





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class        __SCREAMING_SNAKE_CASE					(     _a       ):
							snake_case     :      Tuple						     =       (CMStochasticIterativeScheduler,)
							snake_case     :      Optional[Any]						     =       10






							def 				_lowerCamelCase		(						self  , **__lowerCAmelCase  ):
														UpperCamelCase__				     =			{
														    """num_train_timesteps""": 201,
														    """sigma_min""": 0.002,
														    """sigma_max""": 80.0,
														}

														config.update(**__lowerCAmelCase  )
														return config






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			10

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

														scheduler.set_timesteps(__lowerCAmelCase  )

														UpperCamelCase__				     =			scheduler.timesteps[0]
														UpperCamelCase__				     =			scheduler.timesteps[1]

														UpperCamelCase__				     =			self.dummy_sample
														UpperCamelCase__				     =			0.1 * sample

														UpperCamelCase__				     =			scheduler.step(__lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ).prev_sample
														UpperCamelCase__				     =			scheduler.step(__lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ).prev_sample

														self.assertEqual(output_a.shape  , sample.shape  )
														self.assertEqual(output_a.shape  , output_a.shape  )






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






							def 				_lowerCamelCase		(						self  ):
														for clip_denoised in [True, False]:
																					self.check_over_configs(clip_denoised=__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.scheduler_classes[0]
														UpperCamelCase__				     =			self.get_scheduler_config()
														UpperCamelCase__				     =			scheduler_class(**__lowerCAmelCase  )

														UpperCamelCase__				     =			1
														scheduler.set_timesteps(__lowerCAmelCase  )
														UpperCamelCase__				     =			scheduler.timesteps

														UpperCamelCase__				     =			torch.manual_seed(0  )

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

														for i, t in enumerate(__lowerCAmelCase  ):
																					# 1. scale model input
																					UpperCamelCase__				     =			scheduler.scale_model_input(__lowerCAmelCase  , __lowerCAmelCase  )

																					# 2. predict noise residual
																					UpperCamelCase__				     =			model(__lowerCAmelCase  , __lowerCAmelCase  )

																					# 3. predict previous sample x_t-1
																					UpperCamelCase__				     =			scheduler.step(__lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , generator=__lowerCAmelCase  ).prev_sample

																					UpperCamelCase__				     =			pred_prev_sample

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

														assert abs(result_sum.item() - 192.7614  ) < 1E-2
														assert abs(result_mean.item() - 0.2510  ) < 1E-3






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.scheduler_classes[0]
														UpperCamelCase__				     =			self.get_scheduler_config()
														UpperCamelCase__				     =			scheduler_class(**__lowerCAmelCase  )

														UpperCamelCase__				     =			[106, 0]
														scheduler.set_timesteps(timesteps=__lowerCAmelCase  )
														UpperCamelCase__				     =			scheduler.timesteps

														UpperCamelCase__				     =			torch.manual_seed(0  )

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

														for t in timesteps:
																					# 1. scale model input
																					UpperCamelCase__				     =			scheduler.scale_model_input(__lowerCAmelCase  , __lowerCAmelCase  )

																					# 2. predict noise residual
																					UpperCamelCase__				     =			model(__lowerCAmelCase  , __lowerCAmelCase  )

																					# 3. predict previous sample x_t-1
																					UpperCamelCase__				     =			scheduler.step(__lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , generator=__lowerCAmelCase  ).prev_sample

																					UpperCamelCase__				     =			pred_prev_sample

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

														assert abs(result_sum.item() - 347.6357  ) < 1E-2
														assert abs(result_mean.item() - 0.4527  ) < 1E-3






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.scheduler_classes[0]
														UpperCamelCase__				     =			self.get_scheduler_config()
														UpperCamelCase__				     =			scheduler_class(**__lowerCAmelCase  )

														UpperCamelCase__				     =			[39, 30, 12, 15, 0]

														with self.assertRaises(__lowerCAmelCase  , msg="""`timesteps` must be in descending order."""  ):
																					scheduler.set_timesteps(timesteps=__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.scheduler_classes[0]
														UpperCamelCase__				     =			self.get_scheduler_config()
														UpperCamelCase__				     =			scheduler_class(**__lowerCAmelCase  )

														UpperCamelCase__				     =			[39, 30, 12, 1, 0]
														UpperCamelCase__				     =			len(__lowerCAmelCase  )

														with self.assertRaises(__lowerCAmelCase  , msg="""Can only pass one of `num_inference_steps` or `timesteps`."""  ):
																					scheduler.set_timesteps(num_inference_steps=__lowerCAmelCase  , timesteps=__lowerCAmelCase  )







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.scheduler_classes[0]
														UpperCamelCase__				     =			self.get_scheduler_config()
														UpperCamelCase__				     =			scheduler_class(**__lowerCAmelCase  )

														UpperCamelCase__				     =			[scheduler.config.num_train_timesteps]

														with self.assertRaises(
														    __lowerCAmelCase  , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""  , ):
																					scheduler.set_timesteps(timesteps=__lowerCAmelCase  )











from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any

@dataclass
class        __SCREAMING_SNAKE_CASE					:
							snake_case     :      int
							snake_case     :      Node | None						     =       None
							snake_case     :      Node | None						     =       None






def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			Node(1       )
							UpperCamelCase__				     =			Node(2       )
							UpperCamelCase__				     =			Node(3       )
							UpperCamelCase__				     =			Node(4       )
							UpperCamelCase__				     =			Node(5       )
							return tree





def     _UpperCamelCase     (a__   :Node | None       ):






							"""simple docstring"""





							return [root.data, *preorder(root.left       ), *preorder(root.right       )] if root else []





def     _UpperCamelCase     (a__   :Node | None       ):






							"""simple docstring"""





							return postorder(root.left       ) + postorder(root.right       ) + [root.data] if root else []





def     _UpperCamelCase     (a__   :Node | None       ):






							"""simple docstring"""





							return [*inorder(root.left       ), root.data, *inorder(root.right       )] if root else []





def     _UpperCamelCase     (a__   :Node | None       ):






							"""simple docstring"""





							return (max(height(root.left       )   ,			height(root.right       )       ) + 1) if root else 0





def     _UpperCamelCase     (a__   :Node | None       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[]

							if root is None:
														return output

							UpperCamelCase__				     =			deque([root]       )

							while process_queue:
														UpperCamelCase__				     =			process_queue.popleft()
														output.append(node.data       )

														if node.left:
																					process_queue.append(node.left       )
														if node.right:
																					process_queue.append(node.right       )
							return output





def     _UpperCamelCase     (a__   :Node | None   ,			a__   :int       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[]

							def populate_output(a__   :Node | None   ,			a__   :int       ) -> None:
														if not root:
																					return
														if level == 1:
																					output.append(root.data       )
														elif level > 1:
																					populate_output(root.left   ,			level - 1       )
																					populate_output(root.right   ,			level - 1       )

							populate_output(a__   ,			a__       )
							return output





def     _UpperCamelCase     (a__   :Node | None   ,			a__   :int       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[]

							def populate_output(a__   :Node | None   ,			a__   :int       ) -> None:
														if root is None:
																					return
														if level == 1:
																					output.append(root.data       )
														elif level > 1:
																					populate_output(root.right   ,			level - 1       )
																					populate_output(root.left   ,			level - 1       )

							populate_output(a__   ,			a__       )
							return output





def     _UpperCamelCase     (a__   :Node | None       ):






							"""simple docstring"""





							if root is None:
														return []

							UpperCamelCase__				     =			[]

							UpperCamelCase__				     =			0
							UpperCamelCase__				     =			height(a__       )

							for h in range(1   ,			height_tree + 1       ):
														if not flag:
																					output.append(get_nodes_from_left_to_right(a__   ,			a__       )       )
																					UpperCamelCase__				     =			1
														else:
																					output.append(get_nodes_from_right_to_left(a__   ,			a__       )       )
																					UpperCamelCase__				     =			0

							return output





def     _UpperCamelCase     ():  # Main function for testing.






							"""simple docstring"""





							UpperCamelCase__				     =			make_tree()
							print(f"""In-order Traversal: {inorder(a__       )}"""       )
							print(f"""Pre-order Traversal: {preorder(a__       )}"""       )
							print(f"""Post-order Traversal: {postorder(a__       )}"""   ,			"""\n"""       )

							print(f"""Height of Tree: {height(a__       )}"""   ,			"""\n"""       )

							print("""Complete Level Order Traversal: """       )
							print(level_order(a__       )   ,			"""\n"""       )

							print("""Level-wise order Traversal: """       )

							for level in range(1   ,			height(a__       ) + 1       ):
														print(f"""Level {level}:"""   ,			get_nodes_from_left_to_right(a__   ,			level=a__       )       )

							print("""\nZigZag order Traversal: """       )
							print(zigzag(a__       )       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()




