'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference






class 						A   (   UpperCAmelCase       ,	unittest.TestCase   ):
							a_        =      ShapEImgaImgPipeline
							a_        =      ['''image''']
							a_        =      ['''image''']
							a_        =      [
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							a_        =      False





							@property
							def       snake_case__						(      self				:							Tuple	)							->	List[str]:
								return 3_2





							@property
							def       snake_case__						(      self				:							Optional[Any]	)							->	Optional[int]:
								return 3_2





							@property
							def       snake_case__						(      self				:							Optional[int]	)							->	str:
								return self.time_input_dim * 4





							@property
							def       snake_case__						(      self				:							List[Any]	)							->	int:
								return 8





							@property
							def       snake_case__						(      self				:							Optional[int]	)							->	str:
								torch.manual_seed(0	)
								__UpperCAmelCase							=			CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size     ,		image_size=6_4     ,		projection_dim=self.text_embedder_hidden_size     ,		intermediate_size=3_7     ,		num_attention_heads=4     ,		num_channels=3     ,		num_hidden_layers=5     ,		patch_size=1     ,		)

								__UpperCAmelCase							=			CLIPVisionModel(__a	)
								return model





							@property
							def       snake_case__						(      self				:							int	)							->	Union[str, Any]:
								__UpperCAmelCase							=			CLIPImageProcessor(
								    crop_size=2_2_4     ,		do_center_crop=__a     ,		do_normalize=__a     ,		do_resize=__a     ,		image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]     ,		image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]     ,		resample=3     ,		size=2_2_4     ,		)

								return image_processor





							@property
							def       snake_case__						(      self				:							List[str]	)							->	Dict:
								torch.manual_seed(0	)

								__UpperCAmelCase							=			{
								    '''num_attention_heads''': 2,
								    '''attention_head_dim''': 1_6,
								    '''embedding_dim''': self.time_input_dim,
								    '''num_embeddings''': 3_2,
								    '''embedding_proj_dim''': self.text_embedder_hidden_size,
								    '''time_embed_dim''': self.time_embed_dim,
								    '''num_layers''': 1,
								    '''clip_embed_dim''': self.time_input_dim * 2,
								    '''additional_embeddings''': 0,
								    '''time_embed_act_fn''': '''gelu''',
								    '''norm_in_type''': '''layer''',
								    '''embedding_proj_norm_type''': '''layer''',
								    '''encoder_hid_proj_type''': None,
								    '''added_emb_type''': None,
								}

								__UpperCAmelCase							=			PriorTransformer(**__a	)
								return model





							@property
							def       snake_case__						(      self				:							Dict	)							->	Dict:
								torch.manual_seed(0	)

								__UpperCAmelCase							=			{
								    '''param_shapes''': (
								        (self.renderer_dim, 9_3),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								    ),
								    '''d_latent''': self.time_input_dim,
								    '''d_hidden''': self.renderer_dim,
								    '''n_output''': 1_2,
								    '''background''': (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								__UpperCAmelCase							=			ShapERenderer(**__a	)
								return model





							def       snake_case__						(      self				:							Optional[int]	)							->	Optional[int]:
								__UpperCAmelCase							=			self.dummy_prior
								__UpperCAmelCase							=			self.dummy_image_encoder
								__UpperCAmelCase							=			self.dummy_image_processor
								__UpperCAmelCase							=			self.dummy_renderer

								__UpperCAmelCase							=			HeunDiscreteScheduler(
								    beta_schedule='''exp'''     ,		num_train_timesteps=1_0_2_4     ,		prediction_type='''sample'''     ,		use_karras_sigmas=__a     ,		clip_sample=__a     ,		clip_sample_range=1.0     ,		)
								__UpperCAmelCase							=			{
								    '''prior''': prior,
								    '''image_encoder''': image_encoder,
								    '''image_processor''': image_processor,
								    '''renderer''': renderer,
								    '''scheduler''': scheduler,
								}

								return components





							def       snake_case__						(      self				:							Tuple     ,		__a				:							Dict     ,		__a				:							Union[str, Any]=0	)							->	List[str]:
								__UpperCAmelCase							=			floats_tensor((1, 3, 6_4, 6_4)     ,		rng=random.Random(__a	)	).to(__a	)

								if str(__a	).startswith('''mps'''	):
									__UpperCAmelCase							=			torch.manual_seed(__a	)
								else:
									__UpperCAmelCase							=			torch.Generator(device=__a	).manual_seed(__a	)
								__UpperCAmelCase							=			{
								    '''image''': input_image,
								    '''generator''': generator,
								    '''num_inference_steps''': 1,
								    '''frame_size''': 3_2,
								    '''output_type''': '''np''',
								}
								return inputs





							def       snake_case__						(      self				:							Optional[int]	)							->	Union[str, Any]:
								__UpperCAmelCase							=			'''cpu'''

								__UpperCAmelCase							=			self.get_dummy_components()

								__UpperCAmelCase							=			self.pipeline_class(**__a	)
								__UpperCAmelCase							=			pipe.to(__a	)

								pipe.set_progress_bar_config(disable=__a	)

								__UpperCAmelCase							=			pipe(**self.get_dummy_inputs(__a	)	)
								__UpperCAmelCase							=			output.images[0]
								__UpperCAmelCase							=			image[0, -3:, -3:, -1]

								assert image.shape == (2_0, 3_2, 3_2, 3)

								__UpperCAmelCase							=			np.array(
								    [
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								    ]	)

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





							def       snake_case__						(      self				:							str	)							->	List[str]:
								# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
								self._test_inference_batch_consistent(batch_sizes=[1, 2]	)





							def       snake_case__						(      self				:							Optional[Any]	)							->	Optional[Any]:
								__UpperCAmelCase							=			torch_device == '''cpu'''
								__UpperCAmelCase							=			True
								self._test_inference_batch_single_identical(
								    batch_size=2     ,		test_max_difference=__a     ,		relax_max_difference=__a     ,		)






							def       snake_case__						(      self				:							Dict	)							->	List[str]:
								__UpperCAmelCase							=			self.get_dummy_components()
								__UpperCAmelCase							=			self.pipeline_class(**__a	)
								__UpperCAmelCase							=			pipe.to(__a	)
								pipe.set_progress_bar_config(disable=__a	)

								__UpperCAmelCase							=			1
								__UpperCAmelCase							=			2

								__UpperCAmelCase							=			self.get_dummy_inputs(__a	)

								for key in inputs.keys():
									if key in self.batch_params:
										__UpperCAmelCase							=			batch_size * [inputs[key]]

								__UpperCAmelCase							=			pipe(**__a     ,		num_images_per_prompt=__a	)[0]

								assert images.shape[0] == batch_size * num_images_per_prompt






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





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






							def       snake_case__						(      self				:							Dict	)							->	Any:
								__UpperCAmelCase							=			load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''	)
								__UpperCAmelCase							=			load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/shap_e/test_shap_e_img2img_out.npy'''	)
								__UpperCAmelCase							=			ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''	)
								__UpperCAmelCase							=			pipe.to(__a	)
								pipe.set_progress_bar_config(disable=__a	)

								__UpperCAmelCase							=			torch.Generator(device=__a	).manual_seed(0	)

								__UpperCAmelCase							=			pipe(
								    __a     ,		generator=__a     ,		guidance_scale=3.0     ,		num_inference_steps=6_4     ,		frame_size=6_4     ,		output_type='''np'''     ,		).images[0]

								assert images.shape == (2_0, 6_4, 6_4, 3)

								assert_mean_pixel_difference(__a     ,		__a	)

'''simple docstring'''


import heapq
import sys

import numpy as np

__lowerCAmelCase	:       Any						       =	tuple[int, int]






class 						A   :





							def __init__(      self				:							Optional[int]	)							->	int:
								__UpperCAmelCase							=			[]
								__UpperCAmelCase							=			set()





							def       snake_case__						(      self				:							Optional[Any]	)							->	List[Any]:
								if not self.empty():
									return self.elements[0][0]
								else:
									return float('''inf'''	)





							def       snake_case__						(      self				:							Dict	)							->	Optional[int]:
								return len(self.elements	) == 0





							def       snake_case__						(      self				:							Optional[int]     ,		__a				:							Optional[Any]     ,		__a				:							Dict	)							->	Optional[Any]:
								if item not in self.set:
									heapq.heappush(self.elements     ,		(priority, item)	)
									self.set.add(__a	)
								else:
									# update
									# print("update", item)
									__UpperCAmelCase							=			[]
									((__UpperCAmelCase)				,			(__UpperCAmelCase))							=			heapq.heappop(self.elements	)
									while x != item:
										temp.append((pri, x)	)
										((__UpperCAmelCase)				,			(__UpperCAmelCase))							=			heapq.heappop(self.elements	)
									temp.append((priority, item)	)
									for pro, xxx in temp:
										heapq.heappush(self.elements     ,		(pro, xxx)	)





							def       snake_case__						(      self				:							int     ,		__a				:							Any	)							->	int:
								if item in self.set:
									self.set.remove(__a	)
									__UpperCAmelCase							=			[]
									((__UpperCAmelCase)				,			(__UpperCAmelCase))							=			heapq.heappop(self.elements	)
									while x != item:
										temp.append((pro, x)	)
										((__UpperCAmelCase)				,			(__UpperCAmelCase))							=			heapq.heappop(self.elements	)
									for prito, yyy in temp:
										heapq.heappush(self.elements     ,		(prito, yyy)	)





							def       snake_case__						(      self				:							List[str]	)							->	Dict:
								return self.elements[0][1]






							def       snake_case__						(      self				:							Any	)							->	List[str]:
								((__UpperCAmelCase)				,			(__UpperCAmelCase))							=			heapq.heappop(self.elements	)
								self.set.remove(__a	)
								return (priority, item)





def       lowerCAmelCase					(		UpperCamelCase__				:   TPos    ,					UpperCamelCase__				:   TPos       ):


	"""simple docstring"""
	# euclidean distance
	__UpperCAmelCase							=			np.array(UpperCamelCase__       )
	__UpperCAmelCase							=			np.array(UpperCamelCase__       )
	return np.linalg.norm(a - b       )





def       lowerCAmelCase					(		UpperCamelCase__				:   TPos    ,					UpperCamelCase__				:   TPos       ):


	"""simple docstring"""
	# integer division by time variable
	return consistent_heuristic(UpperCamelCase__    ,					UpperCamelCase__       ) // t





def       lowerCAmelCase					(		UpperCamelCase__				:   TPos    ,					UpperCamelCase__				:   TPos       ):


	"""simple docstring"""
	# manhattan distance
	return abs(p[0] - goal[0]       ) + abs(p[1] - goal[1]       )





def       lowerCAmelCase					(		UpperCamelCase__				:   TPos    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   TPos    ,					UpperCamelCase__				:   dict[TPos, float]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			g_function[start] + Wa * heuristics[i](UpperCamelCase__    ,					UpperCamelCase__       )
	return ans





def       lowerCAmelCase					(		UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   Optional[int]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			np.chararray((n, n)       )
	for i in range(UpperCamelCase__       ):
		for j in range(UpperCamelCase__       ):
			__UpperCAmelCase							=			'''*'''

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

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

	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:-'''       )
	__UpperCAmelCase							=			back_pointer[goal]
	while x != start:
		print(UpperCamelCase__    ,					end=''' '''       )
		__UpperCAmelCase							=			back_pointer[x]
	print(UpperCamelCase__       )
	sys.exit()





def       lowerCAmelCase					(		UpperCamelCase__				:   TPos       ):


	"""simple docstring"""
	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       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   Tuple    ,					):


	"""simple docstring"""
	for itera in range(UpperCamelCase__       ):
		open_list[itera].remove_element(UpperCamelCase__       )
	# print("s", s)
	# print("j", j)
	((__UpperCAmelCase)				,			(__UpperCAmelCase))							=			s
	__UpperCAmelCase							=			(x - 1, y)
	__UpperCAmelCase							=			(x + 1, y)
	__UpperCAmelCase							=			(x, y + 1)
	__UpperCAmelCase							=			(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__       )
				__UpperCAmelCase							=			-1
				__UpperCAmelCase							=			float('''inf'''       )

			if valid(UpperCamelCase__       ) and g_function[neighbours] > g_function[s] + 1:
				__UpperCAmelCase							=			g_function[s] + 1
				__UpperCAmelCase							=			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       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			[]
	for x in range(1    ,					5       ):
		for y in range(1    ,					6       ):
			some_list.append((x, y)       )

	for x in range(1_5    ,					2_0       ):
		some_list.append((x, 1_7)       )

	for x in range(1_0    ,					1_9       ):
		for y in range(1    ,					1_5       ):
			some_list.append((x, y)       )

    # L block
	for x in range(1    ,					4       ):
		for y in range(1_2    ,					1_9       ):
			some_list.append((x, y)       )
	for x in range(3    ,					1_3       ):
		for y in range(1_6    ,					1_9       ):
			some_list.append((x, y)       )
	return some_list


__lowerCAmelCase	:       Optional[Any]						       =	{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

__lowerCAmelCase	:       List[Any]						       =	[
    (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),
]
__lowerCAmelCase	:       Dict						       =	make_common_ground()


__lowerCAmelCase	:       int						       =	blocks_blk
# hyper parameters
__lowerCAmelCase	:       Dict						       =	1
__lowerCAmelCase	:       List[str]						       =	1
__lowerCAmelCase	:       Union[str, Any]						       =	20
__lowerCAmelCase	:       Any						       =	3  # one consistent and two other inconsistent

# start and end destination
__lowerCAmelCase	:       Optional[Any]						       =	(0, 0)
__lowerCAmelCase	:       Any						       =	(n - 1, n - 1)

__lowerCAmelCase	:       Optional[int]						       =	1





def       lowerCAmelCase					(		UpperCamelCase__				:   TPos    ,					UpperCamelCase__				:   TPos    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			{start: 0, goal: float('''inf'''       )}
	__UpperCAmelCase							=			{start: -1, goal: -1}
	__UpperCAmelCase							=			[]
	__UpperCAmelCase							=			set()

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

	__UpperCAmelCase							=			[]
	__UpperCAmelCase							=			[]
	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:
					__UpperCAmelCase				,			__UpperCAmelCase							=			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:
					__UpperCAmelCase							=			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)
