






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_												=					{
    '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_												=					[
        '''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''PegasusXForConditionalGeneration''',
        '''PegasusXModel''',
        '''PegasusXPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_pegasus_x import (
            PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
            PegasusXForConditionalGeneration,
            PegasusXModel,
            PegasusXPreTrainedModel,
        )


else:
    import sys

    lowerCamelCase_												=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







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

import torch_xla.distributed.xla_multiprocessing as xmp

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       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=A_,     default=1,     help="""Number of TPU cores to use (1 or 8)."""					)

  # positional
  parser.add_argument(
      """training_script""",     type=A_,     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=A_					)

  return parser.parse_args()

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       parse_args()

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

  # Patch sys.argv
  __magic_name__				     =       [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()

