






"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				Tuple    =				StableDiffusionInstructPixaPixPipeline
	_A       :				List[str]    =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
	_A       :				int    =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	_A       :				int    =				IMAGE_TO_IMAGE_IMAGE_PARAMS
	_A       :				Dict    =				IMAGE_TO_IMAGE_IMAGE_PARAMS





	def    lowerCamelCase(self							):
		torch.manual_seed(0							)
		A_		:						List[Any]	    =   UNetaDConditionModel(
		    block_out_channels=(32, 64)     ,     layers_per_block=2     ,     sample_size=32     ,     in_channels=8     ,     out_channels=4     ,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")     ,     up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")     ,     cross_attention_dim=32     ,     )
		A_		:						Optional[Any]	    =   PNDMScheduler(skip_prk_steps=lowerCAmelCase_							)
		torch.manual_seed(0							)
		A_		:						Optional[int]	    =   AutoencoderKL(
		    block_out_channels=[32, 64]     ,     in_channels=3     ,     out_channels=3     ,     down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]     ,     up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]     ,     latent_channels=4     ,     )
		torch.manual_seed(0							)
		A_		:						Optional[int]	    =   CLIPTextConfig(
		    bos_token_id=0     ,     eos_token_id=2     ,     hidden_size=32     ,     intermediate_size=37     ,     layer_norm_eps=1e-0_5     ,     num_attention_heads=4     ,     num_hidden_layers=5     ,     pad_token_id=1     ,     vocab_size=1000     ,     )
		A_		:						Optional[Any]	    =   CLIPTextModel(lowerCAmelCase_							)
		A_		:						List[Any]	    =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

		A_		:						Tuple	    =   {
		    """unet""": unet,
		    """scheduler""": scheduler,
		    """vae""": vae,
		    """text_encoder""": text_encoder,
		    """tokenizer""": tokenizer,
		    """safety_checker""": None,
		    """feature_extractor""": None,
		}
		return components





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=0							):
		A_		:						Union[str, Any]	    =   floats_tensor((1, 3, 32, 32)     ,     rng=random.Random(lowerCAmelCase_							)							).to(lowerCAmelCase_							)
		A_		:						int	    =   image.cpu().permute(0     ,     2     ,     3     ,     1							)[0]
		A_		:						Any	    =   Image.fromarray(np.uinta(lowerCAmelCase_							)							).convert("""RGB"""							)
		if str(lowerCAmelCase_							).startswith("""mps"""							):
			A_		:						Union[str, Any]	    =   torch.manual_seed(lowerCAmelCase_							)
		else:
			A_		:						str	    =   torch.Generator(device=lowerCAmelCase_							).manual_seed(lowerCAmelCase_							)
		A_		:						str	    =   {
		    """prompt""": """A painting of a squirrel eating a burger""",
		    """image""": image,
		    """generator""": generator,
		    """num_inference_steps""": 2,
		    """guidance_scale""": 6.0,
		    """image_guidance_scale""": 1,
		    """output_type""": """numpy""",
		}
		return inputs





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
		A_		:						List[Any]	    =   self.get_dummy_components()
		A_		:						Tuple	    =   StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_							)
		A_		:						Optional[Any]	    =   sd_pipe.to(lowerCAmelCase_							)
		sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

		A_		:						Optional[int]	    =   self.get_dummy_inputs(lowerCAmelCase_							)
		A_		:						Optional[int]	    =   sd_pipe(**lowerCAmelCase_							).images
		A_		:						List[Any]	    =   image[0, -3:, -3:, -1]
		assert image.shape == (1, 32, 32, 3)
		A_		:						int	    =   np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]							)

		assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3





	def    lowerCamelCase(self							):
		A_		:						str	    =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
		A_		:						Optional[int]	    =   self.get_dummy_components()
		A_		:						List[Any]	    =   StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_							)
		A_		:						List[str]	    =   sd_pipe.to(lowerCAmelCase_							)
		sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

		A_		:						List[str]	    =   self.get_dummy_inputs(lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   """french fries"""
		A_		:						Dict	    =   sd_pipe(**lowerCAmelCase_     ,     negative_prompt=lowerCAmelCase_							)
		A_		:						Dict	    =   output.images
		A_		:						Union[str, Any]	    =   image[0, -3:, -3:, -1]

		assert image.shape == (1, 32, 32, 3)
		A_		:						List[Any]	    =   np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]							)

		assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3





	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
		A_		:						str	    =   self.get_dummy_components()
		A_		:						Tuple	    =   StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_							)
		A_		:						Optional[Any]	    =   sd_pipe.to(lowerCAmelCase_							)
		sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

		A_		:						List[Any]	    =   self.get_dummy_inputs(lowerCAmelCase_							)
		A_		:						List[Any]	    =   [inputs["""prompt"""]] * 2

		A_		:						str	    =   np.array(inputs["""image"""]							).astype(np.floataa							) / 255.0
		A_		:						int	    =   torch.from_numpy(lowerCAmelCase_							).unsqueeze(0							).to(lowerCAmelCase_							)
		A_		:						Dict	    =   image / 2 + 0.5
		A_		:						Optional[Any]	    =   image.permute(0     ,     3     ,     1     ,     2							)
		A_		:						int	    =   image.repeat(2     ,     1     ,     1     ,     1							)

		A_		:						Optional[int]	    =   sd_pipe(**lowerCAmelCase_							).images
		A_		:						Tuple	    =   image[-1, -3:, -3:, -1]

		assert image.shape == (2, 32, 32, 3)
		A_		:						Optional[Any]	    =   np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]							)

		assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3





	def    lowerCamelCase(self							):
		A_		:						int	    =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
		A_		:						Optional[int]	    =   self.get_dummy_components()
		A_		:						List[str]	    =   EulerAncestralDiscreteScheduler(
		    beta_start=0.00085     ,     beta_end=0.012     ,     beta_schedule="""scaled_linear"""							)
		A_		:						List[Any]	    =   StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_							)
		A_		:						Optional[int]	    =   sd_pipe.to(lowerCAmelCase_							)
		sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

		A_		:						Union[str, Any]	    =   self.get_dummy_inputs(lowerCAmelCase_							)
		A_		:						List[Any]	    =   sd_pipe(**lowerCAmelCase_							).images
		A_		:						Optional[Any]	    =   image[0, -3:, -3:, -1]

		A_		:						List[str]	    =   [round(lowerCAmelCase_     ,     4							) for x in image_slice.flatten().tolist()]
		print(""",""".join([str(lowerCAmelCase_							) for x in slice]							)							)

		assert image.shape == (1, 32, 32, 3)
		A_		:						List[str]	    =   np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]							)

		assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3





	def    lowerCamelCase(self							):
		super().test_inference_batch_single_identical(expected_max_diff=3e-3							)





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   self.get_dummy_components()
		A_		:						Optional[Any]	    =   StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_							)
		A_		:						List[Any]	    =   VaeImageProcessor(do_resize=lowerCAmelCase_     ,     do_normalize=lowerCAmelCase_							)
		A_		:						List[Any]	    =   pipe.to(lowerCAmelCase_							)
		pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

		A_		:						Optional[Any]	    =   pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase_     ,     input_image_type="""pt"""							)							)[0]

		A_		:						Tuple	    =   components["""vae"""]
		A_		:						Dict	    =   self.get_dummy_inputs_by_type(lowerCAmelCase_     ,     input_image_type="""pt"""							)

		for image_param in self.image_latents_params:
			if image_param in inputs.keys():
				A_		:						List[str]	    =   vae.encode(inputs[image_param]							).latent_dist.mode()

		A_		:						Dict	    =   pipe(**lowerCAmelCase_							)[0]

		A_		:						List[Any]	    =   np.abs(out - out_latents_inputs							).max()
		self.assertLess(lowerCAmelCase_     ,     1e-4     ,     """passing latents as image input generate different result from passing image"""							)






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





	"""simple docstring"""




	def    lowerCamelCase(self							):
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def    lowerCamelCase(self     ,     lowerCAmelCase_=0							):
		A_		:						int	    =   torch.manual_seed(lowerCAmelCase_							)
		A_		:						int	    =   load_image(
		    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""							)
		A_		:						Optional[Any]	    =   {
		    """prompt""": """turn him into a cyborg""",
		    """image""": image,
		    """generator""": generator,
		    """num_inference_steps""": 3,
		    """guidance_scale""": 7.5,
		    """image_guidance_scale""": 1.0,
		    """output_type""": """numpy""",
		}
		return inputs





	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    """timbrooks/instruct-pix2pix"""     ,     safety_checker=lowerCAmelCase_							)
		pipe.to(lowerCAmelCase_							)
		pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
		pipe.enable_attention_slicing()

		A_		:						List[Any]	    =   self.get_inputs()
		A_		:						int	    =   pipe(**lowerCAmelCase_							).images
		A_		:						str	    =   image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 512, 512, 3)
		A_		:						int	    =   np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]							)

		assert np.abs(expected_slice - image_slice							).max() < 1e-3





	def    lowerCamelCase(self							):
		A_		:						int	    =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    """timbrooks/instruct-pix2pix"""     ,     safety_checker=lowerCAmelCase_							)
		A_		:						int	    =   LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
		pipe.to(lowerCAmelCase_							)
		pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
		pipe.enable_attention_slicing()

		A_		:						Union[str, Any]	    =   self.get_inputs()
		A_		:						Any	    =   pipe(**lowerCAmelCase_							).images
		A_		:						str	    =   image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 512, 512, 3)
		A_		:						List[str]	    =   np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]							)

		assert np.abs(expected_slice - image_slice							).max() < 1e-3





	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    """timbrooks/instruct-pix2pix"""     ,     safety_checker=lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   DDIMScheduler.from_config(pipe.scheduler.config							)
		pipe.to(lowerCAmelCase_							)
		pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
		pipe.enable_attention_slicing()

		A_		:						List[Any]	    =   self.get_inputs()
		A_		:						Optional[int]	    =   pipe(**lowerCAmelCase_							).images
		A_		:						str	    =   image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 512, 512, 3)
		A_		:						int	    =   np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]							)

		assert np.abs(expected_slice - image_slice							).max() < 1e-3





	def    lowerCamelCase(self							):
		A_		:						int	    =   0

		def callback_fn(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							) -> None:
			A_		:						Optional[Any]	    =   True
			nonlocal number_of_steps
			number_of_steps += 1
			if step == 1:
				A_		:						str	    =   latents.detach().cpu().numpy()
				assert latents.shape == (1, 4, 64, 64)
				A_		:						str	    =   latents[0, -3:, -3:, -1]
				A_		:						int	    =   np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]							)

				assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5e-2
			elif step == 2:
				A_		:						Optional[Any]	    =   latents.detach().cpu().numpy()
				assert latents.shape == (1, 4, 64, 64)
				A_		:						Optional[Any]	    =   latents[0, -3:, -3:, -1]
				A_		:						Union[str, Any]	    =   np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]							)

				assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5e-2

		A_		:						Optional[Any]	    =   False

		A_		:						int	    =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    """timbrooks/instruct-pix2pix"""     ,     safety_checker=lowerCAmelCase_     ,     torch_dtype=torch.floataa							)
		A_		:						Tuple	    =   pipe.to(lowerCAmelCase_							)
		pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
		pipe.enable_attention_slicing()

		A_		:						Optional[int]	    =   self.get_inputs()
		pipe(**lowerCAmelCase_     ,     callback=lowerCAmelCase_     ,     callback_steps=1							)
		assert callback_fn.has_been_called
		assert number_of_steps == 3





	def    lowerCamelCase(self							):
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

		A_		:						List[Any]	    =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    """timbrooks/instruct-pix2pix"""     ,     safety_checker=lowerCAmelCase_     ,     torch_dtype=torch.floataa							)
		A_		:						Optional[Any]	    =   pipe.to(lowerCAmelCase_							)
		pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
		pipe.enable_attention_slicing(1							)
		pipe.enable_sequential_cpu_offload()

		A_		:						Dict	    =   self.get_inputs()
		A_		:						Any	    =   pipe(**lowerCAmelCase_							)

		A_		:						int	    =   torch.cuda.max_memory_allocated()
		# make sure that less than 2.2 GB is allocated
		assert mem_bytes < 2.2 * 10**9





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   self.get_inputs()
		# resize to resolution that is divisible by 8 but not 16 or 32
		A_		:						int	    =   inputs["""image"""].resize((504, 504)							)

		A_		:						Optional[Any]	    =   """timbrooks/instruct-pix2pix"""
		A_		:						List[Any]	    =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    lowerCAmelCase_     ,     safety_checker=lowerCAmelCase_     ,     )
		pipe.to(lowerCAmelCase_							)
		pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
		pipe.enable_attention_slicing()

		A_		:						Tuple	    =   pipe(**lowerCAmelCase_							)
		A_		:						str	    =   output.images[0]

		A_		:						Optional[int]	    =   image[255:258, 383:386, -1]

		assert image.shape == (504, 504, 3)
		A_		:						Dict	    =   np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]							)

		assert np.abs(image_slice.flatten() - expected_slice							).max() < 5e-3









"""simple docstring"""
import heapq
import sys

import numpy as np

_lowerCAmelCase         =							tuple[int, int]



class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self							):
		A_		:						Any	    =   []
		A_		:						str	    =   set()





	def    lowerCamelCase(self							):
		if not self.empty():
			return self.elements[0][0]
		else:
			return float("""inf"""							)





	def    lowerCamelCase(self							):
		return len(self.elements							) == 0





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		if item not in self.set:
			heapq.heappush(self.elements     ,     (priority, item)							)
			self.set.add(lowerCAmelCase_							)
		else:
			# update
			# print("update", item)
			A_		:						Union[str, Any]	    =   []
			((A_)    ,     (A_))		:						Union[str, Any]	    =   heapq.heappop(self.elements							)
			while x != item:
				temp.append((pri, x)							)
				((A_)    ,     (A_))		:						Any	    =   heapq.heappop(self.elements							)
			temp.append((priority, item)							)
			for pro, xxx in temp:
				heapq.heappush(self.elements     ,     (pro, xxx)							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		if item in self.set:
			self.set.remove(lowerCAmelCase_							)
			A_		:						int	    =   []
			((A_)    ,     (A_))		:						Union[str, Any]	    =   heapq.heappop(self.elements							)
			while x != item:
				temp.append((pro, x)							)
				((A_)    ,     (A_))		:						List[Any]	    =   heapq.heappop(self.elements							)
			for prito, yyy in temp:
				heapq.heappush(self.elements     ,     (prito, yyy)							)





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





	def    lowerCamelCase(self							):
		((A_)    ,     (A_))		:						Optional[Any]	    =   heapq.heappop(self.elements							)
		self.set.remove(lowerCAmelCase_							)
		return (priority, item)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	# euclidean distance
	A_		:						Optional[Any]	    =   np.array(snake_case__					)
	A_		:						List[str]	    =   np.array(snake_case__					)
	return np.linalg.norm(a - b					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	# integer division by time variable
	return consistent_heuristic(snake_case__   ,   snake_case__					) // t






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	# manhattan distance
	return abs(p[0] - goal[0]					) + abs(p[1] - goal[1]					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Optional[int]	    =   g_function[start] + Wa * heuristics[i](snake_case__   ,   snake_case__					)
	return ans






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Tuple	    =   np.chararray((n, n)					)
	for i in range(snake_case__					):
		for j in range(snake_case__					):
			A_		:						List[Any]	    =   """*"""

	for i in range(snake_case__					):
		for j in range(snake_case__					):
			if (j, (n - 1) - i) in blocks:
				A_		:						Optional[Any]	    =   """#"""

	A_		:						Optional[Any]	    =   """-"""
	A_		:						Optional[Any]	    =   back_pointer[goal]
	while x != start:
		((A_)    ,     (A_))		:						List[str]	    =   x
		# print(x)
		A_		:						Dict	    =   """-"""
		A_		:						int	    =   back_pointer[x]
	A_		:						Optional[int]	    =   """-"""

	for i in range(snake_case__					):
		for j in range(snake_case__					):
			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_		:						List[str]	    =   back_pointer[goal]
	while x != start:
		print(snake_case__   ,   end=""" """					)
		A_		:						Optional[int]	    =   back_pointer[x]
	print(snake_case__					)
	sys.exit()






def 			__UpperCamelCase	(		snake_case__					):
	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 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   ):
	for itera in range(snake_case__					):
		open_list[itera].remove_element(snake_case__					)
	# print("s", s)
	# print("j", j)
	((A_)    ,     (A_))		:						Any	    =   s
	A_		:						int	    =   (x - 1, y)
	A_		:						List[str]	    =   (x + 1, y)
	A_		:						Union[str, Any]	    =   (x, y + 1)
	A_		:						Any	    =   (x, y - 1)

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

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






def 			__UpperCamelCase	(		):
	A_		:						str	    =   []
	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


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

_lowerCAmelCase         =							[
    (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         =							make_common_ground()


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

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

_lowerCAmelCase         =							1






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Tuple	    =   {start: 0, goal: float("""inf"""					)}
	A_		:						List[str]	    =   {start: -1, goal: -1}
	A_		:						str	    =   []
	A_		:						int	    =   set()

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

	A_		:						list[int]	    =   []
	A_		:						list[int]	    =   []
	while open_list[0].minkey() < float("""inf"""					):
		for i in range(1   ,   snake_case__					):
			# 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(snake_case__   ,   snake_case__   ,   snake_case__					)
				else:
					A_    ,     A_		:						Optional[Any]	    =   open_list[i].top_show()
					visited.add(snake_case__					)
					expand_state(
					    snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   )
					close_list_inad.append(snake_case__					)
			else:
				if g_function[goal] <= open_list[0].minkey():
					if g_function[goal] < float("""inf"""					):
						do_something(snake_case__   ,   snake_case__   ,   snake_case__					)
				else:
					A_		:						int	    =   open_list[0].top_show()
					visited.add(snake_case__					)
					expand_state(
					    snake_case__   ,   0   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   )
					close_list_anchor.append(snake_case__					)
	print("""No path found to goal"""					)
	print()
	for i in range(n - 1   ,   -1   ,   -1					):
		for j in range(snake_case__					):
			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)

