
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class        lowercase	(      __SCREAMING_SNAKE_CASE	):
							lowercase_ : Optional[int]       =(UnCLIPScheduler,)




							def 				A__   (       self     ,**A__):
												lowercase					=		{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''variance_type''': '''fixed_small_log''',
												    '''clip_sample''': True,
												    '''clip_sample_range''': 1.0,
												    '''prediction_type''': '''epsilon''',
												}

												config.update(**_a)
												return config




							def 				A__   (       self):
												for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_a)




							def 				A__   (       self):
												for variance in ["fixed_small_log", "learned_range"]:
																	self.check_over_configs(variance_type=_a)




							def 				A__   (       self):
												for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=_a)




							def 				A__   (       self):
												for clip_sample_range in [1, 5, 1_0, 2_0]:
																	self.check_over_configs(clip_sample_range=_a)




							def 				A__   (       self):
												for prediction_type in ["epsilon", "sample"]:
																	self.check_over_configs(prediction_type=_a)




							def 				A__   (       self):
												for time_step in [0, 5_0_0, 9_9_9]:
																	for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
																						if prev_timestep is not None and prev_timestep >= time_step:
																											continue

																						self.check_over_forward(time_step=_a     ,prev_timestep=_a)




							def 				A__   (       self):
												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config(variance_type='''fixed_small_log''')
												lowercase					=		scheduler_class(**_a)

												assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0_000E-10)) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_8_7) - 0.0549625)) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_9_9) - 0.9994987)) < 1E-5




							def 				A__   (       self):
												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config(variance_type='''learned_range''')
												lowercase					=		scheduler_class(**_a)

												lowercase					=		0.5

												assert scheduler._get_variance(1     ,predicted_variance=_a) - -10.1712790 < 1E-5
												assert scheduler._get_variance(4_8_7     ,predicted_variance=_a) - -5.7998052 < 1E-5
												assert scheduler._get_variance(9_9_9     ,predicted_variance=_a) - -0.0010011 < 1E-5




							def 				A__   (       self):
												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config()
												lowercase					=		scheduler_class(**_a)

												lowercase					=		scheduler.timesteps

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

												for i, t in enumerate(_a):
																	# 1. predict noise residual
																	lowercase					=		model(_a     ,_a)

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

																	lowercase					=		pred_prev_sample

												lowercase					=		torch.sum(torch.abs(_a))
												lowercase					=		torch.mean(torch.abs(_a))

												assert abs(result_sum.item() - 252.2682495) < 1E-2
												assert abs(result_mean.item() - 0.3284743) < 1E-3




							def 				A__   (       self):
												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config()
												lowercase					=		scheduler_class(**_a)

												scheduler.set_timesteps(2_5)

												lowercase					=		scheduler.timesteps

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

												for i, t in enumerate(_a):
																	# 1. predict noise residual
																	lowercase					=		model(_a     ,_a)

																	if i + 1 == timesteps.shape[0]:
																						lowercase					=		None
																	else:
																						lowercase					=		timesteps[i + 1]

																	# 2. predict previous mean of sample x_t-1
																	lowercase					=		scheduler.step(
																	    _a     ,_a     ,_a     ,prev_timestep=_a     ,generator=_a).prev_sample

																	lowercase					=		pred_prev_sample

												lowercase					=		torch.sum(torch.abs(_a))
												lowercase					=		torch.mean(torch.abs(_a))

												assert abs(result_sum.item() - 258.2044983) < 1E-2
												assert abs(result_mean.item() - 0.3362038) < 1E-3




							def 				A__   (       self):
												pass





							def 				A__   (       self):
												pass






from __future__ import annotations



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if len(lowerCAmelCase__	) < 2:
										raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''	)
					if any(i <= 0 for i in nums	):
										raise ValueError('''All values must be greater than 0'''	)
					lowercase					=		nums.copy()
					copy_nums.sort()
					return copy_nums[-1] < sum(copy_nums[:-1]	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




