





'''simple docstring'''

import os
import re
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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {"""vocab_file""": """spiece.model"""}

lowercase_					= {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

lowercase_					= {
    """google/bigbird-roberta-base""": 4_096,
    """google/bigbird-roberta-large""": 4_096,
    """google/bigbird-base-trivia-itc""": 4_096,
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = VOCAB_FILES_NAMES
    UpperCamelCase     = PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase     = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase     = ['''input_ids''', '''attention_mask''']
    UpperCamelCase     = []


    def __init__(		self ,		A ,		A="<unk>" ,		A="<s>" ,		A="</s>" ,		A="<pad>" ,		A="[SEP]" ,		A="[MASK]" ,		A="[CLS]" ,		A = None ,		**A ,		)		->   None:
        _SCREAMING_SNAKE_CASE									=			AddedToken(A ,		lstrip=A ,		rstrip=A       ) if isinstance(A ,		A       ) else bos_token
        _SCREAMING_SNAKE_CASE									=			AddedToken(A ,		lstrip=A ,		rstrip=A       ) if isinstance(A ,		A       ) else eos_token
        _SCREAMING_SNAKE_CASE									=			AddedToken(A ,		lstrip=A ,		rstrip=A       ) if isinstance(A ,		A       ) else unk_token
        _SCREAMING_SNAKE_CASE									=			AddedToken(A ,		lstrip=A ,		rstrip=A       ) if isinstance(A ,		A       ) else pad_token
        _SCREAMING_SNAKE_CASE									=			AddedToken(A ,		lstrip=A ,		rstrip=A       ) if isinstance(A ,		A       ) else cls_token
        _SCREAMING_SNAKE_CASE									=			AddedToken(A ,		lstrip=A ,		rstrip=A       ) if isinstance(A ,		A       ) else sep_token

        # 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 ,		pad_token=A ,		sep_token=A ,		mask_token=A ,		cls_token=A ,		sp_model_kwargs=self.sp_model_kwargs ,		**A ,		)

        _SCREAMING_SNAKE_CASE									=			vocab_file

        _SCREAMING_SNAKE_CASE									=			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
        self.sp_model.Load(A       )


    @property
    def 			snake_case_(		self       )		->   Optional[int]:
        return self.sp_model.get_piece_size()


    def 			snake_case_(		self       )		->   Any:
        _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 __getstate__(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			self.__dict__.copy()
        _SCREAMING_SNAKE_CASE									=			None
        return state


    def __setstate__(		self ,		A       )		->   Dict:
        _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.Load(self.vocab_file       )


    def 			snake_case_(		self ,		A       )		->   List[str]:
        return self.sp_model.encode(A ,		out_type=A       )


    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        return self.sp_model.piece_to_id(A       )


    def 			snake_case_(		self ,		A       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			self.sp_model.IdToPiece(A       )
        return token


    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			[]
        _SCREAMING_SNAKE_CASE									=			""""""
        _SCREAMING_SNAKE_CASE									=			False
        for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                if not prev_is_special:
                    out_string += " "
                out_string += self.sp_model.decode(A       ) + token
                _SCREAMING_SNAKE_CASE									=			True
                _SCREAMING_SNAKE_CASE									=			[]
            else:
                current_sub_tokens.append(A       )
                _SCREAMING_SNAKE_CASE									=			False
        out_string += self.sp_model.decode(A       )
        return out_string.strip()


    def 			snake_case_(		self ,		A ,		A = False ,		A = None ,		A = True ,		**A ,		)		->   str:
        _SCREAMING_SNAKE_CASE									=			kwargs.pop("""use_source_tokenizer""" ,		A       )

        _SCREAMING_SNAKE_CASE									=			self.convert_ids_to_tokens(A ,		skip_special_tokens=A       )

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        _SCREAMING_SNAKE_CASE									=			[]
        _SCREAMING_SNAKE_CASE									=			[]
        for token in filtered_tokens:
            if skip_special_tokens and token in self.all_special_ids:
                continue
            if token in self.added_tokens_encoder:
                if current_sub_text:
                    sub_texts.append(self.convert_tokens_to_string(A       )       )
                    _SCREAMING_SNAKE_CASE									=			[]
                sub_texts.append(A       )
            else:
                current_sub_text.append(A       )
        if current_sub_text:
            sub_texts.append(self.convert_tokens_to_string(A       )       )

        # Mimic the behavior of the Rust tokenizer:
        # No space before [MASK] and [SEP]
        if spaces_between_special_tokens:
            _SCREAMING_SNAKE_CASE									=			re.sub(R""" (\[(MASK|SEP)\])""" ,		R"""\1""" ,		""" """.join(A       )       )
        else:
            _SCREAMING_SNAKE_CASE									=			"""""".join(A       )

        _SCREAMING_SNAKE_CASE									=			(
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
            _SCREAMING_SNAKE_CASE									=			self.clean_up_tokenization(A       )
            return clean_text
        else:
            return text


    def 			snake_case_(		self ,		A ,		A = 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,)


    def 			snake_case_(		self ,		A ,		A = 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 + token_ids_a + sep


    def 			snake_case_(		self ,		A ,		A = None ,		A = 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] + ([0] * len(A       )) + [1]





    def 			snake_case_(		self ,		A ,		A = 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       ) * [0] + len(token_ids_a + sep       ) * [1]










'''simple docstring'''

from __future__ import annotations

import math





def lowerCamelCase						(     __lowerCamelCase :    int    )						->list[int]:

    if num <= 0:
        _SCREAMING_SNAKE_CASE									=			F'{num}: Invalid input, please enter a positive integer.'
        raise ValueError(__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			[True] * (num + 1)
    _SCREAMING_SNAKE_CASE									=			[]
    _SCREAMING_SNAKE_CASE									=			2
    _SCREAMING_SNAKE_CASE									=			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:
                    _SCREAMING_SNAKE_CASE									=			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())))



