






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


a		:	Any      					=			logging.get_logger(__name__)

a		:	Optional[Any]      					=			{
    # pcqm4mv1 now deprecated
    "graphormer-base": "https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}
class    a   (			lowercase__       ):



      """simple docstring"""


      a  :			str								=     'graphormer'
      a  :			Any								=     ['past_key_values']
      def __init__(				self :      Union[str, Any]		,				__lowercase :      int = 1		,				__lowercase :      int = 512 * 9		,				__lowercase :      int = 512 * 3		,				__lowercase :      int = 512		,				__lowercase :      int = 512		,				__lowercase :      int = 512		,				__lowercase :      int = 128		,				__lowercase :      int = 5		,				__lowercase :      int = 1024		,				__lowercase :      str = "multi_hop"		,				__lowercase :      int = 512		,				__lowercase :      bool = False		,				__lowercase :      int = 12		,				__lowercase :      int = 768		,				__lowercase :      int = 768		,				__lowercase :      int = 32		,				__lowercase :      float = 0.1		,				__lowercase :      float = 0.1		,				__lowercase :      float = 0.0		,				__lowercase :      bool = False		,				__lowercase :      bool = False		,				__lowercase :      bool = False		,				__lowercase :      str = "gelu"		,				__lowercase :      float = None		,				__lowercase :      bool = False		,				__lowercase :      int = 0		,				__lowercase :      bool = False		,				__lowercase :      float = 0.0		,				__lowercase :      int = 8		,				__lowercase :      int = None		,				__lowercase :      int = None		,				__lowercase :      bool = True		,				__lowercase :      bool = True		,				__lowercase :      Union[str, Any]=0		,				__lowercase :      Any=1		,				__lowercase :      Tuple=2		,				**__lowercase :      Optional[int]		,				)							->				Union[str, Any]:
        __UpperCAmelCase							:     str									=  num_classes
        __UpperCAmelCase							:     Dict									=  num_atoms
        __UpperCAmelCase							:     Union[str, Any]									=  num_in_degree
        __UpperCAmelCase							:     Optional[Any]									=  num_out_degree
        __UpperCAmelCase							:     List[str]									=  num_edges
        __UpperCAmelCase							:     Dict									=  num_spatial
        __UpperCAmelCase							:     int									=  num_edge_dis
        __UpperCAmelCase							:     int									=  edge_type
        __UpperCAmelCase							:     List[Any]									=  multi_hop_max_dist
        __UpperCAmelCase							:     str									=  spatial_pos_max
        __UpperCAmelCase							:     str									=  max_nodes
        __UpperCAmelCase							:     Optional[int]									=  num_hidden_layers
        __UpperCAmelCase							:     List[Any]									=  embedding_dim
        __UpperCAmelCase							:     int									=  embedding_dim
        __UpperCAmelCase							:     Any									=  ffn_embedding_dim
        __UpperCAmelCase							:     int									=  num_attention_heads
        __UpperCAmelCase							:     Optional[int]									=  dropout
        __UpperCAmelCase							:     List[Any]									=  attention_dropout
        __UpperCAmelCase							:     Any									=  layerdrop
        __UpperCAmelCase							:     str									=  encoder_normalize_before
        __UpperCAmelCase							:     int									=  pre_layernorm
        __UpperCAmelCase							:     Dict									=  apply_graphormer_init
        __UpperCAmelCase							:     Any									=  activation_fn
        __UpperCAmelCase							:     Any									=  embed_scale
        __UpperCAmelCase							:     Union[str, Any]									=  freeze_embeddings
        __UpperCAmelCase							:     List[Any]									=  num_trans_layers_to_freeze
        __UpperCAmelCase							:     Tuple									=  share_input_output_embed
        __UpperCAmelCase							:     str									=  traceable
        __UpperCAmelCase							:     Union[str, Any]									=  q_noise
        __UpperCAmelCase							:     Union[str, Any]									=  qn_block_size

        # These parameters are here for future extensions
        # atm, the model only supports self attention
        __UpperCAmelCase							:     Optional[Any]									=  kdim
        __UpperCAmelCase							:     List[Any]									=  vdim
        __UpperCAmelCase							:     List[str]									=  self_attention
        __UpperCAmelCase							:     int									=  bias

        super().__init__(
            pad_token_id=__lowercase		,				bos_token_id=__lowercase		,				eos_token_id=__lowercase		,				**__lowercase		,				)










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()


