


'''simple docstring'''






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

a_    :			List[Any]      				=			logging.get_logger(__name__)


a_    :			Tuple      				=			{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

a_    :			Union[str, Any]      				=			{
    "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"
    },
}

a_    :			List[Any]      				=			{"facebook/blenderbot-3B": 1_2_8}




class        a   (   snake_case_				):
 _lowerCAmelCase           =		VOCAB_FILES_NAMES
 _lowerCAmelCase           =		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase           =		["""input_ids""", """attention_mask"""]
 _lowerCAmelCase           =		BlenderbotTokenizer
 def __init__(       self					, __magic_name__=None					, __magic_name__=None					, __magic_name__=None					, __magic_name__="replace"					, __magic_name__="<s>"					, __magic_name__="</s>"					, __magic_name__="</s>"					, __magic_name__="<s>"					, __magic_name__="<unk>"					, __magic_name__="<pad>"					, __magic_name__="<mask>"					, __magic_name__=False					, __magic_name__=True					, **__magic_name__					, )    ->							Any:
      super().__init__(
          __magic_name__					, __magic_name__					, tokenizer_file=__magic_name__					, errors=__magic_name__					, bos_token=__magic_name__					, eos_token=__magic_name__					, sep_token=__magic_name__					, cls_token=__magic_name__					, unk_token=__magic_name__					, pad_token=__magic_name__					, mask_token=__magic_name__					, add_prefix_space=__magic_name__					, trim_offsets=__magic_name__					, **__magic_name__					, )

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

      _a						=							add_prefix_space

      _a						=							'post_processor'
      _a						=							getattr(self.backend_tokenizer					, __magic_name__					, __magic_name__  )
      if tokenizer_component_instance:
           _a						=							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:
                _a						=							tuple(state['sep']  )
           if "cls" in state:
                _a						=							tuple(state['cls']  )

           _a						=							False

           if state.get('add_prefix_space'					, __magic_name__  ) != add_prefix_space:
                _a						=							add_prefix_space
                _a						=							True

           if state.get('trim_offsets'					, __magic_name__  ) != trim_offsets:
                _a						=							trim_offsets
                _a						=							True

           if changes_to_apply:
                _a						=							getattr(__magic_name__					, state.pop('type'  )  )
                _a						=							component_class(**__magic_name__  )
                setattr(self.backend_tokenizer					, __magic_name__					, __magic_name__  )
 @property
 # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
 def 							__UpperCAmelCase						(       self  )    ->							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 							__UpperCAmelCase						(       self					, __magic_name__  )    ->							List[str]:
      _a						=							AddedToken(__magic_name__					, lstrip=__magic_name__					, rstrip=__magic_name__  ) if isinstance(__magic_name__					, __magic_name__  ) else value
      _a						=							value
 def 							__UpperCAmelCase						(       self					, *__magic_name__					, **__magic_name__  )    ->							BatchEncoding:
      _a						=							kwargs.get('is_split_into_words'					, __magic_name__  )
      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(*__magic_name__					, **__magic_name__  )
 def 							__UpperCAmelCase						(       self					, *__magic_name__					, **__magic_name__  )    ->							BatchEncoding:
      _a						=							kwargs.get('is_split_into_words'					, __magic_name__  )

      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(*__magic_name__					, **__magic_name__  )
 def 							__UpperCAmelCase						(       self					, __magic_name__					, __magic_name__ = None  )    ->							Tuple[str]:
      _a						=							self._tokenizer.model.save(__magic_name__					, name=__magic_name__  )
      return tuple(__magic_name__  )
 def 							__UpperCAmelCase						(       self					, __magic_name__					, __magic_name__ = None  )    ->							List[int]:
      _a						=							[self.sep_token_id]
      _a						=							[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 							__UpperCAmelCase						(       self					, __magic_name__					, __magic_name__ = None  )    ->							int:
      return token_ids_a + [self.eos_token_id]




 def 							__UpperCAmelCase						(       self					, __magic_name__  )    ->							List[int]:
      _a						=							[]
      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(__magic_name__  )

      _a						=							'  '.join(__magic_name__  )
      _a						=							self.encode(__magic_name__  )
      if len(__magic_name__  ) > self.model_max_length:
           _a						=							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



__A				={str(digit): digit**5 for digit in range(1_0)}





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCamelCase__    )    )





def 			lowerCamelCase_ (				):
				return sum(
				    number
				    for number in range(1_0_0_0	, 1_0_0_0_0_0_0    )
				    if number == digits_fifth_powers_sum(lowerCamelCase__    )    )


if __name__ == "__main__":
			print(solution())


