






"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_								=	{
    'configuration_gptsan_japanese': ['GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTSanJapaneseConfig'],
    'tokenization_gptsan_japanese': ['GPTSanJapaneseTokenizer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
           'GPTSanJapaneseForConditionalGeneration',
           'GPTSanJapaneseModel',
           'GPTSanJapanesePreTrainedModel',
       ]
       lowercase_								=	[
           'GPTSanJapaneseTokenizer',
       ]


if TYPE_CHECKING:
       from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
       from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gptsan_japanese import (
                  GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTSanJapaneseForConditionalGeneration,
                  GPTSanJapaneseModel,
                  GPTSanJapanesePreTrainedModel,
              )
              from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
       import sys

       lowercase_								=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""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()



