






"""simple docstring"""







import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler





class   UpperCamelCase_ (       a_  ):



  def __init__(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		)			->			int:



         """simple docstring"""





         super().__init__()
         UpperCAmelCase	    =			value_function
         UpperCAmelCase	    =			unet
         UpperCAmelCase	    =			scheduler
         UpperCAmelCase	    =			env
         UpperCAmelCase	    =			env.get_dataset()
         UpperCAmelCase	    =			{}
         for key in self.data.keys():
                try:
                       UpperCAmelCase	    =			self.data[key].mean()
                except:  # noqa: E722
                       pass
         UpperCAmelCase	    =			{}
         for key in self.data.keys():
                try:
                       UpperCAmelCase	    =			self.data[key].std()
                except:  # noqa: E722
                       pass
         UpperCAmelCase	    =			env.observation_space.shape[0]
         UpperCAmelCase	    =			env.action_space.shape[0]



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__				)			->			Any:



         """simple docstring"""





         return (x_in - self.means[key]) / self.stds[key]



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__				)			->			Tuple:



         """simple docstring"""





         return x_in * self.stds[key] + self.means[key]



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			Tuple:



         """simple docstring"""





         if type(snake_case__				) is dict:
                return {k: self.to_torch(snake_case__				) for k, v in x_in.items()}
         elif torch.is_tensor(snake_case__				):
                return x_in.to(self.unet.device				)
         return torch.tensor(snake_case__ ,		device=self.unet.device				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			str:



         """simple docstring"""





         for key, val in cond.items():
                UpperCAmelCase	    =			val.clone()
         return x_in



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			x.shape[0]
         UpperCAmelCase	    =			None
         for i in tqdm.tqdm(self.scheduler.timesteps				):
                # create batch of timesteps to pass into model
                UpperCAmelCase	    =			torch.full((batch_size,) ,		snake_case__ ,		device=self.unet.device ,		dtype=torch.long				)
                for _ in range(snake_case__				):
                       with torch.enable_grad():
                              x.requires_grad_()

                              # permute to match dimension for pre-trained models
                              UpperCAmelCase	    =			self.value_function(x.permute(0 ,		2 ,		1				) ,		snake_case__				).sample
                              UpperCAmelCase	    =			torch.autograd.grad([y.sum()] ,		[x]				)[0]

                              UpperCAmelCase	    =			self.scheduler._get_variance(snake_case__				)
                              UpperCAmelCase	    =			torch.exp(0.5 * posterior_variance				)
                              UpperCAmelCase	    =			model_std * grad

                       UpperCAmelCase	    =			0
                       UpperCAmelCase	    =			x.detach()
                       UpperCAmelCase	    =			x + scale * grad
                       UpperCAmelCase	    =			self.reset_xa(snake_case__ ,		snake_case__ ,		self.action_dim				)

                UpperCAmelCase	    =			self.unet(x.permute(0 ,		2 ,		1				) ,		snake_case__				).sample.permute(0 ,		2 ,		1				)

                # TODO: verify deprecation of this kwarg
                UpperCAmelCase	    =			self.scheduler.step(snake_case__ ,		snake_case__ ,		snake_case__ ,		predict_epsilon=snake_case__				)["""prev_sample"""]

                # apply conditions to the trajectory (set the initial state)
                UpperCAmelCase	    =			self.reset_xa(snake_case__ ,		snake_case__ ,		self.action_dim				)
                UpperCAmelCase	    =			self.to_torch(snake_case__				)
         return x, y



  def __call__(      self ,		snake_case__ ,		snake_case__=64 ,		snake_case__=32 ,		snake_case__=2 ,		snake_case__=0.1				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			self.normalize(snake_case__ ,		"""observations"""				)
         UpperCAmelCase	    =			obs[None].repeat(snake_case__ ,		axis=0				)

         UpperCAmelCase	    =			{0: self.to_torch(snake_case__				)}
         UpperCAmelCase	    =			(batch_size, planning_horizon, self.state_dim + self.action_dim)

         # generate initial noise and apply our conditions (to make the trajectories start at current state)
         UpperCAmelCase	    =			randn_tensor(snake_case__ ,		device=self.unet.device				)
         UpperCAmelCase	    =			self.reset_xa(snake_case__ ,		snake_case__ ,		self.action_dim				)
         UpperCAmelCase	    =			self.to_torch(snake_case__				)

         # run the diffusion process
         UpperCAmelCase    ,       UpperCAmelCase	    =			self.run_diffusion(snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__				)

         # sort output trajectories by value
         UpperCAmelCase	    =			y.argsort(0 ,		descending=snake_case__				).squeeze()
         UpperCAmelCase	    =			x[sorted_idx]
         UpperCAmelCase	    =			sorted_values[:, :, : self.action_dim]
         UpperCAmelCase	    =			actions.detach().cpu().numpy()
         UpperCAmelCase	    =			self.de_normalize(snake_case__ ,		key="""actions"""				)

         # select the action with the highest value
         if y is not None:
                UpperCAmelCase	    =			0
         else:
                # if we didn't run value guiding, select a random action
                UpperCAmelCase	    =			np.random.randint(0 ,		snake_case__				)

         UpperCAmelCase	    =			denorm_actions[selected_index, 0]
         return denorm_actions








"""simple docstring"""







from __future__ import annotations

import pandas as pd






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[0] * no_of_processes
       UpperCAmelCase	    =			[0] * no_of_processes
       # Copy the burst time into remaining_time[]
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			burst_time[i]

       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			999999999
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			False

       # Process until all processes are completed
       while complete != no_of_processes:
              for j in range(lowerCAmelCase		):
                     if arrival_time[j] <= increment_time and remaining_time[j] > 0:
                            if remaining_time[j] < minm:
                                   UpperCAmelCase	    =			remaining_time[j]
                                   UpperCAmelCase	    =			j
                                   UpperCAmelCase	    =			True

              if not check:
                     increment_time += 1
                     continue
              remaining_time[short] -= 1

              UpperCAmelCase	    =			remaining_time[short]
              if minm == 0:
                     UpperCAmelCase	    =			999999999

              if remaining_time[short] == 0:
                     complete += 1
                     UpperCAmelCase	    =			False

                     # Find finish time of current process
                     UpperCAmelCase	    =			increment_time + 1

                     # Calculate waiting time
                     UpperCAmelCase	    =			finish_time - arrival_time[short]
                     UpperCAmelCase	    =			finar - burst_time[short]

                     if waiting_time[short] < 0:
                            UpperCAmelCase	    =			0

        # Increment time
              increment_time += 1
       return waiting_time






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[0] * no_of_processes
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			burst_time[i] + waiting_time[i]
       return turn_around_time






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			0
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			total_waiting_time + waiting_time[i]
              UpperCAmelCase	    =			total_turn_around_time + turn_around_time[i]
       print(F'''Average waiting time = {total_waiting_time / no_of_processes:.5f}'''		)
       print("""Average turn around time ="""	,      total_turn_around_time / no_of_processes		)


if __name__ == "__main__":
 print('''Enter how many process you want to analyze''')
 lowerCAmelCase_	:    Optional[Any]            =      int(input())
 lowerCAmelCase_	:    List[Any]            =      [0] * no_of_processes
 lowerCAmelCase_	:    Optional[Any]            =      [0] * no_of_processes
 lowerCAmelCase_	:    int            =      list(range(1, no_of_processes + 1))

 for i in range(no_of_processes):
  print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
  lowerCAmelCase_     ,					lowerCAmelCase_	:    Optional[Any]            =      map(int, input().split())

 lowerCAmelCase_	:    str            =      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

 lowerCAmelCase_	:    str            =      burst_time
 lowerCAmelCase_	:    List[Any]            =      no_of_processes
 lowerCAmelCase_	:    int            =      waiting_time
 lowerCAmelCase_	:    int            =      calculate_turnaroundtime(bt, n, wt)

 calculate_average_times(waiting_time, turn_around_time, no_of_processes)

 lowerCAmelCase_	:    List[Any]            =      pd.DataFrame(
     list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
     columns=[
         '''Process''',
         '''BurstTime''',
         '''ArrivalTime''',
         '''WaitingTime''',
         '''TurnAroundTime''',
     ],
 )

 # Printing the dataFrame
 pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
 print(fcfs)
