




from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class 		UpperCAmelCase   (				UpperCAmelCase__			):







						'''simple docstring'''





						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							return [
							    {"col_1": 3, "col_2": "a"},
							    {"col_1": 2, "col_2": "b"},
							    {"col_1": 1, "col_2": "c"},
							    {"col_1": 0, "col_2": "d"},
							]






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							snake_case_		    =     {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
							return Dataset.from_dict(__lowercase						)






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							snake_case_		    =     self._create_example_records()
							snake_case_		    =     Dataset.from_list(__lowercase						)
							self.assertListEqual(dset.column_names       ,	["col_1", "col_2"]						)
							for i, r in enumerate(__lowercase						):
								self.assertDictEqual(__lowercase       ,	example_records[i]						)






						def        snake_case__						(						self							:     List[str]						):






							"""simple docstring"""






							snake_case_		    =     self._create_example_records()
							snake_case_		    =     Dataset.from_list(__lowercase						)
							snake_case_		    =     Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}						)
							self.assertEqual(dset.info       ,	dset_from_dict.info						)






						def        snake_case__						(						self							:     List[Any]						):  # checks what happens with missing columns






							"""simple docstring"""






							snake_case_		    =     [{"col_1": 1}, {"col_2": "x"}]
							snake_case_		    =     Dataset.from_list(__lowercase						)
							self.assertDictEqual(dset[0]       ,	{"col_1": 1}						)
							self.assertDictEqual(dset[1]       ,	{"col_1": None}						)  # NB: first record is used for columns






						def        snake_case__						(						self							:     int						):  # checks if the type can be inferred from the second record






							"""simple docstring"""






							snake_case_		    =     [{"col_1": []}, {"col_1": [1, 2]}]
							snake_case_		    =     Dataset.from_list(__lowercase						)
							self.assertEqual(dset.info.features["col_1"]       ,	Sequence(Value("int64"						)						)						)






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     Dataset.from_list([]						)
							self.assertEqual(len(__lowercase						)       ,	0						)
							self.assertListEqual(dset.column_names       ,	[]						)






from __future__ import annotations

lowercase__				:     str 		=						[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A     ,	_A     ,	):







	'''simple docstring'''





	snake_case_		    =     [
	    [0 for col in range(len(grid[0]							)							)] for row in range(len(_A							)							)
	]  # the reference grid
	snake_case_		    =     1
	snake_case_		    =     [
	    [0 for col in range(len(grid[0]							)							)] for row in range(len(_A							)							)
	]  # 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(_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()
			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(_A							)							):  # to try out different valid actions
					snake_case_		    =     x + DIRECTIONS[i][0]
					snake_case_		    =     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:
							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(_A							)							):
		path.append(invpath[len(_A							) - 1 - i]							)
	return path, action


if __name__ == "__main__":
		lowercase__				:     Union[str, 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__				:     Optional[Any] 		=						[0, 0]
		# all coordinates are given in format [y,x]
		lowercase__				:     Tuple 		=						[len(grid) - 1, len(grid[0]) - 1]
		lowercase__				:     Dict 		=						1

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

		lowercase__		,       lowercase__				:     Tuple 		=						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])
