


def 							_UpperCAmelCase				(UpperCamelCase__     :  list				,							UpperCamelCase__     :  list				,							UpperCamelCase__     :  int						):
       if len(UpperCamelCase__						) != len(UpperCamelCase__						):
              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.
       _A      :       Tuple				    = [p / w for p, w in zip(UpperCamelCase__				,							UpperCamelCase__						)]

       # Creating a copy of the list and sorting profit/weight in ascending order
       _A      :       Optional[int]				    = sorted(UpperCamelCase__						)

       # declaring useful variables
       _A      :       List[Any]				    = len(UpperCamelCase__						)
       _A      :       Dict				    = 0
       _A      :       List[Any]				    = 0
       _A      :       List[Any]				    = 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
              _A      :       Union[str, Any]				    = sorted_profit_by_weight[length - i - 1]
              _A      :       Optional[int]				    = profit_by_weight.index(UpperCamelCase__						)
              _A      :       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)






import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
						import torch

						from transformers import AutoModelForImageClassification

if is_vision_available():
						from transformers import AutoImageProcessor


@require_torch
@require_vision
class  A		(  unittest.TestCase     ):



		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								lowerCAmelCase_      			=  AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								model.to(UpperCamelCase__   )

								from datasets import load_dataset

								lowerCAmelCase_      			=  load_dataset('''nielsr/rvlcdip-demo'''   )

								lowerCAmelCase_      			=  dataset['''train'''][0]['''image'''].convert('''RGB'''   )

								lowerCAmelCase_      			=  image_processor(UpperCamelCase__,	return_tensors='''pt'''   ).to(UpperCamelCase__   )

								# forward pass
								with torch.no_grad():
														lowerCAmelCase_      			=  model(**UpperCamelCase__   )
														lowerCAmelCase_      			=  outputs.logits

								lowerCAmelCase_      			=  torch.Size((1, 16)   )
								self.assertEqual(logits.shape,	UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.tensor(
								    [-0.4_158, -0.4_092, -0.4_347],	device=UpperCamelCase__,	dtype=torch.float,	)
								self.assertTrue(torch.allclose(logits[0, :3],	UpperCamelCase__,	atol=1E-4   )   )




