






'''simple docstring'''
def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				list[tuple[int, int, int]]:






					"""simple docstring"""







					a_									=   sorted(_UpperCAmelCase					,  key=lambda _UpperCAmelCase : edge[2] )

					a_									=   list(range(_UpperCAmelCase ) )

					def find_parent(_UpperCAmelCase ):
										if i != parent[i]:
															a_									=   find_parent(parent[i] )
										return parent[i]

					a_									=   0
					a_									=   []

					for edge in edges:
										a_									=   find_parent(edge[0] )
										a_									=   find_parent(edge[1] )
										if parent_a != parent_b:
															minimum_spanning_tree_cost += edge[2]
															minimum_spanning_tree.append(_UpperCAmelCase )
															a_									=   parent_b

					return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
						__lowerCAmelCase							,				__lowerCAmelCase							=list(map(int, input().strip().split()))
						__lowerCAmelCase							=[]

						for _ in range(num_edges):
												__lowerCAmelCase							,				__lowerCAmelCase							,				__lowerCAmelCase							=(int(x) for x in input().strip().split())
												edges.append((nodea, nodea, cost))

						kruskal(num_nodes, edges)









'''simple docstring'''
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , )      ->	List[str]:
										super().__init__()

										self.register_modules(vae=UpperCAmelCase__   , text_encoder=UpperCAmelCase__   , tokenizer=UpperCAmelCase__   , unet=UpperCAmelCase__   , scheduler=UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	int:
										with torch.no_grad():
															a_									=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase__     ).unsqueeze(0     ).to(self.device     ) * 2 - 1     )
															a_									=   0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	List[str]:
										a_									=   (1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
															a_									=   self.vae.decode(UpperCAmelCase__     ).sample
										a_									=   (img / 2 + 0.5).clamp(0   , 1     )
										a_									=   img.detach().cpu().permute(0   , 2   , 3   , 1     ).numpy()
										a_									=   (img * 255).round().astype('uint8'     )
										return Image.fromarray(img[0]     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	Any:
										a_									=   self.tokenizer(
										    UpperCAmelCase__   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(text_input.input_ids.to(self.device     )     )[0]

										a_									=   self.tokenizer(
										    ''   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]

										return torch.cat([uncond_embedding, text_embedding]     )




					def __call__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = 0.3   , UpperCAmelCase__ = 0.6   , UpperCAmelCase__ = 0.5   , UpperCAmelCase__ = 42   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = 7.5   , )      ->	Image.Image:
										a_									=   steps - int(kmin * steps     )
										a_									=   steps - int(kmax * steps     )

										a_									=   self.prep_text(UpperCAmelCase__     )

										self.scheduler.set_timesteps(UpperCAmelCase__     )

										a_							,					a_									=   img.size
										a_									=   self.encode(UpperCAmelCase__     )

										torch.manual_seed(UpperCAmelCase__     )
										a_									=   torch.randn(
										    (1, self.unet.config.in_channels, height // 8, width // 8)   , ).to(self.device     )

										a_									=   self.scheduler.add_noise(
										    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=self.scheduler.timesteps[tmax]   , )

										a_									=   torch.cat([latents] * 2     )

										a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , self.scheduler.timesteps[tmax]     )

										with torch.no_grad():
															a_									=   self.unet(
															    UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , encoder_hidden_states=UpperCAmelCase__   , ).sample

										a_							,					a_									=   pred.chunk(2     )
										a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

										a_									=   self.scheduler.step(UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , UpperCAmelCase__     ).prev_sample

										for i, t in enumerate(tqdm(self.scheduler.timesteps     )     ):
															if i > tmax:
																				if i < tmin:  # layout generation phase
																									a_									=   self.scheduler.add_noise(
																									    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=UpperCAmelCase__   , )

																									a_									=   (mix_factor * latents) + (
																									    1 - mix_factor
																									) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																									a_									=   torch.cat([input] * 2     )

																				else:  # content generation phase
																									a_									=   torch.cat([latents] * 2     )

																				a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , UpperCAmelCase__     )

																				with torch.no_grad():
																									a_									=   self.unet(
																									    UpperCAmelCase__   , UpperCAmelCase__   , encoder_hidden_states=UpperCAmelCase__   , ).sample

																				a_							,					a_									=   pred.chunk(2     )
																				a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

																				a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

										return self.decode(UpperCAmelCase__     )

