
"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase			=		{
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase			=		["""XLMProphetNetTokenizer"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase			=		[
							    """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XLMProphetNetDecoder""",
							    """XLMProphetNetEncoder""",
							    """XLMProphetNetForCausalLM""",
							    """XLMProphetNetForConditionalGeneration""",
							    """XLMProphetNetModel""",
							    """XLMProphetNetPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xlm_prophetnet import (
														    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XLMProphetNetDecoder,
														    XLMProphetNetEncoder,
														    XLMProphetNetForCausalLM,
														    XLMProphetNetForConditionalGeneration,
														    XLMProphetNetModel,
														    XLMProphetNetPreTrainedModel,
														)

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()