

from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm

def    A_  (							a					,   a  ):

      """simple docstring"""



      return math.sqrt(sum(pow(a - b					,   2  ) for a, b in zip(a					,   a  )  )  )

def    A_  (							a					,   a  ):

      """simple docstring"""



      if dataset.ndim != value_array.ndim:
            SCREAMING_SNAKE_CASE_				:  Tuple           =     (
                'Wrong input data\'s dimensions... '
                f"dataset : {dataset.ndim}, value_array : {value_array.ndim}"
            )
            raise ValueError(a  )

      try:
            if dataset.shape[1] != value_array.shape[1]:
                  SCREAMING_SNAKE_CASE_				:  List[Any]           =     (
                      'Wrong input data\'s shape... '
                      f"dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"
                  )
                  raise ValueError(a  )
      except IndexError:
            if dataset.ndim != value_array.ndim:
                  raise TypeError('Wrong shape'  )

      if dataset.dtype != value_array.dtype:
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     (
                'Input data have different datatype... '
                f"dataset : {dataset.dtype}, value_array : {value_array.dtype}"
            )
            raise TypeError(a  )

      SCREAMING_SNAKE_CASE_				:  str           =     []

      for value in value_array:
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     euclidean(a					,   dataset[0]  )
            SCREAMING_SNAKE_CASE_				:  List[str]           =     dataset[0].tolist()

            for dataset_value in dataset[1:]:
                  SCREAMING_SNAKE_CASE_				:  Tuple           =     euclidean(a					,   a  )

                  if dist > temp_dist:
                        SCREAMING_SNAKE_CASE_				:  Dict           =     temp_dist
                        SCREAMING_SNAKE_CASE_				:  List[Any]           =     dataset_value.tolist()

            answer.append([vector, dist]  )

      return answer

def    A_  (							a					,   a  ):

      """simple docstring"""



      return np.dot(a					,   a  ) / (norm(a  ) * norm(a  ))


if __name__ == "__main__":
    import doctest

    doctest.testmod()









def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      if len(a  ) != len(a  ):
            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.
      SCREAMING_SNAKE_CASE_				:  Tuple           =     [p / w for p, w in zip(a					,   a  )]

      # Creating a copy of the list and sorting profit/weight in ascending order
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     sorted(a  )

      # declaring useful variables
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     len(a  )
      SCREAMING_SNAKE_CASE_				:  str           =     0
      SCREAMING_SNAKE_CASE_				:  List[str]           =     0
      SCREAMING_SNAKE_CASE_				:  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
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     sorted_profit_by_weight[length - i - 1]
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     profit_by_weight.index(a  )
            SCREAMING_SNAKE_CASE_				:  Dict           =     -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     :   Tuple						     =  [int(x) for x in input('Input profits separated by spaces: ').split()]
    lowerCAmelCase     :   Union[str, Any]						     =  [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)






