






'''simple docstring'''





from sklearn.neural_network import MLPClassifier

_UpperCamelCase		:		Union[str, Any]								=			[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
_UpperCamelCase		:		List[Any]								=			[0, 1, 0, 0]


_UpperCamelCase		:		Tuple								=			MLPClassifier(
    solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


_UpperCamelCase		:		Optional[int]								=			[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
_UpperCamelCase		:		str								=			clf.predict(test)
def __UpperCAmelCase (			A			:	Tuple						)  -> str:
			return list(A						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_UpperCamelCase		:		str								=			{}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		List[Any]								=			['MBart50Tokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		List[str]								=			['MBart50TokenizerFast']


if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbartaa import MBartaaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
						import sys

						_UpperCamelCase		:		Optional[int]								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


