

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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 				lowercase       ( lowercase_			,						lowercase_			,						lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				Tuple     =    StableDiffusionInpaintPipeline
				__SCREAMING_SNAKE_CASE      :				Any     =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				__SCREAMING_SNAKE_CASE      :				Optional[Any]     =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				__SCREAMING_SNAKE_CASE      :				List[Any]     =    frozenset(
				    []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				__SCREAMING_SNAKE_CASE      :				int     =    frozenset([]    )




				def 					a							(      self ):
											torch.manual_seed(0 )
											snake_case_           =					UNetaDConditionModel(
											    block_out_channels=(32, 64)   ,     layers_per_block=2   ,     sample_size=32   ,     in_channels=9   ,     out_channels=4   ,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,     cross_attention_dim=32   ,     attention_head_dim=(2, 4)   ,     use_linear_projection=snake_case   ,     )
											snake_case_           =					PNDMScheduler(skip_prk_steps=snake_case )
											torch.manual_seed(0 )
											snake_case_           =					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   ,     sample_size=128   ,     )
											torch.manual_seed(0 )
											snake_case_           =					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   ,     hidden_act='gelu'   ,     projection_dim=512   ,     )
											snake_case_           =					CLIPTextModel(snake_case )
											snake_case_           =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

											snake_case_           =					{
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'safety_checker': None,
											    'feature_extractor': None,
											}
											return components




				def 					a							(      self   ,     snake_case   ,     snake_case=0 ):
											# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
											snake_case_           =					floats_tensor((1, 3, 32, 32)   ,     rng=random.Random(snake_case ) ).to(snake_case )
											snake_case_           =					image.cpu().permute(0   ,     2   ,     3   ,     1 )[0]
											snake_case_           =					Image.fromarray(np.uinta(snake_case ) ).convert('RGB' ).resize((64, 64) )
											snake_case_           =					Image.fromarray(np.uinta(image + 4 ) ).convert('RGB' ).resize((64, 64) )
											if str(snake_case ).startswith('mps' ):
																		snake_case_           =					torch.manual_seed(snake_case )
											else:
																		snake_case_           =					torch.Generator(device=snake_case ).manual_seed(snake_case )
											snake_case_           =					{
											    'prompt': 'A painting of a squirrel eating a burger',
											    'image': init_image,
											    'mask_image': mask_image,
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs




				def 					a							(      self ):
											snake_case_           =					'cpu'  # ensure determinism for the device-dependent torch.Generator
											snake_case_           =					self.get_dummy_components()
											snake_case_           =					StableDiffusionInpaintPipeline(**snake_case )
											snake_case_           =					sd_pipe.to(snake_case )
											sd_pipe.set_progress_bar_config(disable=snake_case )

											snake_case_           =					self.get_dummy_inputs(snake_case )
											snake_case_           =					sd_pipe(**snake_case ).images
											snake_case_           =					image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											snake_case_           =					np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76] )

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




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







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




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




				def 					a							(      self ):
											snake_case_           =					load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png' )
											snake_case_           =					load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )
											snake_case_           =					load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
											    '/yellow_cat_sitting_on_a_park_bench.npy' )

											snake_case_           =					'stabilityai/stable-diffusion-2-inpainting'
											snake_case_           =					StableDiffusionInpaintPipeline.from_pretrained(snake_case   ,     safety_checker=snake_case )
											pipe.to(snake_case )
											pipe.set_progress_bar_config(disable=snake_case )
											pipe.enable_attention_slicing()

											snake_case_           =					'Face of a yellow cat, high resolution, sitting on a park bench'

											snake_case_           =					torch.manual_seed(0 )
											snake_case_           =					pipe(
											    prompt=snake_case   ,     image=snake_case   ,     mask_image=snake_case   ,     generator=snake_case   ,     output_type='np'   ,     )
											snake_case_           =					output.images[0]

											assert image.shape == (512, 512, 3)
											assert np.abs(expected_image - image ).max() < 9e-3




				def 					a							(      self ):
											snake_case_           =					load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png' )
											snake_case_           =					load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )
											snake_case_           =					load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
											    '/yellow_cat_sitting_on_a_park_bench_fp16.npy' )

											snake_case_           =					'stabilityai/stable-diffusion-2-inpainting'
											snake_case_           =					StableDiffusionInpaintPipeline.from_pretrained(
											    snake_case   ,     torch_dtype=torch.floataa   ,     safety_checker=snake_case   ,     )
											pipe.to(snake_case )
											pipe.set_progress_bar_config(disable=snake_case )
											pipe.enable_attention_slicing()

											snake_case_           =					'Face of a yellow cat, high resolution, sitting on a park bench'

											snake_case_           =					torch.manual_seed(0 )
											snake_case_           =					pipe(
											    prompt=snake_case   ,     image=snake_case   ,     mask_image=snake_case   ,     generator=snake_case   ,     output_type='np'   ,     )
											snake_case_           =					output.images[0]

											assert image.shape == (512, 512, 3)
											assert np.abs(expected_image - image ).max() < 5e-1




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

											snake_case_           =					load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png' )
											snake_case_           =					load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )

											snake_case_           =					'stabilityai/stable-diffusion-2-inpainting'
											snake_case_           =					PNDMScheduler.from_pretrained(snake_case   ,     subfolder='scheduler' )
											snake_case_           =					StableDiffusionInpaintPipeline.from_pretrained(
											    snake_case   ,     safety_checker=snake_case   ,     scheduler=snake_case   ,     torch_dtype=torch.floataa   ,     )
											pipe.to(snake_case )
											pipe.set_progress_bar_config(disable=snake_case )
											pipe.enable_attention_slicing(1 )
											pipe.enable_sequential_cpu_offload()

											snake_case_           =					'Face of a yellow cat, high resolution, sitting on a park bench'

											snake_case_           =					torch.manual_seed(0 )
											snake_case_           =					pipe(
											    prompt=snake_case   ,     image=snake_case   ,     mask_image=snake_case   ,     generator=snake_case   ,     num_inference_steps=2   ,     output_type='np'   ,     )

											snake_case_           =					torch.cuda.max_memory_allocated()
											# make sure that less than 2.65 GB is allocated
											assert mem_bytes < 2.65 * 10**9







import heapq
import sys

import numpy as np

_UpperCAmelCase	:     str		       = tuple[int, int]







class 				lowercase       :




				def __init__(      self ):
											snake_case_           =					[]
											snake_case_           =					set()




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




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




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											if item not in self.set:
																		heapq.heappush(self.elements   ,     (priority, item) )
																		self.set.add(snake_case )
											else:
																		# update
																		# print("update", item)
																		snake_case_           =					[]
																		((snake_case_)       ,					(snake_case_))           =					heapq.heappop(self.elements )
																		while x != item:
																									temp.append((pri, x) )
																									((snake_case_)       ,					(snake_case_))           =					heapq.heappop(self.elements )
																		temp.append((priority, item) )
																		for pro, xxx in temp:
																									heapq.heappush(self.elements   ,     (pro, xxx) )




				def 					a							(      self   ,     snake_case ):
											if item in self.set:
																		self.set.remove(snake_case )
																		snake_case_           =					[]
																		((snake_case_)       ,					(snake_case_))           =					heapq.heappop(self.elements )
																		while x != item:
																									temp.append((pro, x) )
																									((snake_case_)       ,					(snake_case_))           =					heapq.heappop(self.elements )
																		for prito, yyy in temp:
																									heapq.heappush(self.elements   ,     (prito, yyy) )




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




				def 					a							(      self ):
											((snake_case_)       ,					(snake_case_))           =					heapq.heappop(self.elements )
											self.set.remove(snake_case )
											return (priority, item)





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					np.array(UpperCamelCase__   )
							snake_case_           =					np.array(UpperCamelCase__   )
							return np.linalg.norm(a - b   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							return consistent_heuristic(UpperCamelCase__     ,							UpperCamelCase__   ) // t





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							return abs(p[0] - goal[0]   ) + abs(p[1] - goal[1]   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					g_function[start] + Wa * heuristics[i](UpperCamelCase__     ,							UpperCamelCase__   )
							return ans





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					np.chararray((n, n)   )
							for i in range(UpperCamelCase__   ):
														for j in range(UpperCamelCase__   ):
																					snake_case_           =					'*'

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

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

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





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''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__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							):







							'''simple docstring'''






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

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






							snake_case_           =					[]
							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


_UpperCAmelCase	:     Tuple		       = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

_UpperCAmelCase	:     List[str]		       = [
    (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),
]
_UpperCAmelCase	:     Tuple		       = make_common_ground()


_UpperCAmelCase	:     Dict		       = blocks_blk
# hyper parameters
_UpperCAmelCase	:     int		       = 1
_UpperCAmelCase	:     int		       = 1
_UpperCAmelCase	:     Tuple		       = 20
_UpperCAmelCase	:     Tuple		       = 3  # one consistent and two other inconsistent

# start and end destination
_UpperCAmelCase	:     Tuple		       = (0, 0)
_UpperCAmelCase	:     List[Any]		       = (n - 1, n - 1)

_UpperCAmelCase	:     Optional[int]		       = 1





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					{start: 0, goal: float('inf'   )}
							snake_case_           =					{start: -1, goal: -1}
							snake_case_           =					[]
							snake_case_           =					set()

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

							snake_case_           =					[]
							snake_case_           =					[]
							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:
																																			snake_case_       ,					snake_case_           =					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:
																																			snake_case_           =					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)




