

from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
A_	:			List[Any]  	=      0

A_	:			Tuple  	=      [
    [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_	:			Any  	=      [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

A_	:			Dict  	=      tuple[int, int]



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   int					       =		pos_x
						A__							:   Dict					       =		pos_y
						A__							:   Tuple					       =		(pos_y, pos_x)
						A__							:   List[Any]					       =		goal_x
						A__							:   Dict					       =		goal_y
						A__							:   Union[str, Any]					       =		g_cost
						A__							:   Union[str, Any]					       =		parent
						A__							:   List[str]					       =		self.calculate_heuristic()
						A__							:   Optional[int]					       =		self.g_cost + self.h_cost






					def   __A      (			self      ):
						A__							:   Any					       =		self.pos_x - self.goal_x
						A__							:   int					       =		self.pos_y - self.goal_y
						if HEURISTIC == 1:
							return abs(A__      ) + abs(A__      )
						else:
							return sqrt(dy**2 + dx**2      )






					def __lt__(			self   ,	A__      ):
						return self.f_cost < other.f_cost



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__      ):
						A__							:   Optional[Any]					       =		Node(start[1]   ,	start[0]   ,	goal[1]   ,	goal[0]   ,	0   ,	A__      )
						A__							:   Optional[int]					       =		Node(goal[1]   ,	goal[0]   ,	goal[1]   ,	goal[0]   ,	9_9999   ,	A__      )

						A__							:   Union[str, Any]					       =		[self.start]
						A__							:   list[Node]					       =		[]

						A__							:   Any					       =		False






					def   __A      (			self      ):
						while self.open_nodes:
							# Open Nodes are sorted using __lt__
							self.open_nodes.sort()
							A__							:   str					       =		self.open_nodes.pop(0      )

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

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

							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(A__      )
								else:
									# retrieve the best current path
									A__							:   Optional[Any]					       =		self.open_nodes.pop(self.open_nodes.index(A__      )      )

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

						return [self.start.pos]






					def   __A      (			self   ,	A__      ):
						A__							:   List[str]					       =		[]
						for action in delta:
							A__							:   int					       =		parent.pos_x + action[1]
							A__							:   Union[str, Any]					       =		parent.pos_y + action[0]
							if not (0 <= pos_x <= len(grid[0]      ) - 1 and 0 <= pos_y <= len(A__      ) - 1):
								continue

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

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






					def   __A      (			self   ,	A__      ):
						A__							:   Dict					       =		node
						A__							:   List[str]					       =		[]
						while current_node is not None:
							path.append((current_node.pos_y, current_node.pos_x)      )
							A__							:   Dict					       =		current_node.parent
						path.reverse()
						return path



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__      ):
						A__							:   str					       =		AStar(A__   ,	A__      )
						A__							:   str					       =		AStar(A__   ,	A__      )
						A__							:   Tuple					       =		False






					def   __A      (			self      ):
						while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
							self.fwd_astar.open_nodes.sort()
							self.bwd_astar.open_nodes.sort()
							A__							:   List[str]					       =		self.fwd_astar.open_nodes.pop(0      )
							A__							:   List[Any]					       =		self.bwd_astar.open_nodes.pop(0      )

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

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

							A__							:   int					       =		current_bwd_node
							A__							:   Tuple					       =		current_fwd_node

							A__							:   str					       =		{
							    self.fwd_astar: self.fwd_astar.get_successors(A__      ),
							    self.bwd_astar: self.bwd_astar.get_successors(A__      ),
							}

							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(A__      )
									else:
										# retrieve the best current path
										A__							:   str					       =		astar.open_nodes.pop(
										    astar.open_nodes.index(A__      )      )

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

						return [self.fwd_astar.start.pos]






					def   __A      (			self   ,	A__   ,	A__      ):
						A__							:   List[Any]					       =		self.fwd_astar.retrace_path(A__      )
						A__							:   Optional[Any]					       =		self.bwd_astar.retrace_path(A__      )
						bwd_path.pop()
						bwd_path.reverse()
						A__							:   Any					       =		fwd_path + bwd_path
						return path


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

	A_	:			Optional[int]  	=      time.time()
	A_	:			Union[str, Any]  	=      AStar(init, goal)
	A_	:			Optional[Any]  	=      a_star.search()
	A_	:			Optional[int]  	=      time.time() - start_time
	print(f'''AStar execution time = {end_time:f} seconds''')

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






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class    _a						(__magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Any  							=	KandinskyVaaImgaImgPipeline
					UpperCAmelCase__:       Optional[Any]  							=	['''image_embeds''', '''negative_image_embeds''', '''image''']
					UpperCAmelCase__:       str  							=	[
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					]
					UpperCAmelCase__:       int  							=	[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''strength''',
					    '''guidance_scale''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					UpperCAmelCase__:       Union[str, Any]  							=	False






					@property
					def   __A      (			self      ):
						return 32






					@property
					def   __A      (			self      ):
						return 32






					@property
					def   __A      (			self      ):
						return self.time_input_dim






					@property
					def   __A      (			self      ):
						return self.time_input_dim * 4






					@property
					def   __A      (			self      ):
						return 100






					@property
					def   __A      (			self      ):
						torch.manual_seed(0      )

						A__							:   Dict					       =		{
						    """in_channels""": 4,
						    # Out channels is double in channels because predicts mean and variance
						    """out_channels""": 8,
						    """addition_embed_type""": """image""",
						    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
						    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
						    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
						    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    """layers_per_block""": 1,
						    """encoder_hid_dim""": self.text_embedder_hidden_size,
						    """encoder_hid_dim_type""": """image_proj""",
						    """cross_attention_dim""": self.cross_attention_dim,
						    """attention_head_dim""": 4,
						    """resnet_time_scale_shift""": """scale_shift""",
						    """class_embed_type""": None,
						}

						A__							:   List[str]					       =		UNetaDConditionModel(**A__      )
						return model






					@property
					def   __A      (			self      ):
						return {
						    "block_out_channels": [32, 64],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 12,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}






					@property
					def   __A      (			self      ):
						torch.manual_seed(0      )
						A__							:   Tuple					       =		VQModel(**self.dummy_movq_kwargs      )
						return model






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.dummy_unet
						A__							:   Dict					       =		self.dummy_movq

						A__							:   List[Any]					       =		{
						    """num_train_timesteps""": 1000,
						    """beta_schedule""": """linear""",
						    """beta_start""": 0.0_0_0_8_5,
						    """beta_end""": 0.0_1_2,
						    """clip_sample""": False,
						    """set_alpha_to_one""": False,
						    """steps_offset""": 0,
						    """prediction_type""": """epsilon""",
						    """thresholding""": False,
						}

						A__							:   List[str]					       =		DDIMScheduler(**A__      )

						A__							:   List[str]					       =		{
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}

						return components






					def   __A      (			self   ,	A__   ,	A__=0      ):
						A__							:   List[str]					       =		floats_tensor((1, self.text_embedder_hidden_size)   ,	rng=random.Random(A__      )      ).to(A__      )
						A__							:   Any					       =		floats_tensor((1, self.text_embedder_hidden_size)   ,	rng=random.Random(seed + 1      )      ).to(
						    A__      )
						# create init_image
						A__							:   Dict					       =		floats_tensor((1, 3, 64, 64)   ,	rng=random.Random(A__      )      ).to(A__      )
						A__							:   Optional[int]					       =		image.cpu().permute(0   ,	2   ,	3   ,	1      )[0]
						A__							:   Dict					       =		Image.fromarray(np.uinta(A__      )      ).convert("""RGB"""      ).resize((256, 256)      )

						if str(A__      ).startswith("""mps"""      ):
							A__							:   Any					       =		torch.manual_seed(A__      )
						else:
							A__							:   List[Any]					       =		torch.Generator(device=A__      ).manual_seed(A__      )
						A__							:   Optional[int]					       =		{
						    """image""": init_image,
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """generator""": generator,
						    """height""": 64,
						    """width""": 64,
						    """num_inference_steps""": 10,
						    """guidance_scale""": 7.0,
						    """strength""": 0.2,
						    """output_type""": """np""",
						}
						return inputs






					def   __A      (			self      ):
						A__							:   str					       =		"""cpu"""

						A__							:   Any					       =		self.get_dummy_components()

						A__							:   Union[str, Any]					       =		self.pipeline_class(**A__      )
						A__							:   List[str]					       =		pipe.to(A__      )

						pipe.set_progress_bar_config(disable=A__      )

						A__							:   Dict					       =		pipe(**self.get_dummy_inputs(A__      )      )
						A__							:   Any					       =		output.images

						A__							:   List[str]					       =		pipe(
						    **self.get_dummy_inputs(A__      )   ,	return_dict=A__   ,	)[0]

						A__							:   Optional[int]					       =		image[0, -3:, -3:, -1]
						A__							:   str					       =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						A__							:   str					       =		np.array(
						    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]      )
						assert (
						    np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
						), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
						), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""



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


					'''simple docstring'''






					def   __A      (			self      ):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinskyv22/kandinskyv22_img2img_frog.npy"""      )

						A__							:   List[Any]					       =		load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""      )
						A__							:   str					       =		"""A red cartoon frog, 4k"""

						A__							:   int					       =		KandinskyVaaPriorPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-prior"""   ,	torch_dtype=torch.floataa      )
						pipe_prior.to(A__      )

						A__							:   List[Any]					       =		KandinskyVaaImgaImgPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-decoder"""   ,	torch_dtype=torch.floataa      )
						A__							:   List[str]					       =		pipeline.to(A__      )

						pipeline.set_progress_bar_config(disable=A__      )

						A__							:   Tuple					       =		torch.Generator(device="""cpu"""      ).manual_seed(0      )
						A__       ,       A__							:   Optional[Any]					       =		pipe_prior(
						    A__   ,	generator=A__   ,	num_inference_steps=5   ,	negative_prompt=""""""   ,	).to_tuple()

						A__							:   str					       =		pipeline(
						    image=A__   ,	image_embeds=A__   ,	negative_image_embeds=A__   ,	generator=A__   ,	num_inference_steps=100   ,	height=768   ,	width=768   ,	strength=0.2   ,	output_type="""np"""   ,	)

						A__							:   Optional[int]					       =		output.images[0]

						assert image.shape == (768, 768, 3)

						assert_mean_pixel_difference(A__   ,	A__      )



