



'''simple docstring'''

from __future__ import annotations

snake_case_			=     list[tuple[int, int]]

snake_case_			=     [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

snake_case_			=     ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right


class 	a__     :


	def __init__(self : List[Any],					__UpperCAmelCase : int,					__UpperCAmelCase : int,					__UpperCAmelCase : int,					__UpperCAmelCase : int,					__UpperCAmelCase : float,					__UpperCAmelCase : Node | None,					)     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= pos_x
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= pos_y
					SCREAMING_SNAKE_CASE  :						Dict      					= (pos_y, pos_x)
					SCREAMING_SNAKE_CASE  :						List[str]      					= goal_x
					SCREAMING_SNAKE_CASE  :						List[Any]      					= goal_y
					SCREAMING_SNAKE_CASE  :						str      					= g_cost
					SCREAMING_SNAKE_CASE  :						int      					= parent
					SCREAMING_SNAKE_CASE  :						int      					= self.calculate_heuristic()


	def 				lowercase__  (self : int      )     ->			float:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= abs(self.pos_x - self.goal_x      )
					SCREAMING_SNAKE_CASE  :						int      					= abs(self.pos_y - self.goal_y      )
					return dx + dy






	def __lt__(self : List[str],					__UpperCAmelCase : Any      )     ->			bool:

					"""simple docstring"""

					return self.f_cost < other.f_cost



class 	a__     :


	def __init__(self : Tuple,					__UpperCAmelCase : tuple[int, int],					__UpperCAmelCase : tuple[int, int]      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= Node(start[1],					start[0],					goal[1],					goal[0],					0,					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= Node(goal[1],					goal[0],					goal[1],					goal[0],					99999,					__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [self.start]
					SCREAMING_SNAKE_CASE  :						list[Node]      					= []

					SCREAMING_SNAKE_CASE  :						int      					= False


	def 				lowercase__  (self : Union[str, Any]      )     ->			Path | None:

					"""simple docstring"""

					while self.open_nodes:
									# Open Nodes are sorted using __lt__
									self.open_nodes.sort()
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.open_nodes.pop(0      )

									if current_node.pos == self.target.pos:
													SCREAMING_SNAKE_CASE  :						int      					= True
													return self.retrace_path(__UpperCAmelCase      )

									self.closed_nodes.append(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_successors(__UpperCAmelCase      )

									for child_node in successors:
													if child_node in self.closed_nodes:
																	continue

													if child_node not in self.open_nodes:
																	self.open_nodes.append(__UpperCAmelCase      )
													else:
																	# retrieve the best current path
																	SCREAMING_SNAKE_CASE  :						Tuple      					= self.open_nodes.pop(self.open_nodes.index(__UpperCAmelCase      )      )

																	if child_node.g_cost < better_node.g_cost:
																					self.open_nodes.append(__UpperCAmelCase      )
																	else:
																					self.open_nodes.append(__UpperCAmelCase      )

					if not self.reached:
									return [self.start.pos]
					return None


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : Node      )     ->			list[Node]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= []
					for action in delta:
									SCREAMING_SNAKE_CASE  :						Dict      					= parent.pos_x + action[1]
									SCREAMING_SNAKE_CASE  :						str      					= parent.pos_y + action[0]

									if not (0 <= pos_x <= len(grid[0]      ) - 1 and 0 <= pos_y <= len(__UpperCAmelCase      ) - 1):
													continue

									if grid[pos_y][pos_x] != 0:
													continue

									successors.append(
									    Node(
									        __UpperCAmelCase,					__UpperCAmelCase,					self.target.pos_y,					self.target.pos_x,					parent.g_cost + 1,					__UpperCAmelCase,					)      )
					return successors






	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Node | None      )     ->			Path:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= node
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
					while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x)      )
									SCREAMING_SNAKE_CASE  :						Any      					= current_node.parent
					path.reverse()
					return path


if __name__ == "__main__":
	snake_case_			=     (0, 0)
	snake_case_			=     (len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	print("""------""")

	snake_case_			=     GreedyBestFirst(init, goal)
	snake_case_			=     greedy_bf.search()
	if path:
		for pos_x, pos_y in path:
			snake_case_			=     2

		for elem in grid:
			print(elem)









'''simple docstring'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
	from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




