
'''simple docstring'''
def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							int:


		'''simple docstring'''


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

		# Creating a copy of the list and sorting profit/weight in ascending order
		snake_case  :       List[str]     			=						sorted(SCREAMING_SNAKE_CASE__			)

		# declaring useful variables
		snake_case  :       Optional[int]     			=						len(SCREAMING_SNAKE_CASE__			)
		snake_case  :       Any     			=						0
		snake_case  :       Dict     			=						0
		snake_case  :       Union[str, 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
				snake_case  :       str     			=						sorted_profit_by_weight[length - i - 1]
				snake_case  :       Any     			=						profit_by_weight.index(SCREAMING_SNAKE_CASE__			)
				snake_case  :       int     			=						-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."
						)

						lowercase__						       =      [int(x) for x in input("Input profits separated by spaces: ").split()]
						lowercase__						       =      [int(x) for x in input("Input weights separated by spaces: ").split()]
						lowercase__						       =      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_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase__						       =      {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["EncoderDecoderModel"]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["TFEncoderDecoderModel"]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["FlaxEncoderDecoderModel"]

if TYPE_CHECKING:
						from .configuration_encoder_decoder import EncoderDecoderConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_encoder_decoder import EncoderDecoderModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
						import sys

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


