


from __future__ import annotations

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : list[list[int]]      ,					SCREAMING_SNAKE_CASE__      : list[int]      ,					SCREAMING_SNAKE_CASE__      : list[int]      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : list[list[int]]      ,					):
     __UpperCamelCase   							=[
         [0 for col in range(len(grid[0]    )    )] for row in range(len(SCREAMING_SNAKE_CASE__    )    )
     ]  # the reference grid
     __UpperCamelCase   							=1
     __UpperCamelCase   							=[
         [0 for col in range(len(grid[0]    )    )] for row in range(len(SCREAMING_SNAKE_CASE__    )    )
     ]  # 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(SCREAMING_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()
               __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(SCREAMING_SNAKE_CASE__    )    ):  # to try out different valid actions
                         __UpperCamelCase   							=x + DIRECTIONS[i][0]
                         __UpperCamelCase   							=y + DIRECTIONS[i][1]
                         if xa >= 0 and xa < len(SCREAMING_SNAKE_CASE__    ) 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(SCREAMING_SNAKE_CASE__    )    ):
          path.append(invpath[len(SCREAMING_SNAKE_CASE__    ) - 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])









import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


_A			=						logging.get_logger(__name__)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       *A_ ,       **A_   )  ->	None:
         warnings.warn(
             'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
             ' Please use ImageGPTImageProcessor instead.' ,       A_ ,       )
         super().__init__(*A_ ,       **A_   )





