






"""simple docstring"""







import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase    	= (DPMSolverSinglestepScheduler,)
						__lowerCamelCase    	= (("num_inference_steps", 25),)




						def 		UpperCAmelCase_	(       self	,  **snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	{
												    "num_train_timesteps": 1000,
												    "beta_start": 0.00_01,
												    "beta_end": 0.02,
												    "beta_schedule": "linear",
												    "solver_order": 2,
												    "prediction_type": "epsilon",
												    "thresholding": False,
												    "sample_max_value": 1.0,
												    "algorithm_type": "dpmsolver++",
												    "solver_type": "midpoint",
												    "lambda_min_clipped": -float("inf"      ),
												    "variance_type": None,
												}

												config.update(**snake_case__      )
												return config




						def 		UpperCAmelCase_	(       self	,  snake_case__=0	,  **snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	dict(self.forward_default_kwargs      )
												lowercase__    :				int=	kwargs.pop("num_inference_steps"	,  snake_case__      )
												lowercase__    :				Tuple=	self.dummy_sample
												lowercase__    :				Union[str, Any]=	0.1 * sample
												lowercase__    :				Any=	[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																		lowercase__    :				Optional[Any]=	self.get_scheduler_config(**snake_case__      )
																		lowercase__    :				Any=	scheduler_class(**snake_case__      )
																		scheduler.set_timesteps(snake_case__      )
																		# copy over dummy past residuals
																		lowercase__    :				Tuple=	dummy_past_residuals[: scheduler.config.solver_order]

																		with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(snake_case__      )
																								lowercase__    :				str=	scheduler_class.from_pretrained(snake_case__      )
																								new_scheduler.set_timesteps(snake_case__      )
																								# copy over dummy past residuals
																								lowercase__    :				Tuple=	dummy_past_residuals[: new_scheduler.config.solver_order]

																		lowercase__,     lowercase__    :				Tuple=	sample, sample
																		for t in range(snake_case__	,  time_step + scheduler.config.solver_order + 1      ):
																								lowercase__    :				Dict=	scheduler.step(snake_case__	,  snake_case__	,  snake_case__	,  **snake_case__      ).prev_sample
																								lowercase__    :				Any=	new_scheduler.step(snake_case__	,  snake_case__	,  snake_case__	,  **snake_case__      ).prev_sample

																								assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_	(       self	,  snake_case__=0	,  **snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	dict(self.forward_default_kwargs      )
												lowercase__    :				Union[str, Any]=	kwargs.pop("num_inference_steps"	,  snake_case__      )
												lowercase__    :				str=	self.dummy_sample
												lowercase__    :				Optional[Any]=	0.1 * sample
												lowercase__    :				Tuple=	[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																		lowercase__    :				List[str]=	self.get_scheduler_config()
																		lowercase__    :				int=	scheduler_class(**snake_case__      )
																		scheduler.set_timesteps(snake_case__      )

																		# copy over dummy past residuals (must be after setting timesteps)
																		lowercase__    :				Tuple=	dummy_past_residuals[: scheduler.config.solver_order]

																		with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(snake_case__      )
																								lowercase__    :				Tuple=	scheduler_class.from_pretrained(snake_case__      )
																								# copy over dummy past residuals
																								new_scheduler.set_timesteps(snake_case__      )

																								# copy over dummy past residual (must be after setting timesteps)
																								lowercase__    :				Any=	dummy_past_residuals[: new_scheduler.config.solver_order]

																		lowercase__    :				List[str]=	scheduler.step(snake_case__	,  snake_case__	,  snake_case__	,  **snake_case__      ).prev_sample
																		lowercase__    :				Tuple=	new_scheduler.step(snake_case__	,  snake_case__	,  snake_case__	,  **snake_case__      ).prev_sample

																		assert torch.sum(torch.abs(output - new_output      )      ) < 1e-5, "Scheduler outputs are not identical"




						def 		UpperCAmelCase_	(       self	,  snake_case__=None	,  **snake_case__      ):
												'''simple docstring'''
												if scheduler is None:
																		lowercase__    :				Optional[Any]=	self.scheduler_classes[0]
																		lowercase__    :				Optional[int]=	self.get_scheduler_config(**snake_case__      )
																		lowercase__    :				int=	scheduler_class(**snake_case__      )

												lowercase__    :				Union[str, Any]=	self.scheduler_classes[0]
												lowercase__    :				Union[str, Any]=	self.get_scheduler_config(**snake_case__      )
												lowercase__    :				List[str]=	scheduler_class(**snake_case__      )

												lowercase__    :				str=	10
												lowercase__    :				Any=	self.dummy_model()
												lowercase__    :				Any=	self.dummy_sample_deter
												scheduler.set_timesteps(snake_case__      )

												for i, t in enumerate(scheduler.timesteps      ):
																		lowercase__    :				Tuple=	model(snake_case__	,  snake_case__      )
																		lowercase__    :				Dict=	scheduler.step(snake_case__	,  snake_case__	,  snake_case__      ).prev_sample

												return sample




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				List[str]=	DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
												lowercase__    :				Union[str, Any]=	50
												lowercase__    :				Union[str, Any]=	self.dummy_model()
												lowercase__    :				Union[str, Any]=	self.dummy_sample_deter
												scheduler.set_timesteps(snake_case__      )

												# make sure that the first t is uneven
												for i, t in enumerate(scheduler.timesteps[3:]      ):
																		lowercase__    :				str=	model(snake_case__	,  snake_case__      )
																		lowercase__    :				str=	scheduler.step(snake_case__	,  snake_case__	,  snake_case__      ).prev_sample

												lowercase__    :				Tuple=	torch.mean(torch.abs(snake_case__      )      )

												assert abs(result_mean.item() - 0.25_74      ) < 1e-3




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												for timesteps in [25, 50, 100, 999, 1000]:
																		self.check_over_configs(num_train_timesteps=snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# make sure that iterating over schedulers with same config names gives same results
												# for defaults
												lowercase__    :				Any=	DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
												lowercase__    :				List[Any]=	self.full_loop(scheduler=snake_case__      )
												lowercase__    :				Dict=	torch.mean(torch.abs(snake_case__      )      )

												assert abs(result_mean.item() - 0.27_91      ) < 1e-3

												lowercase__    :				int=	DEISMultistepScheduler.from_config(scheduler.config      )
												lowercase__    :				Tuple=	DPMSolverMultistepScheduler.from_config(scheduler.config      )
												lowercase__    :				Tuple=	UniPCMultistepScheduler.from_config(scheduler.config      )
												lowercase__    :				int=	DPMSolverSinglestepScheduler.from_config(scheduler.config      )

												lowercase__    :				List[str]=	self.full_loop(scheduler=snake_case__      )
												lowercase__    :				Tuple=	torch.mean(torch.abs(snake_case__      )      )

												assert abs(result_mean.item() - 0.27_91      ) < 1e-3




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												self.check_over_configs(thresholding=snake_case__      )
												for order in [1, 2, 3]:
																		for solver_type in ["midpoint", "heun"]:
																								for threshold in [0.5, 1.0, 2.0]:
																														for prediction_type in ["epsilon", "sample"]:
																																				self.check_over_configs(
																																				    thresholding=snake_case__	,  prediction_type=snake_case__	,  sample_max_value=snake_case__	,  algorithm_type="dpmsolver++"	,  solver_order=snake_case__	,  solver_type=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 algorithm_type in ["dpmsolver", "dpmsolver++"]:
																		for solver_type in ["midpoint", "heun"]:
																								for order in [1, 2, 3]:
																														for prediction_type in ["epsilon", "sample"]:
																																				self.check_over_configs(
																																				    solver_order=snake_case__	,  solver_type=snake_case__	,  prediction_type=snake_case__	,  algorithm_type=snake_case__	,  )
																																				lowercase__    :				str=	self.full_loop(
																																				    solver_order=snake_case__	,  solver_type=snake_case__	,  prediction_type=snake_case__	,  algorithm_type=snake_case__	,  )
																																				assert not torch.isnan(snake_case__      ).any(), "Samples have nan numbers"




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												self.check_over_configs(lower_order_final=snake_case__      )
												self.check_over_configs(lower_order_final=snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												self.check_over_configs(lambda_min_clipped=-float("inf"      )      )
												self.check_over_configs(lambda_min_clipped=-5.1      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												self.check_over_configs(variance_type=snake_case__      )
												self.check_over_configs(variance_type="learned_range"      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
																		self.check_over_forward(num_inference_steps=snake_case__	,  time_step=0      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	self.full_loop()
												lowercase__    :				List[str]=	torch.mean(torch.abs(snake_case__      )      )

												assert abs(result_mean.item() - 0.27_91      ) < 1e-3




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Any=	self.full_loop(use_karras_sigmas=snake_case__      )
												lowercase__    :				List[Any]=	torch.mean(torch.abs(snake_case__      )      )

												assert abs(result_mean.item() - 0.22_48      ) < 1e-3




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	self.full_loop(prediction_type="v_prediction"      )
												lowercase__    :				List[Any]=	torch.mean(torch.abs(snake_case__      )      )

												assert abs(result_mean.item() - 0.14_53      ) < 1e-3




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				str=	self.full_loop(prediction_type="v_prediction"	,  use_karras_sigmas=snake_case__      )
												lowercase__    :				Optional[int]=	torch.mean(torch.abs(snake_case__      )      )

												assert abs(result_mean.item() - 0.06_49      ) < 1e-3




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

												lowercase__    :				str=	10
												lowercase__    :				Tuple=	self.dummy_model()
												lowercase__    :				str=	self.dummy_sample_deter.half()
												scheduler.set_timesteps(snake_case__      )

												for i, t in enumerate(scheduler.timesteps      ):
																		lowercase__    :				Dict=	model(snake_case__	,  snake_case__      )
																		lowercase__    :				Tuple=	scheduler.step(snake_case__	,  snake_case__	,  snake_case__      ).prev_sample

												assert sample.dtype == torch.floataa














"""simple docstring"""







from __future__ import annotations

from collections.abc import Iterator
from typing import Any


class    __UpperCAmelCase:




						"""simple docstring"""




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


class    __UpperCAmelCase:




						"""simple docstring"""




						def __init__(       self      ):
												'''simple docstring'''
												lowercase__    :				List[str]=	None
												lowercase__    :				int=	None




						def __iter__(       self      ):
												'''simple docstring'''
												lowercase__    :				Any=	self.head
												while self.head:
																		yield node.data
																		lowercase__    :				str=	node.next
																		if node == self.head:
																								break




						def __len__(       self      ):
												'''simple docstring'''
												return sum(1 for _ in self      )




						def __repr__(       self      ):
												'''simple docstring'''
												return "->".join(str(snake_case__      ) for item in iter(self      )      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												self.insert_nth(len(self      )	,  snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												self.insert_nth(0	,  snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												if index < 0 or index > len(self      ):
																		raise IndexError("list index out of range."      )
												lowercase__    :				Optional[Any]=	Node(snake_case__      )
												if self.head is None:
																		lowercase__    :				Tuple=	new_node  # first node points itself
																		lowercase__    :				str=	new_node
												elif index == 0:  # insert at head
																		lowercase__    :				Dict=	self.head
																		lowercase__    :				int=	new_node
												else:
																		lowercase__    :				Dict=	self.head
																		for _ in range(index - 1      ):
																								lowercase__    :				Any=	temp.next
																		lowercase__    :				str=	temp.next
																		lowercase__    :				Dict=	new_node
																		if index == len(self      ) - 1:  # insert at tail
																								lowercase__    :				int=	new_node




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return self.delete_nth(0      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return self.delete_nth(len(self      ) - 1      )




						def 		UpperCAmelCase_	(       self	,  snake_case__ = 0      ):
												'''simple docstring'''
												if not 0 <= index < len(self      ):
																		raise IndexError("list index out of range."      )
												lowercase__    :				int=	self.head
												if self.head == self.tail:  # just one node
																		lowercase__    :				Union[str, Any]=	None
												elif index == 0:  # delete head node
																		lowercase__    :				Tuple=	self.tail.next.next
																		lowercase__    :				Union[str, Any]=	self.head.next
												else:
																		lowercase__    :				Union[str, Any]=	self.head
																		for _ in range(index - 1      ):
																								lowercase__    :				Dict=	temp.next
																		lowercase__    :				List[str]=	temp.next
																		lowercase__    :				Optional[Any]=	temp.next.next
																		if index == len(self      ) - 1:  # delete at tail
																								lowercase__    :				Optional[Any]=	temp
												return delete_node.data




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return len(self      ) == 0





def 						lowercase__()    ->None:




						"""simple docstring"""



						lowercase__    :				Dict=	CircularLinkedList()
						assert len(A						) == 0
						assert circular_linked_list.is_empty() is True
						assert str(A						) == ""

						try:
												circular_linked_list.delete_front()
												raise AssertionError  # This should not happen
						except IndexError:
												assert True  # This should happen

						try:
												circular_linked_list.delete_tail()
												raise AssertionError  # This should not happen
						except IndexError:
												assert True  # This should happen

						try:
												circular_linked_list.delete_nth(-1						)
												raise AssertionError
						except IndexError:
												assert True

						try:
												circular_linked_list.delete_nth(0						)
												raise AssertionError
						except IndexError:
												assert True

						assert circular_linked_list.is_empty() is True
						for i in range(5						):
												assert len(A						) == i
												circular_linked_list.insert_nth(A ,		i + 1						)
						assert str(A						) == "->".join(str(A						) for i in range(1 ,		6						)						)

						circular_linked_list.insert_tail(6						)
						assert str(A						) == "->".join(str(A						) for i in range(1 ,		7						)						)
						circular_linked_list.insert_head(0						)
						assert str(A						) == "->".join(str(A						) for i in range(0 ,		7						)						)

						assert circular_linked_list.delete_front() == 0
						assert circular_linked_list.delete_tail() == 6
						assert str(A						) == "->".join(str(A						) for i in range(1 ,		6						)						)
						assert circular_linked_list.delete_nth(2						) == 3

						circular_linked_list.insert_nth(2 ,		3						)
						assert str(A						) == "->".join(str(A						) for i in range(1 ,		6						)						)

						assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
			import doctest

			doctest.testmod()






