






"""simple docstring"""


from sklearn.neural_network import MLPClassifier

_UpperCamelCase   :						Any     						= [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
_UpperCamelCase   :						Any     						= [0, 1, 0, 0]


_UpperCamelCase   :						List[str]     						= 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   :						Optional[int]     						= clf.predict(test)




def 				a (     UpperCamelCase_							:				Any		)  ->		List[Any]:
 return list(UpperCamelCase_		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()













"""simple docstring"""


import math




def 				a (     UpperCamelCase_							:				int		)  ->		list[int]:
 if not isinstance(UpperCamelCase_     ,	UpperCamelCase_		):
  __A	:       Any       =	F"Input value of [number={number}] must be an integer"
  raise TypeError(UpperCamelCase_		)
 if number < 1:
  __A	:       int       =	F"Input value of [number={number}] must be a positive integer"
  raise ValueError(UpperCamelCase_		)
 __A	:       Optional[Any]       =	[number]
 while number != 1:
  if number % 2 == 0:
   __A	:       Optional[int]       =	math.floor(math.sqrt(UpperCamelCase_		)		)
  else:
   __A	:       List[str]       =	math.floor(
       math.sqrt(UpperCamelCase_		) * math.sqrt(UpperCamelCase_		) * math.sqrt(UpperCamelCase_		)		)
  sequence.append(UpperCamelCase_		)
 return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()





