code: 
  
  
  
  
  
  
  from __future__ import annotations
  
  from enum import Enum
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      Union[str, Any]     =24
        UpperCamelCase_       :      Union[str, Any]     =21
        UpperCamelCase_       :      int     =18
        UpperCamelCase_       :      int     =15
        UpperCamelCase_       :      Dict     =12
        UpperCamelCase_       :      Dict     =9
        UpperCamelCase_       :      str     =6
        UpperCamelCase_       :      str     =3
        UpperCamelCase_       :      int     =2
        UpperCamelCase_       :      Optional[int]     =1
        UpperCamelCase_       :      List[Any]     =-1
        UpperCamelCase_       :      int     =-2
        UpperCamelCase_       :      Tuple     =-3
        UpperCamelCase_       :      int     =-6
        UpperCamelCase_       :      str     =-9
        UpperCamelCase_       :      Any     =-12
        UpperCamelCase_       :      Dict     =-15
        UpperCamelCase_       :      Union[str, Any]     =-18
        UpperCamelCase_       :      Tuple     =-21
        UpperCamelCase_       :      Union[str, Any]     =-24
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      Tuple     =8
        UpperCamelCase_       :      Tuple     =7
        UpperCamelCase_       :      Optional[Any]     =6
        UpperCamelCase_       :      List[Any]     =5
        UpperCamelCase_       :      Dict     =4
        UpperCamelCase_       :      Dict     =3
        UpperCamelCase_       :      Optional[int]     =2
        UpperCamelCase_       :      Optional[Any]     =1
  
  
  
  
  
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			float  ,						SCREAMING_SNAKE_CASE__						:			str | SIUnit  ,						SCREAMING_SNAKE_CASE__						:			str | SIUnit  ,						):
      if isinstance(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ):
          UpperCamelCase :Union[str, Any]										=     SIUnit[known_prefix.lower()]
      if isinstance(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ):
          UpperCamelCase :Optional[Any]										=     SIUnit[unknown_prefix.lower()]
      UpperCamelCase :float										=     known_amount * (
          10 ** (known_prefix.value - unknown_prefix.value)
      )
      return unknown_amount
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			float  ,						SCREAMING_SNAKE_CASE__						:			str | BinaryUnit  ,						SCREAMING_SNAKE_CASE__						:			str | BinaryUnit  ,						):
      if isinstance(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ):
          UpperCamelCase :Tuple										=     BinaryUnit[known_prefix.lower()]
      if isinstance(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ):
          UpperCamelCase :Optional[Any]										=     BinaryUnit[unknown_prefix.lower()]
      UpperCamelCase :float										=     known_amount * (
          2 ** ((known_prefix.value - unknown_prefix.value) * 10)
      )
      return unknown_amount
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  # 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_		(      lowercase				):
  
  
  
        """simple docstring"""
        @classmethod
        def 					UpperCAmelCase		(       cls				)       ->   List[Any]:
            return cls
  
  
  
  
        @slow
        @require_torch_gpu
        def 					UpperCAmelCase		(       self				)       ->   int:
            UpperCamelCase :int										=     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 :List[str]										=     get_gpu_count()
            UpperCamelCase :Optional[Any]										=     F'''
              -m torch.distributed.launch
              --nproc_per_node={n_gpu}
              {self.test_file_dir}/run_distributed_eval.py
          '''.split()
            UpperCamelCase :List[Any]										=     [sys.executable] + distributed_args + args
            execute_subprocess_async(SCREAMING_SNAKE_CASE_					,       env=self.get_env()				)
  
            UpperCamelCase :Tuple										=     os.path.join(SCREAMING_SNAKE_CASE_					,       '''test_bleu.json'''				)
            UpperCamelCase :str										=     load_json(SCREAMING_SNAKE_CASE_				)
            # print(metrics)
            self.assertGreaterEqual(metrics['''bleu''']					,       25				)
  
  
  
  
  
  