"""simple docstring"""

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 		_lowercase (						__a      ):



						"""simple docstring"""




						lowercase__       =  (LMSDiscreteScheduler,)
						lowercase__       =  10





						def   UpperCAmelCase_ (				self							:    Optional[Any]     , **UpperCamelCase__							:    Any				)				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							={
													    '''num_train_timesteps''': 1100,
													    '''beta_start''': 0.00_01,
													    '''beta_end''': 0.02,
													    '''beta_schedule''': '''linear''',
													}

													config.update(**UpperCamelCase__				)
													return config





						def   UpperCAmelCase_ (				self							:    List[str]				)				->				str:
													'''simple docstring'''



													for timesteps in [10, 50, 100, 1000]:
																				self.check_over_configs(num_train_timesteps=UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				int:
													'''simple docstring'''



													for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]     , [0.00_02, 0.0_02, 0.02]				):
																				self.check_over_configs(beta_start=UpperCamelCase__     , beta_end=UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				Any:
													'''simple docstring'''



													for schedule in ["linear", "scaled_linear"]:
																				self.check_over_configs(beta_schedule=UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    Optional[int]				)				->				int:
													'''simple docstring'''



													for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(prediction_type=UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				Union[str, Any]:
													'''simple docstring'''



													for t in [0, 500, 800]:
																				self.check_over_forward(time_step=UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=self.scheduler_classes[0]
													__UpperCamelCase							=self.get_scheduler_config()
													__UpperCamelCase							=scheduler_class(**UpperCamelCase__				)

													scheduler.set_timesteps(self.num_inference_steps				)

													__UpperCamelCase							=self.dummy_model()
													__UpperCamelCase							=self.dummy_sample_deter * scheduler.init_noise_sigma

													for i, t in enumerate(scheduler.timesteps				):
																				__UpperCamelCase							=scheduler.scale_model_input(UpperCamelCase__     , UpperCamelCase__				)

																				__UpperCamelCase							=model(UpperCamelCase__     , UpperCamelCase__				)

																				__UpperCamelCase							=scheduler.step(UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__				)
																				__UpperCamelCase							=output.prev_sample

													__UpperCamelCase							=torch.sum(torch.abs(UpperCamelCase__				)				)
													__UpperCamelCase							=torch.mean(torch.abs(UpperCamelCase__				)				)

													assert abs(result_sum.item() - 10_06.3_88				) < 1E-2
													assert abs(result_mean.item() - 1.31				) < 1E-3





						def   UpperCAmelCase_ (				self							:    str				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=self.scheduler_classes[0]
													__UpperCamelCase							=self.get_scheduler_config(prediction_type='''v_prediction'''				)
													__UpperCamelCase							=scheduler_class(**UpperCamelCase__				)

													scheduler.set_timesteps(self.num_inference_steps				)

													__UpperCamelCase							=self.dummy_model()
													__UpperCamelCase							=self.dummy_sample_deter * scheduler.init_noise_sigma

													for i, t in enumerate(scheduler.timesteps				):
																				__UpperCamelCase							=scheduler.scale_model_input(UpperCamelCase__     , UpperCamelCase__				)

																				__UpperCamelCase							=model(UpperCamelCase__     , UpperCamelCase__				)

																				__UpperCamelCase							=scheduler.step(UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__				)
																				__UpperCamelCase							=output.prev_sample

													__UpperCamelCase							=torch.sum(torch.abs(UpperCamelCase__				)				)
													__UpperCamelCase							=torch.mean(torch.abs(UpperCamelCase__				)				)

													assert abs(result_sum.item() - 0.00_17				) < 1E-2
													assert abs(result_mean.item() - 2.2676E-06				) < 1E-3





						def   UpperCAmelCase_ (				self							:    List[str]				)				->				Union[str, Any]:
													'''simple docstring'''



													__UpperCamelCase							=self.scheduler_classes[0]
													__UpperCamelCase							=self.get_scheduler_config()
													__UpperCamelCase							=scheduler_class(**UpperCamelCase__				)

													scheduler.set_timesteps(self.num_inference_steps     , device=UpperCamelCase__				)

													__UpperCamelCase							=self.dummy_model()
													__UpperCamelCase							=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													__UpperCamelCase							=sample.to(UpperCamelCase__				)

													for i, t in enumerate(scheduler.timesteps				):
																				__UpperCamelCase							=scheduler.scale_model_input(UpperCamelCase__     , UpperCamelCase__				)

																				__UpperCamelCase							=model(UpperCamelCase__     , UpperCamelCase__				)

																				__UpperCamelCase							=scheduler.step(UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__				)
																				__UpperCamelCase							=output.prev_sample

													__UpperCamelCase							=torch.sum(torch.abs(UpperCamelCase__				)				)
													__UpperCamelCase							=torch.mean(torch.abs(UpperCamelCase__				)				)

													assert abs(result_sum.item() - 10_06.3_88				) < 1E-2
													assert abs(result_mean.item() - 1.31				) < 1E-3





						def   UpperCAmelCase_ (				self							:    Dict				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=self.scheduler_classes[0]
													__UpperCamelCase							=self.get_scheduler_config()
													__UpperCamelCase							=scheduler_class(**UpperCamelCase__     , use_karras_sigmas=UpperCamelCase__				)

													scheduler.set_timesteps(self.num_inference_steps     , device=UpperCamelCase__				)

													__UpperCamelCase							=self.dummy_model()
													__UpperCamelCase							=self.dummy_sample_deter.to(UpperCamelCase__				) * scheduler.init_noise_sigma
													__UpperCamelCase							=sample.to(UpperCamelCase__				)

													for t in scheduler.timesteps:
																				__UpperCamelCase							=scheduler.scale_model_input(UpperCamelCase__     , UpperCamelCase__				)

																				__UpperCamelCase							=model(UpperCamelCase__     , UpperCamelCase__				)

																				__UpperCamelCase							=scheduler.step(UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__				)
																				__UpperCamelCase							=output.prev_sample

													__UpperCamelCase							=torch.sum(torch.abs(UpperCamelCase__				)				)
													__UpperCamelCase							=torch.mean(torch.abs(UpperCamelCase__				)				)

													assert abs(result_sum.item() - 38_12.99_27				) < 2E-2
													assert abs(result_mean.item() - 4.96_48				) < 1E-3




"""simple docstring"""

class 		_lowercase :



						"""simple docstring"""




						def __init__(				self							:    List[str]     , UpperCamelCase__							:    int				)				->				None:
													'''simple docstring'''



													__UpperCamelCase							=data
													__UpperCamelCase							=None
													__UpperCamelCase							=None






def    lowerCAmelCase			(__UpperCamelCase							:    BinaryTreeNode | None							,					__UpperCamelCase							:    int			):
							"""simple docstring"""


							if node is None:
														__UpperCamelCase							=BinaryTreeNode(__UpperCamelCase			)
														return node

							# binary search tree is not empty,
							# so we will insert it into the tree
							# if new_value is less than value of data in node,
							#  add it to left subtree and proceed recursively
							if new_value < node.data:
														__UpperCamelCase							=insert(node.left_child							,					__UpperCamelCase			)
							else:
														# if new_value is greater than value of data in node,
														#  add it to right subtree and proceed recursively
														__UpperCamelCase							=insert(node.right_child							,					__UpperCamelCase			)
							return node


def    lowerCAmelCase			(__UpperCamelCase							:    None | BinaryTreeNode			):  # if node is None,return
							"""simple docstring"""


							if node:
														__UpperCamelCase							=inorder(node.left_child			)
														__UpperCamelCase							=[*inorder_array, node.data]
														__UpperCamelCase							=inorder_array + inorder(node.right_child			)
							else:
														__UpperCamelCase							=[]
							return inorder_array


def    lowerCAmelCase			():
							"""simple docstring"""


							__UpperCamelCase							=insert(__UpperCamelCase							,					1_5			)
							insert(__UpperCamelCase							,					1_0			)
							insert(__UpperCamelCase							,					2_5			)
							insert(__UpperCamelCase							,					6			)
							insert(__UpperCamelCase							,					1_4			)
							insert(__UpperCamelCase							,					2_0			)
							insert(__UpperCamelCase							,					6_0			)
							return root


def    lowerCAmelCase			():
							"""simple docstring"""


							__UpperCamelCase							=make_tree()
							print('''Printing values of binary search tree in Inorder Traversal.'''			)
							inorder(__UpperCamelCase			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()



