






def 	__magic_name__( _A						,      _A						,      _A					):
						'''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.
						UpperCamelCase__         = [p / w for p, w in zip(lowerCamelCase_						,      lowerCamelCase_					)]

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

						# declaring useful variables
						UpperCamelCase__         = len(lowerCamelCase_					)
						UpperCamelCase__         = 0
						UpperCamelCase__         = 0
						UpperCamelCase__         = 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
												UpperCamelCase__         = sorted_profit_by_weight[length - i - 1]
												UpperCamelCase__         = profit_by_weight.index(lowerCamelCase_					)
												UpperCamelCase__         = -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[Any]    	=							[int(x) for x in input('''Input profits separated by spaces: ''').split()]
					lowerCamelCase_				: str    	=							[int(x) for x in input('''Input weights separated by spaces: ''').split()]
					lowerCamelCase_				: Dict    	=							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_				: Optional[int]    	=							logging.get_logger(__name__)
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Optional[Any]					     =					["pixel_values"]






			def __init__(   self							:						str ,      lowercase							:						bool = True ,      lowercase							:						int = 3_2 ,      lowercase							:						List[Any]=PILImageResampling.BILINEAR ,      lowercase							:						bool = True ,      **lowercase							:						str ,      )							->    None:

									'''simple docstring'''







									UpperCamelCase__         = do_resize
									UpperCamelCase__         = do_rescale
									UpperCamelCase__         = size_divisor
									UpperCamelCase__         = resample
									super().__init__(**lowercase			)






			def 				A      (   self							:						Optional[int] ,      lowercase							:						np.ndarray ,      lowercase							:						int ,      lowercase							:						List[Any] ,      lowercase							:						Optional[ChannelDimension] = None ,      **lowercase							:						Any			)							->    np.ndarray:

									'''simple docstring'''







									UpperCamelCase__    ,   UpperCamelCase__         = get_image_size(lowercase			)
									# Rounds the height and width down to the closest multiple of size_divisor
									UpperCamelCase__         = height // size_divisor * size_divisor
									UpperCamelCase__         = width // size_divisor * size_divisor
									UpperCamelCase__         = resize(lowercase ,      (new_h, new_w) ,      resample=lowercase ,      data_format=lowercase ,      **lowercase			)
									return image






			def 				A      (   self							:						int ,      lowercase							:						np.ndarray ,      lowercase							:						float ,      lowercase							:						Optional[ChannelDimension] = None ,      **lowercase							:						List[str]			)							->    np.ndarray:

									'''simple docstring'''







									return rescale(image=lowercase ,      scale=lowercase ,      data_format=lowercase ,      **lowercase			)







			def 				A      (   self							:						int ,      lowercase							:						Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[int] = None ,      lowercase							:						Optional[Any]=None ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[Union[TensorType, str]] = None ,      lowercase							:						ChannelDimension = ChannelDimension.FIRST ,      **lowercase							:						str ,      )							->    BatchFeature:

									'''simple docstring'''







									UpperCamelCase__         = do_resize if do_resize is not None else self.do_resize
									UpperCamelCase__         = do_rescale if do_rescale is not None else self.do_rescale
									UpperCamelCase__         = size_divisor if size_divisor is not None else self.size_divisor
									UpperCamelCase__         = 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"""			)

									UpperCamelCase__         = make_list_of_images(lowercase			)

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

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

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

									if do_rescale:
															UpperCamelCase__         = [self.rescale(lowercase ,      scale=1 / 2_5_5			) for image in images]

									UpperCamelCase__         = [to_channel_dimension_format(lowercase ,      lowercase			) for image in images]

									UpperCamelCase__         = {"""pixel_values""": images}
									return BatchFeature(data=lowercase ,      tensor_type=lowercase			)
