
from sklearn.neural_network import MLPClassifier

UpperCAmelCase__				:   Dict   							=			[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
UpperCAmelCase__				:   Optional[int]   							=			[0, 1, 0, 0]


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

clf.fit(X, y)


UpperCAmelCase__				:   Union[str, Any]   							=			[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
UpperCAmelCase__				:   List[str]   							=			clf.predict(test)

def lowercase_  (       _snake_case						):
							return list(_snake_case						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__				:   Optional[Any]   							=			{'configuration_umt5': ['UMT5Config', 'UMT5OnnxConfig']}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__				:   Tuple   							=			[
					    'UMT5EncoderModel',
					    'UMT5ForConditionalGeneration',
					    'UMT5ForQuestionAnswering',
					    'UMT5Model',
					    'UMT5PreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_umta import UMTaConfig, UMTaOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_umta import (
										    UMTaEncoderModel,
										    UMTaForConditionalGeneration,
										    UMTaForQuestionAnswering,
										    UMTaModel,
										    UMTaPreTrainedModel,
										)
else:
					import sys

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




