


'''simple docstring'''



from __future__ import annotations

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






def   lowerCamelCase							(_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]]						,				):
					__a      :	Dict    =  [
					    [0 for col in range(len(grid[0]       )       )] for row in range(len(_lowercase       )       )
					]  # the reference grid
					__a      :	Any    =  1
					__a      :	List[Any]    =  [
					    [0 for col in range(len(grid[0]       )       )] for row in range(len(_lowercase       )       )
					]  # the action grid

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

					__a      :	List[Any]    =  False  # flag that is set when search is complete
					__a      :	Optional[int]    =  False  # flag set if we can't find expand

					while not found and not resign:
										if len(_lowercase       ) == 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      :	Optional[int]    =  cell.pop()
															__a      :	Dict    =  next_cell[2]
															__a      :	int    =  next_cell[3]
															__a      :	Union[str, Any]    =  next_cell[1]

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

					__a      :	Optional[int]    =  []
					for i in range(len(_lowercase       )       ):
										path.append(invpath[len(_lowercase       ) - 1 - i]       )
					return path, action


if __name__ == "__main__":
							__lowercase     :						str	=     [
							    [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     :						int	=     [0, 0]
							# all coordinates are given in format [y,x]
							__lowercase     :						Dict	=     [len(grid) - 1, len(grid[0]) - 1]
							__lowercase     :						int	=     1

							# the cost map which pushes the path closer to the goal
							__lowercase     :						Optional[int]	=     [[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     :						Optional[Any]	=     abs(i - goal[0]) + abs(j - goal[1])
																					if grid[i][j] == 1:
																												# added extra penalty in the heuristic map
																												__lowercase     :						Optional[Any]	=     99

							__lowercase     :						Optional[int]	=     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])







'''simple docstring'''



from .imports import is_rich_available


if is_rich_available():
	from rich.traceback import install

	install(show_locals=False)

else:
	raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')



