


'''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 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				'''dandelin/vilt-b32-finetuned-vqa'''
     UpperCAmelCase 					=				(
         '''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.'''
     )
     UpperCAmelCase 					=				'''image_qa'''
     UpperCAmelCase 					=				AutoProcessor
     UpperCAmelCase 					=				AutoModelForVisualQuestionAnswering

     UpperCAmelCase 					=				['''image''', '''text''']
     UpperCAmelCase 					=				['''text''']






     def __init__(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    int:
          requires_backends(self     ,							["vision"]		)
          super().__init__(*__UpperCamelCase     ,							**__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    List[Any]:
          return self.pre_processor(__UpperCamelCase     ,							__UpperCamelCase     ,							return_tensors="pt"		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Dict:
          with torch.no_grad():
               return self.model(**__UpperCamelCase		).logits






     def  a_					(			self     ,							__UpperCamelCase		)       ->    List[str]:
          _a						=		outputs.argmax(-1		).item()
          return self.model.config.idalabel[idx]





'''simple docstring'''

class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase		)       ->    Optional[Any]:
          # we need a list not a string, so do something to change the type
          _a						=		arr.split(","		)






     def  a_					(			self		)       ->    List[Any]:
          _a						=		[int(self.array[0]		)] * len(self.array		)
          _a						=		[int(self.array[0]		)] * len(self.array		)
          for i in range(1     ,							len(self.array		)		):
               _a						=		max(
                   int(self.array[i]		) + sum_value[i - 1]     ,							int(self.array[i]		)		)
               _a						=		max(sum_value[i]     ,							rear[i - 1]		)
          return rear[len(self.array		) - 1]


if __name__ == "__main__":
   lowercase__				     =		input("please input some numbers:")
   lowercase__				     =		SubArray(whole_array)
   lowercase__				     =		array.solve_sub_array()
   print(("the results is:", re))

