
"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase			=		{
    """configuration_nystromformer""": ["""NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NystromformerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase			=		[
							    """NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """NystromformerForMaskedLM""",
							    """NystromformerForMultipleChoice""",
							    """NystromformerForQuestionAnswering""",
							    """NystromformerForSequenceClassification""",
							    """NystromformerForTokenClassification""",
							    """NystromformerLayer""",
							    """NystromformerModel""",
							    """NystromformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nystromformer import (
														    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NystromformerForMaskedLM,
														    NystromformerForMultipleChoice,
														    NystromformerForQuestionAnswering,
														    NystromformerForSequenceClassification,
														    NystromformerForTokenClassification,
														    NystromformerLayer,
														    NystromformerModel,
														    NystromformerPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase			=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

"""simple docstring"""


from sklearn.neural_network import MLPClassifier

lowerCAmelCase			=		[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase			=		[0, 1, 0, 0]


lowerCAmelCase			=		MLPClassifier(
    solver="""lbfgs""", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCAmelCase			=		[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCAmelCase			=		clf.predict(test)






def      lowerCAmelCase_     (	snake_case_	:    Tuple    )     ->List[Any]:
			return list(snake_case_    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()