






from __future__ import annotations

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







def       lowercase__	(					A_:						list[list[int]]  ,      A_:						list[int]  ,      A_:						list[int]  ,      A_:						int  ,      A_:						list[list[int]]  ,      )	->				tuple[list[list[int]], list[list[int]]]:







       """simple docstring"""






       __UpperCAmelCase     				=[
           [0 for col in range(len(grid[0]		)		)] for row in range(len(A_		)		)
       ]  # the reference grid
       __UpperCAmelCase     				=1
       __UpperCAmelCase     				=[
           [0 for col in range(len(grid[0]		)		)] for row in range(len(A_		)		)
       ]  # the action grid

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

       __UpperCAmelCase     				=False  # flag that is set when search is complete
       __UpperCAmelCase     				=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()
                     __UpperCAmelCase     				=cell.pop()
                     __UpperCAmelCase     				=next_cell[2]
                     __UpperCAmelCase     				=next_cell[3]
                     __UpperCAmelCase     				=next_cell[1]

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

       __UpperCAmelCase     				=[]
       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])





def 		snake_case__	(		lowercase							,				lowercase					):

   if density <= 0:
      raise ValueError("Impossible fluid density"					)
   if bulk_modulus <= 0:
      raise ValueError("Impossible bulk modulus"					)

   return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
 import doctest

 doctest.testmod()