

def 			A_      (    _UpperCAmelCase = 2_00_00_00    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       [0 for i in range(n + 1    )]
						SCREAMING_SNAKE_CASE_: Any  		=       1
						SCREAMING_SNAKE_CASE_: List[str]  		=       1

						for i in range(2				,	int(n**0.5    ) + 1    ):
												if primality_list[i] == 0:
																		for j in range(i * i				,	n + 1				,	_UpperCAmelCase    ):
																								SCREAMING_SNAKE_CASE_: Tuple  		=       1
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       0
						for i in range(_UpperCAmelCase    ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


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












from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest





@require_tokenizers
class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Dict    		=   DistilBertTokenizer
      UpperCAmelCase__      :	Dict    		=   DistilBertTokenizerFast
      UpperCAmelCase__      :	Tuple    		=   True
      @slow
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						DistilBertTokenizer.from_pretrained("""distilbert-base-uncased"""   )

             __lowerCamelCase   =						tokenizer.encode("""sequence builders"""    ,       add_special_tokens=UpperCamelCase_   )
             __lowerCamelCase   =						tokenizer.encode("""multi-sequence build"""    ,       add_special_tokens=UpperCamelCase_   )

             __lowerCamelCase   =						tokenizer.build_inputs_with_special_tokens(UpperCamelCase_   )
             __lowerCamelCase   =						tokenizer.build_inputs_with_special_tokens(UpperCamelCase_    ,       UpperCamelCase_   )

             assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
             assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
                 tokenizer.sep_token_id
             ]

