from __future__ import annotations

from typing import Any





class       lowercase__		:


						def __init__( self			:       Any					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							num_of_nodes
												SCREAMING_SNAKE_CASE							:					list[list[int]]           =							[]
												SCREAMING_SNAKE_CASE							:					dict[int, int]           =							{}


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												self.m_edges.append([u_node, v_node, weight]		)


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												if self.m_component[u_node] == u_node:
																		return u_node
												return self.find_component(self.m_component[u_node]		)


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												if self.m_component[u_node] != u_node:
																		for k in self.m_component:
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.find_component(UpperCamelCase__		)


						def  __A ( self			:       str					,	UpperCamelCase__			:       list[int]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												if component_size[u_node] <= component_size[v_node]:
																		SCREAMING_SNAKE_CASE							:					int           =							v_node
																		component_size[v_node] += component_size[u_node]
																		self.set_component(UpperCamelCase__		)

												elif component_size[u_node] >= component_size[v_node]:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.find_component(UpperCamelCase__		)
																		component_size[u_node] += component_size[v_node]
																		self.set_component(UpperCamelCase__		)



						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[]
												SCREAMING_SNAKE_CASE							:					int           =							0

												SCREAMING_SNAKE_CASE							:					list[Any]           =							[-1] * self.m_num_of_nodes

												# A list of components (initialized to all of the nodes)
												for node in range(self.m_num_of_nodes		):
																		self.m_component.update({node: node}		)
																		component_size.append(1		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							self.m_num_of_nodes

												while num_of_components > 1:
																		for edge in self.m_edges:
																								SCREAMING_SNAKE_CASE							:					int           =							edge

																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.m_component[u]
																								SCREAMING_SNAKE_CASE							:					List[str]           =							self.m_component[v]

																								if u_component != v_component:

																														for component in (u_component, v_component):
																																				if (
																																				    minimum_weight_edge[component] == -1
																																				    or minimum_weight_edge[component][2] > w
																																				):
																																										SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[u, v, w]

																		for edge in minimum_weight_edge:
																								if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																														SCREAMING_SNAKE_CASE							:					str           =							edge

																														SCREAMING_SNAKE_CASE							:					List[str]           =							self.m_component[u]
																														SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.m_component[v]

																														if u_component != v_component:
																																				mst_weight += w
																																				self.union(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)
																																				print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""		)
																																				num_of_components -= 1

																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[-1] * self.m_num_of_nodes
												print(f"""The total weight of the minimal spanning tree is: {mst_weight}"""		)






def    A			(					):
						pass


if __name__ == "__main__":
					import doctest

					doctest.testmod()






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__UpperCamelCase	: str    		=  False





class       lowercase__		(     unittest.TestCase):
						pass






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


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Dict           =							pipe.dual_guided(
												    prompt='''first prompt'''					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=2					,	output_type='''numpy'''					,	).images

												with tempfile.TemporaryDirectory() as tmpdirname:
																		pipe.save_pretrained(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							VersatileDiffusionPipeline.from_pretrained(UpperCamelCase__					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							generator.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					int           =							pipe.dual_guided(
												    prompt='''first prompt'''					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=2					,	output_type='''numpy'''					,	).images

												assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''cyberpunk 2077'''
												SCREAMING_SNAKE_CASE							:					Tuple           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							pipe.dual_guided(
												    prompt=UpperCamelCase__					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=50					,	output_type='''numpy'''					,	).images

												SCREAMING_SNAKE_CASE							:					Tuple           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					int           =							np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]		)

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

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''A painting of a squirrel eating a burger '''
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Any           =							pipe.text_to_image(
												    prompt=UpperCamelCase__					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=50					,	output_type='''numpy'''		).images

												SCREAMING_SNAKE_CASE							:					List[str]           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					Any           =							np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]		)

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

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							pipe.image_variation(UpperCamelCase__					,	generator=UpperCamelCase__					,	output_type='''numpy'''		).images

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]		)

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





