






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()













# 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				)





