






def 		_UpperCamelCase				(      UpperCamelCase_    :   list      ,    UpperCamelCase_    :   list      ,    UpperCamelCase_    :   int						)					->				int:

						"""simple docstring"""



						if len(UpperCamelCase_						) != len(UpperCamelCase_						):
												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__        =  [p / w for p, w in zip(UpperCamelCase_      ,    UpperCamelCase_						)]

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

						# declaring useful variables
						lowerCAmelCase__        =  len(UpperCamelCase_						)
						lowerCAmelCase__        =  0
						lowerCAmelCase__        =  0
						lowerCAmelCase__        =  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__        =  sorted_profit_by_weight[length - i - 1]
												lowerCAmelCase__        =  profit_by_weight.index(UpperCamelCase_						)
												lowerCAmelCase__        =  -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      :      Dict             =       [int(x) for x in input("""Input profits separated by spaces: """).split()]
					__snake_case      :      List[Any]             =       [int(x) for x in input("""Input weights separated by spaces: """).split()]
					__snake_case      :      List[Any]             =       int(input("""Max weight allowed: """))

					# Function Call
					calc_profit(profit, weight, max_weight)













from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case      :      Dict             =       {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      str             =       [
					    """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FocalNetForImageClassification""",
					    """FocalNetForMaskedImageModeling""",
					    """FocalNetBackbone""",
					    """FocalNetModel""",
					    """FocalNetPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_focalnet import (
										    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FocalNetBackbone,
										    FocalNetForImageClassification,
										    FocalNetForMaskedImageModeling,
										    FocalNetModel,
										    FocalNetPreTrainedModel,
										)

else:
					import sys

					__snake_case      :      List[Any]             =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





