
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class     _UpperCamelCase			(   A__					):






			'''simple docstring'''

			__UpperCamelCase :  List[str]    =	(UnCLIPScheduler,)

			def  lowerCAmelCase__		(    self	:    Tuple  ,		**snake_case_	:    List[str] ):
							UpperCamelCase_:							Optional[int]		=  {
							    """num_train_timesteps""": 1000,
							    """variance_type""": """fixed_small_log""",
							    """clip_sample""": True,
							    """clip_sample_range""": 1.0,
							    """prediction_type""": """epsilon""",
							}

							config.update(**__A )
							return config

			def  lowerCAmelCase__		(    self	:    List[str] ):
							for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=__A )

			def  lowerCAmelCase__		(    self	:    str ):
							for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=__A )

			def  lowerCAmelCase__		(    self	:    List[str] ):
							for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=__A )

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=__A )

			def  lowerCAmelCase__		(    self	:    List[Any] ):
							for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(prediction_type=__A )

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							for time_step in [0, 500, 999]:
											for prev_timestep in [None, 5, 100, 250, 500, 750]:
															if prev_timestep is not None and prev_timestep >= time_step:
																			continue

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

			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							Optional[int]		=  self.scheduler_classes[0]
							UpperCamelCase_:							List[str]		=  self.get_scheduler_config(variance_type="""fixed_small_log""" )
							UpperCamelCase_:							Optional[Any]		=  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(487 ) - 0.054_9625 ) ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.999_4987 ) ) < 1e-5

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							Dict		=  self.scheduler_classes[0]
							UpperCamelCase_:							List[Any]		=  self.get_scheduler_config(variance_type="""learned_range""" )
							UpperCamelCase_:							Union[str, Any]		=  scheduler_class(**__A )

							UpperCamelCase_:							Union[str, Any]		=  0.5

							assert scheduler._get_variance(1  ,		predicted_variance=__A ) - -10.171_2790 < 1e-5
							assert scheduler._get_variance(487  ,		predicted_variance=__A ) - -5.799_8052 < 1e-5
							assert scheduler._get_variance(999  ,		predicted_variance=__A ) - -0.001_0011 < 1e-5

			def  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							Any		=  self.scheduler_classes[0]
							UpperCamelCase_:							int		=  self.get_scheduler_config()
							UpperCamelCase_:							int		=  scheduler_class(**__A )

							UpperCamelCase_:							Tuple		=  scheduler.timesteps

							UpperCamelCase_:							List[str]		=  self.dummy_model()
							UpperCamelCase_:							Optional[Any]		=  self.dummy_sample_deter
							UpperCamelCase_:							str		=  torch.manual_seed(0 )

							for i, t in enumerate(__A ):
											# 1. predict noise residual
											UpperCamelCase_:							List[str]		=  model(__A  ,		__A )

											# 2. predict previous mean of sample x_t-1
											UpperCamelCase_:							Optional[int]		=  scheduler.step(__A  ,		__A  ,		__A  ,		generator=__A ).prev_sample

											UpperCamelCase_:							int		=  pred_prev_sample

							UpperCamelCase_:							Union[str, Any]		=  torch.sum(torch.abs(__A ) )
							UpperCamelCase_:							Union[str, Any]		=  torch.mean(torch.abs(__A ) )

							assert abs(result_sum.item() - 252.268_2495 ) < 1e-2
							assert abs(result_mean.item() - 0.328_4743 ) < 1e-3

			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							str		=  self.scheduler_classes[0]
							UpperCamelCase_:							Union[str, Any]		=  self.get_scheduler_config()
							UpperCamelCase_:							Dict		=  scheduler_class(**__A )

							scheduler.set_timesteps(25 )

							UpperCamelCase_:							Optional[Any]		=  scheduler.timesteps

							UpperCamelCase_:							Any		=  self.dummy_model()
							UpperCamelCase_:							Tuple		=  self.dummy_sample_deter
							UpperCamelCase_:							Tuple		=  torch.manual_seed(0 )

							for i, t in enumerate(__A ):
											# 1. predict noise residual
											UpperCamelCase_:							Optional[int]		=  model(__A  ,		__A )

											if i + 1 == timesteps.shape[0]:
															UpperCamelCase_:							int		=  None
											else:
															UpperCamelCase_:							str		=  timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											UpperCamelCase_:							Optional[Any]		=  scheduler.step(
											    __A  ,		__A  ,		__A  ,		prev_timestep=__A  ,		generator=__A ).prev_sample

											UpperCamelCase_:							List[str]		=  pred_prev_sample

							UpperCamelCase_:							Tuple		=  torch.sum(torch.abs(__A ) )
							UpperCamelCase_:							Dict		=  torch.mean(torch.abs(__A ) )

							assert abs(result_sum.item() - 258.204_4983 ) < 1e-2
							assert abs(result_mean.item() - 0.336_2038 ) < 1e-3

			def  lowerCAmelCase__		(    self	:    str ):
							pass




			def  lowerCAmelCase__		(    self	:    Any ):
							pass








def     A__   (  lowerCamelCase  )					->		list:
				return [
				    txt[:a] + txt[a].upper() + txt[a + 1 :]
				    for a in range(len(lowerCamelCase  )  )
				    if txt[a].isalpha()
				]


if __name__ == "__main__":
							__import__("""doctest""").testmod()






