


"""simple docstring"""
from __future__ import annotations

__SCREAMING_SNAKE_CASE							:    Dict		=    [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, )    ->				tuple[list[list[int]], list[list[int]]]:
							snake_case_           =		[
							    [0 for col in range(len(grid[0]				)				)] for row in range(len(_SCREAMING_SNAKE_CASE				)				)
							]  # the reference grid
							snake_case_           =		1
							snake_case_           =		[
							    [0 for col in range(len(grid[0]				)				)] for row in range(len(_SCREAMING_SNAKE_CASE				)				)
							]  # the action grid

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

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

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

							snake_case_           =		[]
							for i in range(len(_SCREAMING_SNAKE_CASE				)				):
														path.append(invpath[len(_SCREAMING_SNAKE_CASE				) - 1 - i]				)
							return path, action


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    List[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],
				]

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

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

				__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    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"""
__SCREAMING_SNAKE_CASE							:    str		=    'Input must be a string of 8 numbers plus letter'
__SCREAMING_SNAKE_CASE							:    Dict		=    'TRWAGMYFPDXBNJZSQVHLCKE'







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				bool:

							if not isinstance(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				):
														snake_case_           =		f"""Expected string as input, found {type(_SCREAMING_SNAKE_CASE				).__name__}"""
														raise TypeError(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		spanish_id.replace("""-"""				, """"""				).upper()
							if len(_SCREAMING_SNAKE_CASE				) != 9:
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							try:
														snake_case_           =		int(spanish_id_clean[0:8]				)
														snake_case_           =		spanish_id_clean[8]
							except ValueError as ex:
														raise ValueError(_SCREAMING_SNAKE_CASE				) from ex

							if letter.isdigit():
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
				import doctest

				doctest.testmod()



