





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


_A :   Tuple  			=  logging.get_logger(__name__)

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

_A :   str  			=  {
    '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'
        ),
    },
}

_A :   List[Any]  			=  {
    'roberta-base': 5_12,
    'roberta-large': 5_12,
    'roberta-large-mnli': 5_12,
    'distilroberta-base': 5_12,
    'roberta-base-openai-detector': 5_12,
    'roberta-large-openai-detector': 5_12,
}


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  List[str]			   =   VOCAB_FILES_NAMES
       _UpperCAmelCase				:  Dict			   =   PRETRAINED_VOCAB_FILES_MAP
       _UpperCAmelCase				:  List[str]			   =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCAmelCase				:  Union[str, Any]			   =   ["input_ids", "attention_mask"]
       _UpperCAmelCase				:  str			   =   RobertaTokenizer







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

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

          lowerCamelCase__       :						List[str]        =							add_prefix_space

          lowerCamelCase__       :						Optional[Any]        =							'''post_processor'''
          lowerCamelCase__       :						Optional[int]        =							getattr(self.backend_tokenizer		, A		, A      )
          if tokenizer_component_instance:
             lowerCamelCase__       :						Optional[int]        =							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:
                lowerCamelCase__       :						Tuple        =							tuple(state['''sep''']      )
             if "cls" in state:
                lowerCamelCase__       :						List[Any]        =							tuple(state['''cls''']      )

             lowerCamelCase__       :						Any        =							False

             if state.get('''add_prefix_space'''		, A      ) != add_prefix_space:
                lowerCamelCase__       :						Tuple        =							add_prefix_space
                lowerCamelCase__       :						List[Any]        =							True

             if state.get('''trim_offsets'''		, A      ) != trim_offsets:
                lowerCamelCase__       :						int        =							trim_offsets
                lowerCamelCase__       :						int        =							True

             if changes_to_apply:
                lowerCamelCase__       :						Optional[int]        =							getattr(A		, state.pop('''type'''      )      )
                lowerCamelCase__       :						Any        =							component_class(**A      )
                setattr(self.backend_tokenizer		, A		, A      )







       @property
       def 						__lowerCamelCase   (				self					:     List[str]      )       ->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 						__lowerCamelCase   (				self					:     str		, A					:     Optional[Any]      )       ->Optional[int]:
          lowerCamelCase__       :						Tuple        =							AddedToken(A		, lstrip=A		, rstrip=A      ) if isinstance(A		, A      ) else value
          lowerCamelCase__       :						int        =							value







       def 						__lowerCamelCase   (				self					:     Optional[int]		, *A					:     Tuple		, **A					:     int      )       ->BatchEncoding:
          lowerCamelCase__       :						Dict        =							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 						__lowerCamelCase   (				self					:     Dict		, *A					:     int		, **A					:     List[str]      )       ->BatchEncoding:
          lowerCamelCase__       :						int        =							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 						__lowerCamelCase   (				self					:     List[Any]		, A					:     str		, A					:     Optional[str] = None      )       ->Tuple[str]:
          lowerCamelCase__       :						Union[str, Any]        =							self._tokenizer.model.save(A		, name=A      )
          return tuple(A      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     Tuple		, A					:     Union[str, Any]=None      )       ->Optional[Any]:
          lowerCamelCase__       :						Any        =							[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 						__lowerCamelCase   (				self					:     Any		, A					:     List[int]		, A					:     Optional[List[int]] = None      )       ->List[int]:
          lowerCamelCase__       :						Optional[int]        =							[self.sep_token_id]
          lowerCamelCase__       :						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 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   if b == 0:
      return 1
   if (b % 2) == 0:
      return actual_power(UpperCAmelCase      ,  int(b / 2					)					) * actual_power(UpperCAmelCase      ,  int(b / 2					)					)
   else:
      return a * actual_power(UpperCAmelCase      ,  int(b / 2					)					) * actual_power(UpperCAmelCase      ,  int(b / 2					)					)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    float:







   """simple docstring"""
   if b < 0:
      return 1 / actual_power(UpperCAmelCase      ,  UpperCAmelCase					)
   return actual_power(UpperCAmelCase      ,  UpperCAmelCase					)


if __name__ == "__main__":
       print(power(-2, -3))



