




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


lowercase__      :			int           =					logging.get_logger(__name__)

lowercase__      :			int           =					{
    # 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__       (					UpperCamelCase__      ):
     a		:			Optional[Any]							=	"""graphormer"""
     a		:			int							=	["""past_key_values"""]







     def __init__(	self  ,		A = 1  ,		A = 512 * 9  ,		A = 512 * 3  ,		A = 512  ,		A = 512  ,		A = 512  ,		A = 128  ,		A = 5  ,		A = 1024  ,		A = "multi_hop"  ,		A = 512  ,		A = False  ,		A = 12  ,		A = 768  ,		A = 768  ,		A = 32  ,		A = 0.1  ,		A = 0.1  ,		A = 0.0  ,		A = False  ,		A = False  ,		A = False  ,		A = "gelu"  ,		A = None  ,		A = False  ,		A = 0  ,		A = False  ,		A = 0.0  ,		A = 8  ,		A = None  ,		A = None  ,		A = True  ,		A = True  ,		A=0  ,		A=1  ,		A=2  ,		**A  ,		)  ->    List[str]:



          '''simple docstring'''



          a      =      num_classes
          a      =      num_atoms
          a      =      num_in_degree
          a      =      num_out_degree
          a      =      num_edges
          a      =      num_spatial
          a      =      num_edge_dis
          a      =      edge_type
          a      =      multi_hop_max_dist
          a      =      spatial_pos_max
          a      =      max_nodes
          a      =      num_hidden_layers
          a      =      embedding_dim
          a      =      embedding_dim
          a      =      ffn_embedding_dim
          a      =      num_attention_heads
          a      =      dropout
          a      =      attention_dropout
          a      =      layerdrop
          a      =      encoder_normalize_before
          a      =      pre_layernorm
          a      =      apply_graphormer_init
          a      =      activation_fn
          a      =      embed_scale
          a      =      freeze_embeddings
          a      =      num_trans_layers_to_freeze
          a      =      share_input_output_embed
          a      =      traceable
          a      =      q_noise
          a      =      qn_block_size

          # These parameters are here for future extensions
          # atm, the model only supports self attention
          a      =      kdim
          a      =      vdim
          a      =      self_attention
          a      =      bias

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







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


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




class 			a__       (					UpperCamelCase__      ):







     def __init__(	self  ,		*A  ,		**A )  ->    None:



          '''simple docstring'''



          warnings.warn(
              "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV2ImageProcessor instead."  ,		A  ,		)
          super().__init__(*A  ,		**A )

