



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	(KDPMaAncestralDiscreteScheduler,)
			UpperCamelCase      						=	10




			def 			snake_case__					(       self							:			Optional[int]	,	**a_							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Any													=     {
					    '''num_train_timesteps''': 11_00,
					    '''beta_start''': 0.0_0_0_1,
					    '''beta_end''': 0.0_2,
					    '''beta_schedule''': '''linear''',
					}

					config.update(**a_			)
					return config




			def 			snake_case__					(       self							:			Union[str, Any]			):
					'''simple docstring'''





					for timesteps in [10, 50, 1_00, 10_00]:
							self.check_over_configs(num_train_timesteps=a_			)




			def 			snake_case__					(       self							:			int			):
					'''simple docstring'''





					for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]	,	[0.0_0_0_2, 0.0_0_2, 0.0_2]			):
							self.check_over_configs(beta_start=a_	,	beta_end=a_			)




			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





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




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





					if torch_device == "mps":
							return
					__UpperCAmelCase   :					str													=     self.scheduler_classes[0]
					__UpperCAmelCase   :					Union[str, Any]													=     self.get_scheduler_config()
					__UpperCAmelCase   :					Any													=     scheduler_class(**a_			)

					scheduler.set_timesteps(self.num_inference_steps			)

					__UpperCAmelCase   :					List[str]													=     torch.manual_seed(0			)

					__UpperCAmelCase   :					Dict													=     self.dummy_model()
					__UpperCAmelCase   :					List[Any]													=     self.dummy_sample_deter * scheduler.init_noise_sigma
					__UpperCAmelCase   :					Any													=     sample.to(a_			)

					for i, t in enumerate(scheduler.timesteps			):
							__UpperCAmelCase   :					Dict													=     scheduler.scale_model_input(a_	,	a_			)

							__UpperCAmelCase   :					Optional[Any]													=     model(a_	,	a_			)

							__UpperCAmelCase   :					int													=     scheduler.step(a_	,	a_	,	a_	,	generator=a_			)
							__UpperCAmelCase   :					Tuple													=     output.prev_sample

					__UpperCAmelCase   :					Optional[int]													=     torch.sum(torch.abs(a_			)			)
					__UpperCAmelCase   :					int													=     torch.mean(torch.abs(a_			)			)

					assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7			) < 1e-2
					assert abs(result_mean.item() - 1_8.0_3_3_1			) < 5e-3




			def 			snake_case__					(       self							:			List[str]			):
					'''simple docstring'''





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




			def 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





					if torch_device == "mps":
							return
					__UpperCAmelCase   :					List[str]													=     self.scheduler_classes[0]
					__UpperCAmelCase   :					Dict													=     self.get_scheduler_config(prediction_type='''v_prediction'''			)
					__UpperCAmelCase   :					Dict													=     scheduler_class(**a_			)

					scheduler.set_timesteps(self.num_inference_steps			)

					__UpperCAmelCase   :					Union[str, Any]													=     self.dummy_model()
					__UpperCAmelCase   :					List[str]													=     self.dummy_sample_deter * scheduler.init_noise_sigma
					__UpperCAmelCase   :					str													=     sample.to(a_			)

					__UpperCAmelCase   :					str													=     torch.manual_seed(0			)

					for i, t in enumerate(scheduler.timesteps			):
							__UpperCAmelCase   :					Optional[Any]													=     scheduler.scale_model_input(a_	,	a_			)

							__UpperCAmelCase   :					Dict													=     model(a_	,	a_			)

							__UpperCAmelCase   :					Optional[Any]													=     scheduler.step(a_	,	a_	,	a_	,	generator=a_			)
							__UpperCAmelCase   :					str													=     output.prev_sample

					__UpperCAmelCase   :					Tuple													=     torch.sum(torch.abs(a_			)			)
					__UpperCAmelCase   :					str													=     torch.mean(torch.abs(a_			)			)

					assert abs(result_sum.item() - 3_2_8.9_9_7_0			) < 1e-2
					assert abs(result_mean.item() - 0.4_2_8_4			) < 1e-3




			def 			snake_case__					(       self							:			Union[str, Any]			):
					'''simple docstring'''





					if torch_device == "mps":
							return
					__UpperCAmelCase   :					Union[str, Any]													=     self.scheduler_classes[0]
					__UpperCAmelCase   :					Dict													=     self.get_scheduler_config()
					__UpperCAmelCase   :					Tuple													=     scheduler_class(**a_			)

					scheduler.set_timesteps(self.num_inference_steps	,	device=a_			)
					__UpperCAmelCase   :					Optional[int]													=     torch.manual_seed(0			)

					__UpperCAmelCase   :					Tuple													=     self.dummy_model()
					__UpperCAmelCase   :					Tuple													=     self.dummy_sample_deter.to(a_			) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
							__UpperCAmelCase   :					Tuple													=     scheduler.scale_model_input(a_	,	a_			)

							__UpperCAmelCase   :					int													=     model(a_	,	a_			)

							__UpperCAmelCase   :					Any													=     scheduler.step(a_	,	a_	,	a_	,	generator=a_			)
							__UpperCAmelCase   :					Union[str, Any]													=     output.prev_sample

					__UpperCAmelCase   :					List[Any]													=     torch.sum(torch.abs(a_			)			)
					__UpperCAmelCase   :					Optional[Any]													=     torch.mean(torch.abs(a_			)			)

					assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8			) < 1e-1
					assert abs(result_mean.item() - 1_8.0_3_3_1			) < 1e-3










from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

__A             =TypeVar("T", bound=bool)

class 		UpperCAmelCase__      (      Generic[T]       ):




			'''simple docstring'''






			def __init__(       self							:			List[Any]	,	a_							:			T			):
					'''simple docstring'''





					__UpperCAmelCase   :					T													=     value
					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None
					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None




			@property
			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					return self._value




			@staticmethod
			def 			snake_case__					(       a_							:			RandomizedHeapNode[T] | None	,	a_							:			RandomizedHeapNode[T] | None			):
					'''simple docstring'''





					if not roota:
							return roota

					if not roota:
							return roota

					if roota.value > roota.value:
							__UpperCAmelCase							, __UpperCAmelCase   :					int													=     roota, roota

					if random.choice([True, False]			):
							__UpperCAmelCase							, __UpperCAmelCase   :					Union[str, Any]													=     roota.right, roota.left

					__UpperCAmelCase   :					Optional[Any]													=     RandomizedHeapNode.merge(roota.left	,	a_			)

					return roota

class 		UpperCAmelCase__      (      Generic[T]       ):




			'''simple docstring'''






			def __init__(       self							:			Tuple	,	a_							:			Iterable[T] | None = ()			):
					'''simple docstring'''





					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None

					if data:
							for item in data:
									self.insert(a_			)




			def 			snake_case__					(       self							:			List[str]	,	a_							:			T			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     RandomizedHeapNode.merge(self._root	,	RandomizedHeapNode(a_			)			)




			def 			snake_case__					(       self							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     self.top()

					if self._root is None:
							return None

					__UpperCAmelCase   :					Tuple													=     RandomizedHeapNode.merge(self._root.left	,	self._root.right			)

					return result




			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					if not self._root:
							raise IndexError('''Can\'t get top element for the empty heap.'''			)
					return self._root.value




			def 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					str													=     None




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





					__UpperCAmelCase   :					List[Any]													=     []
					while self:
							result.append(self.pop()			)

					return result




			def __bool__(       self							:			List[Any]			):
					'''simple docstring'''





					return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





