

"""simple docstring"""




# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

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 						__magic_name__      (							__lowerCamelCase							):

   '''simple docstring'''




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






     """simple docstring"""






     return cls



   @slow
   @require_torch_gpu
   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     a__  =self.get_auto_remove_tmp_dir()
     a__  =F"""
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        """.split()

     # we want this test to run even if there is only one GPU, but if there are more we use them all
     a__  =get_gpu_count()
     a__  =F"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
     a__  =[sys.executable] + distributed_args + args
     execute_subprocess_async(_UpperCamelCase,   env=self.get_env()	)

     a__  =os.path.join(_UpperCamelCase,   '''test_bleu.json'''	)
     a__  =load_json(_UpperCamelCase	)
     # print(metrics)
     self.assertGreaterEqual(metrics['''bleu'''],   25	)



import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

lowerCamelCase									= [(1,)]




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  a__  =bool(os.getenv('''RUN_SLOW'''     ,				0							)							)
  if run_slow:
    assert True
  else:
    assert False, "should have been skipped"




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  a__  =bool(os.getenv('''RUN_SLOW'''     ,				0							)							)
  if run_slow and torch_device == "cuda":
    assert True
  else:
    assert False, "should have been skipped"







@require_torch
class 						__magic_name__      (							unittest.TestCase							):

   '''simple docstring'''




   @slow
   @require_torch_gpu
   def 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






     """simple docstring"""






     check_slow_torch_cuda()



   @require_torch_gpu
   @slow
   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     check_slow_torch_cuda()



   @parameterized.expand(lowercase_	)
   @slow
   def 			_UpperCAmelCase			(    self,   lowercase_=None	)							->   Tuple:






     """simple docstring"""






     check_slow()





@slow
@require_torch_gpu
def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  check_slow_torch_cuda()




@require_torch_gpu
@slow
def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  check_slow_torch_cuda()




@slow
@pytest.mark.parametrize('''param'''     ,				[1]							)
def       UpperCAmelCase__    (      _A				:			str							):





  '''simple docstring'''







  check_slow()




@pytest.mark.parametrize('''param'''     ,				[1]							)
@slow
def       UpperCAmelCase__    (      _A				:			Tuple							):





  '''simple docstring'''







  check_slow()
