




import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class    __lowerCAmelCase       (							unittest.TestCase):






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         torch.manual_seed(0				)
         lowercase    :Optional[int]      	=						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")       ,					)
         return model






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :Any      	=						self.dummy_uncond_unet
         lowercase    :Optional[Any]      	=						ScoreSdeVeScheduler()

         lowercase    :str      	=						ScoreSdeVePipeline(unet=_lowerCamelCase       ,					scheduler=_lowerCamelCase				)
         sde_ve.to(_lowerCamelCase				)
         sde_ve.set_progress_bar_config(disable=_lowerCamelCase				)

         lowercase    :Optional[Any]      	=						torch.manual_seed(0				)
         lowercase    :List[Any]      	=						sde_ve(num_inference_steps=2       ,					output_type="numpy"       ,					generator=_lowerCamelCase				).images

         lowercase    :int      	=						torch.manual_seed(0				)
         lowercase    :str      	=						sde_ve(num_inference_steps=2       ,					output_type="numpy"       ,					generator=_lowerCamelCase       ,					return_dict=_lowerCamelCase				)[
             0
         ]

         lowercase    :List[str]      	=						image[0, -3:, -3:, -1]
         lowercase    :Optional[int]      	=						image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         lowercase    :List[Any]      	=						np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2







@slow
@require_torch
class    __lowerCAmelCase       (							unittest.TestCase):






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :Tuple      	=						"google/ncsnpp-church-256"
         lowercase    :Dict      	=						UNetaDModel.from_pretrained(_lowerCamelCase				)

         lowercase    :Optional[Any]      	=						ScoreSdeVeScheduler.from_pretrained(_lowerCamelCase				)

         lowercase    :List[Any]      	=						ScoreSdeVePipeline(unet=_lowerCamelCase       ,					scheduler=_lowerCamelCase				)
         sde_ve.to(_lowerCamelCase				)
         sde_ve.set_progress_bar_config(disable=_lowerCamelCase				)

         lowercase    :str      	=						torch.manual_seed(0				)
         lowercase    :List[Any]      	=						sde_ve(num_inference_steps=10       ,					output_type="numpy"       ,					generator=_lowerCamelCase				).images

         lowercase    :Dict      	=						image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_56, 2_56, 3)

         lowercase    :Tuple      	=						np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2









from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst



def       UpperCAmelCase__  (     ):
  lowercase	,    lowercase    :Union[str, Any]      	=						9, 14  # noqa: F841
  lowercase    :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],
  ]

  lowercase    :Optional[Any]      	=						defaultdict(lowerCamelCase   )
  for nodea, nodea, cost in edges:
    adjancency[nodea].append([nodea, cost]   )
    adjancency[nodea].append([nodea, cost]   )

  lowercase    :Optional[int]      	=						mst(lowerCamelCase   )

  lowercase    :Any      	=						[
      [7, 6, 1],
      [2, 8, 2],
      [6, 5, 2],
      [0, 1, 4],
      [2, 5, 4],
      [2, 3, 7],
      [0, 7, 8],
      [3, 4, 9],
  ]

  for answer in expected:
    lowercase    :Any      	=						tuple(answer[:2]   )
    lowercase    :str      	=						tuple(edge[::-1]   )
    assert edge in result or reverse in result



