simple docstring'''







from sklearn.neural_network import MLPClassifier

_lowerCamelCase							:						int								=  [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
_lowerCamelCase							:						Tuple								=  [0, 1, 0, 0]


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

clf.fit(X, y)


_lowerCamelCase							:						Tuple								=  [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
_lowerCamelCase							:						Dict								=  clf.predict(test)






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		return list(lowercase_  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()











'''simple docstring'''







from __future__ import annotations

_lowerCamelCase							:						Union[str, Any]								=  {1: 1}






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		if n in COLLATZ_SEQUENCE_LENGTHS:
				return COLLATZ_SEQUENCE_LENGTHS[n]
		_snake_case	       =       n // 2 if n % 2 == 0 else 3 * n + 1
		_snake_case	       =       collatz_sequence_length(lowercase_  ) + 1
		_snake_case	       =       sequence_length
		return sequence_length






def 		_lowercase		(	lowercase_ = 1000000  ):



		'''simple docstring'''







		_snake_case	       =       max((collatz_sequence_length(lowercase_  ), i) for i in range(1						,							lowercase_  )  )
		return result[1]


if __name__ == "__main__":
						print(solution(int(input().strip())))





