


'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


_a   :   List[str]												=   logging.get_logger(__name__)


_a   :   List[str]												=   {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
_a   :   Dict												=   {
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

_a   :   List[str]												=   {
    '''RUCAIBox/mvp''': 1_024,
}




class 			_lowercase (	_UpperCamelCase							):
   _SCREAMING_SNAKE_CASE  :  Tuple          =	VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE  :  List[str]          =	PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE  :  Dict          =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE  :  str          =	["input_ids", "attention_mask"]
   _SCREAMING_SNAKE_CASE  :  List[str]          =	MvpTokenizer
   def __init__(						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      Any=None       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=None       ,   SCREAMING_SNAKE_CASE_       :      Tuple=None       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]="replace"       ,   SCREAMING_SNAKE_CASE_       :      Dict="<s>"       ,   SCREAMING_SNAKE_CASE_       :      Any="</s>"       ,   SCREAMING_SNAKE_CASE_       :      Tuple="</s>"       ,   SCREAMING_SNAKE_CASE_       :      int="<s>"       ,   SCREAMING_SNAKE_CASE_       :      Any="<unk>"       ,   SCREAMING_SNAKE_CASE_       :      Dict="<pad>"       ,   SCREAMING_SNAKE_CASE_       :      int="<mask>"       ,   SCREAMING_SNAKE_CASE_       :      int=False       ,   SCREAMING_SNAKE_CASE_       :      int=True       ,   **SCREAMING_SNAKE_CASE_       :      int       ,   ) ->							Tuple:
        super().__init__(
            UpperCamelCase__       ,   UpperCamelCase__       ,   tokenizer_file=UpperCamelCase__       ,   errors=UpperCamelCase__       ,   bos_token=UpperCamelCase__       ,   eos_token=UpperCamelCase__       ,   sep_token=UpperCamelCase__       ,   cls_token=UpperCamelCase__       ,   unk_token=UpperCamelCase__       ,   pad_token=UpperCamelCase__       ,   mask_token=UpperCamelCase__       ,   add_prefix_space=UpperCamelCase__       ,   trim_offsets=UpperCamelCase__       ,   **UpperCamelCase__       ,   )

        __snake_case           =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
        if pre_tok_state.get('add_prefix_space'       ,   UpperCamelCase__      ) != add_prefix_space:
             __snake_case           =						getattr(UpperCamelCase__       ,   pre_tok_state.pop('type'      )      )
             __snake_case           =						add_prefix_space
             __snake_case           =						pre_tok_class(**UpperCamelCase__      )

        __snake_case           =						add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        __snake_case           =						'post_processor'
        __snake_case           =						getattr(self.backend_tokenizer       ,   UpperCamelCase__       ,   UpperCamelCase__      )
        if tokenizer_component_instance:
             __snake_case           =						json.loads(tokenizer_component_instance.__getstate__()      )

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                  __snake_case           =						tuple(state['sep']      )
             if "cls" in state:
                  __snake_case           =						tuple(state['cls']      )

             __snake_case           =						False

             if state.get('add_prefix_space'       ,   UpperCamelCase__      ) != add_prefix_space:
                  __snake_case           =						add_prefix_space
                  __snake_case           =						True

             if state.get('trim_offsets'       ,   UpperCamelCase__      ) != trim_offsets:
                  __snake_case           =						trim_offsets
                  __snake_case           =						True

             if changes_to_apply:
                  __snake_case           =						getattr(UpperCamelCase__       ,   state.pop('type'      )      )
                  __snake_case           =						component_class(**UpperCamelCase__      )
                  setattr(self.backend_tokenizer       ,   UpperCamelCase__       ,   UpperCamelCase__      )
   @property
   def a  (						self       :      List[Any]      ) ->							str:
        if self._mask_token is None:
             if self.verbose:
                  logger.error('Using mask_token, but it is not set yet.'      )
             return None
        return str(self._mask_token      )
   @mask_token.setter
   def a  (						self       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      Any      ) ->							List[str]:
        __snake_case           =						AddedToken(UpperCamelCase__       ,   lstrip=UpperCamelCase__       ,   rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__       ,   UpperCamelCase__      ) else value
        __snake_case           =						value
   def a  (						self       :      str       ,   *SCREAMING_SNAKE_CASE_       :      Dict       ,   **SCREAMING_SNAKE_CASE_       :      Any      ) ->							BatchEncoding:
        __snake_case           =						kwargs.get('is_split_into_words'       ,   UpperCamelCase__      )

        if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                 'to use it with pretokenized inputs.'      )

        return super()._batch_encode_plus(*UpperCamelCase__       ,   **UpperCamelCase__      )
   def a  (						self       :      Optional[int]       ,   *SCREAMING_SNAKE_CASE_       :      Optional[int]       ,   **SCREAMING_SNAKE_CASE_       :      Any      ) ->							BatchEncoding:
        __snake_case           =						kwargs.get('is_split_into_words'       ,   UpperCamelCase__      )

        if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                 'to use it with pretokenized inputs.'      )

        return super()._encode_plus(*UpperCamelCase__       ,   **UpperCamelCase__      )
   def a  (						self       :      Dict       ,   SCREAMING_SNAKE_CASE_       :      str       ,   SCREAMING_SNAKE_CASE_       :      Optional[str] = None      ) ->							Tuple[str]:
        __snake_case           =						self._tokenizer.model.save(UpperCamelCase__       ,   name=UpperCamelCase__      )
        return tuple(UpperCamelCase__      )
   def a  (						self       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=None      ) ->							List[Any]:
        __snake_case           =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
             return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





   def a  (						self       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      List[int]       ,   SCREAMING_SNAKE_CASE_       :      Optional[List[int]] = None      ) ->							List[int]:
        __snake_case           =						[self.sep_token_id]
        __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]





'''simple docstring'''






import math





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     _A    		=  []
     _A    		=  2
     _A    		=  int(math.sqrt(__snake_case				)				)  # Size of every segment
     _A    		=  [True] * (end + 1)
     _A    		=  []

     while start <= end:
          if temp[start] is True:
               in_prime.append(__snake_case				)
               for i in range(start * start   ,		end + 1   ,		__snake_case				):
                    _A    		=  False
          start += 1
     prime += in_prime

     _A    		=  end + 1
     _A    		=  min(2 * end   ,		__snake_case				)

     while low <= n:
          _A    		=  [True] * (high - low + 1)
          for each in in_prime:
               _A    		=  math.floor(low / each				) * each
               if t < low:
                    t += each

               for j in range(__snake_case   ,		high + 1   ,		__snake_case				):
                    _A    		=  False

          for j in range(len(__snake_case				)				):
               if temp[j] is True:
                    prime.append(j + low				)

          _A    		=  high + 1
          _A    		=  min(high + end   ,		__snake_case				)

     return prime
print(sieve(10**6))



