



'''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

lowercase_									=    logging.get_logger(__name__)


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

lowercase_									=    {
    "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"
    },
}

lowercase_									=    {"facebook/blenderbot-3B": 128}






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			List[str]		=			VOCAB_FILES_NAMES
 __lowerCamelCase       :			Dict		=			PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase       :			List[Any]		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase       :			List[Any]		=			['input_ids', 'attention_mask']
 __lowerCamelCase       :			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					,  )       ->	Optional[Any]:


      """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					,  )

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

      _a           =    add_prefix_space

      _a           =    '''post_processor'''
      _a           =    getattr(self.backend_tokenizer					,  A					,  A			)
      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'''					,  A			) != add_prefix_space:
                _a           =    add_prefix_space
                _a           =    True

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

           if changes_to_apply:
                _a           =    getattr(A					,  state.pop('''type'''			)			)
                _a           =    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    a__    (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    a__    (self					,  A			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    AddedToken(A					,  lstrip=A					,  rstrip=A			) if isinstance(A					,  A			) else value
      _a           =    value



 def    a__    (self					,  *A					,  **A			)       ->	BatchEncoding:


      """simple docstring"""



      _a           =    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					,  *A					,  **A			)       ->	BatchEncoding:


      """simple docstring"""



      _a           =    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					,  A					,  A = None			)       ->	Tuple[str]:


      """simple docstring"""



      _a           =    self._tokenizer.model.save(A					,  name=A			)
      return tuple(A			)



 def    a__    (self					,  A					,  A = None			)       ->	List[int]:


      """simple docstring"""



      _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    a__    (self					,  A					,  A = None			)       ->	Tuple:


      """simple docstring"""



      return token_ids_a + [self.eos_token_id]



 def    a__    (self					,  A			)       ->	List[int]:


      """simple docstring"""



      _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(A			)

      _a           =    '''  '''.join(A			)
      _a           =    self.encode(A			)
      if len(A			) > 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








'''simple docstring'''





from __future__ import annotations




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    2
     _a           =    []
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(__A)
     if n > 1:
          factors.append(__A)
     return factors


if __name__ == "__main__":
  import doctest

  doctest.testmod()



