
'''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        UpperCAmelCase_       (						A							):

 '''simple docstring'''







 @classmethod
 def        UpperCamelCase     (				cls		:     Optional[Any]   ):



   '''simple docstring'''




   return cls







 @slow
 @require_torch_gpu
 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Dict												=       self.get_auto_remove_tmp_dir()
   UpperCAmelCase__			:				List[str]												=       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
   UpperCAmelCase__			:				Any												=       get_gpu_count()
   UpperCAmelCase__			:				Union[str, Any]												=       F"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
   UpperCAmelCase__			:				List[str]												=       [sys.executable] + distributed_args + args
   execute_subprocess_async(snake_case__ ,							env=self.get_env()   )

   UpperCAmelCase__			:				List[str]												=       os.path.join(snake_case__ ,							"test_bleu.json"   )
   UpperCAmelCase__			:				Dict												=       load_json(snake_case__   )
   # print(metrics)
   self.assertGreaterEqual(metrics["bleu"] ,							25   )





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE    =					{"""configuration_visual_bert""": ["""VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VisualBertConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =					[
          """VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """VisualBertForMultipleChoice""",
          """VisualBertForPreTraining""",
          """VisualBertForQuestionAnswering""",
          """VisualBertForRegionToPhraseAlignment""",
          """VisualBertForVisualReasoning""",
          """VisualBertLayer""",
          """VisualBertModel""",
          """VisualBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_visual_bert import (
                VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                VisualBertForMultipleChoice,
                VisualBertForPreTraining,
                VisualBertForQuestionAnswering,
                VisualBertForRegionToPhraseAlignment,
                VisualBertForVisualReasoning,
                VisualBertLayer,
                VisualBertModel,
                VisualBertPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE    =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



