



"""simple docstring"""






def 	snake_case_  (				A_ :		list,    A_ :		list,    A_ :		int						):


       '''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.
       _lowerCamelCase						:    Dict         =			[p / w for p, w in zip(A_,    A_						)]

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

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







"""simple docstring"""






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def 	snake_case_  (				A_ :		Optional[int]						):


       '''simple docstring'''





       def wrapper(*A_ :		int,    **A_ :		List[str]						):
              _lowerCamelCase						:    Tuple         =			timeit.default_timer()
              _lowerCamelCase						:    Optional[Any]         =			func(*A_,    **A_						)
              _lowerCamelCase						:    Tuple         =			timeit.default_timer() - starttime
              return delta

       _lowerCamelCase						:    Optional[Any]         =			func.__name__

       return wrapper






def 	snake_case_  (				A_ :		dict,    A_ :		List[str]=1_00,    A_ :		Optional[Any]=None						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			[]
       _lowerCamelCase						:    Optional[int]         =			seq_shapes or {}
       for i in range(A_						):
              _lowerCamelCase						:    Any         =			{}
              for col_id, (k, v) in enumerate(features.items()						):
                     if isinstance(A_,    _ArrayXD						):
                            _lowerCamelCase						:    str         =			np.random.rand(*v.shape						).astype(v.dtype						)
                     elif isinstance(A_,    datasets.Value						):
                            if v.dtype == "string":
                                   _lowerCamelCase						:    List[str]         =			'''The small grey turtle was surprisingly fast when challenged.'''
                            else:
                                   _lowerCamelCase						:    int         =			np.random.randint(10,    size=1						).astype(v.dtype						).item()
                     elif isinstance(A_,    datasets.Sequence						):
                            while isinstance(A_,    datasets.Sequence						):
                                   _lowerCamelCase						:    Union[str, Any]         =			v.feature
                            _lowerCamelCase						:    Dict         =			seq_shapes[k]
                            _lowerCamelCase						:    int         =			np.random.rand(*A_						).astype(v.dtype						)
                     _lowerCamelCase						:    Optional[Any]         =			data

              dummy_data.append((i, example)						)

       return dummy_data







def 	snake_case_  (				A_ :		List[str],    A_ :		Optional[int],    A_ :		Optional[int]=1_00,    A_ :		Optional[Any]=None						):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[int]         =			generate_examples(A_,    num_examples=A_,    seq_shapes=A_						)

       with ArrowWriter(features=A_,    path=A_						) as writer:
              for key, record in dummy_data:
                     _lowerCamelCase						:    Optional[Any]         =			features.encode_example(A_						)
                     writer.write(A_						)

              _lowerCamelCase      ,   _lowerCamelCase						:    Dict         =			writer.finalize()

       if not num_final_examples == num_examples:
              raise ValueError(
                  F'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'''						)

       _lowerCamelCase						:    List[Any]         =			datasets.Dataset.from_file(filename=A_,    info=datasets.DatasetInfo(features=A_						)						)

       return dataset


