



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class     lowerCAmelCase_		(  lowercase__							):
       UpperCAmelCase__    : UNetaDModel
       UpperCAmelCase__    : ScoreSdeVeScheduler
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Optional[Any]:
          super().__init__()
          self.register_modules(unet=lowercase_,      scheduler=lowercase_	)






       @torch.no_grad()
       def __call__(      self,      SCREAMING_SNAKE_CASE_ = 1,      SCREAMING_SNAKE_CASE_ = 2000,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = "pil",      SCREAMING_SNAKE_CASE_ = True,      **SCREAMING_SNAKE_CASE_,      )					->		str:
          UpperCamelCase   :  Optional[Any]						     =     self.unet.config.sample_size
          UpperCamelCase   :  Tuple						     =     (batch_size, 3, img_size, img_size)

          UpperCamelCase   :  List[Any]						     =     self.unet

          UpperCamelCase   :  Dict						     =     randn_tensor(lowercase_,      generator=lowercase_	) * self.scheduler.init_noise_sigma
          UpperCamelCase   :  List[str]						     =     sample.to(self.device	)

          self.scheduler.set_timesteps(lowercase_	)
          self.scheduler.set_sigmas(lowercase_	)

          for i, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
             UpperCamelCase   :  Dict						     =     self.scheduler.sigmas[i] * torch.ones(shape[0],      device=self.device	)

             # correction step
             for _ in range(self.scheduler.config.correct_steps	):
                UpperCamelCase   :  Union[str, Any]						     =     self.unet(lowercase_,      lowercase_	).sample
                UpperCamelCase   :  str						     =     self.scheduler.step_correct(lowercase_,      lowercase_,      generator=lowercase_	).prev_sample

             # prediction step
             UpperCamelCase   :  Any						     =     model(lowercase_,      lowercase_	).sample
             UpperCamelCase   :  int						     =     self.scheduler.step_pred(lowercase_,      lowercase_,      lowercase_,      generator=lowercase_	)

             UpperCamelCase   :  Union[str, Any]						     =     output.prev_sample, output.prev_sample_mean

          UpperCamelCase   :  str						     =     sample_mean.clamp(0,      1	)
          UpperCamelCase   :  Union[str, Any]						     =     sample.cpu().permute(0,      2,      3,      1	).numpy()
          if output_type == "pil":
             UpperCamelCase   :  Optional[int]						     =     self.numpy_to_pil(lowercase_	)

          if not return_dict:
             return (sample,)

          return ImagePipelineOutput(images=lowercase_	)








from graphs.minimum_spanning_tree_kruskal import kruskal
def 		UpperCamelCase     (       )     ->					Tuple:
   UpperCamelCase   :  List[str]						     =     9
   UpperCamelCase   :  Optional[Any]						     =     [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   UpperCamelCase   :  int						     =     kruskal(snake_case__	,					snake_case__     )

   UpperCamelCase   :  List[str]						     =     [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(snake_case__     ) == sorted(snake_case__     )



