

from __future__ import annotations

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





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__      ,			):





	'''simple docstring'''
	snake_case_   		=			[
	    [0 for col in range(len(grid[0]						)						)] for row in range(len(lowercase__						)						)
	]  # the reference grid
	snake_case_   		=			1
	snake_case_   		=			[
	    [0 for col in range(len(grid[0]						)						)] for row in range(len(lowercase__						)						)
	]  # 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(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()
			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(lowercase__						)						):  # to try out different valid actions
					snake_case_   		=			x + DIRECTIONS[i][0]
					snake_case_   		=			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:
							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(lowercase__						)						):
		path.append(invpath[len(lowercase__						) - 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 unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script


@require_cpu
class SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				debug_launcher(test_script.main				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				debug_launcher(test_ops.main				)
