





from __future__ import annotations

__A									=				[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]





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







  """simple docstring"""





  lowerCamelCase__:      List[str]			  =[
      [0 for col in range(len(grid[0]		)		)] for row in range(len(__a		)		)
  ]  # the reference grid
  lowerCamelCase__:      Optional[int]			  =1
  lowerCamelCase__:      int			  =[
      [0 for col in range(len(grid[0]		)		)] for row in range(len(__a		)		)
  ]  # the action grid

  lowerCamelCase__:      Dict			  =init[0]
  lowerCamelCase__:      Dict			  =init[1]
  lowerCamelCase__:      List[str]			  =0
  lowerCamelCase__:      int			  =g + heuristic[x][y]  # cost from starting cell to destination cell
  lowerCamelCase__:      Optional[int]			  =[[f, g, x, y]]

  lowerCamelCase__:      Tuple			  =False  # flag that is set when search is complete
  lowerCamelCase__:      Union[str, Any]			  =False  # flag set if we can't find expand

  while not found and not resign:
    if len(__a		) == 0:
      raise ValueError("Algorithm is unable to find solution"		)
    else:  # to choose the least costliest action so as to move closer to the goal
      cell.sort()
      cell.reverse()
      lowerCamelCase__:      int			  =cell.pop()
      lowerCamelCase__:      List[str]			  =next_cell[2]
      lowerCamelCase__:      List[Any]			  =next_cell[3]
      lowerCamelCase__:      Dict			  =next_cell[1]

      if x == goal[0] and y == goal[1]:
        lowerCamelCase__:      Union[str, Any]			  =True
      else:
        for i in range(len(__a		)		):  # to try out different valid actions
          lowerCamelCase__:      Any			  =x + DIRECTIONS[i][0]
          lowerCamelCase__:      List[Any]			  =y + DIRECTIONS[i][1]
          if xa >= 0 and xa < len(__a		) and ya >= 0 and ya < len(grid[0]		):
            if closed[xa][ya] == 0 and grid[xa][ya] == 0:
              lowerCamelCase__:      Optional[int]			  =g + cost
              lowerCamelCase__:      Optional[int]			  =ga + heuristic[xa][ya]
              cell.append([fa, ga, xa, ya]		)
              lowerCamelCase__:      int			  =1
              lowerCamelCase__:      Any			  =i
  lowerCamelCase__:      Tuple			  =[]
  lowerCamelCase__:      Any			  =goal[0]
  lowerCamelCase__:      Union[str, Any]			  =goal[1]
  invpath.append([x, y]		)  # we get the reverse path from here
  while x != init[0] or y != init[1]:
    lowerCamelCase__:      Any			  =x - DIRECTIONS[action[x][y]][0]
    lowerCamelCase__:      int			  =y - DIRECTIONS[action[x][y]][1]
    lowerCamelCase__:      Any			  =xa
    lowerCamelCase__:      int			  =ya
    invpath.append([x, y]		)

  lowerCamelCase__:      str			  =[]
  for i in range(len(__a		)		):
    path.append(invpath[len(__a		) - 1 - i]		)
  return path, action


if __name__ == "__main__":
     __A									=				[
         [0, 1, 0, 0, 0, 0],
         [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
         [0, 1, 0, 0, 0, 0],
         [0, 1, 0, 0, 1, 0],
         [0, 0, 0, 0, 1, 0],
     ]

     __A									=				[0, 0]
     # all coordinates are given in format [y,x]
     __A									=				[len(grid) - 1, len(grid[0]) - 1]
     __A									=				1

     # the cost map which pushes the path closer to the goal
     __A									=				[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
     for i in range(len(grid)):
          for j in range(len(grid[0])):
               __A									=				abs(i - goal[0]) + abs(j - goal[1])
               if grid[i][j] == 1:
                    # added extra penalty in the heuristic map
                    __A									=				99

     __A					,	__A									=				search(grid, init, goal, cost, heuristic)

     print("ACTION MAP")
     for i in range(len(action)):
          print(action[i])

     for i in range(len(path)):
          print(path[i])





'''simple docstring'''




from __future__ import annotations

from statistics import mean





def 	a_					( lowerCamelCase	:     list[int]	, lowerCamelCase	:     list[int]	, lowerCamelCase	:     int				):
   lowerCAmelCase     =			[0] * no_of_processes
   lowerCAmelCase     =			[0] * no_of_processes

   # Initialize remaining_time to waiting_time.

   for i in range(lowerCamelCase				):
      lowerCAmelCase     =			burst_time[i]
   lowerCAmelCase     =			[]

   lowerCAmelCase     =			0
   lowerCAmelCase     =			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:
      lowerCAmelCase     =			[]
      lowerCAmelCase     =			-1

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

      if len(lowerCamelCase				) > 0:
         lowerCAmelCase     =			ready_process[0]
         for i in ready_process:
            if remaining_time[i] < remaining_time[target_process]:
               lowerCAmelCase     =			i
         total_time += burst_time[target_process]
         completed += 1
         lowerCAmelCase     =			0
         lowerCAmelCase     =			(
             total_time - arrival_time[target_process] - burst_time[target_process]
         )
      else:
         total_time += 1

   return waiting_time





def 	a_					( lowerCamelCase	:     list[int]	, lowerCamelCase	:     int	, lowerCamelCase	:     list[int]				):
   lowerCAmelCase     =			[0] * no_of_processes
   for i in range(lowerCamelCase				):
      lowerCAmelCase     =			burst_time[i] + waiting_time[i]
   return turn_around_time


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

       __snake_case    						=4
       __snake_case    						=[2, 5, 3, 7]
       __snake_case    						=[0, 0, 0, 0]
       __snake_case    						=calculate_waitingtime(arrival_time, burst_time, no_of_processes)
       __snake_case    						=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}''')





