

import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 						__lowercase       (				UpperCAmelCase_	, unittest.TestCase			):




			"""simple docstring"""







			_UpperCAmelCase     :							Optional[int]    							=  RePaintPipeline
			_UpperCAmelCase     :							List[str]    							=  IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
			_UpperCAmelCase     :							Union[str, Any]    							=  PipelineTesterMixin.required_optional_params - {
			    '''latents''',
			    '''num_images_per_prompt''',
			    '''callback''',
			    '''callback_steps''',
			}
			_UpperCAmelCase     :							Dict    							=  IMAGE_INPAINTING_BATCH_PARAMS
			_UpperCAmelCase     :							Optional[Any]    							=  False



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									torch.manual_seed(0)
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:				Dict							= UNetaDModel(
									    block_out_channels=(32, 64)     ,		layers_per_block=2     ,		sample_size=32     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=("DownBlock2D", "AttnDownBlock2D")     ,		up_block_types=("AttnUpBlock2D", "UpBlock2D")     ,		)
									SCREAMING_SNAKE_CASE_:				str							= RePaintScheduler()
									SCREAMING_SNAKE_CASE_:				str							= {"unet": unet, "scheduler": scheduler}
									return components



			def  _SCREAMING_SNAKE_CASE    (				self						:  int     ,		lowerCAmelCase__						:  Tuple     ,		lowerCAmelCase__						:  Any=0):
									if str(lowerCAmelCase__).startswith("mps"):
															SCREAMING_SNAKE_CASE_:				Optional[int]							= torch.manual_seed(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Tuple							= np.random.RandomState(lowerCAmelCase__).standard_normal((1, 3, 32, 32))
									SCREAMING_SNAKE_CASE_:				Dict							= torch.from_numpy(lowerCAmelCase__).to(device=lowerCAmelCase__     ,		dtype=torch.floataa)
									SCREAMING_SNAKE_CASE_:				Optional[int]							= (image > 0).to(device=lowerCAmelCase__     ,		dtype=torch.floataa)
									SCREAMING_SNAKE_CASE_:				str							= {
									    "image": image,
									    "mask_image": mask,
									    "generator": generator,
									    "num_inference_steps": 5,
									    "eta": 0.0,
									    "jump_length": 2,
									    "jump_n_sample": 2,
									    "output_type": "numpy",
									}
									return inputs



			def  _SCREAMING_SNAKE_CASE    (				self						:  str):
									SCREAMING_SNAKE_CASE_:				str							= "cpu"  # ensure determinism for the device-dependent torch.Generator
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.get_dummy_components()
									SCREAMING_SNAKE_CASE_:				Dict							= RePaintPipeline(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				List[Any]							= sd_pipe.to(lowerCAmelCase__)
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				List[Any]							= self.get_dummy_inputs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= sd_pipe(**lowerCAmelCase__).images
									SCREAMING_SNAKE_CASE_:				List[Any]							= image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									SCREAMING_SNAKE_CASE_:				List[Any]							= np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274])

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



			@skip_mps
			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									return super().test_save_load_local()



			@unittest.skip("non-deterministic pipeline")
			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]):
									return super().test_inference_batch_single_identical()



			@skip_mps
			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									return super().test_dict_tuple_outputs_equivalent()



			@skip_mps
			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]):
									return super().test_save_load_optional_components()




			@skip_mps
			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):
									return super().test_attention_slicing_forward_pass()







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




			"""simple docstring"""







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




			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									SCREAMING_SNAKE_CASE_:				List[Any]							= load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
									    "repaint/celeba_hq_256.png")
									SCREAMING_SNAKE_CASE_:				Tuple							= load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png")
									SCREAMING_SNAKE_CASE_:				Dict							= load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
									    "repaint/celeba_hq_256_result.npy")

									SCREAMING_SNAKE_CASE_:				Dict							= "google/ddpm-ema-celebahq-256"
									SCREAMING_SNAKE_CASE_:				int							= UNetaDModel.from_pretrained(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Any							= RePaintScheduler.from_pretrained(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				Dict							= RePaintPipeline(unet=lowerCAmelCase__     ,		scheduler=lowerCAmelCase__).to(lowerCAmelCase__)
									repaint.set_progress_bar_config(disable=lowerCAmelCase__)
									repaint.enable_attention_slicing()

									SCREAMING_SNAKE_CASE_:				str							= torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:				Dict							= repaint(
									    lowerCAmelCase__     ,		lowerCAmelCase__     ,		num_inference_steps=250     ,		eta=0.0     ,		jump_length=10     ,		jump_n_sample=10     ,		generator=lowerCAmelCase__     ,		output_type="np"     ,		)
									SCREAMING_SNAKE_CASE_:				int							= output.images[0]

									assert image.shape == (256, 256, 3)
									assert np.abs(expected_image - image).mean() < 1E-2









import heapq







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				List[Any]							= [(0, start)]  # cost from start node,end node
						SCREAMING_SNAKE_CASE_:				Any							= set()
						while heap:
												((SCREAMING_SNAKE_CASE_)	,					(SCREAMING_SNAKE_CASE_)):				Tuple							= heapq.heappop(_UpperCAmelCase					)
												if u in visited:
																		continue
												visited.add(_UpperCAmelCase					)
												if u == end:
																		return cost
												for v, c in graph[u]:
																		if v in visited:
																								continue
																		SCREAMING_SNAKE_CASE_:				List[str]							= cost + c
																		heapq.heappush(_UpperCAmelCase       ,			(next_item, v)					)
						return -1


lowerCAmelCase							:						Optional[Any]     			=		{
    """A""": [["""B""", 2], ["""C""", 5]],
    """B""": [["""A""", 2], ["""D""", 3], ["""E""", 1], ["""F""", 1]],
    """C""": [["""A""", 5], ["""F""", 3]],
    """D""": [["""B""", 3]],
    """E""": [["""B""", 4], ["""F""", 3]],
    """F""": [["""C""", 3], ["""E""", 3]],
}
lowerCAmelCase							:						Tuple     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""F""", 3]],
    """F""": [],
}
lowerCAmelCase							:						str     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(G, """E""", """C""")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

lowerCAmelCase							:						Tuple     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 3 --> F == 3

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
						import doctest

						doctest.testmod()






