






import torch






def   UpperCamelCase  (       ):




     '''simple docstring'''






     if torch.cuda.is_available():
          __snake_case			:Optional[int]          =						torch.cuda.device_count()
     else:
          __snake_case			:List[str]          =						0
     print(f'''Successfully ran on {num_gpus} GPUs'''  )


if __name__ == "__main__":
     main()










# 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]


