






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 			snake_case__   (							UpperCAmelCase_):

       '''simple docstring'''
       lowerCamelCase							:				Union[str, Any] 				= VOCAB_FILES_NAMES
       lowerCamelCase							:				Any 				= PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase							:				List[str] 				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCamelCase							:				Union[str, Any] 				= ['input_ids', 'attention_mask']
       lowerCamelCase							:				Optional[Any] 				= BlenderbotTokenizer



       def __init__(							self			,						a__=None			,						a__=None			,						a__=None			,						a__="replace"			,						a__="<s>"			,						a__="</s>"			,						a__="</s>"			,						a__="<s>"			,						a__="<unk>"			,						a__="<pad>"			,						a__="<mask>"			,						a__=False			,						a__=True			,						**a__			,						) ->      Optional[Any]:






            '''simple docstring'''





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

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

            __snake_case			:List[Any]          =						add_prefix_space

            __snake_case			:Optional[int]          =						'post_processor'
            __snake_case			:Optional[int]          =						getattr(self.backend_tokenizer			,						_lowercase			,						_lowercase    )
            if tokenizer_component_instance:
                 __snake_case			:Any          =						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			:Dict          =						tuple(state["""sep"""]    )
                 if "cls" in state:
                      __snake_case			:Dict          =						tuple(state["""cls"""]    )

                 __snake_case			:Optional[int]          =						False

                 if state.get("""add_prefix_space"""			,						_lowercase    ) != add_prefix_space:
                      __snake_case			:int          =						add_prefix_space
                      __snake_case			:str          =						True

                 if state.get("""trim_offsets"""			,						_lowercase    ) != trim_offsets:
                      __snake_case			:Dict          =						trim_offsets
                      __snake_case			:Any          =						True

                 if changes_to_apply:
                      __snake_case			:Optional[Any]          =						getattr(_lowercase			,						state.pop("""type"""    )    )
                      __snake_case			:Tuple          =						component_class(**_lowercase    )
                      setattr(self.backend_tokenizer			,						_lowercase			,						_lowercase    )



       @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			,						a__    ) ->      str:






            '''simple docstring'''





            __snake_case			:Dict          =						AddedToken(_lowercase			,						lstrip=_lowercase			,						rstrip=_lowercase    ) if isinstance(_lowercase			,						_lowercase    ) else value
            __snake_case			:Any          =						value



       def 					__lowercase						(							self			,						*a__			,						**a__    ) ->      BatchEncoding:






            '''simple docstring'''





            __snake_case			:Tuple          =						kwargs.get("""is_split_into_words"""			,						_lowercase    )
            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(*_lowercase			,						**_lowercase    )



       def 					__lowercase						(							self			,						*a__			,						**a__    ) ->      BatchEncoding:






            '''simple docstring'''





            __snake_case			:str          =						kwargs.get("""is_split_into_words"""			,						_lowercase    )

            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(*_lowercase			,						**_lowercase    )



       def 					__lowercase						(							self			,						a__			,						a__ = None    ) ->      Tuple[str]:






            '''simple docstring'''





            __snake_case			:List[str]          =						self._tokenizer.model.save(_lowercase			,						name=_lowercase    )
            return tuple(_lowercase    )



       def 					__lowercase						(							self			,						a__			,						a__ = None    ) ->      List[int]:






            '''simple docstring'''





            __snake_case			:Tuple          =						[self.sep_token_id]
            __snake_case			:Union[str, 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			,						a__			,						a__ = None    ) ->      Dict:






            '''simple docstring'''





            return token_ids_a + [self.eos_token_id]



       def 					__lowercase						(							self			,						a__    ) ->      List[int]:






            '''simple docstring'''





            __snake_case			:Dict          =						[]
            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(_lowercase    )

            __snake_case			:Optional[Any]          =						'  '.join(_lowercase    )
            __snake_case			:Optional[int]          =						self.encode(_lowercase    )
            if len(_lowercase    ) > self.model_max_length:
                 __snake_case			: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










# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Tuple 				= "dandelin/vilt-b32-finetuned-vqa"
       lowerCamelCase							:				List[str] 				= (
           "This is a tool that answers a question about an image. It takes an input named `image` which should be the "
           "image containing the information, as well as a `question` which should be the question in English. It "
           "returns a text that is the answer to the question."
       )
       lowerCamelCase							:				Optional[Any] 				= "image_qa"
       lowerCamelCase							:				str 				= AutoProcessor
       lowerCamelCase							:				Union[str, Any] 				= AutoModelForVisualQuestionAnswering

       lowerCamelCase							:				Any 				= ["image", "text"]
       lowerCamelCase							:				Dict 				= ["text"]



       def __init__(							self			,						*a__			,						**a__    ) ->      Any:






            '''simple docstring'''





            requires_backends(self			,						["""vision"""]    )
            super().__init__(*a__			,						**a__    )



       def 					__lowercase						(							self			,						a__			,						a__    ) ->      int:






            '''simple docstring'''





            return self.pre_processor(a__			,						a__			,						return_tensors="""pt"""    )



       def 					__lowercase						(							self			,						a__    ) ->      Union[str, Any]:






            '''simple docstring'''





            with torch.no_grad():
                 return self.model(**a__    ).logits



       def 					__lowercase						(							self			,						a__    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:str          =						outputs.argmax(-1    ).item()
            return self.model.config.idalabel[idx]


