




'''simple docstring'''



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

import torch_xla.distributed.xla_multiprocessing as xmp




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      str    					=						parse_args()

      # Import training_script as a module.
      SCREAMING_SNAKE_CASE   :      int    					=						Path(args.training_script    )
      sys.path.append(str(script_fpath.parent.resolve()    )    )
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						script_fpath.stem
      SCREAMING_SNAKE_CASE   :      str    					=						importlib.import_module(__UpperCamelCase    )

      # Patch sys.argv
      SCREAMING_SNAKE_CASE   :      Dict    					=						[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 ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Any				  =		"""T5Config"""
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig


