'''simple docstring'''






from typing import TYPE_CHECKING

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


_UpperCAmelCase						:    Any     					=					{
    '''configuration_deformable_detr''': ['''DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeformableDetrConfig'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase						:    Optional[Any]     					=					['''DeformableDetrFeatureExtractor''']
   _UpperCAmelCase						:    str     					=					['''DeformableDetrImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase						:    List[str]     					=					[
       '''DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DeformableDetrForObjectDetection''',
       '''DeformableDetrModel''',
       '''DeformableDetrPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
      from .image_processing_deformable_detr import DeformableDetrImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deformable_detr import (
          DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
          DeformableDetrForObjectDetection,
          DeformableDetrModel,
          DeformableDetrPreTrainedModel,
      )

else:
   import sys

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




'''simple docstring'''






from sklearn.neural_network import MLPClassifier

_UpperCAmelCase						:    str     					=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
_UpperCAmelCase						:    Any     					=					[0, 1, 0, 0]


_UpperCAmelCase						:    List[Any]     					=					MLPClassifier(
    solver='''lbfgs''', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


_UpperCAmelCase						:    Optional[Any]     					=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
_UpperCAmelCase						:    Tuple     					=					clf.predict(test)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]							):
     return list(__snake_case							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



