

import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase   :      List[Any]     =     logging.getLogger()

def       A_     (							a    ,      a     ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     Optional[int]							=   '\n'.join(_UpperCamelCase     )
      Path(_UpperCamelCase     ).open('w'     ).writelines(_UpperCamelCase     )


lowerCAmelCase   :      Tuple     =     "patrickvonplaten/t5-tiny-random"
lowerCAmelCase   :      Tuple     =     "sshleifer/bart-tiny-random"
lowerCAmelCase   :      List[Any]     =     "sshleifer/tiny-mbart"

lowerCAmelCase   :      str     =     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



class        _A						(     A__):

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   Path(self.get_auto_remove_tmp_dir()    ) / 'utest_input.source'
        SCREAMING_SNAKE_CASE_ :     Tuple							=   input_file_name.parent / 'utest_output.txt'
        assert not output_file_name.exists()
        SCREAMING_SNAKE_CASE_ :     Tuple							=   [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
        _dump_articles(_a       ,    _a    )

        SCREAMING_SNAKE_CASE_ :     Tuple							=   str(Path(self.get_auto_remove_tmp_dir()    ) / 'scores.json'    )
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   'translation_en_to_de' if model == T5_TINY else 'summarization'
        SCREAMING_SNAKE_CASE_ :     List[str]							=   f"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

        with patch.object(_a       ,    'argv'       ,    _a    ):
              run_generate()
              assert Path(_a    ).exists()
              # os.remove(Path(output_file_name))

  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        self.run_eval_tester(_a    )

  @parameterized.expand([BART_TINY, MBART_TINY]    )
  @slow
  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        self.run_eval_tester(_a    )



  @parameterized.expand([T5_TINY, MBART_TINY]    )
  @slow
  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   Path(self.get_auto_remove_tmp_dir()    ) / 'utest_input.source'
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   input_file_name.parent / 'utest_output.txt'
        assert not output_file_name.exists()

        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   {
            'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
            'de': [
                'Maschinelles Lernen ist großartig, oder?',
                'Ich esse gerne Bananen',
                'Morgen ist wieder ein toller Tag!',
            ],
        }

        SCREAMING_SNAKE_CASE_ :     Dict							=   Path(self.get_auto_remove_tmp_dir()    )
        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   str(tmp_dir / 'scores.json'    )
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   str(tmp_dir / 'val.target'    )
        _dump_articles(_a       ,    text['en']    )
        _dump_articles(_a       ,    text['de']    )
        SCREAMING_SNAKE_CASE_ :     Any							=   'translation_en_to_de' if model == T5_TINY else 'summarization'
        SCREAMING_SNAKE_CASE_ :     str							=   f"\n            run_eval_search.py\n            {model}\n            {str(_a    )}\n            {str(_a    )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            ".split()
        testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']    )

        with patch.object(_a       ,    'argv'       ,    _a    ):
              with CaptureStdout() as cs:
                    run_search()
              SCREAMING_SNAKE_CASE_ :     List[str]							=   [' num_beams | length_penalty', model, 'Best score args']
              SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   ['Info']
              if "translation" in task:
                    expected_strings.append('bleu'    )
              else:
                    expected_strings.extend(_a    )
              for w in expected_strings:
                    assert w in cs.out
              for w in un_expected_strings:
                    assert w not in cs.out
              assert Path(_a    ).exists()
              os.remove(Path(_a    )    )









'''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					(						)      ->			Any:
     """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					(						)      ->			Optional[int]:
     """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()
