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


lowerCamelCase_				:		Tuple       						=       logging.get_logger(__name__)

lowerCamelCase_				:		Optional[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__				:   Tuple								=			'graphormer'
			A__				:   Dict								=			['past_key_values']



			def __init__(						self      ,						UpperCAmelCase = 1      ,						UpperCAmelCase = 5_1_2 * 9      ,						UpperCAmelCase = 5_1_2 * 3      ,						UpperCAmelCase = 5_1_2      ,						UpperCAmelCase = 5_1_2      ,						UpperCAmelCase = 5_1_2      ,						UpperCAmelCase = 1_2_8      ,						UpperCAmelCase = 5      ,						UpperCAmelCase = 1_0_2_4      ,						UpperCAmelCase = "multi_hop"      ,						UpperCAmelCase = 5_1_2      ,						UpperCAmelCase = False      ,						UpperCAmelCase = 1_2      ,						UpperCAmelCase = 7_6_8      ,						UpperCAmelCase = 7_6_8      ,						UpperCAmelCase = 3_2      ,						UpperCAmelCase = 0.1      ,						UpperCAmelCase = 0.1      ,						UpperCAmelCase = 0.0      ,						UpperCAmelCase = False      ,						UpperCAmelCase = False      ,						UpperCAmelCase = False      ,						UpperCAmelCase = "gelu"      ,						UpperCAmelCase = None      ,						UpperCAmelCase = False      ,						UpperCAmelCase = 0      ,						UpperCAmelCase = False      ,						UpperCAmelCase = 0.0      ,						UpperCAmelCase = 8      ,						UpperCAmelCase = None      ,						UpperCAmelCase = None      ,						UpperCAmelCase = True      ,						UpperCAmelCase = True      ,						UpperCAmelCase=0      ,						UpperCAmelCase=1      ,						UpperCAmelCase=2      ,						**UpperCAmelCase      ,						) ->    List[str]:
								__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      ,						)







def 						UpperCamelCase_(			snake_case__:  list   )       -> list:
 UpperCAmelCase__							=				len(snake_case__   )

 # Set the gap value to be decreased by a factor of 1.3
 # after each iteration
 UpperCAmelCase__							=				1.3

 # Continue sorting until the gap is 1
 while gap > 1:
  # Decrease the gap value
  UpperCAmelCase__							=				int(gap / shrink   )

  # Sort the elements using insertion sort
  for i in range(snake_case__  ,    len(snake_case__   )   ):
   UpperCAmelCase__							=				collection[i]
   UpperCAmelCase__							=				i
   while j >= gap and collection[j - gap] > temp:
    UpperCAmelCase__							=				collection[j - gap]
    j -= gap
   UpperCAmelCase__							=				temp

 return collection


if __name__ == "__main__":
 import doctest

 doctest.testmod()



