'''simple docstring'''






from __future__ import annotations

import sys





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int							):
     if n < 0:
          raise ValueError('Negative arguments are not supported'							)
     return _fib(__snake_case							)[0]





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int							):
     if n == 0:  # (F(0), F(1))
          return (0, 1)

     # F(2n) = F(n)[2F(n+1) − F(n)]
     # F(2n+1) = F(n+1)^2+F(n)^2
     _A		,					_A								=     _fib(n // 2							)
     _A								=     a * (b * 2 - a)
     _A								=     a * a + b * b
     return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
   _UpperCAmelCase						:    List[str]     					=					int(sys.argv[1])
   print(F'''fibonacci({n}) is {fibonacci(n)}''')




'''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()



