



'''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

_lowerCAmelCase 		=      logging.get_logger(__name__)


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

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






class 			lowerCAmelCase_(							__lowerCAmelCase	):
  '''simple docstring'''




  __lowercase				: str           = VOCAB_FILES_NAMES
  __lowercase				: List[str]           = PRETRAINED_VOCAB_FILES_MAP
  __lowercase				: Optional[Any]           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase				: List[str]           = ['''input_ids''', '''attention_mask''']
  __lowercase				: Any           = BlenderbotTokenizer




  def __init__(       self    ,__UpperCAmelCase=None    ,__UpperCAmelCase=None    ,__UpperCAmelCase=None    ,__UpperCAmelCase="replace"    ,__UpperCAmelCase="<s>"    ,__UpperCAmelCase="</s>"    ,__UpperCAmelCase="</s>"    ,__UpperCAmelCase="<s>"    ,__UpperCAmelCase="<unk>"    ,__UpperCAmelCase="<pad>"    ,__UpperCAmelCase="<mask>"    ,__UpperCAmelCase=False    ,__UpperCAmelCase=True    ,**__UpperCAmelCase    ,)       ->						Dict:
      super().__init__(
          _UpperCAmelCase    ,_UpperCAmelCase    ,tokenizer_file=_UpperCAmelCase    ,errors=_UpperCAmelCase    ,bos_token=_UpperCAmelCase    ,eos_token=_UpperCAmelCase    ,sep_token=_UpperCAmelCase    ,cls_token=_UpperCAmelCase    ,unk_token=_UpperCAmelCase    ,pad_token=_UpperCAmelCase    ,mask_token=_UpperCAmelCase    ,add_prefix_space=_UpperCAmelCase    ,trim_offsets=_UpperCAmelCase    ,**_UpperCAmelCase    ,)

      lowerCAmelCase__					:    List[str]				 =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
      if pre_tok_state.get("""add_prefix_space"""    ,_UpperCAmelCase ) != add_prefix_space:
          lowerCAmelCase__					:    int				 =   getattr(_UpperCAmelCase    ,pre_tok_state.pop("""type""" ) )
          lowerCAmelCase__					:    Tuple				 =   add_prefix_space
          lowerCAmelCase__					:    Optional[int]				 =   pre_tok_class(**_UpperCAmelCase )

      lowerCAmelCase__					:    Optional[Any]				 =   add_prefix_space

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

          lowerCAmelCase__					:    List[str]				 =   False

          if state.get("""add_prefix_space"""    ,_UpperCAmelCase ) != add_prefix_space:
              lowerCAmelCase__					:    Dict				 =   add_prefix_space
              lowerCAmelCase__					:    Union[str, Any]				 =   True

          if state.get("""trim_offsets"""    ,_UpperCAmelCase ) != trim_offsets:
              lowerCAmelCase__					:    int				 =   trim_offsets
              lowerCAmelCase__					:    Dict				 =   True

          if changes_to_apply:
              lowerCAmelCase__					:    Tuple				 =   getattr(_UpperCAmelCase    ,state.pop("""type""" ) )
              lowerCAmelCase__					:    List[str]				 =   component_class(**_UpperCAmelCase )
              setattr(self.backend_tokenizer    ,_UpperCAmelCase    ,_UpperCAmelCase )




  @property
  # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
  def 				UpperCAmelCase_      (       self )       ->						Union[str, Any]:
      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    ,__UpperCAmelCase )       ->						Optional[Any]:
      lowerCAmelCase__					:    Optional[Any]				 =   AddedToken(_UpperCAmelCase    ,lstrip=_UpperCAmelCase    ,rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase    ,_UpperCAmelCase ) else value
      lowerCAmelCase__					:    List[Any]				 =   value




  def 				UpperCAmelCase_      (       self    ,*__UpperCAmelCase    ,**__UpperCAmelCase )       ->						int:
      lowerCAmelCase__					:    str				 =   kwargs.get("""is_split_into_words"""    ,_UpperCAmelCase )
      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(*_UpperCAmelCase    ,**_UpperCAmelCase )




  def 				UpperCAmelCase_      (       self    ,*__UpperCAmelCase    ,**__UpperCAmelCase )       ->						Optional[int]:
      lowerCAmelCase__					:    Any				 =   kwargs.get("""is_split_into_words"""    ,_UpperCAmelCase )

      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(*_UpperCAmelCase    ,**_UpperCAmelCase )




  def 				UpperCAmelCase_      (       self    ,__UpperCAmelCase    ,__UpperCAmelCase = None )       ->						Dict:
      lowerCAmelCase__					:    int				 =   self._tokenizer.model.save(_UpperCAmelCase    ,name=_UpperCAmelCase )
      return tuple(_UpperCAmelCase )




  def 				UpperCAmelCase_      (       self    ,__UpperCAmelCase    ,__UpperCAmelCase = None )       ->						Dict:
      lowerCAmelCase__					:    Optional[int]				 =   [self.sep_token_id]
      lowerCAmelCase__					:    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 				UpperCAmelCase_      (       self    ,__UpperCAmelCase    ,__UpperCAmelCase = None )       ->						Any:
      return token_ids_a + [self.eos_token_id]






  def 				UpperCAmelCase_      (       self    ,__UpperCAmelCase )       ->						Dict:
      lowerCAmelCase__					:    Tuple				 =   []
      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(_UpperCAmelCase )

      lowerCAmelCase__					:    Dict				 =   """  """.join(_UpperCAmelCase )
      lowerCAmelCase__					:    Tuple				 =   self.encode(_UpperCAmelCase )
      if len(_UpperCAmelCase ) > self.model_max_length:
          lowerCAmelCase__					:    Optional[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"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Any     =      ['''image_processor''', '''tokenizer''']
 UpperCAmelCase       :	Optional[int]     =      '''ViTImageProcessor'''
 UpperCAmelCase       :	int     =      ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

 def __init__(					self			:			Tuple       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Tuple=None       ,				**_UpperCAmelCase			:			Dict						):
       _A     =  None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'       ,				_UpperCAmelCase       ,				)
             _A     =  kwargs.pop('feature_extractor'						)

       _A     =  image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'						)
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'						)

       super().__init__(_UpperCAmelCase       ,				_UpperCAmelCase						)

 def __call__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Optional[Any]=None       ,				_UpperCAmelCase			:			Optional[int]=None       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       if text is None and visual_prompt is None and images is None:
             raise ValueError('You have to specify either text, visual prompt or images.'						)

       if text is not None and visual_prompt is not None:
             raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'						)

       if text is not None:
             _A     =  self.tokenizer(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if visual_prompt is not None:
             _A     =  self.image_processor(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if images is not None:
             _A     =  self.image_processor(_UpperCAmelCase       ,				return_tensors=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       if visual_prompt is not None and images is not None:
             _A     =  {
                 'pixel_values': image_features.pixel_values,
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
       elif text is not None and images is not None:
             _A     =  image_features.pixel_values
             return encoding
       elif text is not None:
             return encoding
       elif visual_prompt is not None:
             _A     =  {
                 'conditional_pixel_values': prompt_features.pixel_values,
             }
             return encoding
       else:
             return BatchEncoding(data=dict(**_UpperCAmelCase						)       ,				tensor_type=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			str       ,				*_UpperCAmelCase			:			List[Any]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       return self.tokenizer.batch_decode(*_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				*_UpperCAmelCase			:			List[str]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       return self.tokenizer.decode(*_UpperCAmelCase       ,				**_UpperCAmelCase						)

 @property
 def      lowerCAmelCase_				(					self			:			Dict						):
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,				_UpperCAmelCase       ,				)
       return self.image_processor_class



 @property
 def      lowerCAmelCase_				(					self			:			Tuple						):
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,				_UpperCAmelCase       ,				)
       return self.image_processor

