






from __future__ import annotations

def      _lowerCamelCase	(						__A    :							int )      ->					list[int]:
    _UpperCAmelCase				:      List[str]			  =				[True] * limit
    _UpperCAmelCase				:      Optional[int]			  =				False
    _UpperCAmelCase				:      Dict			  =				False
    _UpperCAmelCase				:      List[str]			  =				True

    for i in range(3		,		int(limit**0.5 + 1 )		,		2 ):
        _UpperCAmelCase				:      List[str]			  =				i * 2
        while index < limit:
            _UpperCAmelCase				:      Union[str, Any]			  =				False
            _UpperCAmelCase				:      int			  =				index + i

    _UpperCAmelCase				:      Optional[int]			  =				[2]

    for i in range(3		,		__A		,		2 ):
        if is_prime[i]:
            primes.append(__A )

    return primes

def      _lowerCamelCase	(						__A    :							int = 1_000_000 )      ->					int:
    _UpperCAmelCase				:      Any			  =				prime_sieve(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				0
    _UpperCAmelCase				:      Tuple			  =				0

    for i in range(len(__A ) ):
        for j in range(i + length		,		len(__A ) ):
            _UpperCAmelCase				:      List[Any]			  =				sum(primes[i:j] )
            if sol >= ceiling:
                break

            if sol in primes:
                _UpperCAmelCase				:      List[str]			  =				j - i
                _UpperCAmelCase				:      Optional[Any]			  =				sol

    return largest


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












import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE 							=						{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

SCREAMING_SNAKE_CASE 							=						{
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

SCREAMING_SNAKE_CASE 							=						{'facebook/blenderbot-3B': 128}
class 							A_    (						__lowercase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Tuple =					VOCAB_FILES_NAMES
 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					PRETRAINED_VOCAB_FILES_MAP
 _SCREAMING_SNAKE_CASE							:			Optional[int] =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _SCREAMING_SNAKE_CASE							:			List[Any] =					["input_ids", "attention_mask"]
 _SCREAMING_SNAKE_CASE							:			Dict =					BlenderbotTokenizer


 def __init__(	self							,  _A=None							,  _A=None							,  _A=None							,  _A="replace"							,  _A="<s>"							,  _A="</s>"							,  _A="</s>"							,  _A="<s>"							,  _A="<unk>"							,  _A="<pad>"							,  _A="<mask>"							,  _A=False							,  _A=True							,  **_A							,  ) ->		str:

     """simple docstring"""



     super().__init__(
         _A							,  _A							,  tokenizer_file=_A							,  errors=_A							,  bos_token=_A							,  eos_token=_A							,  sep_token=_A							,  cls_token=_A							,  unk_token=_A							,  pad_token=_A							,  mask_token=_A							,  add_prefix_space=_A							,  trim_offsets=_A							,  **_A							,  )

     _UpperCAmelCase				:      Any			  =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
     if pre_tok_state.get('''add_prefix_space'''							,  _A) != add_prefix_space:
         _UpperCAmelCase				:      Optional[int]			  =				getattr(_A							,  pre_tok_state.pop('''type'''))
         _UpperCAmelCase				:      Optional[int]			  =				add_prefix_space
         _UpperCAmelCase				:      Dict			  =				pre_tok_class(**_A)

     _UpperCAmelCase				:      Dict			  =				add_prefix_space

     _UpperCAmelCase				:      Tuple			  =				'''post_processor'''
     _UpperCAmelCase				:      str			  =				getattr(self.backend_tokenizer							,  _A							,  _A)
     if tokenizer_component_instance:
         _UpperCAmelCase				:      str			  =				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:
             _UpperCAmelCase				:      str			  =				tuple(state['''sep'''])
         if "cls" in state:
             _UpperCAmelCase				:      List[str]			  =				tuple(state['''cls'''])

         _UpperCAmelCase				:      List[str]			  =				False

         if state.get('''add_prefix_space'''							,  _A) != add_prefix_space:
             _UpperCAmelCase				:      int			  =				add_prefix_space
             _UpperCAmelCase				:      Optional[int]			  =				True

         if state.get('''trim_offsets'''							,  _A) != trim_offsets:
             _UpperCAmelCase				:      List[Any]			  =				trim_offsets
             _UpperCAmelCase				:      Any			  =				True

         if changes_to_apply:
             _UpperCAmelCase				:      Optional[Any]			  =				getattr(_A							,  state.pop('''type'''))
             _UpperCAmelCase				:      Union[str, Any]			  =				component_class(**_A)
             setattr(self.backend_tokenizer							,  _A							,  _A)


 @property
 # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     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  snake_case__				(	self							,  _A) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				AddedToken(_A							,  lstrip=_A							,  rstrip=_A) if isinstance(_A							,  _A) else value
     _UpperCAmelCase				:      Union[str, Any]			  =				value


 def  snake_case__				(	self							,  *_A							,  **_A) ->		BatchEncoding:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				kwargs.get('''is_split_into_words'''							,  _A)
     assert self.add_prefix_space or not is_split_into_words, (
         f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
         "to use it with pretokenized inputs."
     )

     return super()._batch_encode_plus(*_A							,  **_A)


 def  snake_case__				(	self							,  *_A							,  **_A) ->		BatchEncoding:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				kwargs.get('''is_split_into_words'''							,  _A)

     assert self.add_prefix_space or not is_split_into_words, (
         f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
         "to use it with pretokenized inputs."
     )

     return super()._encode_plus(*_A							,  **_A)


 def  snake_case__				(	self							,  _A							,  _A = None) ->		Tuple[str]:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				self._tokenizer.model.save(_A							,  name=_A)
     return tuple(_A)


 def  snake_case__				(	self							,  _A							,  _A = None) ->		List[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				[self.sep_token_id]
     _UpperCAmelCase				:      str			  =				[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]


 def  snake_case__				(	self							,  _A							,  _A = None) ->		Optional[Any]:

     """simple docstring"""



     return token_ids_a + [self.eos_token_id]


 def  snake_case__				(	self							,  _A) ->		List[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				[]
     for is_user, text in conversation.iter_texts():
         if is_user:
             # We need to space prefix as it's being done within blenderbot
             inputs.append(''' ''' + text)
         else:
             # Generated responses should contain them already.
             inputs.append(_A)

     _UpperCAmelCase				:      int			  =				'''  '''.join(_A)
     _UpperCAmelCase				:      Optional[int]			  =				self.encode(_A)
     if len(_A) > self.model_max_length:
         _UpperCAmelCase				:      str			  =				input_ids[-self.model_max_length :]
         logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''')
     return input_ids




