
"""simple docstring"""


def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						):
			if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
						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(lowerCAmelCase__	,lowerCAmelCase__						)]

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

			# declaring useful variables
			lowerCamelCase_					=  len(lowerCAmelCase__						)
			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(lowerCAmelCase__						)
						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."""
						)

						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"""


import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      Adafactor,
      AdamW,
      get_constant_schedule,
      get_constant_schedule_with_warmup,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_inverse_sqrt_schedule,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
  )




def        lowercase__     (       lowercase_		,lowercase_=10      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						str    =							[]
      for _ in range(lowercase_      ):
            lrs.append(scheduler.get_lr()[0]      )
            scheduler.step()
      return lrs




def        lowercase__     (       lowercase_		,lowercase_=10      )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							[]
      for step in range(lowercase_      ):
            lrs.append(scheduler.get_lr()[0]      )
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        _UpperCamelCase   :						List[str]    =							os.path.join(lowercase_		,"schedule.bin"      )
                        torch.save(scheduler.state_dict()		,lowercase_      )

                        _UpperCamelCase   :						Optional[int]    =							torch.load(lowercase_      )
                        scheduler.load_state_dict(lowercase_      )
      return lrs



@require_torch
class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  def  __SCREAMING_SNAKE_CASE					(		self :							Dict       , __a :							Dict       , __a :							Any       , __a :							List[str]						)		->					List[str]:
        self.assertEqual(len(__a						)       , len(__a						)						)
        for a, b in zip(__a       , __a						):
              self.assertAlmostEqual(__a       , __a       , delta=__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					str:
        _UpperCamelCase   :						Optional[int]    =							torch.tensor([0.1, -0.2, -0.1]       , requires_grad=__a						)
        _UpperCamelCase   :						List[Any]    =							torch.tensor([0.4, 0.2, -0.5]						)
        _UpperCamelCase   :						Any    =							nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        _UpperCamelCase   :						Dict    =							AdamW(params=[w]       , lr=2e-1       , weight_decay=0.0						)
        for _ in range(100						):
              _UpperCamelCase   :						List[Any]    =							criterion(__a       , __a						)
              loss.backward()
              optimizer.step()
              w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
              w.grad.zero_()
        self.assertListAlmostEqual(w.tolist()       , [0.4, 0.2, -0.5]       , tol=1e-2						)


  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Tuple:
        _UpperCamelCase   :						List[Any]    =							torch.tensor([0.1, -0.2, -0.1]       , requires_grad=__a						)
        _UpperCamelCase   :						Any    =							torch.tensor([0.4, 0.2, -0.5]						)
        _UpperCamelCase   :						List[str]    =							nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        _UpperCamelCase   :						Dict    =							Adafactor(
            params=[w]       , lr=1e-2       , eps=(1e-3_0, 1e-3)       , clip_threshold=1.0       , decay_rate=-0.8       , betaa=__a       , weight_decay=0.0       , relative_step=__a       , scale_parameter=__a       , warmup_init=__a       , )
        for _ in range(1000						):
              _UpperCamelCase   :						Optional[Any]    =							criterion(__a       , __a						)
              loss.backward()
              optimizer.step()
              w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
              w.grad.zero_()
        self.assertListAlmostEqual(w.tolist()       , [0.4, 0.2, -0.5]       , tol=1e-2						)





@require_torch
class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       nn.Linear(50		,	50 ) if is_torch_available() else None
  SCREAMING_SNAKE_CASE__				:List[Any]						  =       AdamW(m.parameters()		,	lr=10.0 ) if is_torch_available() else None
  SCREAMING_SNAKE_CASE__				:List[str]						  =       10

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							Tuple       , __a :							List[Any]       , __a :							Dict       , __a :							str=None						)		->					List[Any]:
        self.assertEqual(len(__a						)       , len(__a						)						)
        for a, b in zip(__a       , __a						):
              self.assertAlmostEqual(__a       , __a       , delta=__a       , msg=__a						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					List[Any]:
        _UpperCamelCase   :						Union[str, Any]    =							{"num_warmup_steps": 2, "num_training_steps": 10}
        # schedulers doct format
        # function: (sched_args_dict, expected_learning_rates)
        _UpperCamelCase   :						Optional[Any]    =							{
            get_constant_schedule: ({}, [10.0] * self.num_steps),
            get_constant_schedule_with_warmup: (
                {"num_warmup_steps": 4},
                [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
            ),
            get_linear_schedule_with_warmup: (
                {**common_kwargs},
                [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
            ),
            get_cosine_schedule_with_warmup: (
                {**common_kwargs},
                [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
            ),
            get_cosine_with_hard_restarts_schedule_with_warmup: (
                {**common_kwargs, "num_cycles": 2},
                [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
            ),
            get_polynomial_decay_schedule_with_warmup: (
                {**common_kwargs, "power": 2.0, "lr_end": 1e-7},
                [0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
            ),
            get_inverse_sqrt_schedule: (
                {"num_warmup_steps": 2},
                [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
            ),
        }

        for scheduler_func, data in scheds.items():
              _UpperCamelCase,	_UpperCamelCase   :						List[str]    =							data

              _UpperCamelCase   :						List[str]    =							scheduler_func(self.optimizer       , **__a						)
              self.assertEqual(len([scheduler.get_lr()[0]]						)       , 1						)
              _UpperCamelCase   :						Optional[int]    =							unwrap_schedule(__a       , self.num_steps						)
              self.assertListAlmostEqual(
                  __a       , __a       , tol=1e-2       , msg=F'''failed for {scheduler_func} in normal scheduler'''       , )

              _UpperCamelCase   :						List[str]    =							scheduler_func(self.optimizer       , **__a						)
              if scheduler_func.__name__ != "get_constant_schedule":
                    LambdaScheduleWrapper.wrap_scheduler(__a						)  # wrap to test picklability of the schedule
              _UpperCamelCase   :						int    =							unwrap_and_save_reload_schedule(__a       , self.num_steps						)
              self.assertListEqual(__a       , __a       , msg=F'''failed for {scheduler_func} in save and reload'''						)





class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


  def __init__(		self :							List[Any]       , __a :							Optional[int]						)		->					Tuple:
        _UpperCamelCase   :						Any    =							fn

  def __call__(		self :							Tuple       , *__a :							Optional[Any]       , **__a :							str						)		->					Any:
        return self.fn(*__a       , **__a						)


  @classmethod
  def  __SCREAMING_SNAKE_CASE					(		self :							Any       , __a :							Any						)		->					Tuple:
        _UpperCamelCase   :						Any    =							list(map(self       , scheduler.lr_lambdas						)						)
