
"""simple docstring"""





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      ):




			"""simple docstring"""







			@property
			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Dict:







								"""simple docstring"""
								torch.manual_seed(0	)
								A_				 =						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      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						self.dummy_uncond_unet
								A_				 =						ScoreSdeVeScheduler()

								A_				 =						ScoreSdeVePipeline(unet=_snake_case       ,   scheduler=_snake_case	)
								sde_ve.to(_snake_case	)
								sde_ve.set_progress_bar_config(disable=_snake_case	)

								A_				 =						torch.manual_seed(0	)
								A_				 =						sde_ve(num_inference_steps=2       ,   output_type="numpy"       ,   generator=_snake_case	).images

								A_				 =						torch.manual_seed(0	)
								A_				 =						sde_ve(num_inference_steps=2       ,   output_type="numpy"       ,   generator=_snake_case       ,   return_dict=_snake_case	)[
								    0
								]

								A_				 =						image[0, -3:, -3:, -1]
								A_				 =						image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								A_				 =						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      ):




			"""simple docstring"""







			def      lowerCamelCase__     (				self						:					Tuple	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						"google/ncsnpp-church-256"
								A_				 =						UNetaDModel.from_pretrained(_snake_case	)

								A_				 =						ScoreSdeVeScheduler.from_pretrained(_snake_case	)

								A_				 =						ScoreSdeVePipeline(unet=_snake_case       ,   scheduler=_snake_case	)
								sde_ve.to(_snake_case	)
								sde_ve.set_progress_bar_config(disable=_snake_case	)

								A_				 =						torch.manual_seed(0	)
								A_				 =						sde_ve(num_inference_steps=10       ,   output_type="numpy"       ,   generator=_snake_case	).images

								A_				 =						image[0, -3:, -3:, -1]

								assert image.shape == (1, 256, 256, 3)

								A_				 =						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






"""simple docstring"""





from __future__ import annotations

from collections import Counter
from random import random



class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						{}




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					str	)      -> None:







								"""simple docstring"""
								A_				 =						{}




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					float	)      -> None:







								"""simple docstring"""
								if nodea not in self.connections:
													self.add_node(_snake_case	)
								if nodea not in self.connections:
													self.add_node(_snake_case	)
								A_				 =						probability




			def      lowerCamelCase__     (				self						:					Tuple	)      -> list[str]:







								"""simple docstring"""
								return list(self.connections	)






			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					str	)      -> str:







								"""simple docstring"""
								A_				 =						0
								A_				 =						random()

								for dest in self.connections[node]:
													current_probability += self.connections[node][dest]
													if current_probability > random_value:
																		return dest
								return ""





def 							A_     (__a				,   __a				,   __a				):

					'''simple docstring'''





					A_				 =						MarkovChainGraphUndirectedUnweighted()

					for nodea, nodea, probability in transitions:
										graph.add_transition_probability(__a				,   __a				,   __a				)

					A_				 =						Counter(graph.get_nodes()				)
					A_				 =						start

					for _ in range(__a				):
										A_				 =						graph.transition(__a				)
										visited[node] += 1

					return visited


if __name__ == "__main__":
		import doctest

		doctest.testmod()




