



"""simple docstring"""






def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				int:
  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.
  _snake_case   	=      [p / w for p, w in zip(lowerCAmelCase_  ,						lowerCAmelCase_	)]

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

  # declaring useful variables
  _snake_case   	=      len(lowerCAmelCase_	)
  _snake_case   	=      0
  _snake_case   	=      0
  _snake_case   	=      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
    _snake_case   	=      sorted_profit_by_weight[length - i - 1]
    _snake_case   	=      profit_by_weight.index(lowerCAmelCase_	)
    _snake_case   	=      -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.'''
     )

     snake_case   					=				[int(x) for x in input('''Input profits separated by spaces: ''').split()]
     snake_case   					=				[int(x) for x in input('''Input weights separated by spaces: ''').split()]
     snake_case   					=				int(input('''Max weight allowed: '''))

     # Function Call
     calc_profit(profit, weight, max_weight)










"""simple docstring"""






from ...configuration_utils import PretrainedConfig



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	str           =  '''bert-generation'''



   def __init__(       self    :	Tuple							,       __lowerCamelCase    :	Optional[int]=5_0_3_5_8							,       __lowerCamelCase    :	List[str]=1_0_2_4							,       __lowerCamelCase    :	Optional[Any]=2_4							,       __lowerCamelCase    :	Any=1_6							,       __lowerCamelCase    :	Union[str, Any]=4_0_9_6							,       __lowerCamelCase    :	Optional[Any]="gelu"							,       __lowerCamelCase    :	str=0.1							,       __lowerCamelCase    :	List[str]=0.1							,       __lowerCamelCase    :	int=5_1_2							,       __lowerCamelCase    :	Dict=0.0_2							,       __lowerCamelCase    :	Tuple=1E-12							,       __lowerCamelCase    :	Any=0							,       __lowerCamelCase    :	Union[str, Any]=2							,       __lowerCamelCase    :	List[Any]=1							,       __lowerCamelCase    :	Optional[Any]="absolute"							,       __lowerCamelCase    :	str=True							,       **__lowerCamelCase    :	List[Any]							,       ):



     """simple docstring"""
     super().__init__(pad_token_id=__lowerCamelCase							,       bos_token_id=__lowerCamelCase							,       eos_token_id=__lowerCamelCase							,       **__lowerCamelCase				)

     _snake_case   	=      vocab_size
     _snake_case   	=      hidden_size
     _snake_case   	=      num_hidden_layers
     _snake_case   	=      num_attention_heads
     _snake_case   	=      hidden_act
     _snake_case   	=      intermediate_size
     _snake_case   	=      hidden_dropout_prob
     _snake_case   	=      attention_probs_dropout_prob
     _snake_case   	=      max_position_embeddings
     _snake_case   	=      initializer_range
     _snake_case   	=      layer_norm_eps
     _snake_case   	=      position_embedding_type
     _snake_case   	=      use_cache





