


import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case				:    Optional[int]       				=     logging.get_logger(__name__)
snake_case				:    Any       				=     {'''vocab_file''': '''spiece.model'''}

snake_case				:    List[Any]       				=     {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    }
}

snake_case				:    int       				=     {
    '''albert-base-v1''': 5_12,
    '''albert-large-v1''': 5_12,
    '''albert-xlarge-v1''': 5_12,
    '''albert-xxlarge-v1''': 5_12,
    '''albert-base-v2''': 5_12,
    '''albert-large-v2''': 5_12,
    '''albert-xlarge-v2''': 5_12,
    '''albert-xxlarge-v2''': 5_12,
}

snake_case				:    List[Any]       				=     '''▁'''







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__      =       PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase=True					,  _lowerCamelCase=True					,  _lowerCamelCase=False					,  _lowerCamelCase="[CLS]"					,  _lowerCamelCase="[SEP]"					,  _lowerCamelCase="<unk>"					,  _lowerCamelCase="[SEP]"					,  _lowerCamelCase="<pad>"					,  _lowerCamelCase="[CLS]"					,  _lowerCamelCase="[MASK]"					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       # Mask token behave like a normal word, i.e. include the space before it and
       # is included in the raw text, there should be a match in a non-normalized sentence.
       a						:str 							=       (
           AddedToken(_lowerCamelCase					,  lstrip=_lowerCamelCase					,  rstrip=_lowerCamelCase					,  normalized=_lowerCamelCase    )
           if isinstance(_lowerCamelCase					,  _lowerCamelCase    )
           else mask_token
       )

       a						:str 							=       {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           do_lower_case=_lowerCamelCase					,  remove_space=_lowerCamelCase					,  keep_accents=_lowerCamelCase					,  bos_token=_lowerCamelCase					,  eos_token=_lowerCamelCase					,  unk_token=_lowerCamelCase					,  sep_token=_lowerCamelCase					,  pad_token=_lowerCamelCase					,  cls_token=_lowerCamelCase					,  mask_token=_lowerCamelCase					,  sp_model_kwargs=self.sp_model_kwargs					,  **_lowerCamelCase					,  )

       a						:List[Any] 							=       do_lower_case
       a						:int 							=       remove_space
       a						:List[Any] 							=       keep_accents
       a						:Optional[int] 							=       vocab_file

       a						:Tuple 							=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.Load(_lowerCamelCase    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return len(self.sp_model    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[Any] 							=       {self.convert_ids_to_tokens(_lowerCamelCase    ): i for i in range(self.vocab_size    )}
       vocab.update(self.added_tokens_encoder    )
       return vocab




 def __getstate__(		self    ):
       a						:Optional[int] 							=       self.__dict__.copy()
       a						:int 							=       None
       return state




 def __setstate__(		self					,  _lowerCamelCase    ):
       a						:Union[str, Any] 							=       d

       # for backward compatibility
       if not hasattr(self					,  '''sp_model_kwargs'''    ):
             a						:Any 							=       {}

       a						:List[Any] 							=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.Load(self.vocab_file    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       if self.remove_space:
             a						:int 							=       ''' '''.join(inputs.strip().split()    )
       else:
             a						:Tuple 							=       inputs
       a						:Tuple 							=       outputs.replace('''``'''					,  '''"'''    ).replace('''\'\''''					,  '''"'''    )

       if not self.keep_accents:
             a						:List[str] 							=       unicodedata.normalize('''NFKD'''					,  _lowerCamelCase    )
             a						:List[str] 							=       ''''''.join([c for c in outputs if not unicodedata.combining(_lowerCamelCase    )]    )
       if self.do_lower_case:
             a						:Tuple 							=       outputs.lower()

       return outputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:Optional[Any] 							=       self.preprocess_text(_lowerCamelCase    )
       a						:int 							=       self.sp_model.encode(_lowerCamelCase					,  out_type=_lowerCamelCase    )
       a						:Tuple 							=       []
       for piece in pieces:
             if len(_lowerCamelCase    ) > 1 and piece[-1] == str(''','''    ) and piece[-2].isdigit():
                   a						:int 							=       self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowerCamelCase					,  ''''''    )    )
                   if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                         if len(cur_pieces[0]    ) == 1:
                               a						:List[str] 							=       cur_pieces[1:]
                         else:
                               a						:Optional[Any] 							=       cur_pieces[0][1:]
                   cur_pieces.append(piece[-1]    )
                   new_pieces.extend(_lowerCamelCase    )
             else:
                   new_pieces.append(_lowerCamelCase    )

       return new_pieces




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       return self.sp_model.PieceToId(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       return self.sp_model.IdToPiece(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:Dict 							=       []
       a						:List[Any] 							=       ''''''
       a						:str 							=       False
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   if not prev_is_special:
                         out_string += " "
                   out_string += self.sp_model.decode(_lowerCamelCase    ) + token
                   a						:Optional[int] 							=       True
                   a						:str 							=       []
             else:
                   current_sub_tokens.append(_lowerCamelCase    )
                   a						:Optional[int] 							=       False
       out_string += self.sp_model.decode(_lowerCamelCase    )
       return out_string.strip()




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None    ):
       a						:str 							=       [self.sep_token_id]
       a						:str 							=       [self.cls_token_id]
       if token_ids_a is None:
             return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None					,  _lowerCamelCase = False    ):

       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_lowerCamelCase					,  token_ids_a=_lowerCamelCase					,  already_has_special_tokens=_lowerCamelCase    )

       if token_ids_a is not None:
             return [1] + ([0] * len(_lowerCamelCase    )) + [1] + ([0] * len(_lowerCamelCase    )) + [1]
       return [1] + ([0] * len(_lowerCamelCase    )) + [1]




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None    ):
       a						:Union[str, Any] 							=       [self.sep_token_id]
       a						:Optional[int] 							=       [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    ) * [0] + len(token_ids_a + sep    ) * [1]




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None    ):
       if not os.path.isdir(_lowerCamelCase    ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
             return
       a						:Dict 							=       os.path.join(
           _lowerCamelCase					,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

       if os.path.abspath(self.vocab_file    ) != os.path.abspath(_lowerCamelCase    ) and os.path.isfile(self.vocab_file    ):
             copyfile(self.vocab_file					,  _lowerCamelCase    )
       elif not os.path.isfile(self.vocab_file    ):
             with open(_lowerCamelCase					,  '''wb'''    ) as fi:
                   a						:Dict 							=       self.sp_model.serialized_model_proto()
                   fi.write(_lowerCamelCase    )

       return (out_vocab_file,)













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

lowerCamelCase	:							Union[str, Any]				=logging.get_logger(__name__)


lowerCamelCase	:							str				={
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCamelCase	:							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'''
    },
}

lowerCamelCase	:							str				={'''facebook/blenderbot-3B''': 128}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Union[str, Any]        = VOCAB_FILES_NAMES
 _lowerCAmelCase       :       Dict        = PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase       :       List[Any]        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase       :       int        = ['''input_ids''', '''attention_mask''']
 _lowerCAmelCase       :       Tuple        = BlenderbotTokenizer







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


      '''simple docstring'''






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

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

      UpperCamelCase__			:   Tuple       				=  add_prefix_space

      UpperCamelCase__			:   Optional[int]       				=  "post_processor"
      UpperCamelCase__			:   Union[str, Any]       				=  getattr(self.backend_tokenizer							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      if tokenizer_component_instance:
           UpperCamelCase__			:   Tuple       				=  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:
                UpperCamelCase__			:   Tuple       				=  tuple(state["sep"]						)
           if "cls" in state:
                UpperCamelCase__			:   Optional[int]       				=  tuple(state["cls"]						)

           UpperCamelCase__			:   List[Any]       				=  False

           if state.get("add_prefix_space"							,   SCREAMING_SNAKE_CASE						) != add_prefix_space:
                UpperCamelCase__			:   str       				=  add_prefix_space
                UpperCamelCase__			:   Optional[Any]       				=  True

           if state.get("trim_offsets"							,   SCREAMING_SNAKE_CASE						) != trim_offsets:
                UpperCamelCase__			:   Optional[Any]       				=  trim_offsets
                UpperCamelCase__			:   Tuple       				=  True

           if changes_to_apply:
                UpperCamelCase__			:   Optional[int]       				=  getattr(SCREAMING_SNAKE_CASE							,   state.pop("type"						)						)
                UpperCamelCase__			:   Dict       				=  component_class(**SCREAMING_SNAKE_CASE						)
                setattr(self.backend_tokenizer							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 @property
 # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
 def 					__lowercase					(		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 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  AddedToken(SCREAMING_SNAKE_CASE							,   lstrip=SCREAMING_SNAKE_CASE							,   rstrip=SCREAMING_SNAKE_CASE						) if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						) else value
      UpperCamelCase__			:   str       				=  value







 def 					__lowercase					(		self					:   Any							,   *SCREAMING_SNAKE_CASE					:   List[str]							,   **SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  kwargs.get("is_split_into_words"							,   SCREAMING_SNAKE_CASE						)
      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(*SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Dict							,   *SCREAMING_SNAKE_CASE					:   int							,   **SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  kwargs.get("is_split_into_words"							,   SCREAMING_SNAKE_CASE						)

      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(*SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Optional[str] = None						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self._tokenizer.model.save(SCREAMING_SNAKE_CASE							,   name=SCREAMING_SNAKE_CASE						)
      return tuple(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[int]							,   SCREAMING_SNAKE_CASE					:   Optional[List[int]] = None						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  [self.sep_token_id]
      UpperCamelCase__			:   int       				=  [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]							,   SCREAMING_SNAKE_CASE					:   List[int]							,   SCREAMING_SNAKE_CASE					:   Optional[List[int]] = None						):


      '''simple docstring'''






      return token_ids_a + [self.eos_token_id]







 def 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   "Conversation"						):


      '''simple docstring'''






      UpperCamelCase__			:   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(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   List[str]       				=  "  ".join(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  self.encode(SCREAMING_SNAKE_CASE						)
      if len(SCREAMING_SNAKE_CASE						) > self.model_max_length:
           UpperCamelCase__			:   Dict       				=  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