






'''simple docstring'''







from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_A	:			Dict         =0

_A	:			Optional[Any]         =[
    [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],
]

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

_A	:			Optional[Any]         =tuple[int, int]

class _lowercase	:




							def __init__(							self:				List[str]			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int			,							UpperCamelCase__:				Node | None			,							):
														lowerCamelCase__					:	Optional[int]         =       pos_x
														lowerCamelCase__					:	List[Any]         =       pos_y
														lowerCamelCase__					:	Dict         =       (pos_y, pos_x)
														lowerCamelCase__					:	str         =       goal_x
														lowerCamelCase__					:	Optional[int]         =       goal_y
														lowerCamelCase__					:	List[str]         =       g_cost
														lowerCamelCase__					:	Tuple         =       parent
														lowerCamelCase__					:	Any         =       self.calculate_heuristic()
														lowerCamelCase__					:	Tuple         =       self.g_cost + self.h_cost




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.pos_x - self.goal_x
														lowerCamelCase__					:	Tuple         =       self.pos_y - self.goal_y
														if HEURISTIC == 1:
																					return abs(UpperCamelCase__     ) + abs(UpperCamelCase__     )
														else:
																					return sqrt(dy**2 + dx**2     )




							def __lt__(							self:				List[str]			,							UpperCamelCase__:				Node     ):
														return self.f_cost < other.f_cost



class _lowercase	:




							def __init__(							self:				Tuple			,							UpperCamelCase__:				TPosition			,							UpperCamelCase__:				TPosition     ):
														lowerCamelCase__					:	Any         =       Node(start[1]			,							start[0]			,							goal[1]			,							goal[0]			,							0			,							UpperCamelCase__     )
														lowerCamelCase__					:	int         =       Node(goal[1]			,							goal[0]			,							goal[1]			,							goal[0]			,							99_999			,							UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       [self.start]
														lowerCamelCase__					:	list[Node]         =       []

														lowerCamelCase__					:	Dict         =       False




							def 		lowerCamelCase_				(							self:				int     ):
														while self.open_nodes:
																					# Open Nodes are sorted using __lt__
																					self.open_nodes.sort()
																					lowerCamelCase__					:	int         =       self.open_nodes.pop(0     )

																					if current_node.pos == self.target.pos:
																												return self.retrace_path(UpperCamelCase__     )

																					self.closed_nodes.append(UpperCamelCase__     )
																					lowerCamelCase__					:	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
																																			lowerCamelCase__					:	Optional[Any]         =       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__     )

														return [self.start.pos]




							def 		lowerCamelCase_				(							self:				Dict			,							UpperCamelCase__:				Node     ):
														lowerCamelCase__					:	Tuple         =       []
														for action in delta:
																					lowerCamelCase__					:	Any         =       parent.pos_x + action[1]
																					lowerCamelCase__					:	List[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 		lowerCamelCase_				(							self:				Any			,							UpperCamelCase__:				Node | None     ):
														lowerCamelCase__					:	Optional[Any]         =       node
														lowerCamelCase__					:	Optional[Any]         =       []
														while current_node is not None:
																					path.append((current_node.pos_y, current_node.pos_x)     )
																					lowerCamelCase__					:	List[Any]         =       current_node.parent
														path.reverse()
														return path



class _lowercase	:




							def __init__(							self:				str			,							UpperCamelCase__:				TPosition			,							UpperCamelCase__:				TPosition     ):
														lowerCamelCase__					:	str         =       AStar(UpperCamelCase__			,							UpperCamelCase__     )
														lowerCamelCase__					:	Dict         =       AStar(UpperCamelCase__			,							UpperCamelCase__     )
														lowerCamelCase__					:	str         =       False




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
																					self.fwd_astar.open_nodes.sort()
																					self.bwd_astar.open_nodes.sort()
																					lowerCamelCase__					:	Any         =       self.fwd_astar.open_nodes.pop(0     )
																					lowerCamelCase__					:	int         =       self.bwd_astar.open_nodes.pop(0     )

																					if current_bwd_node.pos == current_fwd_node.pos:
																												return self.retrace_bidirectional_path(
																												    UpperCamelCase__			,							UpperCamelCase__     )

																					self.fwd_astar.closed_nodes.append(UpperCamelCase__     )
																					self.bwd_astar.closed_nodes.append(UpperCamelCase__     )

																					lowerCamelCase__					:	Union[str, Any]         =       current_bwd_node
																					lowerCamelCase__					:	Optional[int]         =       current_fwd_node

																					lowerCamelCase__					:	int         =       {
																					    self.fwd_astar: self.fwd_astar.get_successors(UpperCamelCase__     ),
																					    self.bwd_astar: self.bwd_astar.get_successors(UpperCamelCase__     ),
																					}

																					for astar in [self.fwd_astar, self.bwd_astar]:
																												for child_node in successors[astar]:
																																			if child_node in astar.closed_nodes:
																																										continue

																																			if child_node not in astar.open_nodes:
																																										astar.open_nodes.append(UpperCamelCase__     )
																																			else:
																																										# retrieve the best current path
																																										lowerCamelCase__					:	Optional[Any]         =       astar.open_nodes.pop(
																																										    astar.open_nodes.index(UpperCamelCase__     )     )

																																										if child_node.g_cost < better_node.g_cost:
																																																	astar.open_nodes.append(UpperCamelCase__     )
																																										else:
																																																	astar.open_nodes.append(UpperCamelCase__     )

														return [self.fwd_astar.start.pos]




							def 		lowerCamelCase_				(							self:				List[Any]			,							UpperCamelCase__:				Node			,							UpperCamelCase__:				Node     ):
														lowerCamelCase__					:	Any         =       self.fwd_astar.retrace_path(UpperCamelCase__     )
														lowerCamelCase__					:	Any         =       self.bwd_astar.retrace_path(UpperCamelCase__     )
														bwd_path.pop()
														bwd_path.reverse()
														lowerCamelCase__					:	int         =       fwd_path + bwd_path
														return path


if __name__ == "__main__":
			# all coordinates are given in format [y,x]
			_A	:			int         =(0, 0)
			_A	:			Any         =(len(grid) - 1, len(grid[0]) - 1)
			for elem in grid:
						print(elem)

			_A	:			List[Any]         =time.time()
			_A	:			Optional[Any]         =AStar(init, goal)
			_A	:			List[Any]         =a_star.search()
			_A	:			Dict         =time.time() - start_time
			print(F'AStar execution time = {end_time:f} seconds')

			_A	:			List[Any]         =time.time()
			_A	:			int         =BidirectionalAStar(init, goal)
			_A	:			List[str]         =time.time() - bd_start_time
			print(F'BidirectionalAStar execution time = {bd_end_time:f} seconds')









'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_A	:			Union[str, Any]         =False

class _lowercase	(				unittest.TestCase		):
							pass



@slow
@require_torch_gpu
class _lowercase	(				unittest.TestCase		):




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Tuple         =       VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""     )
														pipe.to(UpperCamelCase__     )
														pipe.set_progress_bar_config(disable=UpperCamelCase__     )

														lowerCamelCase__					:	Optional[int]         =       load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""     )
														lowerCamelCase__					:	List[Any]         =       torch.manual_seed(0     )
														lowerCamelCase__					:	List[Any]         =       pipe(
														    image=UpperCamelCase__			,							generator=UpperCamelCase__			,							guidance_scale=7.5			,							num_inference_steps=50			,							output_type="""numpy"""			,							).images

														lowerCamelCase__					:	List[str]         =       image[0, 253:256, 253:256, -1]

														assert image.shape == (1, 512, 512, 3)
														lowerCamelCase__					:	Tuple         =       np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

														assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2

