



"""simple docstring"""
from ..utils import DummyObject, requires_backends
class 							lowerCamelCase    (     metaclass=_lowerCAmelCase							):






					'''simple docstring'''





					_A						:     List[str]		=	["""torch""", """transformers""", """invisible_watermark"""]






					def __init__(			self:				List[str]		,	*snake_case:				List[str]		,	**snake_case:				Tuple		)  ->      Optional[int]:
											requires_backends(self		,	["""torch""", """transformers""", """invisible_watermark"""]		)






					@classmethod
					def   lowerCAmelCase_							(			cls:				List[str]		,	*snake_case:				List[Any]		,	**snake_case:				List[str]		)  ->      Any:
											requires_backends(cls		,	["""torch""", """transformers""", """invisible_watermark"""]		)






					@classmethod
					def   lowerCAmelCase_							(			cls:				str		,	*snake_case:				Tuple		,	**snake_case:				str		)  ->      List[Any]:
											requires_backends(cls		,	["""torch""", """transformers""", """invisible_watermark"""]		)

class 							lowerCamelCase    (     metaclass=_lowerCAmelCase							):






					'''simple docstring'''





					_A						:     str		=	["""torch""", """transformers""", """invisible_watermark"""]






					def __init__(			self:				List[Any]		,	*snake_case:				Tuple		,	**snake_case:				Tuple		)  ->      List[str]:
											requires_backends(self		,	["""torch""", """transformers""", """invisible_watermark"""]		)






					@classmethod
					def   lowerCAmelCase_							(			cls:				int		,	*snake_case:				Optional[int]		,	**snake_case:				Any		)  ->      Any:
											requires_backends(cls		,	["""torch""", """transformers""", """invisible_watermark"""]		)






					@classmethod
					def   lowerCAmelCase_							(			cls:				int		,	*snake_case:				List[Any]		,	**snake_case:				Optional[int]		)  ->      int:
											requires_backends(cls		,	["""torch""", """transformers""", """invisible_watermark"""]		)





"""simple docstring"""
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'''


						snake_case_					:str		=     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   A_    (     ):







						'''simple docstring'''


						snake_case_					:Any		=     parse_args()

						# Import training_script as a module.
						snake_case_					:str		=     Path(args.training_script      )
						sys.path.append(str(script_fpath.parent.resolve()      )      )
						snake_case_					:Dict		=     script_fpath.stem
						snake_case_					:int		=     importlib.import_module(_lowercase      )

						# Patch sys.argv
						snake_case_					:Optional[Any]		=     [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()
