'''simple docstring'''
def  _UpperCamelCase (     _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.
    __UpperCamelCase	:	Union[str, Any]		    =  [p / w for p, w in zip(_a				,     _a			)]

    # Creating a copy of the list and sorting profit/weight in ascending order
    __UpperCamelCase	:	Union[str, Any]		    =  sorted(_a			)

    # declaring useful variables
    __UpperCamelCase	:	Any		    =  len(_a			)
    __UpperCamelCase	:	Dict		    =  0
    __UpperCamelCase	:	List[Any]		    =  0
    __UpperCamelCase	:	str		    =  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	:	Any		    =  sorted_profit_by_weight[length - i - 1]
        __UpperCamelCase	:	Tuple		    =  profit_by_weight.index(_a			)
        __UpperCamelCase	:	Union[str, 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.'''
  )

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

  # Function Call
  calc_profit(profit, weight, max_weight)




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a=  {
    '''configuration_bridgetower''': [
        '''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BridgeTowerConfig''',
        '''BridgeTowerTextConfig''',
        '''BridgeTowerVisionConfig''',
    ],
    '''processing_bridgetower''': ['''BridgeTowerProcessor'''],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''BridgeTowerImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BridgeTowerForContrastiveLearning''',
      '''BridgeTowerForImageAndTextRetrieval''',
      '''BridgeTowerForMaskedLM''',
      '''BridgeTowerModel''',
      '''BridgeTowerPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_bridgetower import (
      BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BridgeTowerConfig,
      BridgeTowerTextConfig,
      BridgeTowerVisionConfig,
  )
  from .processing_bridgetower import BridgeTowerProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_bridgetower import BridgeTowerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bridgetower import (
        BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
        BridgeTowerForContrastiveLearning,
        BridgeTowerForImageAndTextRetrieval,
        BridgeTowerForMaskedLM,
        BridgeTowerModel,
        BridgeTowerPreTrainedModel,
    )


else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)



