






from ..utils import DummyObject, requires_backends
class    a   (			metaclass=SCREAMING_SNAKE_CASE__       ):
      a  :			Tuple								=     ['torch', 'transformers', 'invisible_watermark']
      def __init__(				self :      Union[str, Any]		,				*__lowercase :      Optional[int]		,				**__lowercase :      Optional[Any] )							->				Tuple:
        requires_backends(self		,				["""torch""", """transformers""", """invisible_watermark"""] )
      @classmethod
      def      UpperCAmelCase				(				cls :      Tuple		,				*__lowercase :      int		,				**__lowercase :      str )							->				Any:
        requires_backends(cls		,				["""torch""", """transformers""", """invisible_watermark"""] )




      @classmethod
      def      UpperCAmelCase				(				cls :      Dict		,				*__lowercase :      Union[str, Any]		,				**__lowercase :      Any )							->				List[str]:
        requires_backends(cls		,				["""torch""", """transformers""", """invisible_watermark"""] )


class    a   (			metaclass=SCREAMING_SNAKE_CASE__       ):
      a  :			Optional[Any]								=     ['torch', 'transformers', 'invisible_watermark']
      def __init__(				self :      str		,				*__lowercase :      Dict		,				**__lowercase :      Optional[Any] )							->				List[str]:
        requires_backends(self		,				["""torch""", """transformers""", """invisible_watermark"""] )
      @classmethod
      def      UpperCAmelCase				(				cls :      List[str]		,				*__lowercase :      str		,				**__lowercase :      Union[str, Any] )							->				List[Any]:
        requires_backends(cls		,				["""torch""", """transformers""", """invisible_watermark"""] )




      @classmethod
      def      UpperCAmelCase				(				cls :      Dict		,				*__lowercase :      int		,				**__lowercase :      Any )							->				Optional[Any]:
        requires_backends(cls		,				["""torch""", """transformers""", """invisible_watermark"""] )










from sklearn.neural_network import MLPClassifier

a		:	int      					=			[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
a		:	List[str]      					=			[0, 1, 0, 0]


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

clf.fit(X, y)


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






def     lowerCamelCase__				(   __lowerCamelCase      :					Dict		):
  return list(__lowerCamelCase		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


