



# 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  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			="""dandelin/vilt-b32-finetuned-vqa"""
		snake_case_			=(
		    """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."""
		)
		snake_case_			="""image_qa"""
		snake_case_			=AutoProcessor
		snake_case_			=AutoModelForVisualQuestionAnswering

		snake_case_			=["""image""", """text"""]
		snake_case_			=["""text"""]



		def __init__(self	,*__lowerCamelCase	,**__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						requires_backends(self	,['''vision''']				)
						super().__init__(*__lowerCamelCase	,**__lowerCamelCase				)



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

						"""simple docstring"""






						return self.pre_processor(__lowerCamelCase	,__lowerCamelCase	,return_tensors='''pt'''				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






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






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case       :	Any            =logging.get_logger(__name__)



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			="""encoder-decoder"""
		snake_case_			=True



		def __init__(self	,**__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase				)
						assert (
						    "encoder" in kwargs and "decoder" in kwargs
						), "Config has to be initialized with encoder and decoder config"
						lowerCAmelCase__  :       Optional[Any]				=  kwargs.pop('''encoder'''				)
						lowerCAmelCase__  :       Any				=  encoder_config.pop('''model_type'''				)
						lowerCAmelCase__  :       str				=  kwargs.pop('''decoder'''				)
						lowerCAmelCase__  :       Tuple				=  decoder_config.pop('''model_type'''				)

						from ..auto.configuration_auto import AutoConfig

						lowerCAmelCase__  :       Tuple				=  AutoConfig.for_model(__lowerCamelCase	,**__lowerCamelCase				)
						lowerCAmelCase__  :       Any				=  AutoConfig.for_model(__lowerCamelCase	,**__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  True



		@classmethod
		def 	lowerCAmelCase__   (cls	,__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase				)  ->    PretrainedConfig:

						"""simple docstring"""






						logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''				)
						lowerCAmelCase__  :       Optional[int]				=  True
						lowerCAmelCase__  :       List[str]				=  True

						return cls(encoder=encoder_config.to_dict()	,decoder=decoder_config.to_dict()	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  copy.deepcopy(self.__dict__				)
						lowerCAmelCase__  :       Optional[Any]				=  self.encoder.to_dict()
						lowerCAmelCase__  :       str				=  self.decoder.to_dict()
						lowerCAmelCase__  :       Optional[int]				=  self.__class__.model_type
						return output

