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


_A       =					logging.get_logger(__name__)

_A       =					{
    # 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 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				List[Any] 	=						"graphormer"
     UpperCAmelCase__							:				List[Any] 	=						["past_key_values"]





     def __init__(self :       List[Any]							,       _A :       int = 1							,       _A :       int = 5_1_2 * 9							,       _A :       int = 5_1_2 * 3							,       _A :       int = 5_1_2							,       _A :       int = 5_1_2							,       _A :       int = 5_1_2							,       _A :       int = 1_2_8							,       _A :       int = 5							,       _A :       int = 1_0_2_4							,       _A :       str = "multi_hop"							,       _A :       int = 5_1_2							,       _A :       bool = False							,       _A :       int = 1_2							,       _A :       int = 7_6_8							,       _A :       int = 7_6_8							,       _A :       int = 3_2							,       _A :       float = 0.1							,       _A :       float = 0.1							,       _A :       float = 0.0							,       _A :       bool = False							,       _A :       bool = False							,       _A :       bool = False							,       _A :       str = "gelu"							,       _A :       float = None							,       _A :       bool = False							,       _A :       int = 0							,       _A :       bool = False							,       _A :       float = 0.0							,       _A :       int = 8							,       _A :       int = None							,       _A :       int = None							,       _A :       bool = True							,       _A :       bool = True							,       _A :       Tuple=0							,       _A :       Optional[Any]=1							,       _A :       Optional[Any]=2							,       **_A :       Any							,       )		->		Union[str, Any]:
         snake_case						=   num_classes
         snake_case						=   num_atoms
         snake_case						=   num_in_degree
         snake_case						=   num_out_degree
         snake_case						=   num_edges
         snake_case						=   num_spatial
         snake_case						=   num_edge_dis
         snake_case						=   edge_type
         snake_case						=   multi_hop_max_dist
         snake_case						=   spatial_pos_max
         snake_case						=   max_nodes
         snake_case						=   num_hidden_layers
         snake_case						=   embedding_dim
         snake_case						=   embedding_dim
         snake_case						=   ffn_embedding_dim
         snake_case						=   num_attention_heads
         snake_case						=   dropout
         snake_case						=   attention_dropout
         snake_case						=   layerdrop
         snake_case						=   encoder_normalize_before
         snake_case						=   pre_layernorm
         snake_case						=   apply_graphormer_init
         snake_case						=   activation_fn
         snake_case						=   embed_scale
         snake_case						=   freeze_embeddings
         snake_case						=   num_trans_layers_to_freeze
         snake_case						=   share_input_output_embed
         snake_case						=   traceable
         snake_case						=   q_noise
         snake_case						=   qn_block_size

         # These parameters are here for future extensions
         # atm, the model only supports self attention
         snake_case						=   kdim
         snake_case						=   vdim
         snake_case						=   self_attention
         snake_case						=   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_levit import LevitImageProcessor


_A       =					logging.get_logger(__name__)







class 	lowerCamelCase						(    A_    ):





     def __init__(self :       Any							,       *_A :       Optional[Any]							,       **_A :       List[Any]  )		->		None:
         warnings.warn(
             "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
             " use LevitImageProcessor instead."							,       _A							,       )
         super().__init__(*_A							,       **_A  )

