






'''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__					(     UpperCamelCase_			):

		"""simple docstring"""




		def __init__( self						:						Dict							,    __A						:						UNetaDModel							,    __A						:						UNetaDModel							,    __A						:						DDPMScheduler							,    __A						:						Union[str, Any]							,    ):







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




		def       snake_case     ( self						:						str							,    __A						:						int							,    __A						:						List[Any]    ):







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




		def       snake_case     ( self						:						List[Any]							,    __A						:						Dict							,    __A						:						Any    ):







							"""simple docstring"""
							return x_in * self.stds[key] + self.means[key]




		def       snake_case     ( self						:						int							,    __A						:						Union[str, Any]    ):







							"""simple docstring"""

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




		def       snake_case     ( self						:						str							,    __A						:						Any							,    __A						:						int							,    __A						:						Optional[int]    ):







							"""simple docstring"""

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




		def       snake_case     ( self						:						List[Any]							,    __A						:						Any							,    __A						:						Dict							,    __A						:						Tuple							,    __A						:						str    ):







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

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

																						_lowercase			= self.scheduler._get_variance(a__    )
																						_lowercase			= torch.exp(0.5 * posterior_variance    )
																						_lowercase			= model_std * grad

																	_lowercase			= 0
																	_lowercase			= x.detach()
																	_lowercase			= x + scale * grad
																	_lowercase			= self.reset_xa(a__							,    a__							,    self.action_dim    )

												_lowercase			= self.unet(x.permute(0							,    2							,    1    )							,    a__    ).sample.permute(0							,    2							,    1    )

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

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




		def __call__( self						:						Any							,    __A						:						int							,    __A						:						Union[str, Any]=6_4							,    __A						:						int=3_2							,    __A						:						Any=2							,    __A						:						Optional[Any]=0.1    ):







							"""simple docstring"""
							_lowercase			= self.normalize(a__							,    "observations"    )
							_lowercase			= obs[None].repeat(a__							,    axis=0    )

							_lowercase			= {0: self.to_torch(a__    )}
							_lowercase			= (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)
							_lowercase			= randn_tensor(a__							,    device=self.unet.device    )
							_lowercase			= self.reset_xa(a__							,    a__							,    self.action_dim    )
							_lowercase			= self.to_torch(a__    )

							# run the diffusion process
							_lowercase			= self.run_diffusion(a__							,    a__							,    a__							,    a__    )

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

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

							_lowercase			= denorm_actions[selected_index, 0]
							return denorm_actions



'''simple docstring'''

from __future__ import annotations

import pandas as pd


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Tuple     		=	[0] * no_of_processes
 lowerCAmelCase__     :   Any     		=	[0] * no_of_processes
 # Copy the burst time into remaining_time[]
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Dict     		=	burst_time[i]

 lowerCAmelCase__     :   str     		=	0
 lowerCAmelCase__     :   Optional[Any]     		=	0
 lowerCAmelCase__     :   Optional[Any]     		=	9_9_9_9_9_9_9_9_9
 lowerCAmelCase__     :   int     		=	0
 lowerCAmelCase__     :   str     		=	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:
     lowerCAmelCase__     :   Any     		=	remaining_time[j]
     lowerCAmelCase__     :   str     		=	j
     lowerCAmelCase__     :   List[Any]     		=	True

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

  lowerCAmelCase__     :   Optional[Any]     		=	remaining_time[short]
  if minm == 0:
   lowerCAmelCase__     :   Optional[int]     		=	9_9_9_9_9_9_9_9_9

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

   # Find finish time of current process
   lowerCAmelCase__     :   Tuple     		=	increment_time + 1

   # Calculate waiting time
   lowerCAmelCase__     :   Dict     		=	finish_time - arrival_time[short]
   lowerCAmelCase__     :   Optional[Any]     		=	finar - burst_time[short]

   if waiting_time[short] < 0:
    lowerCAmelCase__     :   Tuple     		=	0

        # Increment time
  increment_time += 1
 return waiting_time


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[Any]     		=	[0] * no_of_processes
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Dict     		=	burst_time[i] + waiting_time[i]
 return turn_around_time


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Union[str, Any]     		=	0
 lowerCAmelCase__     :   Optional[int]     		=	0
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Tuple     		=	total_waiting_time + waiting_time[i]
  lowerCAmelCase__     :   str     		=	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""")
     snake_case									=    int(input())
     snake_case									=    [0] * no_of_processes
     snake_case									=    [0] * no_of_processes
     snake_case									=    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))
          snake_case					,					snake_case									=    map(int, input().split())

     snake_case									=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)

     snake_case									=    burst_time
     snake_case									=    no_of_processes
     snake_case									=    waiting_time
     snake_case									=    calculate_turnaroundtime(bt, n, wt)

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     snake_case									=    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)





