


'''simple docstring'''

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel





def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Tuple=False	):
 lowerCamelCase_  				=						OmegaConf.load(__UpperCAmelCase	)
 if display:
  print(yaml.dump(OmegaConf.to_container(__UpperCAmelCase	)	)	)
 return config





def __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Dict=None      ,       UpperCAmelCase_		:		Union[str, Any]=None	):







 if conf_path is None:
  lowerCamelCase_  				=						"./model_checkpoints/vqgan_only.yaml"
 lowerCamelCase_  				=						load_config(__UpperCAmelCase      ,       display=__UpperCAmelCase	)
 lowerCamelCase_  				=						VQModel(**config.model.params	)
 if ckpt_path is None:
  lowerCamelCase_  				=						"./model_checkpoints/vqgan_only.pt"
 lowerCamelCase_  				=						torch.load(__UpperCAmelCase      ,       map_location=__UpperCAmelCase	)
 if ".ckpt" in ckpt_path:
  lowerCamelCase_  				=						sd["state_dict"]
 model.load_state_dict(__UpperCAmelCase      ,       strict=__UpperCAmelCase	)
 model.to(__UpperCAmelCase	)
 del sd
 return model





def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Optional[Any]	):
 lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						model.encode(__UpperCAmelCase	)
 print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''	)
 lowerCamelCase_  				=						model.decode(__UpperCAmelCase	)
 return xrec





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		int=False	):
 lowerCamelCase_				,lowerCamelCase_  				=						string.rsplit("."      ,       1	)
 if reload:
  lowerCamelCase_  				=						importlib.import_module(__UpperCAmelCase	)
  importlib.reload(__UpperCAmelCase	)
 return getattr(importlib.import_module(__UpperCAmelCase      ,       package=__UpperCAmelCase	)      ,       cls	)





def __snake_case      (   UpperCAmelCase_		:		List[str]	):







 if "target" not in config:
  raise KeyError("Expected key `target` to instantiate."	)
 return get_obj_from_str(config["target"]	)(**config.get("params"      ,       {}	)	)





def __snake_case      (   UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		List[str]=True      ,       UpperCAmelCase_		:		Dict=True	):
 lowerCamelCase_  				=						instantiate_from_config(__UpperCAmelCase	)
 if sd is not None:
  model.load_state_dict(__UpperCAmelCase	)
 if gpu:
  model.cuda()
 if eval_mode:
  model.eval()
 return {"model": model}







def __snake_case      (   UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		Tuple	):







 if ckpt:
  lowerCamelCase_  				=						torch.load(__UpperCAmelCase      ,       map_location="cpu"	)
  lowerCamelCase_  				=						pl_sd["global_step"]
  print(F'''loaded model from global step {global_step}.'''	)
 else:
  lowerCamelCase_  				=						{"state_dict": None}
  lowerCamelCase_  				=						None
 lowerCamelCase_  				=						load_model_from_config(config.model      ,       pl_sd["state_dict"]      ,       gpu=__UpperCAmelCase      ,       eval_mode=__UpperCAmelCase	)["model"]
 return model, global_step







'''simple docstring'''

from __future__ import annotations

import pandas as pd





def __snake_case      (   UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_  				=						[0] * no_of_processes
 lowerCamelCase_  				=						[0] * no_of_processes
 # Copy the burst time into remaining_time[]
 for i in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						burst_time[i]

 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						999999999
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						False

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

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

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

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

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

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

   if waiting_time[short] < 0:
    lowerCamelCase_  				=						0

        # Increment time
  increment_time += 1
 return waiting_time





def __snake_case      (   UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		list[int]	):
 lowerCamelCase_  				=						[0] * no_of_processes
 for i in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						burst_time[i] + waiting_time[i]
 return turn_around_time





def __snake_case      (   UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0
 for i in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						total_waiting_time + waiting_time[i]
  lowerCamelCase_  				=						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""")
     a_						:       Dict      			=				int(input())
     a_						:       Any      			=				[0] * no_of_processes
     a_						:       Optional[int]      			=				[0] * no_of_processes
     a_						:       Tuple      			=				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))
          a_			,     a_						:       str      			=				map(int, input().split())

     a_						:       List[Any]      			=				calculate_waitingtime(arrival_time, burst_time, no_of_processes)

     a_						:       int      			=				burst_time
     a_						:       Union[str, Any]      			=				no_of_processes
     a_						:       Optional[int]      			=				waiting_time
     a_						:       Any      			=				calculate_turnaroundtime(bt, n, wt)

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     a_						:       Optional[int]      			=				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)



