






"""simple docstring"""







import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase    	= (DDIMParallelScheduler,)
						__lowerCamelCase    	= (("eta", 0.0), ("num_inference_steps", 50))




						def 		UpperCAmelCase_	(       self	,  **snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	{
												    "num_train_timesteps": 1000,
												    "beta_start": 0.00_01,
												    "beta_end": 0.02,
												    "beta_schedule": "linear",
												    "clip_sample": True,
												}

												config.update(**snake_case__      )
												return config




						def 		UpperCAmelCase_	(       self	,  **snake_case__      ):
												'''simple docstring'''
												lowercase__    :				int=	self.scheduler_classes[0]
												lowercase__    :				Tuple=	self.get_scheduler_config(**snake_case__      )
												lowercase__    :				Union[str, Any]=	scheduler_class(**snake_case__      )

												lowercase__,     lowercase__    :				Optional[int]=	10, 0.0

												lowercase__    :				Dict=	self.dummy_model()
												lowercase__    :				Any=	self.dummy_sample_deter

												scheduler.set_timesteps(snake_case__      )

												for t in scheduler.timesteps:
																		lowercase__    :				int=	model(snake_case__	,  snake_case__      )
																		lowercase__    :				int=	scheduler.step(snake_case__	,  snake_case__	,  snake_case__	,  snake_case__      ).prev_sample

												return sample




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												for timesteps in [100, 500, 1000]:
																		self.check_over_configs(num_train_timesteps=snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												for steps_offset in [0, 1]:
																		self.check_over_configs(steps_offset=snake_case__      )

												lowercase__    :				Any=	self.scheduler_classes[0]
												lowercase__    :				str=	self.get_scheduler_config(steps_offset=1      )
												lowercase__    :				Tuple=	scheduler_class(**snake_case__      )
												scheduler.set_timesteps(5      )
												assert torch.equal(scheduler.timesteps	,  torch.LongTensor([801, 601, 401, 201, 1]      )      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]	,  [0.0_02, 0.02, 0.2, 2]      ):
																		self.check_over_configs(beta_start=snake_case__	,  beta_end=snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												for schedule in ["linear", "squaredcos_cap_v2"]:
																		self.check_over_configs(beta_schedule=snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(prediction_type=snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												for timestep_spacing in ["trailing", "leading"]:
																		self.check_over_configs(timestep_spacing=snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												for rescale_betas_zero_snr in [True, False]:
																		self.check_over_configs(rescale_betas_zero_snr=snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												self.check_over_configs(thresholding=snake_case__      )
												for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "v_prediction"]:
																								self.check_over_configs(
																								    thresholding=snake_case__	,  prediction_type=snake_case__	,  sample_max_value=snake_case__	,  )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												for t in [1, 10, 49]:
																		self.check_over_forward(time_step=snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												for t, num_inference_steps in zip([1, 10, 50]	,  [10, 50, 500]      ):
																		self.check_over_forward(time_step=snake_case__	,  num_inference_steps=snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												for t, eta in zip([1, 10, 49]	,  [0.0, 0.5, 1.0]      ):
																		self.check_over_forward(time_step=snake_case__	,  eta=snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	self.scheduler_classes[0]
												lowercase__    :				List[Any]=	self.get_scheduler_config()
												lowercase__    :				str=	scheduler_class(**snake_case__      )

												assert torch.sum(torch.abs(scheduler._get_variance(0	,  0      ) - 0.0      )      ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(420	,  400      ) - 0.1_47_71      )      ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(980	,  960      ) - 0.3_24_60      )      ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(0	,  0      ) - 0.0      )      ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(487	,  486      ) - 0.0_09_79      )      ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(999	,  998      ) - 0.02      )      ) < 1e-5




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				str=	self.scheduler_classes[0]
												lowercase__    :				int=	self.get_scheduler_config()
												lowercase__    :				List[str]=	scheduler_class(**snake_case__      )

												lowercase__,     lowercase__    :				Optional[int]=	10, 0.0
												scheduler.set_timesteps(snake_case__      )

												lowercase__    :				Dict=	self.dummy_model()
												lowercase__    :				int=	self.dummy_sample_deter
												lowercase__    :				List[str]=	self.dummy_sample_deter + 0.1
												lowercase__    :				Optional[Any]=	self.dummy_sample_deter - 0.1

												lowercase__    :				str=	samplea.shape[0]
												lowercase__    :				str=	torch.stack([samplea, samplea, samplea]	,  dim=0      )
												lowercase__    :				Optional[Any]=	torch.arange(snake_case__      )[0:3, None].repeat(1	,  snake_case__      )

												lowercase__    :				Optional[int]=	model(samples.flatten(0	,  1      )	,  timesteps.flatten(0	,  1      )      )
												lowercase__    :				Any=	scheduler.batch_step_no_noise(snake_case__	,  timesteps.flatten(0	,  1      )	,  samples.flatten(0	,  1      )	,  snake_case__      )

												lowercase__    :				Tuple=	torch.sum(torch.abs(snake_case__      )      )
												lowercase__    :				List[str]=	torch.mean(torch.abs(snake_case__      )      )

												assert abs(result_sum.item() - 11_47.79_04      ) < 1e-2
												assert abs(result_mean.item() - 0.49_82      ) < 1e-3




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	self.full_loop()

												lowercase__    :				Tuple=	torch.sum(torch.abs(snake_case__      )      )
												lowercase__    :				List[Any]=	torch.mean(torch.abs(snake_case__      )      )

												assert abs(result_sum.item() - 1_72.00_67      ) < 1e-2
												assert abs(result_mean.item() - 0.22_39_67      ) < 1e-3




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	self.full_loop(prediction_type="v_prediction"      )

												lowercase__    :				Any=	torch.sum(torch.abs(snake_case__      )      )
												lowercase__    :				Dict=	torch.mean(torch.abs(snake_case__      )      )

												assert abs(result_sum.item() - 52.53_02      ) < 1e-2
												assert abs(result_mean.item() - 0.06_84      ) < 1e-3




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# We specify different beta, so that the first alpha is 0.99
												lowercase__    :				str=	self.full_loop(set_alpha_to_one=snake_case__	,  beta_start=0.01      )
												lowercase__    :				List[str]=	torch.sum(torch.abs(snake_case__      )      )
												lowercase__    :				List[Any]=	torch.mean(torch.abs(snake_case__      )      )

												assert abs(result_sum.item() - 1_49.82_95      ) < 1e-2
												assert abs(result_mean.item() - 0.19_51      ) < 1e-3




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# We specify different beta, so that the first alpha is 0.99
												lowercase__    :				Union[str, Any]=	self.full_loop(set_alpha_to_one=snake_case__	,  beta_start=0.01      )
												lowercase__    :				str=	torch.sum(torch.abs(snake_case__      )      )
												lowercase__    :				Optional[Any]=	torch.mean(torch.abs(snake_case__      )      )

												assert abs(result_sum.item() - 1_49.07_84      ) < 1e-2
												assert abs(result_mean.item() - 0.19_41      ) < 1e-3














"""simple docstring"""







from __future__ import annotations


class    __UpperCAmelCase:




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	data
												lowercase__    :				Node | None=	None
												lowercase__    :				Node | None=	None





def 						lowercase__(A						)    ->None:  # In Order traversal of the tree




						"""simple docstring"""



						if tree:
												display(tree.left						)
												print(tree.data						)
												display(tree.right						)





def 						lowercase__(A						)    ->int:




						"""simple docstring"""



						return 1 + max(depth_of_tree(tree.left						) ,		depth_of_tree(tree.right						)						) if tree else 0





def 						lowercase__(A						)    ->bool:




						"""simple docstring"""



						if not tree:
												return True
						if tree.left and tree.right:
												return is_full_binary_tree(tree.left						) and is_full_binary_tree(tree.right						)
						else:
												return not tree.left and not tree.right





def 						lowercase__()    ->None:  # Main function for testing.




						"""simple docstring"""



						lowercase__    :				Tuple=	Node(1						)
						lowercase__    :				Optional[int]=	Node(2						)
						lowercase__    :				List[str]=	Node(3						)
						lowercase__    :				Tuple=	Node(4						)
						lowercase__    :				Optional[int]=	Node(5						)
						lowercase__    :				Any=	Node(6						)
						lowercase__    :				Optional[Any]=	Node(7						)
						lowercase__    :				Optional[int]=	Node(8						)
						lowercase__    :				List[str]=	Node(9						)

						print(is_full_binary_tree(A						)						)
						print(depth_of_tree(A						)						)
						print("Tree is: "						)
						display(A						)


if __name__ == "__main__":
			main()






