
'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				str									=       (DDPMScheduler,)




						def  _lowercase     ( self   ,       **_lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			{
												    """num_train_timesteps""": 1_000,
												    """beta_start""": 0.0001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												    """variance_type""": """fixed_small""",
												    """clip_sample""": True,
												}

												config.update(**_lowercase				)
												return config




						def  _lowercase     ( self				):

												"""simple docstring"""



												for timesteps in [1, 5, 100, 1_000]:
																		self.check_over_configs(num_train_timesteps=_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,       [0.002, 0.02, 0.2, 2]				):
																		self.check_over_configs(beta_start=_lowercase   ,       beta_end=_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												for schedule in ["linear", "squaredcos_cap_v2"]:
																		self.check_over_configs(beta_schedule=_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												for variance in ["fixed_small", "fixed_large", "other"]:
																		self.check_over_configs(variance_type=_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												self.check_over_configs(thresholding=_lowercase				)
												for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "sample", "v_prediction"]:
																								self.check_over_configs(
																								    thresholding=_lowercase   ,       prediction_type=_lowercase   ,       sample_max_value=_lowercase   ,       )




						def  _lowercase     ( self				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



												for t in [0, 500, 999]:
																		self.check_over_forward(time_step=_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.scheduler_classes[0]
												_lowerCAmelCase  						=			self.get_scheduler_config()
												_lowerCAmelCase  						=			scheduler_class(**_lowercase				)

												assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_0979				)				) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.02				)				) < 1e-5




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.scheduler_classes[0]
												_lowerCAmelCase  						=			self.get_scheduler_config()
												_lowerCAmelCase  						=			scheduler_class(**_lowercase				)

												_lowerCAmelCase  						=			len(_lowercase				)

												_lowerCAmelCase  						=			self.dummy_model()
												_lowerCAmelCase  						=			self.dummy_sample_deter
												_lowerCAmelCase  						=			torch.manual_seed(0				)

												for t in reversed(range(_lowercase				)				):
																		# 1. predict noise residual
																		_lowerCAmelCase  						=			model(_lowercase   ,       _lowercase				)

																		# 2. predict previous mean of sample x_t-1
																		_lowerCAmelCase  						=			scheduler.step(_lowercase   ,       _lowercase   ,       _lowercase   ,       generator=_lowercase				).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		_lowerCAmelCase  						=			pred_prev_sample

												_lowerCAmelCase  						=			torch.sum(torch.abs(_lowercase				)				)
												_lowerCAmelCase  						=			torch.mean(torch.abs(_lowercase				)				)

												assert abs(result_sum.item() - 258.9606				) < 1e-2
												assert abs(result_mean.item() - 0.3372				) < 1e-3




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.scheduler_classes[0]
												_lowerCAmelCase  						=			self.get_scheduler_config(prediction_type="""v_prediction"""				)
												_lowerCAmelCase  						=			scheduler_class(**_lowercase				)

												_lowerCAmelCase  						=			len(_lowercase				)

												_lowerCAmelCase  						=			self.dummy_model()
												_lowerCAmelCase  						=			self.dummy_sample_deter
												_lowerCAmelCase  						=			torch.manual_seed(0				)

												for t in reversed(range(_lowercase				)				):
																		# 1. predict noise residual
																		_lowerCAmelCase  						=			model(_lowercase   ,       _lowercase				)

																		# 2. predict previous mean of sample x_t-1
																		_lowerCAmelCase  						=			scheduler.step(_lowercase   ,       _lowercase   ,       _lowercase   ,       generator=_lowercase				).prev_sample

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		_lowerCAmelCase  						=			pred_prev_sample

												_lowerCAmelCase  						=			torch.sum(torch.abs(_lowercase				)				)
												_lowerCAmelCase  						=			torch.mean(torch.abs(_lowercase				)				)

												assert abs(result_sum.item() - 202.0296				) < 1e-2
												assert abs(result_mean.item() - 0.2631				) < 1e-3




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.scheduler_classes[0]
												_lowerCAmelCase  						=			self.get_scheduler_config()
												_lowerCAmelCase  						=			scheduler_class(**_lowercase				)

												_lowerCAmelCase  						=			[100, 87, 50, 1, 0]

												scheduler.set_timesteps(timesteps=_lowercase				)

												_lowerCAmelCase  						=			scheduler.timesteps

												for i, timestep in enumerate(_lowercase				):
																		if i == len(_lowercase				) - 1:
																								_lowerCAmelCase  						=			-1
																		else:
																								_lowerCAmelCase  						=			timesteps[i + 1]

																		_lowerCAmelCase  						=			scheduler.previous_timestep(_lowercase				)
																		_lowerCAmelCase  						=			prev_t.item()

																		self.assertEqual(_lowercase   ,       _lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.scheduler_classes[0]
												_lowerCAmelCase  						=			self.get_scheduler_config()
												_lowerCAmelCase  						=			scheduler_class(**_lowercase				)

												_lowerCAmelCase  						=			[100, 87, 50, 51, 0]

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.scheduler_classes[0]
												_lowerCAmelCase  						=			self.get_scheduler_config()
												_lowerCAmelCase  						=			scheduler_class(**_lowercase				)

												_lowerCAmelCase  						=			[100, 87, 50, 1, 0]
												_lowerCAmelCase  						=			len(_lowercase				)

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.scheduler_classes[0]
												_lowerCAmelCase  						=			self.get_scheduler_config()
												_lowerCAmelCase  						=			scheduler_class(**_lowercase				)

												_lowerCAmelCase  						=			[scheduler.config.num_train_timesteps]

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


'''simple docstring'''



from __future__ import annotations

from typing import Any




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						pass






class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			data
												_lowerCAmelCase  						=			None




						def __iter__( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self
												_lowerCAmelCase  						=			[]
												while node:
																		if node in visited:
																								raise ContainsLoopError
																		visited.append(_lowercase				)
																		yield node.data
																		_lowerCAmelCase  						=			node.next_node




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												try:
																		list(self				)
																		return False
												except ContainsLoopError:
																		return True


if __name__ == "__main__":
						_lowercase    =		Node(1)
						_lowercase    =		Node(2)
						_lowercase    =		Node(3)
						_lowercase    =		Node(4)
						print(root_node.has_loop)  # False
						_lowercase    =		root_node.next_node
						print(root_node.has_loop)  # True

						_lowercase    =		Node(5)
						_lowercase    =		Node(6)
						_lowercase    =		Node(5)
						_lowercase    =		Node(6)
						print(root_node.has_loop)  # False

						_lowercase    =		Node(1)
						print(root_node.has_loop)  # False
