






from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available


def __A       ( __lowerCAmelCase      )->    List[Any]:





						"""simple docstring"""



						if is_torch_available():
												import torch

												if isinstance(__lowerCAmelCase					,						torch.Tensor      ):
																		return True

						return isinstance(__lowerCAmelCase					,						np.ndarray      )





class __lowerCamelCase					( __UpperCAmelCase):





	"""simple docstring"""






	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     fields(self       )

							# Safety and consistency checks
							if not len(lowerCamelCase__       ):
													raise ValueError(F"""{self.__class__.__name__} has no fields."""       )

							_UpperCAmelCase							   =     getattr(self			,       class_fields[0].name       )
							_UpperCAmelCase							   =     all(getattr(self			,       field.name       ) is None for field in class_fields[1:]       )

							if other_fields_are_none and isinstance(lowerCamelCase__			,       lowerCamelCase__       ):
													for key, value in first_field.items():
																			_UpperCAmelCase							   =     value
							else:
													for field in class_fields:
																			_UpperCAmelCase							   =     getattr(self			,       field.name       )
																			if v is not None:
																									_UpperCAmelCase							   =     v

	def __delitem__(				self			,       *UpperCAmelCase			,       **UpperCAmelCase       ):
							"""simple docstring"""







							raise Exception(F"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."""       )

	def 			UpperCamelCase       (				self			,       *UpperCAmelCase			,       **UpperCAmelCase       ):
							"""simple docstring"""







							raise Exception(F"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance."""       )

	def 			UpperCamelCase       (				self			,       *UpperCAmelCase			,       **UpperCAmelCase       ):
							"""simple docstring"""







							raise Exception(F"""You cannot use ``pop`` on a {self.__class__.__name__} instance."""       )

	def 			UpperCamelCase       (				self			,       *UpperCAmelCase			,       **UpperCAmelCase       ):
							"""simple docstring"""







							raise Exception(F"""You cannot use ``update`` on a {self.__class__.__name__} instance."""       )

	def __getitem__(				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							if isinstance(lowerCamelCase__			,       lowerCamelCase__       ):
													_UpperCAmelCase							   =     dict(self.items()       )
													return inner_dict[k]
							else:
													return self.to_tuple()[k]

	def __setattr__(				self			,       UpperCAmelCase			,       UpperCAmelCase       ):
							"""simple docstring"""







							if name in self.keys() and value is not None:
													# Don't call self.__setitem__ to avoid recursion errors
													super().__setitem__(lowerCamelCase__			,       lowerCamelCase__       )
							super().__setattr__(lowerCamelCase__			,       lowerCamelCase__       )

	def __setitem__(				self			,       UpperCAmelCase			,       UpperCAmelCase       ):
							"""simple docstring"""







							super().__setitem__(lowerCamelCase__			,       lowerCamelCase__       )
							# Don't call self.__setattr__ to avoid recursion errors
							super().__setattr__(lowerCamelCase__			,       lowerCamelCase__       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							return tuple(self[k] for k in self.keys()       )

"""simple docstring"""






from sklearn.neural_network import MLPClassifier

lowerCAmelCase__    :    Any			=      [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase__    :    Optional[int]			=      [0, 1, 0, 0]


lowerCAmelCase__    :    Dict			=      MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


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



def     a_		(   lowerCamelCase			):
				return list(lowerCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



