





'''simple docstring'''




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          torch.manual_seed(0)
          __A						: List[str]      =       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):

          '''simple docstring'''






          __A						: Dict      =       self.dummy_uncond_unet
          __A						: str      =       KarrasVeScheduler()

          __A						: int      =       KarrasVePipeline(unet=_UpperCAmelCase						,	scheduler=_UpperCAmelCase)
          pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						: Union[str, Any]      =       torch.manual_seed(0)
          __A						: int      =       pipe(num_inference_steps=2						,	generator=_UpperCAmelCase						,	output_type='numpy').images

          __A						: Tuple      =       torch.manual_seed(0)
          __A						: Optional[Any]      =       pipe(num_inference_steps=2						,	generator=_UpperCAmelCase						,	output_type='numpy'						,	return_dict=_UpperCAmelCase)[0]

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

          assert image.shape == (1, 32, 32, 3)
          __A						: Tuple      =       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  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       'google/ncsnpp-celebahq-256'
          __A						: Optional[Any]      =       UNetaDModel.from_pretrained(_UpperCAmelCase)
          __A						: Optional[Any]      =       KarrasVeScheduler()

          __A						: Tuple      =       KarrasVePipeline(unet=_UpperCAmelCase						,	scheduler=_UpperCAmelCase)
          pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						: Optional[int]      =       torch.manual_seed(0)
          __A						: Dict      =       pipe(num_inference_steps=20						,	generator=_UpperCAmelCase						,	output_type='numpy').images

          __A						: Tuple      =       image[0, -3:, -3:, -1]
          assert image.shape == (1, 256, 256, 3)
          __A						: int      =       np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586])

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






'''simple docstring'''




import random






def        _lowerCAmelCase			(  __snake_case  : int ,							__snake_case  : float ,							__snake_case  : bool = False			)     ->     dict:
     __A						: dict      =       {i: [] for i in range(__snake_case			)}

     # if probability is greater or equal than 1, then generate a complete graph
     if probability >= 1:
          return complete_graph(__snake_case			)
     # if probability is lower or equal than 0, then return a graph without edges
     if probability <= 0:
          return graph

     # for each couple of nodes, add an edge from u to v
     # if the number randomly generated is greater than probability probability
     for i in range(__snake_case			):
          for j in range(i + 1 ,							__snake_case			):
               if random.random() < probability:
                    graph[i].append(__snake_case			)
                    if not directed:
                         # if the graph is undirected, add an edge in from j to i, either
                         graph[j].append(__snake_case			)
     return graph






def        _lowerCAmelCase			(  __snake_case  : int			)     ->     dict:
     return {
         i: [j for j in range(__snake_case			) if i != j] for i in range(__snake_case			)
     }


if __name__ == "__main__":
  import doctest

  doctest.testmod()