



from typing import TYPE_CHECKING

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


__lowerCamelCase								=      {
    "configuration_align": [
        "ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AlignConfig",
        "AlignTextConfig",
        "AlignVisionConfig",
    ],
    "processing_align": ["AlignProcessor"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase								=      [
       "ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST",
       "AlignModel",
       "AlignPreTrainedModel",
       "AlignTextModel",
       "AlignVisionModel",
   ]

if TYPE_CHECKING:
   from .configuration_align import (
       ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
       AlignConfig,
       AlignTextConfig,
       AlignVisionConfig,
   )
   from .processing_align import AlignProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_align import (
          ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
          AlignModel,
          AlignPreTrainedModel,
          AlignTextModel,
          AlignVisionModel,
      )

else:
   import sys

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


"""simple docstring"""

from sklearn.neural_network import MLPClassifier

UpperCamelCase      :							Any          =					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
UpperCamelCase      :							Any          =					[0, 1, 0, 0]


UpperCamelCase      :							Optional[Any]          =					MLPClassifier(
    solver="lbfgs", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


UpperCamelCase      :							List[Any]          =					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
UpperCamelCase      :							Optional[Any]          =					clf.predict(test)






def 				A		(							snake_case :int						) ->      int:
 return list(snake_case						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



