



'''simple docstring'''





from __future__ import annotations

import math





def   a_   (		_UpperCAmelCase    :      int				)    ->  bool:

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5 ,int(math.sqrt(_UpperCAmelCase				) + 1				) ,6				):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True


A__    :   str           =					[num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]





def   a_   (		_UpperCAmelCase    :      int				)    ->  list[int]:
      if not isinstance(_UpperCAmelCase ,_UpperCAmelCase				):
            raise ValueError('n must be an integer'				)
      if n <= 0:
            raise ValueError('n must be >= 0'				)

      __snake_case :   Optional[Any]													=    []
      for num in range(len(_UpperCAmelCase				)				):
            __snake_case :   str													=    0
            while 2 * i * i <= odd_composites[num]:
                  __snake_case :   List[str]													=    odd_composites[num] - 2 * i * i
                  if is_prime(_UpperCAmelCase				):
                        break
                  i += 1
            else:
                  list_nums.append(odd_composites[num]				)
                  if len(_UpperCAmelCase				) == n:
                        return list_nums

      return []





def   a_   (		)    ->  int:
      return compute_nums(1				)[0]


if __name__ == "__main__":
    print(F"""{solution() = }""")








'''simple docstring'''





import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

A__    :   List[str]           =					logging.getLogger()





def   a_   (		_UpperCAmelCase    :      int				)    ->  Optional[int]:
      __snake_case :   List[str]													=    {}
      __snake_case :   List[str]													=    os.path.join(_UpperCAmelCase ,'all_results.json'				)
      if os.path.exists(_UpperCAmelCase				):
            with open(_UpperCAmelCase ,'r'				) as f:
                  __snake_case :   Dict													=    json.load(_UpperCAmelCase				)
      else:
            raise ValueError(f'''can\'t find {path}'''				)
      return results


A__    :   Dict           =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





@require_torch_tpu
class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 def A_   (   self						:							Optional[Any]  )      ->   List[str]:







       '''simple docstring'''
       import xla_spawn

       __snake_case :   int													=    self.get_auto_remove_tmp_dir()
       __snake_case :   Tuple													=    f'''
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

       with patch.object(__a      ,     'argv'      ,     __a  ):
             __snake_case :   Tuple													=    time()
             xla_spawn.main()
             __snake_case :   Optional[int]													=    time()

             __snake_case :   List[Any]													=    get_results(__a  )
             self.assertGreaterEqual(result['eval_accuracy']      ,     0.7_5  )

             # Assert that the script takes less than 500 seconds to make sure it doesn't hang.
             self.assertLess(end - start      ,     500  )





 def A_   (   self						:							List[str]  )      ->   Optional[int]:







       '''simple docstring'''
       import xla_spawn

       __snake_case :   Optional[Any]													=    '\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            '.split()
       with patch.object(__a      ,     'argv'      ,     __a  ):
             xla_spawn.main()



