code: 
  
  
  
  
  
  
  from itertools import compress, repeat
  from math import ceil, sqrt
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			List[str] ):
  
  				if num <= 2:
  								return []
  				if num == 3:
  								return [2]
  
  				# Odd sieve for numbers in range [3, num - 1]
  				UpperCamelCase :int										=     bytearray(B'''\x01''' ) * ((num >> 1) - 1)
  
  				for i in range(3  ,						int(sqrt(_UpperCAmelCase ) ) + 1  ,						2 ):
  								if sieve[(i >> 1) - 1]:
  												UpperCamelCase :Any										=     i**2
  												UpperCamelCase :Optional[int]										=     repeat(
  												    0  ,						ceil((num - i_squared) / (i << 1) ) )
  
  				return [2] + list(compress(range(3  ,						_UpperCAmelCase  ,						2 )  ,						_UpperCAmelCase ) )
  
  
  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				)
  
  
  
  
  
  