'''simple docstring'''

def __a (	__lowerCamelCase			:						list ,		__lowerCamelCase			:						list ,		__lowerCamelCase			:						int				)	->							int:
      '''simple docstring'''


      if len(__lowerCamelCase				) != len(__lowerCamelCase				):
            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.
      lowercase_     =      [p / w for p, w in zip(__lowerCamelCase ,		__lowerCamelCase				)]

      # Creating a copy of the list and sorting profit/weight in ascending order
      lowercase_     =      sorted(__lowerCamelCase				)

      # declaring useful variables
      lowercase_     =      len(__lowerCamelCase				)
      lowercase_     =      0
      lowercase_     =      0
      lowercase_     =      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
            lowercase_     =      sorted_profit_by_weight[length - i - 1]
            lowercase_     =      profit_by_weight.index(__lowerCamelCase				)
            lowercase_     =      -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_ : Optional[int]						  =      [int(x) for x in input("Input profits separated by spaces: ").split()]
       lowerCAmelCase_ : Dict						  =      [int(x) for x in input("Input weights separated by spaces: ").split()]
       lowerCAmelCase_ : List[Any]						  =      int(input("Max weight allowed: "))

       # Function Call
       calc_profit(profit, weight, max_weight)







'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase_ : Any						  =      logging.get_logger(__name__)





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=['pixel_values']


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	int = 32   ,		__lowerCAmelCase				:	List[str]=PILImageResampling.BILINEAR   ,		__lowerCAmelCase				:	bool = True   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					None:
         lowercase_     =      do_resize
         lowercase_     =      do_rescale
         lowercase_     =      size_divisor
         lowercase_     =      resample
         super().__init__(**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Optional[ChannelDimension] = None   ,		**__lowerCAmelCase				:	Any)      ->					np.ndarray:
         lowercase_ ,							lowercase_     =      get_image_size(__lowerCAmelCase)
         # Rounds the height and width down to the closest multiple of size_divisor
         lowercase_     =      height // size_divisor * size_divisor
         lowercase_     =      width // size_divisor * size_divisor
         lowercase_     =      resize(__lowerCAmelCase   ,		(new_h, new_w)   ,		resample=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)
         return image


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	float   ,		__lowerCAmelCase				:	Optional[ChannelDimension] = None   ,		**__lowerCAmelCase				:	int)      ->					np.ndarray:
         return rescale(image=__lowerCAmelCase   ,		scale=__lowerCAmelCase   ,		data_format=__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[int] = None   ,		__lowerCAmelCase				:	str=None   ,		__lowerCAmelCase				:	Optional[bool] = None   ,		__lowerCAmelCase				:	Optional[Union[TensorType, str]] = None   ,		__lowerCAmelCase				:	ChannelDimension = ChannelDimension.FIRST   ,		**__lowerCAmelCase				:	List[str]   ,		)      ->					BatchFeature:
         lowercase_     =      do_resize if do_resize is not None else self.do_resize
         lowercase_     =      do_rescale if do_rescale is not None else self.do_rescale
         lowercase_     =      size_divisor if size_divisor is not None else self.size_divisor
         lowercase_     =      resample if resample is not None else self.resample

         if do_resize and size_divisor is None:
               raise ValueError("size_divisor is required for resizing")

         lowercase_     =      make_list_of_images(__lowerCAmelCase)

         if not valid_images(__lowerCAmelCase):
               raise ValueError("Invalid image(s)")

         # All transformations expect numpy arrays.
         lowercase_     =      [to_numpy_array(__lowerCAmelCase) for img in images]

         if do_resize:
               lowercase_     =      [self.resize(__lowerCAmelCase   ,		size_divisor=__lowerCAmelCase   ,		resample=__lowerCAmelCase) for image in images]

         if do_rescale:
               lowercase_     =      [self.rescale(__lowerCAmelCase   ,		scale=1 / 255) for image in images]

         lowercase_     =      [to_channel_dimension_format(__lowerCAmelCase   ,		__lowerCAmelCase) for image in images]

         lowercase_     =      {"pixel_values": images}
         return BatchFeature(data=__lowerCAmelCase   ,		tensor_type=__lowerCAmelCase)






