



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						( snake_case__ ,    unittest.TestCase):






	"""simple docstring"""


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




	def   _SCREAMING_SNAKE_CASE		(	self	:						Dict     )			-> int:
							torch.manual_seed(0     )
							torch.manual_seed(0     )
							UpperCAmelCase_=   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""")						,  )
							UpperCAmelCase_=   RePaintScheduler()
							UpperCAmelCase_=   {"""unet""": unet, """scheduler""": scheduler}
							return components




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]						,  __UpperCAmelCase	:						List[str]						,  __UpperCAmelCase	:						Optional[Any]=0     )			-> Dict:
							if str(__UpperCAmelCase     ).startswith("""mps"""     ):
													UpperCAmelCase_=   torch.manual_seed(__UpperCAmelCase     )
							else:
													UpperCAmelCase_=   torch.Generator(device=__UpperCAmelCase     ).manual_seed(__UpperCAmelCase     )
							UpperCAmelCase_=   np.random.RandomState(__UpperCAmelCase     ).standard_normal((1, 3, 32, 32)     )
							UpperCAmelCase_=   torch.from_numpy(__UpperCAmelCase     ).to(device=__UpperCAmelCase						,  dtype=torch.floataa     )
							UpperCAmelCase_=   (image > 0).to(device=__UpperCAmelCase						,  dtype=torch.floataa     )
							UpperCAmelCase_=   {
							    """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	:						Dict     )			-> Any:
							UpperCAmelCase_=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
							UpperCAmelCase_=   self.get_dummy_components()
							UpperCAmelCase_=   RePaintPipeline(**__UpperCAmelCase     )
							UpperCAmelCase_=   sd_pipe.to(__UpperCAmelCase     )
							sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

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

							assert image.shape == (1, 32, 32, 3)
							UpperCAmelCase_=   np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]     )

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




	@skip_mps
	def   _SCREAMING_SNAKE_CASE		(	self	:						Dict     )			-> Dict:
							return super().test_save_load_local()




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




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




	@skip_mps
	def   _SCREAMING_SNAKE_CASE		(	self	:						Dict     )			-> Any:
							return super().test_save_load_optional_components()




	@skip_mps
	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> Any:
							return super().test_attention_slicing_forward_pass()






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






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> List[str]:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




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

							UpperCAmelCase_=   """google/ddpm-ema-celebahq-256"""
							UpperCAmelCase_=   UNetaDModel.from_pretrained(__UpperCAmelCase     )
							UpperCAmelCase_=   RePaintScheduler.from_pretrained(__UpperCAmelCase     )

							UpperCAmelCase_=   RePaintPipeline(unet=__UpperCAmelCase						,  scheduler=__UpperCAmelCase     ).to(__UpperCAmelCase     )
							repaint.set_progress_bar_config(disable=__UpperCAmelCase     )
							repaint.enable_attention_slicing()

							UpperCAmelCase_=   torch.manual_seed(0     )
							UpperCAmelCase_=   repaint(
							    __UpperCAmelCase						,  __UpperCAmelCase						,  num_inference_steps=250						,  eta=0.0						,  jump_length=10						,  jump_n_sample=10						,  generator=__UpperCAmelCase						,  output_type="""np"""						,  )
							UpperCAmelCase_=   output.images[0]

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









from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
__A				    =						{
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}



def        __a     (    lowerCAmelCase_   :					dict[str, list[str]]				)      -> set[tuple[str, str]]:






						'''simple docstring'''



						UpperCAmelCase_=   {node: {node} for node in graph}

						UpperCAmelCase_=   {node: graph[node][:] for node in graph}

						while len(lowerCAmelCase_				) > 2:
												# Choose a random edge.
												UpperCAmelCase_=   random.choice(list(graph_copy.keys()				)				)
												UpperCAmelCase_=   random.choice(graph_copy[u]				)

												# Contract edge (u, v) to new node uv
												UpperCAmelCase_=   u + v
												UpperCAmelCase_=   list(set(graph_copy[u] + graph_copy[v]				)				)
												uv_neighbors.remove(lowerCAmelCase_				)
												uv_neighbors.remove(lowerCAmelCase_				)
												UpperCAmelCase_=   uv_neighbors
												for neighbor in uv_neighbors:
																		graph_copy[neighbor].append(lowerCAmelCase_				)

												UpperCAmelCase_=   set(contracted_nodes[u].union(contracted_nodes[v]				)				)

												# Remove nodes u and v.
												del graph_copy[u]
												del graph_copy[v]
												for neighbor in uv_neighbors:
																		if u in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(lowerCAmelCase_				)
																		if v in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(lowerCAmelCase_				)

    # Find cutset.
						UpperCAmelCase_=   [contracted_nodes[node] for node in graph_copy]
						return {
						    (node, neighbor)
						    for node in groups[0]
						    for neighbor in graph[node]
						    if neighbor in groups[1]
						}


if __name__ == "__main__":
	print(partition_graph(TEST_GRAPH))




