






'''simple docstring'''
import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
a_											=			logging.getLogger()

a_											=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class   __SCREAMING_SNAKE_CASE   (      snake_case__					):




 def   __magic_name__						(   self						:				str       ,   __lowercase						:				int   )						->    str:
       os.makedirs(__lowercase       ,   exist_ok=__lowercase   )
       SCREAMING_SNAKE_CASE__  :					List[str]						={'source': 'What is love ?', 'target': 'life'}
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						={'train': 12, 'val': 2, 'test': 2}
       for split in ["train", "test", "val"]:
             for field in ["source", "target"]:
                   SCREAMING_SNAKE_CASE__  :					Optional[Any]						='\n'.join([contents[field]] * n_lines[split]   )
                   with open(os.path.join(__lowercase       ,   F"{split}.{field}"   )       ,   '''w'''   ) as f:
                         f.write(__lowercase   )




 def   __magic_name__						(   self						:				Union[str, Any]       ,   __lowercase						:				int       ,   __lowercase						:				str = "pytorch"   )						->    int:
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=os.path.join(__lowercase       ,   '''output'''   )
       SCREAMING_SNAKE_CASE__  :					Dict						=os.path.join(__lowercase       ,   '''data'''   )
       self._create_dummy_data(data_dir=__lowercase   )

       SCREAMING_SNAKE_CASE__  :					List[str]						=F"\n                --data_dir {data_dir} \\n                --output_dir {output_dir} \\n                --model_name_or_path facebook/rag-sequence-base \\n                --model_type rag_sequence \\n                --do_train \\n                --do_predict \\n                --n_val -1 \\n                --val_check_interval 1.0 \\n                --train_batch_size 2 \\n                --eval_batch_size 1 \\n                --max_source_length 25 \\n                --max_target_length 25 \\n                --val_max_target_length 25 \\n                --test_max_target_length 25 \\n                --label_smoothing 0.1 \\n                --dropout 0.1 \\n                --attention_dropout 0.1 \\n                --weight_decay 0.001 \\n                --adam_epsilon 1e-08 \\n                --max_grad_norm 0.1 \\n                --lr_scheduler polynomial \\n                --learning_rate 3e-04 \\n                --num_train_epochs 1 \\n                --warmup_steps 4 \\n                --gradient_accumulation_steps 1 \\n                --distributed-port 8787 \\n                --use_dummy_dataset 1 \\n                --distributed_retriever {distributed_retriever} \\n            ".split()

       if gpus > 0:
             testargs.append(F"--gpus={gpus}"   )
             if is_apex_available():
                   testargs.append('''--fp16'''   )
       else:
             testargs.append('''--gpus=0'''   )
             testargs.append('''--distributed_backend=ddp_cpu'''   )
             testargs.append('''--num_processes=2'''   )

       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=[sys.executable, str(Path(finetune_rag.__file__   ).resolve()   )] + testargs
       execute_subprocess_async(__lowercase       ,   env=self.get_env()   )

       SCREAMING_SNAKE_CASE__  :					Dict						=os.path.join(__lowercase       ,   '''metrics.json'''   )
       with open(__lowercase   ) as f:
             SCREAMING_SNAKE_CASE__  :					int						=json.load(__lowercase   )
       return result




 @require_torch_gpu
 def   __magic_name__						(   self						:				List[str]   )						->    Tuple:
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=self._run_finetune(gpus=1   )
       self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']       ,   0.2   )




 @require_torch_multi_gpu
 def   __magic_name__						(   self						:				int   )						->    Dict:
       SCREAMING_SNAKE_CASE__  :					List[Any]						=self._run_finetune(gpus=2   )
       self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']       ,   0.2   )




 @require_torch_gpu
 @require_ray
 def   __magic_name__						(   self						:				List[str]   )						->    int:
       SCREAMING_SNAKE_CASE__  :					List[Any]						=self._run_finetune(gpus=1       ,   distributed_retriever='''ray'''   )
       self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']       ,   0.2   )




 @require_torch_multi_gpu
 @require_ray
 def   __magic_name__						(   self						:				Any   )						->    Union[str, Any]:
       SCREAMING_SNAKE_CASE__  :					str						=self._run_finetune(gpus=1       ,   distributed_retriever='''ray'''   )
       self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']       ,   0.2   )






from __future__ import annotations

import math


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):

		if num <= 0:
				lowerCamelCase_	:     Optional[int] 						=      F"{num}: Invalid input, please enter a positive integer."
				raise ValueError(lowerCAmelCase__						)

		lowerCamelCase_	:     str 						=      [True] * (num + 1)
		lowerCamelCase_	:     List[str] 						=      []
		lowerCamelCase_	:     Optional[int] 						=      2
		lowerCamelCase_	:     List[str] 						=      int(math.sqrt(lowerCAmelCase__						)						)

		while start <= end:
				# If start is a prime
				if sieve[start] is True:
						prime.append(lowerCAmelCase__						)

						# Set multiples of start be False
						for i in range(start * start							,num + 1							,lowerCAmelCase__						):
								if sieve[i] is True:
										lowerCamelCase_	:     Tuple 						=      False

				start += 1

		for j in range(end + 1							,num + 1						):
				if sieve[j] is True:
						prime.append(lowerCAmelCase__						)

		return prime


if __name__ == "__main__":
		print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
