'''simple docstring'''





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 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=0 )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       1.0 if scale is None else scale
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       0.0 if loc is None else loc

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






    @property
    def       _UpperCamelCase    (       self )							-> Tuple:
         return self.base_dist.mean * self.scale + self.loc






    @property
    def       _UpperCamelCase    (       self )							-> str:
         return self.base_dist.variance * self.scale**2






    @property
    def       _UpperCamelCase    (       self )							-> Dict:
         return self.variance.sqrt()






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							**lowercase__ )							-> None:
         super().__init__(**lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       args_dim
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       nn.ModuleList([nn.Linear(lowercase__    ,							lowercase__ ) for dim in args_dim.values()] )
         SCREAMING_SNAKE_CASE					:			int    	=       domain_map






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Tuple[torch.Tensor]:
         SCREAMING_SNAKE_CASE					:			str    	=       [proj(lowercase__ ) for proj in self.proj]

         return self.domain_map(*lowercase__ )






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Any:
         super().__init__()
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       function






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							*lowercase__ )							-> Optional[int]:
         return self.function(lowercase__    ,							*lowercase__ )






class 				UpperCAmelCase :




    '''simple docstring'''







    snake_case__							:   type
    snake_case__							:   int
    snake_case__							:   Dict[str, int]






    def __init__(       self    ,							lowercase__ = 1 )							-> None:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       dim
         SCREAMING_SNAKE_CASE					:			str    	=       {k: dim * self.args_dim[k] for k in self.args_dim}






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Optional[int]:
         if self.dim == 1:
              return self.distribution_class(*lowercase__ )
         else:
              return Independent(self.distribution_class(*lowercase__ )    ,							1 )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = None    ,							)							-> Distribution:
         SCREAMING_SNAKE_CASE					:			int    	=       self._base_distribution(lowercase__ )
         if loc is None and scale is None:
              return distr
         else:
              return AffineTransformed(lowercase__    ,							loc=lowercase__    ,							scale=lowercase__    ,							event_dim=self.event_dim )






    @property
    def       _UpperCamelCase    (       self )							-> Tuple:
         return () if self.dim == 1 else (self.dim,)






    @property
    def       _UpperCamelCase    (       self )							-> int:
         return len(self.event_shape )






    @property
    def       _UpperCamelCase    (       self )							-> float:
         return 0.0






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> nn.Module:
         return ParameterProjection(
             in_features=lowercase__    ,							args_dim=self.args_dim    ,							domain_map=LambdaLayer(self.domain_map )    ,							)






    def       _UpperCamelCase    (       self    ,							*lowercase__ )							-> Dict:
         raise NotImplementedError()






    @staticmethod
    def       _UpperCamelCase    (       lowercase__ )							-> torch.Tensor:
         return (x + torch.sqrt(torch.square(lowercase__ ) + 4.0 )) / 2.0






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Dict[str, int]							=						{"df": 1, "loc": 1, "scale": 1}
    snake_case__							:   type							=						StudentT






    @classmethod
    def       _UpperCamelCase    (       cls    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       cls.squareplus(lowercase__ ).clamp_min(torch.finfo(scale.dtype ).eps )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       2.0 + cls.squareplus(lowercase__ )
         return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Dict[str, int]							=						{"loc": 1, "scale": 1}
    snake_case__							:   type							=						Normal






    @classmethod
    def       _UpperCamelCase    (       cls    ,							lowercase__    ,							lowercase__ )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       cls.squareplus(lowercase__ ).clamp_min(torch.finfo(scale.dtype ).eps )
         return loc.squeeze(-1 ), scale.squeeze(-1 )






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Dict[str, int]							=						{"total_count": 1, "logits": 1}
    snake_case__							:   type							=						NegativeBinomial






    @classmethod
    def       _UpperCamelCase    (       cls    ,							lowercase__    ,							lowercase__ )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       cls.squareplus(lowercase__ )
         return total_count.squeeze(-1 ), logits.squeeze(-1 )






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Distribution:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[Any]    	=       distr_args
         if self.dim == 1:
              return self.distribution_class(total_count=lowercase__    ,							logits=lowercase__ )
         else:
              return Independent(self.distribution_class(total_count=lowercase__    ,							logits=lowercase__ )    ,							1 )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = None )							-> Distribution:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       distr_args

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

         return self._base_distribution((total_count, logits) )




'''simple docstring'''





from __future__ import annotations

from statistics import mean


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [0] * no_of_processes
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [0] * no_of_processes

     # Initialize remaining_time to waiting_time.

     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			Tuple    	=       burst_time[i]
     SCREAMING_SNAKE_CASE					:			list[int]    	=       []

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       0
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       0

     # When processes are not completed,
     # A process whose arrival time has passed \
     # and has remaining execution time is put into the ready_process.
     # The shortest process in the ready_process, target_process is executed.

     while completed != no_of_processes:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       []
          SCREAMING_SNAKE_CASE					:			Dict    	=       -1

          for i in range(a_		):
               if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
                    ready_process.append(a_		)

          if len(a_		) > 0:
               SCREAMING_SNAKE_CASE					:			Dict    	=       ready_process[0]
               for i in ready_process:
                    if remaining_time[i] < remaining_time[target_process]:
                         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       i
               total_time += burst_time[target_process]
               completed += 1
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       0
               SCREAMING_SNAKE_CASE					:			Tuple    	=       (
                   total_time - arrival_time[target_process] - burst_time[target_process]
               )
          else:
               total_time += 1

     return waiting_time


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	list[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       [0] * no_of_processes
     for i in range(a_		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       burst_time[i] + waiting_time[i]
     return turn_around_time


if __name__ == "__main__":
 print("""[TEST CASE 01]""")

 _lowerCAmelCase  :Optional[int]  				=    4
 _lowerCAmelCase  :Optional[int]  				=    [2, 5, 3, 7]
 _lowerCAmelCase  :List[str]  				=    [0, 0, 0, 0]
 _lowerCAmelCase  :Union[str, Any]  				=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
 _lowerCAmelCase  :Any  				=    calculate_turnaroundtime(
     burst_time, no_of_processes, waiting_time
 )

 # Printing the Result
 print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
 for i, process_id in enumerate(list(range(1, 5))):
  print(
      f"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
      f"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
  )
 print(f"""\nAverage waiting time = {mean(waiting_time):.5f}""")
 print(f"""Average turnaround time = {mean(turn_around_time):.5f}""")



