





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

UpperCAmelCase     :     Tuple							=	logging.get_logger(__name__)


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

UpperCAmelCase     :     Union[str, Any]							=	{
    """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"""
    },
}

UpperCAmelCase     :     Optional[int]							=	{"""facebook/blenderbot-3B""": 128}







class     __lowerCAmelCase	(   UpperCamelCase__):
						_lowercase    : Dict            =      VOCAB_FILES_NAMES
						_lowercase    : str            =      PRETRAINED_VOCAB_FILES_MAP
						_lowercase    : str            =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase    : Dict            =      ["""input_ids""", """attention_mask"""]
						_lowercase    : Tuple            =      BlenderbotTokenizer







						def __init__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__="replace"		,				lowerCAmelCase__="<s>"		,				lowerCAmelCase__="</s>"		,				lowerCAmelCase__="</s>"		,				lowerCAmelCase__="<s>"		,				lowerCAmelCase__="<unk>"		,				lowerCAmelCase__="<pad>"		,				lowerCAmelCase__="<mask>"		,				lowerCAmelCase__=False		,				lowerCAmelCase__=True		,				**lowerCAmelCase__		,				)       ->						int:



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

								a__     : Dict  =add_prefix_space

								a__     : Optional[int]  ="post_processor"
								a__     : List[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__     : Dict  =tuple(state["sep"]							)
										if "cls" in state:
												a__     : List[Any]  =tuple(state["cls"]							)

										a__     : List[str]  =False

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

										if state.get("trim_offsets"		,				lowerCAmelCase__							) != trim_offsets:
												a__     : int  =trim_offsets
												a__     : Optional[int]  =True

										if changes_to_apply:
												a__     : str  =getattr(lowerCAmelCase__		,				state.pop("type"							)							)
												a__     : Tuple  =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							)       ->						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        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Optional[int]:



								'''simple docstring'''




								a__     : int  =AddedToken(lowerCAmelCase__		,				lstrip=lowerCAmelCase__		,				rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__		,				lowerCAmelCase__							) else value
								a__     : Union[str, Any]  =value







						def        _lowercase  (   self		,				*lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						BatchEncoding:



								'''simple docstring'''




								a__     : 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()._batch_encode_plus(*lowerCAmelCase__		,				**lowerCAmelCase__							)







						def        _lowercase  (   self		,				*lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						BatchEncoding:



								'''simple docstring'''




								a__     : Dict  =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		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						Tuple[str]:



								'''simple docstring'''




								a__     : Union[str, Any]  =self._tokenizer.model.save(lowerCAmelCase__		,				name=lowerCAmelCase__							)
								return tuple(lowerCAmelCase__							)







						def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						List[int]:



								'''simple docstring'''




								a__     : Any  =[self.sep_token_id]
								a__     : Optional[Any]  =[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		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						Optional[Any]:



								'''simple docstring'''




								return token_ids_a + [self.eos_token_id]







						def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						List[int]:



								'''simple docstring'''




								a__     : int  =[]
								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__     : List[str]  ="  ".join(lowerCAmelCase__							)
								a__     : Union[str, Any]  =self.encode(lowerCAmelCase__							)
								if len(lowerCAmelCase__							) > self.model_max_length:
										a__     : int  =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 warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
     import torch







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Any            =      """char"""
      _lowercase    : str            =      """bpe"""
      _lowercase    : List[Any]            =      """wp"""
UpperCAmelCase     :     List[Any]							=	(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Tuple            =      ["""image_processor""", """char_tokenizer"""]
      _lowercase    : Any            =      """ViTImageProcessor"""
      _lowercase    : Optional[Any]            =      """MgpstrTokenizer"""







      def __init__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				**lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Optional[int]  =None
        if "feature_extractor" in kwargs:
          warnings.warn(
              "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
              " instead."		,				lowerCAmelCase__		,				)
          a__     : List[str]  =kwargs.pop("feature_extractor"							)

        a__     : List[str]  =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`."							)

        a__     : str  =tokenizer
        a__     : List[str]  =AutoTokenizer.from_pretrained("gpt2"							)
        a__     : Optional[int]  =AutoTokenizer.from_pretrained("bert-base-uncased"							)

        super().__init__(lowerCAmelCase__		,				lowerCAmelCase__							)







      def __call__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				**lowerCAmelCase__							)       ->						Union[str, Any]:



        '''simple docstring'''




        if images is None and text is None:
          raise ValueError("You need to specify either an `images` or `text` input to process."							)

        if images is not None:
          a__     : Union[str, Any]  =self.image_processor(lowerCAmelCase__		,				return_tensors=lowerCAmelCase__		,				**lowerCAmelCase__							)
        if text is not None:
          a__     : int  =self.char_tokenizer(lowerCAmelCase__		,				return_tensors=lowerCAmelCase__		,				**lowerCAmelCase__							)

        if text is None:
          return inputs
        elif images is None:
          return encodings
        else:
          a__     : Tuple  =encodings["input_ids"]
          return inputs







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__ ,							a__ ,							a__     : Any  =sequences
        a__     : Union[str, Any]  =char_preds.size(0							)

        a__ ,							a__     : Dict  =self._decode_helper(lowerCAmelCase__		,				"char"							)
        a__ ,							a__     : List[Any]  =self._decode_helper(lowerCAmelCase__		,				"bpe"							)
        a__ ,							a__     : Optional[int]  =self._decode_helper(lowerCAmelCase__		,				"wp"							)

        a__     : List[Any]  =[]
        a__     : Dict  =[]
        for i in range(lowerCAmelCase__							):
          a__     : int  =[char_scores[i], bpe_scores[i], wp_scores[i]]
          a__     : Tuple  =[char_strs[i], bpe_strs[i], wp_strs[i]]
          a__     : Any  =scores.index(max(lowerCAmelCase__							)							)
          final_strs.append(strs[max_score_index]							)
          final_scores.append(scores[max_score_index]							)

        a__     : Dict  ={}
        a__     : str  =final_strs
        a__     : Optional[int]  =final_scores
        a__     : Union[str, Any]  =char_strs
        a__     : List[str]  =bpe_strs
        a__     : Union[str, Any]  =wp_strs
        return out







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        if format == DecodeType.CHARACTER:
          a__     : Optional[Any]  =self.char_decode
          a__     : Dict  =1
          a__     : Tuple  ="[s]"
        elif format == DecodeType.BPE:
          a__     : str  =self.bpe_decode
          a__     : Dict  =2
          a__     : Optional[int]  ="#"
        elif format == DecodeType.WORDPIECE:
          a__     : Union[str, Any]  =self.wp_decode
          a__     : List[Any]  =1_0_2
          a__     : Dict  ="[SEP]"
        else:
          raise ValueError(F'''Format {format} is not supported.'''							)

        a__ ,							a__     : Any  =[], []
        a__     : str  =pred_logits.size(0							)
        a__     : Optional[Any]  =pred_logits.size(1							)
        a__ ,							a__     : Optional[int]  =pred_logits.topk(1		,				dim=-1		,				largest=lowerCAmelCase__		,				sorted=lowerCAmelCase__							)
        a__     : Optional[Any]  =preds_index.view(-1		,				lowerCAmelCase__							)[:, 1:]
        a__     : Dict  =decoder(lowerCAmelCase__							)
        a__ ,							a__     : Any  =torch.nn.functional.softmax(lowerCAmelCase__		,				dim=2							).max(dim=2							)
        a__     : int  =preds_max_prob[:, 1:]

        for index in range(lowerCAmelCase__							):
          a__     : Optional[Any]  =preds_str[index].find(lowerCAmelCase__							)
          a__     : Optional[int]  =preds_str[index][:pred_eos]
          a__     : List[Any]  =preds_index[index].cpu().tolist()
          a__     : List[Any]  =pred_index.index(lowerCAmelCase__							) if eos_token in pred_index else -1
          a__     : Union[str, Any]  =preds_max_prob[index][: pred_eos_index + 1]
          a__     : List[Any]  =pred_max_prob.cumprod(dim=0							)[-1] if pred_max_prob.nelement() != 0 else 0.0
          dec_strs.append(lowerCAmelCase__							)
          conf_scores.append(lowerCAmelCase__							)

        return dec_strs, conf_scores







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        a__     : int  =[seq.replace(" "		,				""							) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase__							)]
        return decode_strs







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        return self.bpe_tokenizer.batch_decode(lowerCAmelCase__							)







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : List[Any]  =[seq.replace(" "		,				""							) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase__							)]
        return decode_strs






