






def _UpperCAmelCase   (	a						:	list  ,					a						:	list  ,					a						:	int ):
							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.
							snake_case__	  =       [p / w for p, w in zip(a  ,					a )]

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

							# declaring useful variables
							snake_case__	  =       len(a )
							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(a )
														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."""
	)

	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)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__       =     {
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """LiltForQuestionAnswering""",
	    """LiltForSequenceClassification""",
	    """LiltForTokenClassification""",
	    """LiltModel""",
	    """LiltPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lilt import (
		    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LiltForQuestionAnswering,
		    LiltForSequenceClassification,
		    LiltForTokenClassification,
		    LiltModel,
		    LiltPreTrainedModel,
		)

else:
	import sys

	a__       =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
