






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

import torch_xla.distributed.xla_multiprocessing as xmp

def 			__lowerCamelCase     (						)				->    int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    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 			__lowerCamelCase     (						)				->    List[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    parse_args()

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

						# Patch sys.argv
						_SCREAMING_SNAKE_CASE       =    [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_sentencepiece_available, is_tokenizers_available


__a      			= {}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BarthezTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BarthezTokenizerFast"]


if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_barthez import BarthezTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_barthez_fast import BarthezTokenizerFast

else:
						import sys

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