






"""simple docstring"""



from __future__ import annotations

from dataclasses import dataclass






@dataclass
class 							__SCREAMING_SNAKE_CASE						:
							snake_case_     =							42
							snake_case_     =							None
							snake_case_     =							None




def      _lowerCAmelCase		(							UpperCAmelCase__       :					TreeNode | None      )		->bool:

						# Validation
						def is_valid_tree(UpperCAmelCase__       :					TreeNode | None      ) -> bool:
												if node is None:
																		return True

												if not isinstance(UpperCAmelCase__,						UpperCAmelCase__      ):
																		return False

												try:
																		float(node.data      )
												except (TypeError, ValueError):
																		return False

												return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

						if not is_valid_tree(UpperCAmelCase__      ):
												raise ValueError(
												    """Each node should be type of TreeNode and data should be float."""      )

						def is_binary_search_tree_recursive_check(
						    UpperCAmelCase__       :					TreeNode | None,						UpperCAmelCase__       :					float,						UpperCAmelCase__       :					float      ) -> bool:

												if node is None:
																		return True

												return (
												    left_bound < node.data < right_bound
												    and is_binary_search_tree_recursive_check(node.left,						UpperCAmelCase__,						node.data      )
												    and is_binary_search_tree_recursive_check(
												        node.right,						node.data,						UpperCAmelCase__      )
												)

						return is_binary_search_tree_recursive_check(UpperCAmelCase__,						-float("""inf"""      ),						float("""inf"""      )      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()














"""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 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							@property
							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													torch.manual_seed(0							)
													A__						:  Optional[Any]		  =  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 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  List[Any]		  =  self.dummy_uncond_unet
													A__						:  Dict		  =  ScoreSdeVeScheduler()

													A__						:  str		  =  ScoreSdeVePipeline(unet=snake_case			,    scheduler=snake_case							)
													sde_ve.to(snake_case							)
													sde_ve.set_progress_bar_config(disable=snake_case							)

													A__						:  Optional[int]		  =  torch.manual_seed(0							)
													A__						:  Any		  =  sde_ve(num_inference_steps=2			,    output_type="""numpy"""			,    generator=snake_case							).images

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

													A__						:  int		  =  image[0, -3:, -3:, -1]
													A__						:  Optional[Any]		  =  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													A__						:  Optional[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 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  """google/ncsnpp-church-256"""
													A__						:  Tuple		  =  UNetaDModel.from_pretrained(snake_case							)

													A__						:  int		  =  ScoreSdeVeScheduler.from_pretrained(snake_case							)

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

													A__						:  Dict		  =  torch.manual_seed(0							)
													A__						:  Optional[int]		  =  sde_ve(num_inference_steps=10			,    output_type="""numpy"""			,    generator=snake_case							).images

													A__						:  Optional[int]		  =  image[0, -3:, -3:, -1]

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

													A__						:  Dict		  =  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






