



from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ..utils.dummy_pt_objects import *  # noqa F403
else:
	from .scheduling_consistency_models import CMStochasticIterativeScheduler
	from .scheduling_ddim import DDIMScheduler
	from .scheduling_ddim_inverse import DDIMInverseScheduler
	from .scheduling_ddim_parallel import DDIMParallelScheduler
	from .scheduling_ddpm import DDPMScheduler
	from .scheduling_ddpm_parallel import DDPMParallelScheduler
	from .scheduling_deis_multistep import DEISMultistepScheduler
	from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
	from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
	from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
	from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
	from .scheduling_euler_discrete import EulerDiscreteScheduler
	from .scheduling_heun_discrete import HeunDiscreteScheduler
	from .scheduling_ipndm import IPNDMScheduler
	from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
	from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
	from .scheduling_karras_ve import KarrasVeScheduler
	from .scheduling_pndm import PNDMScheduler
	from .scheduling_repaint import RePaintScheduler
	from .scheduling_sde_ve import ScoreSdeVeScheduler
	from .scheduling_sde_vp import ScoreSdeVpScheduler
	from .scheduling_unclip import UnCLIPScheduler
	from .scheduling_unipc_multistep import UniPCMultistepScheduler
	from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
	from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ..utils.dummy_flax_objects import *  # noqa F403
else:
	from .scheduling_ddim_flax import FlaxDDIMScheduler
	from .scheduling_ddpm_flax import FlaxDDPMScheduler
	from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
	from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
	from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
	from .scheduling_pndm_flax import FlaxPNDMScheduler
	from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
	from .scheduling_utils_flax import (
	    FlaxKarrasDiffusionSchedulers,
	    FlaxSchedulerMixin,
	    FlaxSchedulerOutput,
	    broadcast_to_shape_from_left,
	)


try:
	if not (is_torch_available() and is_scipy_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
	from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
	if not (is_torch_available() and is_torchsde_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
	from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler








def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	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.
	__lowercase		       =			[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
	__lowercase		       =			sorted(_SCREAMING_SNAKE_CASE   )

	# declaring useful variables
	__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			0
	__lowercase		       =			0
	__lowercase		       =			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
		__lowercase		       =			sorted_profit_by_weight[length - i - 1]
		__lowercase		       =			profit_by_weight.index(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			-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__					:     str    		=   [int(x) for x in input("""Input profits separated by spaces: """).split()]
	snake_case__					:     str    		=   [int(x) for x in input("""Input weights separated by spaces: """).split()]
	snake_case__					:     Optional[Any]    		=   int(input("""Max weight allowed: """))

	# Function Call
	calc_profit(profit, weight, max_weight)



