



from __future__ import annotations

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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list[list[int]]	,   _snake_case	:list[int]	,   _snake_case	:list[int]	,   _snake_case	:int	,   _snake_case	:list[list[int]]	,   )  ->     tuple[list[list[int]], list[list[int]]]:
   _A      =       [
       [0 for col in range(len(grid[0]				)				)] for row in range(len(_snake_case				)				)
   ]  # the reference grid
   _A      =       1
   _A      =       [
       [0 for col in range(len(grid[0]				)				)] for row in range(len(_snake_case				)				)
   ]  # the action grid

   _A      =       init[0]
   _A      =       init[1]
   _A      =       0
   _A      =       g + heuristic[x][y]  # cost from starting cell to destination cell
   _A      =       [[f, g, x, y]]

   _A      =       False  # flag that is set when search is complete
   _A      =       False  # flag set if we can't find expand

   while not found and not resign:
      if len(_snake_case				) == 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()
         _A      =       cell.pop()
         _A      =       next_cell[2]
         _A      =       next_cell[3]
         _A      =       next_cell[1]

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

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


if __name__ == "__main__":
   UpperCAmelCase_		 =					[
       [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],
   ]

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

   # the cost map which pushes the path closer to the goal
   UpperCAmelCase_		 =					[[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])):
         UpperCAmelCase_		 =					abs(i - goal[0]) + abs(j - goal[1])
         if grid[i][j] == 1:
            # added extra penalty in the heuristic map
            UpperCAmelCase_		 =					9_9

   UpperCAmelCase_ ,UpperCAmelCase_		 =					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])








def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   if not isinstance(_snake_case	,   _snake_case				):
      raise ValueError('''Input must be an integer'''				)
   if input_num <= 0:
      raise ValueError('''Input must be positive'''				)
   return sum(
       divisor for divisor in range(1	,   input_num // 2 + 1				) if input_num % divisor == 0				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



