


import heapq
import sys

import numpy as np

lowercase_    		=     tuple[int, int]




class        _UpperCamelCase			:







							'''simple docstring'''







							def __init__(		self	:					List[str]		):
													_a     			=      []
													_a     			=      set()







							def 		_UpperCAmelCase						(		self	:					Tuple		):
													if not self.empty():
																			return self.elements[0][0]
													else:
																			return float('inf'		)







							def 		_UpperCAmelCase						(		self	:					int		):
													return len(self.elements		) == 0







							def 		_UpperCAmelCase						(		self	:					int		,					SCREAMING_SNAKE_CASE_	:					Optional[int]		,					SCREAMING_SNAKE_CASE_	:					Optional[int]		):
													if item not in self.set:
																			heapq.heappush(self.elements		,					(priority, item)		)
																			self.set.add(lowerCamelCase_		)
													else:
																			# update
																			# print("update", item)
																			_a     			=      []
																			((_a)			,						(_a))     			=      heapq.heappop(self.elements		)
																			while x != item:
																									temp.append((pri, x)		)
																									((_a)			,						(_a))     			=      heapq.heappop(self.elements		)
																			temp.append((priority, item)		)
																			for pro, xxx in temp:
																									heapq.heappush(self.elements		,					(pro, xxx)		)







							def 		_UpperCAmelCase						(		self	:					Optional[Any]		,					SCREAMING_SNAKE_CASE_	:					Optional[Any]		):
													if item in self.set:
																			self.set.remove(lowerCamelCase_		)
																			_a     			=      []
																			((_a)			,						(_a))     			=      heapq.heappop(self.elements		)
																			while x != item:
																									temp.append((pro, x)		)
																									((_a)			,						(_a))     			=      heapq.heappop(self.elements		)
																			for prito, yyy in temp:
																									heapq.heappush(self.elements		,					(prito, yyy)		)







							def 		_UpperCAmelCase						(		self	:					Dict		):
													return self.elements[0][1]







							def 		_UpperCAmelCase						(		self	:					Tuple		):
													((_a)			,						(_a))     			=      heapq.heappop(self.elements		)
													self.set.remove(lowerCamelCase_		)
													return (priority, item)




def 				SCREAMING_SNAKE_CASE       (	_UpperCAmelCase				,  _UpperCAmelCase					)     ->	Tuple:
						# euclidean distance
						_a     			=      np.array(_UpperCAmelCase					)
						_a     			=      np.array(_UpperCAmelCase					)
						return np.linalg.norm(a - b					)


def 				SCREAMING_SNAKE_CASE       (	_UpperCAmelCase				,  _UpperCAmelCase					)     ->	List[str]:
						# integer division by time variable
						return consistent_heuristic(_UpperCAmelCase				,  _UpperCAmelCase					) // t


def 				SCREAMING_SNAKE_CASE       (	_UpperCAmelCase				,  _UpperCAmelCase					)     ->	List[Any]:
						# manhattan distance
						return abs(p[0] - goal[0]					) + abs(p[1] - goal[1]					)


def 				SCREAMING_SNAKE_CASE       (	_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					)     ->	str:
						_a     			=      g_function[start] + Wa * heuristics[i](_UpperCAmelCase				,  _UpperCAmelCase					)
						return ans


def 				SCREAMING_SNAKE_CASE       (	_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					)     ->	int:
						_a     			=      np.chararray((n, n)					)
						for i in range(_UpperCAmelCase					):
												for j in range(_UpperCAmelCase					):
																		_a     			=      '*'

						for i in range(_UpperCAmelCase					):
												for j in range(_UpperCAmelCase					):
																		if (j, (n - 1) - i) in blocks:
																								_a     			=      '#'

						_a     			=      '-'
						_a     			=      back_pointer[goal]
						while x != start:
												((_a)			,						(_a))     			=      x
												# print(x)
												_a     			=      '-'
												_a     			=      back_pointer[x]
						_a     			=      '-'

						for i in range(_UpperCAmelCase					):
												for j in range(_UpperCAmelCase					):
																		if (i, j) == (0, n - 1):
																								print(grid[i][j]				,  end=' '					)
																								print('<-- End position'				,  end=' '					)
																		else:
																								print(grid[i][j]				,  end=' '					)
												print()
						print('^'					)
						print('Start position'					)
						print()
						print('# is an obstacle'					)
						print('- is the path taken by algorithm'					)
						print('PATH TAKEN BY THE ALGORITHM IS:-'					)
						_a     			=      back_pointer[goal]
						while x != start:
												print(_UpperCAmelCase				,  end=' '					)
												_a     			=      back_pointer[x]
						print(_UpperCAmelCase					)
						sys.exit()


def 				SCREAMING_SNAKE_CASE       (	_UpperCAmelCase					)     ->	Any:
						if p[0] < 0 or p[0] > n - 1:
												return False
						if p[1] < 0 or p[1] > n - 1:
												return False
						return True


def 				SCREAMING_SNAKE_CASE       (	_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  )     ->	str:
						for itera in range(_UpperCAmelCase					):
												open_list[itera].remove_element(_UpperCAmelCase					)
						# print("s", s)
						# print("j", j)
						((_a)			,						(_a))     			=      s
						_a     			=      (x - 1, y)
						_a     			=      (x + 1, y)
						_a     			=      (x, y + 1)
						_a     			=      (x, y - 1)

						for neighbours in [left, right, up, down]:
												if neighbours not in blocks:
																		if valid(_UpperCAmelCase					) and neighbours not in visited:
																								# print("neighbour", neighbours)
																								visited.add(_UpperCAmelCase					)
																								_a     			=      -1
																								_a     			=      float('inf'					)

																		if valid(_UpperCAmelCase					) and g_function[neighbours] > g_function[s] + 1:
																								_a     			=      g_function[s] + 1
																								_a     			=      s
																								if neighbours not in close_list_anchor:
																														open_list[0].put(_UpperCAmelCase				,  key(_UpperCAmelCase				,  0				,  _UpperCAmelCase				,  _UpperCAmelCase					)					)
																														if neighbours not in close_list_inad:
																																				for var in range(1				,  _UpperCAmelCase					):
																																										if key(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					) <= Wa * key(
																																										    _UpperCAmelCase				,  0				,  _UpperCAmelCase				,  _UpperCAmelCase					):
																																																open_list[j].put(
																																																    _UpperCAmelCase				,  key(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					)					)


def 				SCREAMING_SNAKE_CASE       (	)     ->	str:
						_a     			=      []
						for x in range(1				,  5					):
												for y in range(1				,  6					):
																		some_list.append((x, y)					)

						for x in range(15				,  20					):
												some_list.append((x, 17)					)

						for x in range(10				,  19					):
												for y in range(1				,  15					):
																		some_list.append((x, y)					)

    # L block
						for x in range(1				,  4					):
												for y in range(12				,  19					):
																		some_list.append((x, y)					)
						for x in range(3				,  13					):
												for y in range(16				,  19					):
																		some_list.append((x, y)					)
						return some_list


lowercase_    		=     {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

lowercase_    		=     [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
lowercase_    		=     make_common_ground()


lowercase_    		=     blocks_blk
# hyper parameters
lowercase_    		=     1
lowercase_    		=     1
lowercase_    		=     20
lowercase_    		=     3  # one consistent and two other inconsistent

# start and end destination
lowercase_    		=     (0, 0)
lowercase_    		=     (n - 1, n - 1)

lowercase_    		=     1


def 				SCREAMING_SNAKE_CASE       (	_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					)     ->	Dict:
						_a     			=      {start: 0, goal: float('inf'					)}
						_a     			=      {start: -1, goal: -1}
						_a     			=      []
						_a     			=      set()

						for i in range(_UpperCAmelCase					):
												open_list.append(PriorityQueue()					)
												open_list[i].put(_UpperCAmelCase				,  key(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					)					)

						_a     			=      []
						_a     			=      []
						while open_list[0].minkey() < float('inf'					):
												for i in range(1				,  _UpperCAmelCase					):
																		# print(open_list[0].minkey(), open_list[i].minkey())
																		if open_list[i].minkey() <= Wa * open_list[0].minkey():
																								global t
																								t += 1
																								if g_function[goal] <= open_list[i].minkey():
																														if g_function[goal] < float('inf'					):
																																				do_something(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					)
																								else:
																														_a			,						_a     			=      open_list[i].top_show()
																														visited.add(_UpperCAmelCase					)
																														expand_state(
																														    _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  )
																														close_list_inad.append(_UpperCAmelCase					)
																		else:
																								if g_function[goal] <= open_list[0].minkey():
																														if g_function[goal] < float('inf'					):
																																				do_something(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					)
																								else:
																														_a     			=      open_list[0].top_show()
																														visited.add(_UpperCAmelCase					)
																														expand_state(
																														    _UpperCAmelCase				,  0				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  )
																														close_list_anchor.append(_UpperCAmelCase					)
						print('No path found to goal'					)
						print()
						for i in range(n - 1				,  -1				,  -1					):
												for j in range(_UpperCAmelCase					):
																		if (j, i) in blocks:
																								print('#'				,  end=' '					)
																		elif (j, i) in back_pointer:
																								if (j, i) == (n - 1, n - 1):
																														print('*'				,  end=' '					)
																								else:
																														print('-'				,  end=' '					)
																		else:
																								print('*'				,  end=' '					)
																		if (j, i) == (n - 1, n - 1):
																								print('<-- End position'				,  end=' '					)
												print()
						print('^'					)
						print('Start position'					)
						print()
						print('# is an obstacle'					)
						print('- is the path taken by algorithm'					)


if __name__ == "__main__":
		multi_a_star(start, goal, n_heuristic)






'''simple docstring'''






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__UpperCAmelCase       							=				False




class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							pass




@nightly
@require_torch_gpu
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''  )

														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt='''first prompt'''   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=2   ,       output_type='''numpy'''   ,       ).images

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(lowerCamelCase_  )
																					lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained(lowerCamelCase_   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      generator.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt='''first prompt'''   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=2   ,       output_type='''numpy'''   ,       ).images

														assert np.abs(image - new_image  ).sum() < 1e-5, "Models don't have the same forward pass"







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''cyberpunk 2077'''
														lowerCAmelCase__			  =      load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''  )
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt=lowerCamelCase_   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=50   ,       output_type='''numpy'''   ,       ).images

														lowerCAmelCase__			  =      image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]  )

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

														lowerCAmelCase__			  =      '''A painting of a squirrel eating a burger '''
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.text_to_image(
														    prompt=lowerCamelCase_   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=50   ,       output_type='''numpy'''  ).images

														lowerCAmelCase__			  =      image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]  )

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

														lowerCAmelCase__			  =      pipe.image_variation(lowerCamelCase_   ,       generator=lowerCamelCase_   ,       output_type='''numpy'''  ).images

														lowerCAmelCase__			  =      image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]  )

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