

"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		str       		=				(UnCLIPScheduler,)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]     ,					**__UpperCAmelCase					:    str							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   {
						    """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(**__UpperCAmelCase							)
						return config

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Optional[int]:
						for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
									self.check_over_configs(num_train_timesteps=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Any:
						for variance in ["fixed_small_log", "learned_range"]:
									self.check_over_configs(variance_type=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Optional[Any]:
						for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     List[str]:
						for clip_sample_range in [1, 5, 1_0, 2_0]:
									self.check_over_configs(clip_sample_range=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Any:
						for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(prediction_type=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Dict:
						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=__UpperCAmelCase     ,					prev_timestep=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config(variance_type="""fixed_small_log"""							)
						SCREAMING_SNAKE_CASE__										=   scheduler_class(**__UpperCAmelCase							)

						assert torch.sum(torch.abs(scheduler._get_variance(0							) - 1.0000e-10							)							) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.0_549_625							)							) < 1e-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.9_994_987							)							) < 1e-5

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config(variance_type="""learned_range"""							)
						SCREAMING_SNAKE_CASE__										=   scheduler_class(**__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   0.5

						assert scheduler._get_variance(1     ,					predicted_variance=__UpperCAmelCase							) - -10.1_712_790 < 1e-5
						assert scheduler._get_variance(4_8_7     ,					predicted_variance=__UpperCAmelCase							) - -5.7_998_052 < 1e-5
						assert scheduler._get_variance(9_9_9     ,					predicted_variance=__UpperCAmelCase							) - -0.0_010_011 < 1e-5

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config()
						SCREAMING_SNAKE_CASE__										=   scheduler_class(**__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   scheduler.timesteps

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

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

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

									SCREAMING_SNAKE_CASE__										=   pred_prev_sample

						SCREAMING_SNAKE_CASE__										=   torch.sum(torch.abs(__UpperCAmelCase							)							)
						SCREAMING_SNAKE_CASE__										=   torch.mean(torch.abs(__UpperCAmelCase							)							)

						assert abs(result_sum.item() - 252.2_682_495							) < 1e-2
						assert abs(result_mean.item() - 0.3_284_743							) < 1e-3

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     str:
						SCREAMING_SNAKE_CASE__										=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE__										=   self.get_scheduler_config()
						SCREAMING_SNAKE_CASE__										=   scheduler_class(**__UpperCAmelCase							)

						scheduler.set_timesteps(2_5							)

						SCREAMING_SNAKE_CASE__										=   scheduler.timesteps

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

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

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

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

									SCREAMING_SNAKE_CASE__										=   pred_prev_sample

						SCREAMING_SNAKE_CASE__										=   torch.sum(torch.abs(__UpperCAmelCase							)							)
						SCREAMING_SNAKE_CASE__										=   torch.mean(torch.abs(__UpperCAmelCase							)							)

						assert abs(result_sum.item() - 258.2_044_983							) < 1e-2
						assert abs(result_mean.item() - 0.3_362_038							) < 1e-3

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Dict:
						pass





			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     int:
						pass





"""simple docstring"""




import numpy as np

def 						A							(  snake_case__     ):




			'''simple docstring'''


			return (2 / (1 + np.exp(-2 * vector     ))) - 1


if __name__ == "__main__":
						import doctest

						doctest.testmod()


