






import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


a		:	Optional[int]      					=			TypeVar("T", bound="TaskTemplate")
@dataclass(frozen=lowercase__       )
class    a   (			abc.ABC       ):
      a  :			str
      a  :			ClassVar[Features]
      a  :			ClassVar[Features]
      def      UpperCAmelCase				(				self :      T		,				__lowercase :      Features )							->				T:
        return copy.deepcopy(self )
      @property
      def      UpperCAmelCase				(				self :      int )							->				Features:
        return Features(**self.input_schema		,				**self.label_schema )
      @property
      @abc.abstractmethod
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				Dict[str, str]:
        raise NotImplementedError




      @classmethod
      def      UpperCAmelCase				(				cls :      Type[T]		,				__lowercase :      dict )							->				T:
        __UpperCAmelCase							:     List[str]									=  {f.name for f in dataclasses.fields(cls )}
        return cls(**{k: v for k, v in template_dict.items() if k in field_names} )










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()


