

from __future__ import annotations

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





def      snake_case_				(__A					:						list[list[int]] ,       __A					:						list[int] ,       __A					:						list[int] ,       __A					:						int ,       __A					:						list[list[int]] ,       )      ->     tuple[list[list[int]], list[list[int]]]:
   __lowerCAmelCase  :      Any    =						[
       [0 for col in range(len(grid[0]					)					)] for row in range(len(__A					)					)
   ]  # the reference grid
   __lowerCAmelCase  :      Dict    =						1
   __lowerCAmelCase  :      List[str]    =						[
       [0 for col in range(len(grid[0]					)					)] for row in range(len(__A					)					)
   ]  # the action grid

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

   __lowerCAmelCase  :      Optional[Any]    =						False  # flag that is set when search is complete
   __lowerCAmelCase  :      str    =						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  :      List[str]    =						cell.pop()
         __lowerCAmelCase  :      Optional[Any]    =						next_cell[2]
         __lowerCAmelCase  :      List[str]    =						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  :      List[Any]    =						x + DIRECTIONS[i][0]
               __lowerCAmelCase  :      Optional[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  :      str    =						1
                     __lowerCAmelCase  :      Dict    =						i
   __lowerCAmelCase  :      Optional[Any]    =						[]
   __lowerCAmelCase  :      int    =						goal[0]
   __lowerCAmelCase  :      Tuple    =						goal[1]
   invpath.append([x, y]					)  # we get the reverse path from here
   while x != init[0] or y != init[1]:
      __lowerCAmelCase  :      Tuple    =						x - DIRECTIONS[action[x][y]][0]
      __lowerCAmelCase  :      Optional[Any]    =						y - DIRECTIONS[action[x][y]][1]
      __lowerCAmelCase  :      str    =						xa
      __lowerCAmelCase  :      int    =						ya
      invpath.append([x, y]					)

   __lowerCAmelCase  :      Any    =						[]
   for i in range(len(__A					)					):
      path.append(invpath[len(__A					) - 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								=			99

   __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])





import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


__UpperCAmelCase								=			logging.get_logger(__name__)



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   Optional[Any]	,							*lowerCAmelCase			:   int	,							**lowerCAmelCase			:   Optional[int]   )							->     None:






     """simple docstring"""







     warnings.warn(
         """The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use GLPNImageProcessor instead."""	,							lowerCAmelCase	,							)
     super().__init__(*lowerCAmelCase	,							**lowerCAmelCase   )


