






'''simple docstring'''







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


__A      :       Optional[int]      =	logging.get_logger(__name__)

__A      :       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__    (			lowerCamelCase						):
     '''simple docstring'''







     _snake_case             =  'graphormer'
     _snake_case             =  ['past_key_values']






     def __init__(self    :	Union[str, Any]				,				lowercase__    :	int = 1				,				lowercase__    :	int = 512 * 9				,				lowercase__    :	int = 512 * 3				,				lowercase__    :	int = 512				,				lowercase__    :	int = 512				,				lowercase__    :	int = 512				,				lowercase__    :	int = 128				,				lowercase__    :	int = 5				,				lowercase__    :	int = 1024				,				lowercase__    :	str = "multi_hop"				,				lowercase__    :	int = 512				,				lowercase__    :	bool = False				,				lowercase__    :	int = 12				,				lowercase__    :	int = 768				,				lowercase__    :	int = 768				,				lowercase__    :	int = 32				,				lowercase__    :	float = 0.1				,				lowercase__    :	float = 0.1				,				lowercase__    :	float = 0.0				,				lowercase__    :	bool = False				,				lowercase__    :	bool = False				,				lowercase__    :	bool = False				,				lowercase__    :	str = "gelu"				,				lowercase__    :	float = None				,				lowercase__    :	bool = False				,				lowercase__    :	int = 0				,				lowercase__    :	bool = False				,				lowercase__    :	float = 0.0				,				lowercase__    :	int = 8				,				lowercase__    :	int = None				,				lowercase__    :	int = None				,				lowercase__    :	bool = True				,				lowercase__    :	bool = True				,				lowercase__    :	Any=0				,				lowercase__    :	Optional[Any]=1				,				lowercase__    :	Tuple=2				,				**lowercase__    :	Dict				,				)  ->       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=lowercase__				,				bos_token_id=lowercase__				,				eos_token_id=lowercase__				,				**lowercase__				,				)










'''simple docstring'''







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def 						_UpperCamelCase							(     ):
      __A		   =      ArgumentParser(
          description=(
              "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
          )							)

      # Optional arguments for the launch helper
      parser.add_argument("--num_cores"							,			type=snake_case__							,			default=1							,			help="Number of TPU cores to use (1 or 8)."							)

      # positional
      parser.add_argument(
          "training_script"							,			type=snake_case__							,			help=(
              "The full path to the single TPU training "
              "program/script to be launched in parallel, "
              "followed by all the arguments for the "
              "training script"
          )							,			)

      # rest from the training program
      parser.add_argument("training_script_args"							,			nargs=snake_case__							)

      return parser.parse_args()


def 						_UpperCamelCase							(     ):
      __A		   =      parse_args()

      # Import training_script as a module.
      __A		   =      Path(args.training_script							)
      sys.path.append(str(script_fpath.parent.resolve()							)							)
      __A		   =      script_fpath.stem
      __A		   =      importlib.import_module(snake_case__							)

      # Patch sys.argv
      __A		   =      [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores							)]

      xmp.spawn(mod._mp_fn							,			args=()							,			nprocs=args.num_cores							)


if __name__ == "__main__":
       main()


