



"""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

snake_case   					=				logging.get_logger(__name__)


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

snake_case   					=				{
    '''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'''
    },
}

snake_case   					=				{'''facebook/blenderbot-3B''': 1_2_8}



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	Dict           =  VOCAB_FILES_NAMES
   A__		:	Optional[int]           =  PRETRAINED_VOCAB_FILES_MAP
   A__		:	str           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__		:	int           =  ['''input_ids''', '''attention_mask''']
   A__		:	int           =  BlenderbotTokenizer



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



     """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							,       )

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

     _snake_case   	=      add_prefix_space

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

       _snake_case   	=      False

       if state.get('''add_prefix_space'''							,       __lowerCamelCase				) != add_prefix_space:
         _snake_case   	=      add_prefix_space
         _snake_case   	=      True

       if state.get('''trim_offsets'''							,       __lowerCamelCase				) != trim_offsets:
         _snake_case   	=      trim_offsets
         _snake_case   	=      True

       if changes_to_apply:
         _snake_case   	=      getattr(__lowerCamelCase							,       state.pop('''type'''				)				)
         _snake_case   	=      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    __UpperCAmelCase    (       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    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	Any				):



     """simple docstring"""
     _snake_case   	=      AddedToken(__lowerCamelCase							,       lstrip=__lowerCamelCase							,       rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else value
     _snake_case   	=      value



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       *__lowerCamelCase    :	Optional[int]							,       **__lowerCamelCase    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      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    __UpperCAmelCase    (       self    :	int							,       *__lowerCamelCase    :	Optional[Any]							,       **__lowerCamelCase    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      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    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Optional[str] = None				):



     """simple docstring"""
     _snake_case   	=      self._tokenizer.model.save(__lowerCamelCase							,       name=__lowerCamelCase				)
     return tuple(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	Optional[List[int]] = None				):



     """simple docstring"""
     _snake_case   	=      [self.sep_token_id]
     _snake_case   	=      [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    :	List[Any]							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	Optional[List[int]] = None				):



     """simple docstring"""
     return token_ids_a + [self.eos_token_id]





   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	"Conversation"				):



     """simple docstring"""
     _snake_case   	=      []
     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				)

     _snake_case   	=      '''  '''.join(__lowerCamelCase				)
     _snake_case   	=      self.encode(__lowerCamelCase				)
     if len(__lowerCamelCase				) > self.model_max_length:
       _snake_case   	=      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











import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_ta import TaTokenizer
else:
      __magic_name__  :  Dict					=       None


__magic_name__  :  Optional[Any]					=       logging.get_logger(__name__)

__magic_name__  :  Optional[Any]					=       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__magic_name__  :  Union[str, Any]					=       {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__magic_name__  :  Tuple					=       {
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}





class     SCREAMING_SNAKE_CASE__		(_a			):
     lowercase_ :      Dict				= VOCAB_FILES_NAMES
     lowercase_ :      Any				= PRETRAINED_VOCAB_FILES_MAP
     lowercase_ :      Tuple				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase_ :      Tuple				= ["input_ids", "attention_mask"]
     lowercase_ :      Any				= TaTokenizer

     lowercase_ :      List[int]				= []
     def __init__(						self	:		Tuple       ,						__lowerCamelCase	:		Tuple=None       ,						__lowerCamelCase	:		str=None       ,						__lowerCamelCase	:		Optional[int]="</s>"       ,						__lowerCamelCase	:		Optional[Any]="<unk>"       ,						__lowerCamelCase	:		int="<pad>"       ,						__lowerCamelCase	:		int=1_00       ,						__lowerCamelCase	:		List[str]=None       ,						**__lowerCamelCase	:		Tuple       ,						):



       """simple docstring"""
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
         lowerCAmelCase__										=      [F"""<extra_id_{i}>""" for i in range(__lowerCamelCase						)]
       elif extra_ids > 0 and additional_special_tokens is not None:
         # Check that we have the right number of extra special tokens
         lowerCAmelCase__										=      len(set(filter(lambda __lowerCamelCase						: bool('''extra_id_''' in str(__lowerCamelCase						)						)       ,						__lowerCamelCase						)						)						)
         if extra_tokens != extra_ids:
           raise ValueError(
               F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
               ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
               ''' tokens'''						)

       super().__init__(
           __lowerCamelCase       ,						tokenizer_file=__lowerCamelCase       ,						eos_token=__lowerCamelCase       ,						unk_token=__lowerCamelCase       ,						pad_token=__lowerCamelCase       ,						extra_ids=__lowerCamelCase       ,						additional_special_tokens=__lowerCamelCase       ,						**__lowerCamelCase       ,						)

       lowerCAmelCase__										=      vocab_file
       lowerCAmelCase__										=      False if not self.vocab_file else True
       lowerCAmelCase__										=      extra_ids
     @staticmethod
     def        A__      (						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		List[str]       ,						__lowerCamelCase	:		Any						):



       """simple docstring"""
       if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
         lowerCAmelCase__										=      TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
         if init_max_model_length is not None and init_max_model_length != max_model_length:
           return init_max_model_length
         elif init_max_model_length is None:
           warnings.warn(
               '''This tokenizer was incorrectly instantiated with a model max length of'''
               F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
               ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
               ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
               F""" {pretrained_model_name_or_path} automatically truncating your input to"""
               F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
               F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
               ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
               ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''       ,						__lowerCamelCase       ,						)

       return max_model_length
     def        A__      (						self	:		int       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		Optional[str] = None						):



       """simple docstring"""
       if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.'''						)

       if not os.path.isdir(__lowerCamelCase						):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
         return
       lowerCAmelCase__										=      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						):
         copyfile(self.vocab_file       ,						__lowerCamelCase						)
         logger.info(F"""Copy vocab file to {out_vocab_file}"""						)

       return (out_vocab_file,)
     def        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		List[int]       ,						__lowerCamelCase	:		Optional[List[int]] = None						):



       """simple docstring"""
       lowerCAmelCase__										=      token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
         return self.prefix_tokens + token_ids_a
       else:
         lowerCAmelCase__										=      token_ids_a + [self.eos_token_id]
         return self.prefix_tokens + token_ids_a + token_ids_a
     def        A__      (						self	:		int       ,						__lowerCamelCase	:		List[int]       ,						__lowerCamelCase	:		Optional[List[int]] = None						):



       """simple docstring"""
       lowerCAmelCase__										=      [self.eos_token_id]

       if token_ids_a is None:
         return len(token_ids_a + eos						) * [0]
       return len(token_ids_a + eos + token_ids_a + eos						) * [0]
     def        A__      (						self	:		int						):



       """simple docstring"""
       return list(
           set(filter(lambda __lowerCamelCase						: bool(re.search(r'''<extra_id_\d+>'''       ,						__lowerCamelCase						)						) is not None       ,						self.additional_special_tokens						)						)						)



     def        A__      (						self	:		Dict						):



       """simple docstring"""
       return [self.convert_tokens_to_ids(__lowerCamelCase						) for token in self.get_sentinel_tokens()]

