




import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json

class   UpperCAmelCase    (      UpperCAmelCase_ ):






   @classmethod
   def 			_a    (						cls :							Optional[Any]  )->      Optional[Any]:
     return cls






   @slow
   @require_torch_gpu
   def 			_a    (						self :							str  )->      Any:
     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			List[Any]      =     f'\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        '.split()

     # we want this test to run even if there is only one GPU, but if there are more we use them all
     _lowercase:			List[str]      =     get_gpu_count()
     _lowercase:			Optional[int]      =     f'\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        '.split()
     _lowercase:			Tuple      =     [sys.executable] + distributed_args + args
     execute_subprocess_async(_lowercase    ,       env=self.get_env()  )

     _lowercase:			Optional[int]      =     os.path.join(_lowercase    ,       'test_bleu.json'  )
     _lowercase:			List[str]      =     load_json(_lowercase  )
     # print(metrics)
     self.assertGreaterEqual(metrics['bleu']    ,       25  )








'''simple docstring'''

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

import torch_xla.distributed.xla_multiprocessing as xmp






def 						_UpperCAmelCase     (	)      ->      Optional[Any]:





						"""simple docstring"""
						lowercase_   : List[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=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 						_UpperCAmelCase     (	)      ->      str:





						"""simple docstring"""
						lowercase_   : Optional[int]   	=      parse_args()

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

						# Patch sys.argv
						lowercase_   : List[str]   	=      [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()
