

'''simple docstring'''






from ..utils import DummyObject, requires_backends




class  a     (     metaclass=_SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	["""torch""", """transformers""", """onnx"""]
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Optional[Any]:
      requires_backends(self      ,				['torch', 'transformers', 'onnx']						)
 @classmethod
 def 							__UpperCAmelCase		( cls      ,				*__magic_name__      ,				**__magic_name__						)					->							Dict:
      requires_backends(cls      ,				['torch', 'transformers', 'onnx']						)




 @classmethod
 def 							__UpperCAmelCase		( cls      ,				*__magic_name__      ,				**__magic_name__						)					->							Optional[Any]:
      requires_backends(cls      ,				['torch', 'transformers', 'onnx']						)





class  a     (     metaclass=_SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	["""torch""", """transformers""", """onnx"""]
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							int:
      requires_backends(self      ,				['torch', 'transformers', 'onnx']						)
 @classmethod
 def 							__UpperCAmelCase		( cls      ,				*__magic_name__      ,				**__magic_name__						)					->							Dict:
      requires_backends(cls      ,				['torch', 'transformers', 'onnx']						)




 @classmethod
 def 							__UpperCAmelCase		( cls      ,				*__magic_name__      ,				**__magic_name__						)					->							List[str]:
      requires_backends(cls      ,				['torch', 'transformers', 'onnx']						)





class  a     (     metaclass=_SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	["""torch""", """transformers""", """onnx"""]
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							str:
      requires_backends(self      ,				['torch', 'transformers', 'onnx']						)
 @classmethod
 def 							__UpperCAmelCase		( cls      ,				*__magic_name__      ,				**__magic_name__						)					->							Any:
      requires_backends(cls      ,				['torch', 'transformers', 'onnx']						)




 @classmethod
 def 							__UpperCAmelCase		( cls      ,				*__magic_name__      ,				**__magic_name__						)					->							Tuple:
      requires_backends(cls      ,				['torch', 'transformers', 'onnx']						)





class  a     (     metaclass=_SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	["""torch""", """transformers""", """onnx"""]
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Optional[int]:
      requires_backends(self      ,				['torch', 'transformers', 'onnx']						)
 @classmethod
 def 							__UpperCAmelCase		( cls      ,				*__magic_name__      ,				**__magic_name__						)					->							Optional[int]:
      requires_backends(cls      ,				['torch', 'transformers', 'onnx']						)




 @classmethod
 def 							__UpperCAmelCase		( cls      ,				*__magic_name__      ,				**__magic_name__						)					->							Any:
      requires_backends(cls      ,				['torch', 'transformers', 'onnx']						)





class  a     (     metaclass=_SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	["""torch""", """transformers""", """onnx"""]
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Tuple:
      requires_backends(self      ,				['torch', 'transformers', 'onnx']						)
 @classmethod
 def 							__UpperCAmelCase		( cls      ,				*__magic_name__      ,				**__magic_name__						)					->							str:
      requires_backends(cls      ,				['torch', 'transformers', 'onnx']						)




 @classmethod
 def 							__UpperCAmelCase		( cls      ,				*__magic_name__      ,				**__magic_name__						)					->							Dict:
      requires_backends(cls      ,				['torch', 'transformers', 'onnx']						)





class  a     (     metaclass=_SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	["""torch""", """transformers""", """onnx"""]
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Dict:
      requires_backends(self      ,				['torch', 'transformers', 'onnx']						)
 @classmethod
 def 							__UpperCAmelCase		( cls      ,				*__magic_name__      ,				**__magic_name__						)					->							str:
      requires_backends(cls      ,				['torch', 'transformers', 'onnx']						)




 @classmethod
 def 							__UpperCAmelCase		( cls      ,				*__magic_name__      ,				**__magic_name__						)					->							List[str]:
      requires_backends(cls      ,				['torch', 'transformers', 'onnx']						)





'''simple docstring'''






# 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  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""dandelin/vilt-b32-finetuned-vqa"""
 _lowerCAmelCase 					=	(
     """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 					=	"""image_qa"""
 _lowerCAmelCase 					=	AutoProcessor
 _lowerCAmelCase 					=	AutoModelForVisualQuestionAnswering

 _lowerCAmelCase 					=	["""image""", """text"""]
 _lowerCAmelCase 					=	["""text"""]
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Tuple:
      requires_backends(self      ,				['vision']						)
      super().__init__(*__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Tuple:
      return self.pre_processor(__magic_name__      ,				__magic_name__      ,				return_tensors='pt'						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Any:
      with torch.no_grad():
           return self.model(**__magic_name__						).logits




 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							outputs.argmax(-1						).item()
      return self.model.config.idalabel[idx]

