
import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property




@unittest.skipUnless(os.path.exists(_A     )  ,       """Tatoeba directory does not exist."""     )
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  @cached_property
  def 							_lowercase							( self			: Tuple    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       tempfile.mkdtemp()
    return TatoebaConverter(save_dir=UpperCamelCase__    )
  @slow
  def 							_lowercase							( self			: Tuple    )			->	List[str]:







    """simple docstring"""






    self.resolver.convert_models(["""heb-eng"""]    )





  @slow
  def 							_lowercase							( self			: int    )			->	Tuple:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.resolver.write_model_card("""opus-mt-he-en"""			,							dry_run=UpperCamelCase__    )
    assert mmeta["long_pair"] == "heb-eng"



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'''





  __magic_name__				     =       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=A_,     default=1,     help="""Number of TPU cores to use (1 or 8)."""					)

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

  return parser.parse_args()

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       parse_args()

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

  # Patch sys.argv
  __magic_name__				     =       [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()

