





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_roberta import RobertaTokenizer


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case_           =      {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

snake_case_           =      {
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Tuple							=   VOCAB_FILES_NAMES
      A_						:    Optional[Any]							=   PRETRAINED_VOCAB_FILES_MAP
      A_						:    Any							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A_						:    str							=   ['input_ids', 'attention_mask']
      A_						:    Optional[int]							=   RobertaTokenizer


      def __init__(self	:				int    ,      a__	:				Dict=None    ,      a__	:				List[Any]=None    ,      a__	:				Any=None    ,      a__	:				List[str]="replace"    ,      a__	:				Dict="<s>"    ,      a__	:				str="</s>"    ,      a__	:				List[str]="</s>"    ,      a__	:				Optional[int]="<s>"    ,      a__	:				List[Any]="<unk>"    ,      a__	:				Tuple="<pad>"    ,      a__	:				Optional[int]="<mask>"    ,      a__	:				Tuple=False    ,      a__	:				Tuple=True    ,      **a__	:				int    ,      ):





         """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__    ,      )

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

         __snake_case								=       add_prefix_space

         __snake_case								=       '''post_processor'''
         __snake_case								=       getattr(self.backend_tokenizer    ,      a__    ,      a__				)
         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'''    ,      a__				) != add_prefix_space:
               __snake_case								=       add_prefix_space
               __snake_case								=       True

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

            if changes_to_apply:
               __snake_case								=       getattr(a__    ,      state.pop('''type'''				)				)
               __snake_case								=       component_class(**a__				)
               setattr(self.backend_tokenizer    ,      a__    ,      a__				)


      @property
      def        a			(self	:				Union[str, Any]				):





         """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        a			(self	:				Optional[int]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       AddedToken(a__    ,      lstrip=a__    ,      rstrip=a__				) if isinstance(a__    ,      a__				) else value
         __snake_case								=       value


      def        a			(self	:				str    ,      *a__	:				Union[str, Any]    ,      **a__	:				int				):





         """simple docstring"""



         __snake_case								=       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        a			(self	:				Dict    ,      *a__	:				int    ,      **a__	:				List[str]				):





         """simple docstring"""



         __snake_case								=       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        a			(self	:				Dict    ,      a__	:				str    ,      a__	:				Optional[str] = None				):





         """simple docstring"""



         __snake_case								=       self._tokenizer.model.save(a__    ,      name=a__				)
         return tuple(a__				)


      def        a			(self	:				Optional[int]    ,      a__	:				Any    ,      a__	:				List[str]=None				):





         """simple docstring"""



         __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	:				Tuple    ,      a__	:				List[int]    ,      a__	:				Optional[List[int]] = None				):





         """simple docstring"""



         __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]








from __future__ import annotations

snake_case_           =      [True] * 1000001
snake_case_           =      2
while i * i <= 1000000:
    if seive[i]:
        for j in range(i * i, 1000001, i):
            snake_case_           =      False
    i += 1






def lowerCamelCase__   (							snake_case_     :  int	)				->    bool:
   return seive[n]






def lowerCamelCase__   (							snake_case_     :  int	)				->    bool:
   return any(digit in '''02468''' for digit in str(snake_case_	)	)






def lowerCamelCase__   (							snake_case_     :  int = 100_0000	)				->    list[int]:
   __snake_case								=       [2]  # result already includes the number 2.
   for num in range(3	,				limit + 1	,				2	):
      if is_prime(snake_case_	) and not contains_an_even_digit(snake_case_	):
         __snake_case								=       str(snake_case_	)
         __snake_case								=       [int(str_num[j:] + str_num[:j]	) for j in range(len(snake_case_	)	)]
         if all(is_prime(snake_case_	) for i in list_nums	):
            result.append(snake_case_	)
   return result






def lowerCamelCase__   (							)				->    int:
   return len(find_circular_primes()	)


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

