


"""simple docstring"""


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

import torch_xla.distributed.xla_multiprocessing as xmp







def 			__snake_case							( )   ->		Any:







		"""simple docstring"""





		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=UpperCamelCase__			,      default=1			,      help='Number of TPU cores to use (1 or 8).'						)

		# positional
		parser.add_argument(
		    'training_script'			,      type=UpperCamelCase__			,      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=UpperCamelCase__						)

		return parser.parse_args()







def 			__snake_case							( )   ->		str:







		"""simple docstring"""





		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(UpperCamelCase__						)

		# 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()











'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_      :  Dict				     =  {
    'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  int				     =  [
     'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GraphormerForGraphClassification',
     'GraphormerModel',
     'GraphormerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_      :  Tuple				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
