



"""simple docstring"""







from __future__ import annotations

__a       :	Optional[int]             = [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     ):
					a__          =  [
					    [0 for col in range(len(grid[0]))] for row in range(len(_lowercase))
					]  # the reference grid
					a__          =  1
					a__          =  [
					    [0 for col in range(len(grid[0]))] for row in range(len(_lowercase))
					]  # the action grid

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

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

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

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


if __name__ == "__main__":
	__a       :	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],
	]

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

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

	__a       , __a       :	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"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a       :	str             = {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	int             = [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	__a       :	Dict             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


