

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


_SCREAMING_SNAKE_CASE							 =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							 =			{
    # 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_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	'''graphormer'''
 _lowerCAmelCase									=	['''past_key_values''']




 def __init__(			self  ,  _UpperCAmelCase = 1  ,  _UpperCAmelCase = 512 * 9  ,  _UpperCAmelCase = 512 * 3  ,  _UpperCAmelCase = 512  ,  _UpperCAmelCase = 512  ,  _UpperCAmelCase = 512  ,  _UpperCAmelCase = 128  ,  _UpperCAmelCase = 5  ,  _UpperCAmelCase = 1_024  ,  _UpperCAmelCase = "multi_hop"  ,  _UpperCAmelCase = 512  ,  _UpperCAmelCase = False  ,  _UpperCAmelCase = 12  ,  _UpperCAmelCase = 768  ,  _UpperCAmelCase = 768  ,  _UpperCAmelCase = 32  ,  _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  ,  ):
   __UpperCAmelCase	:  str		=     num_classes
   __UpperCAmelCase	:  List[str]		=     num_atoms
   __UpperCAmelCase	:  List[str]		=     num_in_degree
   __UpperCAmelCase	:  Union[str, Any]		=     num_out_degree
   __UpperCAmelCase	:  List[Any]		=     num_edges
   __UpperCAmelCase	:  Optional[Any]		=     num_spatial
   __UpperCAmelCase	:  List[str]		=     num_edge_dis
   __UpperCAmelCase	:  Any		=     edge_type
   __UpperCAmelCase	:  List[str]		=     multi_hop_max_dist
   __UpperCAmelCase	:  str		=     spatial_pos_max
   __UpperCAmelCase	:  str		=     max_nodes
   __UpperCAmelCase	:  int		=     num_hidden_layers
   __UpperCAmelCase	:  List[Any]		=     embedding_dim
   __UpperCAmelCase	:  Optional[Any]		=     embedding_dim
   __UpperCAmelCase	:  Optional[int]		=     ffn_embedding_dim
   __UpperCAmelCase	:  int		=     num_attention_heads
   __UpperCAmelCase	:  List[Any]		=     dropout
   __UpperCAmelCase	:  Union[str, Any]		=     attention_dropout
   __UpperCAmelCase	:  Dict		=     layerdrop
   __UpperCAmelCase	:  Optional[int]		=     encoder_normalize_before
   __UpperCAmelCase	:  Dict		=     pre_layernorm
   __UpperCAmelCase	:  str		=     apply_graphormer_init
   __UpperCAmelCase	:  List[Any]		=     activation_fn
   __UpperCAmelCase	:  int		=     embed_scale
   __UpperCAmelCase	:  Any		=     freeze_embeddings
   __UpperCAmelCase	:  List[Any]		=     num_trans_layers_to_freeze
   __UpperCAmelCase	:  Union[str, Any]		=     share_input_output_embed
   __UpperCAmelCase	:  Any		=     traceable
   __UpperCAmelCase	:  Optional[int]		=     q_noise
   __UpperCAmelCase	:  str		=     qn_block_size

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

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













'''simple docstring'''





def  lowerCamelCase_  (     lowercase__					,  lowercase__):
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  (
     first_input_list_length
     if first_input_list_length > second_input_list_length
     else second_input_list_length
 )
 lowerCamelCase__	=  []
 for char_count in range(lowercase__):
  if char_count < first_input_list_length:
   output_result_list.append(first_input_list[char_count])
  if char_count < second_input_list_length:
   output_result_list.append(second_input_list[char_count])

 return output_result_list


if __name__ == "__main__":
 print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)

