






from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		:	Union[str, Any]      					=			logging.get_logger(__name__)

a		:	Tuple      					=			{
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class    a   (			lowercase__       ):



      """simple docstring"""


      a  :			List[str]								=     'vilt'
      def __init__(				self :      Optional[Any]		,				__lowercase :      List[str]=30522		,				__lowercase :      Optional[int]=2		,				__lowercase :      str=2		,				__lowercase :      Union[str, Any]=40		,				__lowercase :      Optional[Any]=768		,				__lowercase :      Tuple=12		,				__lowercase :      Any=12		,				__lowercase :      Optional[Any]=3072		,				__lowercase :      Tuple="gelu"		,				__lowercase :      Tuple=0.0		,				__lowercase :      Dict=0.0		,				__lowercase :      Any=0.02		,				__lowercase :      int=1e-1_2		,				__lowercase :      Any=384		,				__lowercase :      List[Any]=32		,				__lowercase :      str=3		,				__lowercase :      Union[str, Any]=True		,				__lowercase :      Tuple=-1		,				__lowercase :      Dict=False		,				__lowercase :      List[Any]=-1		,				**__lowercase :      Union[str, Any]		,				)							->				int:
        super().__init__(tie_word_embeddings=__lowercase		,				**__lowercase )

        __UpperCAmelCase							:     Union[str, Any]									=  vocab_size
        __UpperCAmelCase							:     int									=  type_vocab_size
        __UpperCAmelCase							:     List[Any]									=  modality_type_vocab_size
        __UpperCAmelCase							:     str									=  max_position_embeddings

        __UpperCAmelCase							:     int									=  hidden_size
        __UpperCAmelCase							:     List[Any]									=  num_hidden_layers
        __UpperCAmelCase							:     List[str]									=  num_attention_heads
        __UpperCAmelCase							:     str									=  intermediate_size
        __UpperCAmelCase							:     List[str]									=  hidden_act
        __UpperCAmelCase							:     List[Any]									=  hidden_dropout_prob
        __UpperCAmelCase							:     Optional[Any]									=  attention_probs_dropout_prob
        __UpperCAmelCase							:     int									=  initializer_range
        __UpperCAmelCase							:     Optional[int]									=  layer_norm_eps

        __UpperCAmelCase							:     Optional[int]									=  image_size
        __UpperCAmelCase							:     str									=  patch_size
        __UpperCAmelCase							:     Union[str, Any]									=  num_channels
        __UpperCAmelCase							:     Union[str, Any]									=  qkv_bias
        __UpperCAmelCase							:     Optional[Any]									=  max_image_length
        __UpperCAmelCase							:     Union[str, Any]									=  num_images










from sklearn.neural_network import MLPClassifier

a		:	int      					=			[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
a		:	List[str]      					=			[0, 1, 0, 0]


a		:	str      					=			MLPClassifier(
    solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


a		:	Union[str, Any]      					=			[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
a		:	Optional[int]      					=			clf.predict(test)






def     lowerCamelCase__				(   __lowerCamelCase      :					Dict		):
  return list(__lowerCamelCase		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


