




def 						__A						(      __lowerCamelCase = 200_0000       ) ->			int:
	a									=				[0 for i in range(n + 1       )]
	a									=				1
	a									=				1

	for i in range(2				,     int(n**0.5       ) + 1       ):
		if primality_list[i] == 0:
			for j in range(i * i				,     n + 1				,     __lowerCamelCase       ):
				a									=				1
	a									=				0
	for i in range(__lowerCamelCase       ):
		if primality_list[i] == 0:
			sum_of_primes += i
	return sum_of_primes


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







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase   :			Union[str, Any]          = logging.get_logger(__name__)

__UpperCamelCase   :			Optional[Any]          = "▁"

__UpperCamelCase   :			Any          = {"vocab_file": "sentencepiece.bpe.model"}

__UpperCamelCase   :			int          = {
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

__UpperCamelCase   :			Dict          = {
    "facebook/xglm-564M": 2_048,
}

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    VOCAB_FILES_NAMES
							UpperCamelCase__   	=    PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__   	=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__   	=    ['''input_ids''', '''attention_mask''']
							def __init__(		self		:Optional[Any]						, __magic_name__		:List[str]						, __magic_name__		:Tuple="<s>"						, __magic_name__		:List[Any]="</s>"						, __magic_name__		:List[str]="</s>"						, __magic_name__		:Any="<s>"						, __magic_name__		:Tuple="<unk>"						, __magic_name__		:int="<pad>"						, __magic_name__		:Optional[Dict[str, Any]] = None						, **__magic_name__		:Optional[Any]						, ):







								'''simple docstring'''







								a									=				{} if sp_model_kwargs is None else sp_model_kwargs

								# Compatibility with the original tokenizer
								a									=				7
								a									=				[F'<madeupword{i}>' for i in range(self.num_madeup_words							)]

								a									=				kwargs.get("""additional_special_tokens"""						, []							)
								kwargs["additional_special_tokens"] += [
								    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    bos_token=__magic_name__						, eos_token=__magic_name__						, unk_token=__magic_name__						, sep_token=__magic_name__						, cls_token=__magic_name__						, pad_token=__magic_name__						, sp_model_kwargs=self.sp_model_kwargs						, **__magic_name__						, )

								a									=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
								self.sp_model.Load(str(__magic_name__							)							)
								a									=				vocab_file

								# Original fairseq vocab and spm vocab must be "aligned":
								# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
								# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
								# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
								# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

								# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
								a									=				1

								# Mimic fairseq token-to-id alignment for the first 4 token
								a									=				{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

								a									=				len(self.sp_model							)
								a									=				{F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words							)}
								self.fairseq_tokens_to_ids.update(__magic_name__							)

								a									=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
							def __getstate__(		self		:List[Any]							):







								'''simple docstring'''







								a									=				self.__dict__.copy()
								a									=				None
								a									=				self.sp_model.serialized_model_proto()
								return state
							def __setstate__(		self		:Any						, __magic_name__		:Any							):







								'''simple docstring'''







								a									=				d

								# for backward compatibility
								if not hasattr(self						, """sp_model_kwargs"""							):
									a									=				{}

								a									=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
								self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:List[int]						, __magic_name__		:Optional[List[int]] = None							):







								'''simple docstring'''







								if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
								a									=				[self.sep_token_id]
								return sep + token_ids_a + sep + sep + token_ids_a
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:List[int]						, __magic_name__		:Optional[List[int]] = None						, __magic_name__		:bool = False							):







								'''simple docstring'''







								if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=__magic_name__						, token_ids_a=__magic_name__						, already_has_special_tokens=__magic_name__							)

								if token_ids_a is None:
									return [1] + ([0] * len(__magic_name__							))
								return [1] + ([0] * len(__magic_name__							)) + [1, 1] + ([0] * len(__magic_name__							))
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:List[int]						, __magic_name__		:Optional[List[int]] = None							):







								'''simple docstring'''







								a									=				[self.sep_token_id]

								if token_ids_a is None:
									return len(sep + token_ids_a							) * [0]
								return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]
							@property
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								return len(self.sp_model							) + self.fairseq_offset + self.num_madeup_words
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								a									=				{self.convert_ids_to_tokens(__magic_name__							): i for i in range(self.vocab_size							)}
								vocab.update(self.added_tokens_encoder							)
								return vocab
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:str							):







								'''simple docstring'''







								return self.sp_model.encode(__magic_name__						, out_type=__magic_name__							)
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:Any							):







								'''simple docstring'''







								if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
								a									=				self.sp_model.PieceToId(__magic_name__							)

								# Need to return unknown token if the SP model returned 0
								return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:Dict							):







								'''simple docstring'''







								if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset							)
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:List[str]							):







								'''simple docstring'''







								a									=				"""""".join(__magic_name__							).replace(__magic_name__						, """ """							).strip()
								return out_string





							def  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:str						, __magic_name__		:Optional[str] = None							):







								'''simple docstring'''







								if not os.path.isdir(__magic_name__							):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
									return
								a									=				os.path.join(
								    __magic_name__						, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

								if os.path.abspath(self.vocab_file							) != os.path.abspath(__magic_name__							) and os.path.isfile(self.vocab_file							):
									copyfile(self.vocab_file						, __magic_name__							)
								elif not os.path.isfile(self.vocab_file							):
									with open(__magic_name__						, """wb"""							) as fi:
										a									=				self.sp_model.serialized_model_proto()
										fi.write(__magic_name__							)

								return (out_vocab_file,)

