

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       (UnCLIPScheduler,)
							def lowerCAmelCase_ (     self:      Optional[Any]       ,	**UpperCamelCase:      List[Any]						)		->		Union[str, Any]:
											snake_case__									=     {
											    'num_train_timesteps': 10_00,
											    'variance_type': 'fixed_small_log',
											    'clip_sample': True,
											    'clip_sample_range': 1.0,
											    'prediction_type': 'epsilon',
											}

											config.update(**UpperCamelCase						)
											return config
							def lowerCAmelCase_ (     self:      Dict						)		->		str:
											for timesteps in [1, 5, 1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Any						)		->		Optional[Any]:
											for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Any:
											for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      str						)		->		List[Any]:
											for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      int						)		->		Optional[int]:
											for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(prediction_type=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      int						)		->		List[Any]:
											for time_step in [0, 5_00, 9_99]:
															for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
																			if prev_timestep is not None and prev_timestep >= time_step:
																							continue

																			self.check_over_forward(time_step=UpperCamelCase       ,	prev_timestep=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[str]						)		->		Union[str, Any]:
											snake_case__									=     self.scheduler_classes[0]
											snake_case__									=     self.get_scheduler_config(variance_type='fixed_small_log'						)
											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_87						) - 0.0_549_625						)						) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(9_99						) - 0.9_994_987						)						) < 1e-5
							def lowerCAmelCase_ (     self:      Dict						)		->		Optional[int]:
											snake_case__									=     self.scheduler_classes[0]
											snake_case__									=     self.get_scheduler_config(variance_type='learned_range'						)
											snake_case__									=     scheduler_class(**UpperCamelCase						)

											snake_case__									=     0.5

											assert scheduler._get_variance(1       ,	predicted_variance=UpperCamelCase						) - -10.1_712_790 < 1e-5
											assert scheduler._get_variance(4_87       ,	predicted_variance=UpperCamelCase						) - -5.7_998_052 < 1e-5
											assert scheduler._get_variance(9_99       ,	predicted_variance=UpperCamelCase						) - -0.0_010_011 < 1e-5
							def lowerCAmelCase_ (     self:      str						)		->		List[Any]:
											snake_case__									=     self.scheduler_classes[0]
											snake_case__									=     self.get_scheduler_config()
											snake_case__									=     scheduler_class(**UpperCamelCase						)

											snake_case__									=     scheduler.timesteps

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

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

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

															snake_case__									=     pred_prev_sample

											snake_case__									=     torch.sum(torch.abs(UpperCamelCase						)						)
											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 lowerCAmelCase_ (     self:      str						)		->		str:
											snake_case__									=     self.scheduler_classes[0]
											snake_case__									=     self.get_scheduler_config()
											snake_case__									=     scheduler_class(**UpperCamelCase						)

											scheduler.set_timesteps(25						)

											snake_case__									=     scheduler.timesteps

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

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

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

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

															snake_case__									=     pred_prev_sample

											snake_case__									=     torch.sum(torch.abs(UpperCamelCase						)						)
											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 lowerCAmelCase_ (     self:      Tuple						)		->		Optional[int]:
											pass


							def lowerCAmelCase_ (     self:      Dict						)		->		List[str]:
											pass












from __future__ import annotations
def      a_ (      _A  )			->			bool:







				"""simple docstring"""
				return len(set(_A  )  ) == len(_A  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




