





from __future__ import annotations

lowercase				:	Optional[Any]      						=  [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]



def A_     (		A__		,				A__		,				A__		,				A__		,				A__		,				)  ->		tuple[list[list[int]], list[list[int]]]:
       a__						:     int			=    [
           [0 for col in range(len(grid[0]    )    )] for row in range(len(A__    )    )
       ]  # the reference grid
       a__						:     Any			=    1
       a__						:     Optional[Any]			=    [
           [0 for col in range(len(grid[0]    )    )] for row in range(len(A__    )    )
       ]  # the action grid

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

       a__						:     Optional[Any]			=    False  # flag that is set when search is complete
       a__						:     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()
                     a__						:     Any			=    cell.pop()
                     a__						:     Optional[Any]			=    next_cell[2]
                     a__						:     Any			=    next_cell[3]
                     a__						:     Union[str, Any]			=    next_cell[1]

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

       a__						:     Union[str, Any]			=    []
       for i in range(len(A__    )    ):
              path.append(invpath[len(A__    ) - 1 - i]    )
       return path, action


if __name__ == "__main__":
    lowercase				:	Optional[Any]      						=  [
        [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],
    ]

    lowercase				:	List[Any]      						=  [0, 0]
    # all coordinates are given in format [y,x]
    lowercase				:	Union[str, Any]      						=  [len(grid) - 1, len(grid[0]) - 1]
    lowercase				:	Tuple      						=  1

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

    lowercase						, lowercase				:	List[str]      						=  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 re

from filelock import FileLock


try:
    import nltk

    lowercase				:	List[str]      						=  True
except (ImportError, ModuleNotFoundError):
    lowercase				:	Tuple      						=  False

if NLTK_AVAILABLE:
    with FileLock(""".lock""") as lock:
        nltk.download("""punkt""", quiet=True)



def A_     (		A__    )  ->		str:
       re.sub('<n>'		,				''		,				A__    )  # remove pegasus newline char
       assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
       return "\n".join(nltk.sent_tokenize(A__    )    )


