

'''simple docstring'''




from typing import TYPE_CHECKING

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


_lowerCAmelCase    			=						{
    """configuration_align""": [
        """ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AlignConfig""",
        """AlignTextConfig""",
        """AlignVisionConfig""",
    ],
    """processing_align""": ["""AlignProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase    			=						[
          """ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """AlignModel""",
          """AlignPreTrainedModel""",
          """AlignTextModel""",
          """AlignVisionModel""",
      ]

if TYPE_CHECKING:
      from .configuration_align import (
          ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AlignConfig,
          AlignTextConfig,
          AlignVisionConfig,
      )
      from .processing_align import AlignProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_align import (
                ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
                AlignModel,
                AlignPreTrainedModel,
                AlignTextModel,
                AlignVisionModel,
            )

else:
      import sys

      _lowerCAmelCase    			=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)













'''simple docstring'''







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

import torch_xla.distributed.xla_multiprocessing as xmp





def    _lowerCAmelCase						(						)					->					List[str]:
			__lowerCAmelCase											=				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=lowercase					,	default=1					,	help="""Number of TPU cores to use (1 or 8)."""	)

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

			return parser.parse_args()





def    _lowerCAmelCase						(						)					->					Any:
			__lowerCAmelCase											=				parse_args()

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

			# Patch sys.argv
			__lowerCAmelCase											=				[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()

