




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A						:							Dict							=  logging.get_logger(__name__)

__A						:							Optional[int]							=  "▁"

__A						:							str							=  {"vocab_file": "sentencepiece.bpe.model"}

__A						:							List[str]							=  {
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

__A						:							Tuple							=  {
    "xlm-roberta-base": 5_1_2,
    "xlm-roberta-large": 5_1_2,
    "xlm-roberta-large-finetuned-conll02-dutch": 5_1_2,
    "xlm-roberta-large-finetuned-conll02-spanish": 5_1_2,
    "xlm-roberta-large-finetuned-conll03-english": 5_1_2,
    "xlm-roberta-large-finetuned-conll03-german": 5_1_2,
}




class 			UpperCAmelCase_      (   _UpperCAmelCase       ):




							'''simple docstring'''



							a__						  =     VOCAB_FILES_NAMES
							a__						  =     PRETRAINED_VOCAB_FILES_MAP
							a__						  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__						  =     ['''input_ids''', '''attention_mask''']





							def __init__(					self       :	List[str]       ,     a       :	List[str]       ,     a       :	Tuple="<s>"       ,     a       :	Tuple="</s>"       ,     a       :	int="</s>"       ,     a       :	Dict="<s>"       ,     a       :	Optional[Any]="<unk>"       ,     a       :	Dict="<pad>"       ,     a       :	Tuple="<mask>"       ,     a       :	Optional[Dict[str, Any]] = None       ,     **a       :	List[str]       ,     )			->					None:
										# Mask token behave like a normal word, i.e. include the space before it
										SCREAMING_SNAKE_CASE           =					AddedToken(A_       ,     lstrip=A_       ,     rstrip=A_			) if isinstance(A_       ,     A_			) else mask_token

										SCREAMING_SNAKE_CASE           =					{} if sp_model_kwargs is None else sp_model_kwargs

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

										SCREAMING_SNAKE_CASE           =					spm.SentencePieceProcessor(**self.sp_model_kwargs			)
										self.sp_model.Load(str(A_			)			)
										SCREAMING_SNAKE_CASE           =					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'

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

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

										SCREAMING_SNAKE_CASE           =					len(self.sp_model			) + self.fairseq_offset
										SCREAMING_SNAKE_CASE           =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}





							def __getstate__(					self       :	Optional[int]			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					self.__dict__.copy()
										SCREAMING_SNAKE_CASE           =					None
										SCREAMING_SNAKE_CASE           =					self.sp_model.serialized_model_proto()
										return state





							def __setstate__(					self       :	Dict       ,     a       :	Any			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					d

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

										SCREAMING_SNAKE_CASE           =					spm.SentencePieceProcessor(**self.sp_model_kwargs			)
										self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:

										if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										SCREAMING_SNAKE_CASE           =					[self.cls_token_id]
										SCREAMING_SNAKE_CASE           =					[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None       ,     a       :	bool = False			)			->					List[int]:

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

										if token_ids_a is None:
													return [1] + ([0] * len(A_			)) + [1]
										return [1] + ([0] * len(A_			)) + [1, 1] + ([0] * len(A_			)) + [1]





							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										SCREAMING_SNAKE_CASE           =					[self.sep_token_id]
										SCREAMING_SNAKE_CASE           =					[self.cls_token_id]

										if token_ids_a is None:
													return len(cls + token_ids_a + sep			) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]





							@property
							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					List[Any]:
										return len(self.sp_model			) + self.fairseq_offset + 1  # Add the <mask> token





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					str:
										SCREAMING_SNAKE_CASE           =					{self.convert_ids_to_tokens(A_			): i for i in range(self.vocab_size			)}
										vocab.update(self.added_tokens_encoder			)
										return vocab





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	str			)			->					List[str]:
										return self.sp_model.encode(A_       ,     out_type=A_			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	Union[str, Any]			)			->					Dict:
										if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
										SCREAMING_SNAKE_CASE           =					self.sp_model.PieceToId(A_			)

										# 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    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	List[str]			)			->					Tuple:
										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    _UpperCAmelCase     (					self       :	str       ,     a       :	List[Any]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					"""""".join(A_			).replace(A_       ,     """ """			).strip()
										return out_string







							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	str       ,     a       :	Optional[str] = None			)			->					Tuple[str]:
										if not os.path.isdir(A_			):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""			)
													return
										SCREAMING_SNAKE_CASE           =					os.path.join(
										    A_       ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

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

										return (out_vocab_file,)







from __future__ import annotations

import math
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''

			if num <= 0:
						SCREAMING_SNAKE_CASE           =					f"""{num}: Invalid input, please enter a positive integer."""
						raise ValueError(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					[True] * (num + 1)
			SCREAMING_SNAKE_CASE           =					[]
			SCREAMING_SNAKE_CASE           =					2
			SCREAMING_SNAKE_CASE           =					int(math.sqrt(SCREAMING_SNAKE_CASE   )   )

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

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

						start += 1

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

			return prime


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






