





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

import torch_xla.distributed.xla_multiprocessing as xmp






def 							_lowerCAmelCase (  )	->			List[Any]:
     lowerCAmelCase_					:       Optional[Any]    =		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 							_lowerCAmelCase (  )	->			Optional[Any]:
     lowerCAmelCase_					:       Tuple    =		parse_args()

     # Import training_script as a module.
     lowerCAmelCase_					:       Union[str, Any]    =		Path(args.training_script			)
     sys.path.append(str(script_fpath.parent.resolve()			)			)
     lowerCAmelCase_					:       Optional[Any]    =		script_fpath.stem
     lowerCAmelCase_					:       Optional[int]    =		importlib.import_module(_a			)

     # Patch sys.argv
     lowerCAmelCase_					:       Tuple    =		[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()







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_					:   int 							=						{"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PLBartConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Optional[int] 							=						["""PLBartTokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Union[str, Any] 							=						[
          """PLBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PLBartForCausalLM""",
          """PLBartForConditionalGeneration""",
          """PLBartForSequenceClassification""",
          """PLBartModel""",
          """PLBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_plbart import PLBartTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_plbart import (
                PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                PLBartForCausalLM,
                PLBartForConditionalGeneration,
                PLBartForSequenceClassification,
                PLBartModel,
                PLBartPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   List[str] 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)
