
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    (DDPMScheduler,)

					def    lowercase__      (	self					:     Dict   ,					**__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					{
												    """num_train_timesteps""": 1_0_0_0,
												    """beta_start""": 0.0001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												    """variance_type""": """fixed_small""",
												    """clip_sample""": True,
												}

												config.update(**__UpperCamelCase   )
												return config

					def    lowercase__      (	self					:     Any   ):
												for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																			self.check_over_configs(num_train_timesteps=__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												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=__UpperCamelCase   ,					beta_end=__UpperCamelCase   )

					def    lowercase__      (	self					:     Dict   ):
												for schedule in ["linear", "squaredcos_cap_v2"]:
																			self.check_over_configs(beta_schedule=__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[Any]   ):
												for variance in ["fixed_small", "fixed_large", "other"]:
																			self.check_over_configs(variance_type=__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[Any]   ):
												for clip_sample in [True, False]:
																			self.check_over_configs(clip_sample=__UpperCamelCase   )

					def    lowercase__      (	self					:     List[Any]   ):
												self.check_over_configs(thresholding=__UpperCamelCase   )
												for threshold in [0.5, 1.0, 2.0]:
																			for prediction_type in ["epsilon", "sample", "v_prediction"]:
																										self.check_over_configs(
																										    thresholding=__UpperCamelCase   ,					prediction_type=__UpperCamelCase   ,					sample_max_value=__UpperCamelCase   ,					)

					def    lowercase__      (	self					:     Any   ):
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																			self.check_over_configs(prediction_type=__UpperCamelCase   )

					def    lowercase__      (	self					:     str   ):
												for t in [0, 5_0_0, 9_9_9]:
																			self.check_over_forward(time_step=__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					self.scheduler_classes[0]
												lowerCamelCase_							  =					self.get_scheduler_config()
												lowerCamelCase_							  =					scheduler_class(**__UpperCamelCase   )

												assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_8_7   ) - 0.00979   )   ) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_9_9   ) - 0.02   )   ) < 1E-5

					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					self.scheduler_classes[0]
												lowerCamelCase_							  =					self.get_scheduler_config()
												lowerCamelCase_							  =					scheduler_class(**__UpperCamelCase   )

												lowerCamelCase_							  =					len(__UpperCamelCase   )

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

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

																			# 2. predict previous mean of sample x_t-1
																			lowerCamelCase_							  =					scheduler.step(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					generator=__UpperCamelCase   ).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(__UpperCamelCase   )   )
												lowerCamelCase_							  =					torch.mean(torch.abs(__UpperCamelCase   )   )

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

					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					self.scheduler_classes[0]
												lowerCamelCase_							  =					self.get_scheduler_config(prediction_type="""v_prediction"""   )
												lowerCamelCase_							  =					scheduler_class(**__UpperCamelCase   )

												lowerCamelCase_							  =					len(__UpperCamelCase   )

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

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

																			# 2. predict previous mean of sample x_t-1
																			lowerCamelCase_							  =					scheduler.step(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					generator=__UpperCamelCase   ).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(__UpperCamelCase   )   )
												lowerCamelCase_							  =					torch.mean(torch.abs(__UpperCamelCase   )   )

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

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					self.scheduler_classes[0]
												lowerCamelCase_							  =					self.get_scheduler_config()
												lowerCamelCase_							  =					scheduler_class(**__UpperCamelCase   )

												lowerCamelCase_							  =					[1_0_0, 8_7, 5_0, 1, 0]

												scheduler.set_timesteps(timesteps=__UpperCamelCase   )

												lowerCamelCase_							  =					scheduler.timesteps

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

																			lowerCamelCase_							  =					scheduler.previous_timestep(__UpperCamelCase   )
																			lowerCamelCase_							  =					prev_t.item()

																			self.assertEqual(__UpperCamelCase   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.scheduler_classes[0]
												lowerCamelCase_							  =					self.get_scheduler_config()
												lowerCamelCase_							  =					scheduler_class(**__UpperCamelCase   )

												lowerCamelCase_							  =					[1_0_0, 8_7, 5_0, 5_1, 0]

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

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					self.scheduler_classes[0]
												lowerCamelCase_							  =					self.get_scheduler_config()
												lowerCamelCase_							  =					scheduler_class(**__UpperCamelCase   )

												lowerCamelCase_							  =					[1_0_0, 8_7, 5_0, 1, 0]
												lowerCamelCase_							  =					len(__UpperCamelCase   )

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



					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					self.scheduler_classes[0]
												lowerCamelCase_							  =					self.get_scheduler_config()
												lowerCamelCase_							  =					scheduler_class(**__UpperCamelCase   )

												lowerCamelCase_							  =					[scheduler.config.num_train_timesteps]

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







from ..utils import DummyObject, requires_backends





class       __A(				metaclass=UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''torch''', '''scipy''']

					def __init__(	self					:     Any   ,					*__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     Any   ):
												requires_backends(self   ,					["""torch""", """scipy"""]   )

					@classmethod
					def    lowercase__      (	cls					:     Dict   ,					*__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     Dict   ):
												requires_backends(cls   ,					["""torch""", """scipy"""]   )



					@classmethod
					def    lowercase__      (	cls					:     str   ,					*__UpperCamelCase					:     Optional[int]   ,					**__UpperCamelCase					:     Optional[int]   ):
												requires_backends(cls   ,					["""torch""", """scipy"""]   )





