






def   UpperCamelCase  (       snake_case__     :   int	,snake_case__     :   Dict	,snake_case__     :   Any  ):




     '''simple docstring'''






     if len(__snake_case  ) != len(__snake_case  ):
          raise ValueError("""The length of profit and weight must be same."""  )
     if max_weight <= 0:
          raise ValueError("""max_weight must greater than zero."""  )
     if any(p < 0 for p in profit  ):
          raise ValueError("""Profit can not be negative."""  )
     if any(w < 0 for w in weight  ):
          raise ValueError("""Weight can not be negative."""  )

     # List created to store profit gained for the 1kg in case of each weight
     # respectively.  Calculate and append profit/weight for each element.
     __snake_case			:Optional[int]          =						[p / w for p, w in zip(__snake_case	,__snake_case  )]

     # Creating a copy of the list and sorting profit/weight in ascending order
     __snake_case			:Union[str, Any]          =						sorted(__snake_case  )

     # declaring useful variables
     __snake_case			:Optional[Any]          =						len(__snake_case  )
     __snake_case			:List[str]          =						0
     __snake_case			:Optional[int]          =						0
     __snake_case			:List[str]          =						0

     # loop till the total weight do not reach max limit e.g. 15 kg and till i<length
     while limit <= max_weight and i < length:
          # flag value for encountered greatest element in sorted_profit_by_weight
          __snake_case			:Optional[Any]          =						sorted_profit_by_weight[length - i - 1]
          __snake_case			:List[Any]          =						profit_by_weight.index(__snake_case  )
          __snake_case			:List[str]          =						-1

          # check if the weight encountered is less than the total weight
          # encountered before.
          if max_weight - limit >= weight[index]:
               limit += weight[index]
               # Adding profit gained for the given weight 1 ===
               # weight[index]/weight[index]
               gain += 1 * profit[index]
          else:
               # Since the weight encountered is greater than limit, therefore take the
               # required number of remaining kgs and calculate profit for it.
               # weight remaining / weight[index]
               gain += (max_weight - limit) / weight[index] * profit[index]
               break
          i += 1
     return gain


if __name__ == "__main__":
     print(
         """Input profits, weights, and then max_weight (all positive ints) separated by """
         """spaces."""
     )

     lowerCamelCase__							=     [int(x) for x in input("""Input profits separated by spaces: """).split()]
     lowerCamelCase__							=     [int(x) for x in input("""Input weights separated by spaces: """).split()]
     lowerCamelCase__							=     int(input("""Max weight allowed: """))

     # Function Call
     calc_profit(profit, weight, max_weight)










# 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]


