

# 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						(							_UpperCAmelCase			):







    """simple docstring"""





    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			:	Dict,*lowercase_			:	Optional[Any],**lowercase_			:	int		)->   Any:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Dict,lowercase_			:	"Image",lowercase_			:	str		)->   int:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Optional[int],lowercase_			:	int		)->   Tuple:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	List[str],lowercase_			:	Optional[int]		)->   Optional[int]:





        '''simple docstring'''







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




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int = 1000				)							->				int:





    '''simple docstring'''






    A__   				=       3
    A__   				=       0
    while a < n:
        if a % 3 == 0 or a % 5 == 0:
            result += a
        elif a % 15 == 0:
            result -= a
        a += 1
    return result


if __name__ == "__main__":
     print(f"""{solution() = }""")

