
'''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__   :		Any 		=logging.get_logger(__name__)


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

A__   :		List[str] 		={
    '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__   :		Optional[Any] 		={'facebook/blenderbot-3B': 128}







class  __A				( _SCREAMING_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			:    str				,   lowerCamelCase			:    Dict=None				,   lowerCamelCase			:    Optional[Any]=None				,   lowerCamelCase			:    int=None				,   lowerCamelCase			:    Any="replace"				,   lowerCamelCase			:    List[Any]="<s>"				,   lowerCamelCase			:    int="</s>"				,   lowerCamelCase			:    Optional[Any]="</s>"				,   lowerCamelCase			:    Dict="<s>"				,   lowerCamelCase			:    Union[str, Any]="<unk>"				,   lowerCamelCase			:    int="<pad>"				,   lowerCamelCase			:    List[str]="<mask>"				,   lowerCamelCase			:    Optional[int]=False				,   lowerCamelCase			:    Tuple=True				,   **lowerCamelCase			:    str				,   ):



   """simple docstring"""





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

   __A		:     Union[str, Any]												=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
   if pre_tok_state.get("""add_prefix_space"""				,   lowerCamelCase       ) != add_prefix_space:
     __A		:     str												=		getattr(lowerCamelCase				,   pre_tok_state.pop("""type"""       )       )
     __A		:     Dict												=		add_prefix_space
     __A		:     Union[str, Any]												=		pre_tok_class(**lowerCamelCase       )

   __A		:     Optional[Any]												=		add_prefix_space

   __A		:     Any												=		"""post_processor"""
   __A		:     str												=		getattr(self.backend_tokenizer				,   lowerCamelCase				,   lowerCamelCase       )
   if tokenizer_component_instance:
     __A		:     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:
       __A		:     Any												=		tuple(state["""sep"""]       )
     if "cls" in state:
       __A		:     Tuple												=		tuple(state["""cls"""]       )

     __A		:     Union[str, Any]												=		False

     if state.get("""add_prefix_space"""				,   lowerCamelCase       ) != add_prefix_space:
       __A		:     int												=		add_prefix_space
       __A		:     str												=		True

     if state.get("""trim_offsets"""				,   lowerCamelCase       ) != trim_offsets:
       __A		:     List[Any]												=		trim_offsets
       __A		:     List[str]												=		True

     if changes_to_apply:
       __A		:     Any												=		getattr(lowerCamelCase				,   state.pop("""type"""       )       )
       __A		:     str												=		component_class(**lowerCamelCase       )
       setattr(self.backend_tokenizer				,   lowerCamelCase				,   lowerCamelCase       )
 @property
 # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
 def 		lowercase_(   self			:    int       ):



   """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 		lowercase_(   self			:    Optional[Any]				,   lowerCamelCase			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		AddedToken(lowerCamelCase				,   lstrip=lowerCamelCase				,   rstrip=lowerCamelCase       ) if isinstance(lowerCamelCase				,   lowerCamelCase       ) else value
   __A		:     int												=		value
 def 		lowercase_(   self			:    Optional[int]				,   *lowerCamelCase			:    Tuple				,   **lowerCamelCase			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		kwargs.get("""is_split_into_words"""				,   lowerCamelCase       )
   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(*lowerCamelCase				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    str				,   *lowerCamelCase			:    List[Any]				,   **lowerCamelCase			:    List[Any]       ):



   """simple docstring"""





   __A		:     Optional[int]												=		kwargs.get("""is_split_into_words"""				,   lowerCamelCase       )

   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(*lowerCamelCase				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    Dict				,   lowerCamelCase			:    str				,   lowerCamelCase			:    Optional[str] = None       ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		self._tokenizer.model.save(lowerCamelCase				,   name=lowerCamelCase       )
   return tuple(lowerCamelCase       )
 def 		lowercase_(   self			:    Union[str, Any]				,   lowerCamelCase			:    List[int]				,   lowerCamelCase			:    Optional[List[int]] = None       ):



   """simple docstring"""





   __A		:     int												=		[self.sep_token_id]
   __A		:     List[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 		lowercase_(   self			:    List[Any]				,   lowerCamelCase			:    List[int]				,   lowerCamelCase			:    Optional[List[int]] = None       ):



   """simple docstring"""





   return token_ids_a + [self.eos_token_id]






 def 		lowercase_(   self			:    Optional[Any]				,   lowerCamelCase			:    "Conversation"       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		[]
   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(lowerCamelCase       )

   __A		:     Optional[Any]												=		"""  """.join(lowerCamelCase       )
   __A		:     List[str]												=		self.encode(lowerCamelCase       )
   if len(lowerCamelCase       ) > self.model_max_length:
     __A		:     List[Any]												=		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 packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
     import accelerate







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Optional[int]:





  """simple docstring"""

  if not is_accelerate_available():
    return method
  __A		:     int												=		version.parse(accelerate.__version__ ).base_version
  if version.parse(__SCREAMING_SNAKE_CASE ) < version.parse("""0.17.0""" ):
    return method

  def wrapper(self						:  str			,     *__SCREAMING_SNAKE_CASE						:  Tuple			,     **__SCREAMING_SNAKE_CASE						:  Tuple ):
    if hasattr(self			,     """_hf_hook""" ) and hasattr(self._hf_hook			,     """pre_forward""" ):
      self._hf_hook.pre_forward(self )
    return method(self			,     *__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE )

  return wrapper



