
"""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    (     )  ->    Tuple:
  _SCREAMING_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=lowerCamelCase__,						default=1,						help="Number of TPU cores to use (1 or 8)."				)

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

  return parser.parse_args()





def    _lowerCAmelCase    (     )  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = parse_args()

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

  # Patch sys.argv
  _SCREAMING_SNAKE_CASE   :				Union[str, 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()





"""simple docstring"""
import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str,						**lowerCamelCase__							:							Optional[Any]				)  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = AutoConfig.from_pretrained(lowerCamelCase__,						**lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = AutoModelForSeqaSeqLM.from_config(lowerCamelCase__				)
  model.save_pretrained(lowerCamelCase__				)
  AutoTokenizer.from_pretrained(lowerCamelCase__				).save_pretrained(lowerCamelCase__				)
  return model


if __name__ == "__main__":
      fire.Fire(save_randomly_initialized_version)



