





from __future__ import annotations

import pandas as pd





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		list[int]:







  """simple docstring"""





  lowerCamelCase__:				Any        =[0] * no_of_processes
  lowerCamelCase__:				Any        =[0] * no_of_processes
  # Copy the burst time into remaining_time[]
  for i in range(__a		):
    lowerCamelCase__:				int        =burst_time[i]

  lowerCamelCase__:				int        =0
  lowerCamelCase__:				Any        =0
  lowerCamelCase__:				int        =999999999
  lowerCamelCase__:				List[Any]        =0
  lowerCamelCase__:				Optional[Any]        =False

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

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

    lowerCamelCase__:				Dict        =remaining_time[short]
    if minm == 0:
      lowerCamelCase__:				Union[str, Any]        =999999999

    if remaining_time[short] == 0:
      complete += 1
      lowerCamelCase__:				Optional[Any]        =False

      # Find finish time of current process
      lowerCamelCase__:				Optional[Any]        =increment_time + 1

      # Calculate waiting time
      lowerCamelCase__:				Union[str, Any]        =finish_time - arrival_time[short]
      lowerCamelCase__:				List[Any]        =finar - burst_time[short]

      if waiting_time[short] < 0:
        lowerCamelCase__:				List[str]        =0

        # Increment time
    increment_time += 1
  return waiting_time





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		list[int]:







  """simple docstring"""





  lowerCamelCase__:				Any        =[0] * no_of_processes
  for i in range(__a		):
    lowerCamelCase__:				Tuple        =burst_time[i] + waiting_time[i]
  return turn_around_time





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		None:







  """simple docstring"""





  lowerCamelCase__:				str        =0
  lowerCamelCase__:				List[str]        =0
  for i in range(__a		):
    lowerCamelCase__:				Optional[int]        =total_waiting_time + waiting_time[i]
    lowerCamelCase__:				Any        =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     =    int(input())
     __A     =    [0] * no_of_processes
     __A     =    [0] * no_of_processes
     __A     =    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     =    map(int, input().split())

     __A     =    calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     __A     =    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)








from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 def __init__(self    :       int	,			UpperCAmelCase_    :       Distribution	,			UpperCAmelCase_    :       Dict=None	,			UpperCAmelCase_    :       Tuple=None	,			UpperCAmelCase_    :       Tuple=0)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =1.0 if scale is None else scale
   lowerCamelCase__:				List[Any]        =0.0 if loc is None else loc

   super().__init__(UpperCAmelCase_	,			[AffineTransform(loc=self.loc	,			scale=self.scale	,			event_dim=UpperCAmelCase_)])

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->int:



   '''simple docstring'''



   return self.base_dist.mean * self.scale + self.loc

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Dict:



   '''simple docstring'''



   return self.base_dist.variance * self.scale**2







 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Optional[Any]:



   '''simple docstring'''



   return self.variance.sqrt()



class        _SCREAMING_SNAKE_CASE						(     nn.Module   ):







 '''simple docstring'''

 def __init__(self    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       Dict[str, int]	,			UpperCAmelCase_    :       Callable[..., Tuple[torch.Tensor]]	,			**UpperCAmelCase_    :       Dict)	->None:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =args_dim
   lowerCamelCase__:				Any        =nn.ModuleList([nn.Linear(UpperCAmelCase_	,			UpperCAmelCase_) for dim in args_dim.values()])
   lowerCamelCase__:				Any        =domain_map







 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       torch.Tensor)	->Tuple[torch.Tensor]:



   '''simple docstring'''



   lowerCamelCase__:				Any        =[proj(UpperCAmelCase_) for proj in self.proj]

   return self.domain_map(*UpperCAmelCase_)



class        _SCREAMING_SNAKE_CASE						(     nn.Module   ):







 '''simple docstring'''

 def __init__(self    :       str	,			UpperCAmelCase_    :       Optional[Any])	->Optional[int]:



   '''simple docstring'''



   super().__init__()
   lowerCamelCase__:				Any        =function







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Optional[Any]	,			*UpperCAmelCase_    :       Union[str, Any])	->Union[str, Any]:



   '''simple docstring'''



   return self.function(UpperCAmelCase_	,			*UpperCAmelCase_)



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''
 lowercase_		=      42
 lowercase_		=      42
 lowercase_		=      42

 def __init__(self    :       str	,			UpperCAmelCase_    :       int = 1)	->None:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =dim
   lowerCamelCase__:				int        ={k: dim * self.args_dim[k] for k in self.args_dim}

 def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       str)	->Optional[Any]:



   '''simple docstring'''



   if self.dim == 1:
     return self.distribution_class(*UpperCAmelCase_)
   else:
     return Independent(self.distribution_class(*UpperCAmelCase_)	,			1)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None	,			)	->Distribution:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =self._base_distribution(UpperCAmelCase_)
   if loc is None and scale is None:
     return distr
   else:
     return AffineTransformed(UpperCAmelCase_	,			loc=UpperCAmelCase_	,			scale=UpperCAmelCase_	,			event_dim=self.event_dim)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Tuple:



   '''simple docstring'''



   return () if self.dim == 1 else (self.dim,)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->int:



   '''simple docstring'''



   return len(self.event_shape)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->float:



   '''simple docstring'''



   return 0.0

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			UpperCAmelCase_    :       int)	->nn.Module:



   '''simple docstring'''



   return ParameterProjection(
       in_features=UpperCAmelCase_	,			args_dim=self.args_dim	,			domain_map=LambdaLayer(self.domain_map)	,			)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			*UpperCAmelCase_    :       torch.Tensor)	->Any:



   '''simple docstring'''



   raise NotImplementedError()







 @staticmethod
 def 							SCREAMING_SNAKE_CASE_ (UpperCAmelCase_    :       torch.Tensor)	->torch.Tensor:



   '''simple docstring'''



   return (x + torch.sqrt(torch.square(UpperCAmelCase_) + 4.0)) / 2.0



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      {"df": 1, "loc": 1, "scale": 1}
 lowercase_		=      StudentT

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Optional[int]	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				str        =cls.squareplus(UpperCAmelCase_).clamp_min(torch.finfo(scale.dtype).eps)
   lowerCamelCase__:				Any        =2.0 + cls.squareplus(UpperCAmelCase_)
   return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      {"loc": 1, "scale": 1}
 lowercase_		=      Normal

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Optional[Any]	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor)	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =cls.squareplus(UpperCAmelCase_).clamp_min(torch.finfo(scale.dtype).eps)
   return loc.squeeze(-1), scale.squeeze(-1)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      {"total_count": 1, "logits": 1}
 lowercase_		=      NegativeBinomial

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Optional[int]	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor)	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =cls.squareplus(UpperCAmelCase_)
   return total_count.squeeze(-1), logits.squeeze(-1)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       Tuple)	->Distribution:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Any        =distr_args
   if self.dim == 1:
     return self.distribution_class(total_count=UpperCAmelCase_	,			logits=UpperCAmelCase_)
   else:
     return Independent(self.distribution_class(total_count=UpperCAmelCase_	,			logits=UpperCAmelCase_)	,			1)







 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None)	->Distribution:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Union[str, Any]        =distr_args

   if scale is not None:
     # See scaling property of Gamma.
     logits += scale.log()

   return self._base_distribution((total_count, logits))

