


"""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        __A       (unittest.TestCase):





							'''simple docstring'''

							@property
							def 						lowerCAmelCase    (			self							: List[str]    )   ->int:



														"""simple docstring"""



														torch.manual_seed(0    )
														snake_case_          =						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							: Dict    )   ->str:



														"""simple docstring"""



														snake_case_          =						self.dummy_uncond_unet
														snake_case_          =						ScoreSdeVeScheduler()

														snake_case_          =						ScoreSdeVePipeline(unet=UpperCAmelCase_						,    scheduler=UpperCAmelCase_    )
														sde_ve.to(UpperCAmelCase_    )
														sde_ve.set_progress_bar_config(disable=UpperCAmelCase_    )

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

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

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

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





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: List[Any]    )   ->List[Any]:



														"""simple docstring"""



														snake_case_          =						"""google/ncsnpp-church-256"""
														snake_case_          =						UNetaDModel.from_pretrained(UpperCAmelCase_    )

														snake_case_          =						ScoreSdeVeScheduler.from_pretrained(UpperCAmelCase_    )

														snake_case_          =						ScoreSdeVePipeline(unet=UpperCAmelCase_						,    scheduler=UpperCAmelCase_    )
														sde_ve.to(UpperCAmelCase_    )
														sde_ve.set_progress_bar_config(disable=UpperCAmelCase_    )

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

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

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

														snake_case_          =						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.abc import Iterator





class        __A       :





							'''simple docstring'''

							def __init__(			self							: Any						,    UpperCAmelCase_							: int    )   ->None:



														"""simple docstring"""



														snake_case_          =						value
														snake_case_          =						None
														snake_case_          =						None





class        __A       :





							'''simple docstring'''

							def __init__(			self							: List[Any]						,    UpperCAmelCase_							: Node    )   ->None:



														"""simple docstring"""



														snake_case_          =						tree



							def 						lowerCAmelCase    (			self							: List[str]						,    UpperCAmelCase_							: Node | None    )   ->int:



														"""simple docstring"""



														if node is None:
																					return 0
														return node.value + (
														    self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
														)



							def __iter__(			self							: str    )   ->Iterator[int]:



														"""simple docstring"""



														yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
				import doctest

				doctest.testmod()



