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


__snake_case			      =    logging.get_logger(__name__)

__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 			__snake_case   (     _SCREAMING_SNAKE_CASE   ):
 __lowerCamelCase    :   Tuple     =      """graphormer"""
 __lowerCamelCase    :   Optional[int]     =      ["""past_key_values"""]
 def __init__(  self		,   snake_case__ = 1		,   snake_case__ = 512 * 9		,   snake_case__ = 512 * 3		,   snake_case__ = 512		,   snake_case__ = 512		,   snake_case__ = 512		,   snake_case__ = 128		,   snake_case__ = 5		,   snake_case__ = 1024		,   snake_case__ = "multi_hop"		,   snake_case__ = 512		,   snake_case__ = False		,   snake_case__ = 12		,   snake_case__ = 768		,   snake_case__ = 768		,   snake_case__ = 32		,   snake_case__ = 0.1		,   snake_case__ = 0.1		,   snake_case__ = 0.0		,   snake_case__ = False		,   snake_case__ = False		,   snake_case__ = False		,   snake_case__ = "gelu"		,   snake_case__ = None		,   snake_case__ = False		,   snake_case__ = 0		,   snake_case__ = False		,   snake_case__ = 0.0		,   snake_case__ = 8		,   snake_case__ = None		,   snake_case__ = None		,   snake_case__ = True		,   snake_case__ = True		,   snake_case__=0		,   snake_case__=1		,   snake_case__=2		,   **snake_case__		,   )	->							int:
       '''simple docstring'''





       UpperCAmelCase    : Union[str, Any]      =num_classes
       UpperCAmelCase    : Dict      =num_atoms
       UpperCAmelCase    : Any      =num_in_degree
       UpperCAmelCase    : List[Any]      =num_out_degree
       UpperCAmelCase    : List[str]      =num_edges
       UpperCAmelCase    : List[Any]      =num_spatial
       UpperCAmelCase    : List[Any]      =num_edge_dis
       UpperCAmelCase    : List[str]      =edge_type
       UpperCAmelCase    : List[Any]      =multi_hop_max_dist
       UpperCAmelCase    : List[Any]      =spatial_pos_max
       UpperCAmelCase    : Tuple      =max_nodes
       UpperCAmelCase    : str      =num_hidden_layers
       UpperCAmelCase    : Dict      =embedding_dim
       UpperCAmelCase    : List[Any]      =embedding_dim
       UpperCAmelCase    : str      =ffn_embedding_dim
       UpperCAmelCase    : Optional[Any]      =num_attention_heads
       UpperCAmelCase    : str      =dropout
       UpperCAmelCase    : Optional[int]      =attention_dropout
       UpperCAmelCase    : Dict      =layerdrop
       UpperCAmelCase    : Union[str, Any]      =encoder_normalize_before
       UpperCAmelCase    : Any      =pre_layernorm
       UpperCAmelCase    : List[str]      =apply_graphormer_init
       UpperCAmelCase    : Dict      =activation_fn
       UpperCAmelCase    : List[Any]      =embed_scale
       UpperCAmelCase    : List[Any]      =freeze_embeddings
       UpperCAmelCase    : Optional[int]      =num_trans_layers_to_freeze
       UpperCAmelCase    : Union[str, Any]      =share_input_output_embed
       UpperCAmelCase    : int      =traceable
       UpperCAmelCase    : Tuple      =q_noise
       UpperCAmelCase    : List[Any]      =qn_block_size

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

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

import math





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  list[int]:






      '''simple docstring'''





      if not isinstance(__lowerCAmelCase		,					__lowerCAmelCase						):
            UpperCAmelCase    : Union[str, Any]      =f'''Input value of [number={number}] must be an integer'''
            raise TypeError(__lowerCAmelCase						)
      if number < 1:
            UpperCAmelCase    : Dict      =f'''Input value of [number={number}] must be a positive integer'''
            raise ValueError(__lowerCAmelCase						)
      UpperCAmelCase    : Dict      =[number]
      while number != 1:
            if number % 2 == 0:
                  UpperCAmelCase    : Optional[Any]      =math.floor(math.sqrt(__lowerCAmelCase						)						)
            else:
                  UpperCAmelCase    : List[str]      =math.floor(
                      math.sqrt(__lowerCAmelCase						) * math.sqrt(__lowerCAmelCase						) * math.sqrt(__lowerCAmelCase						)						)
            sequence.append(__lowerCAmelCase						)
      return sequence


if __name__ == "__main__":
     import doctest

     doctest.testmod()
