


"""simple docstring"""




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class  __snake_case      (lowerCamelCase     ):
							__a				   =					(DDIMScheduler,)
							__a				   =					(('''eta''', 0.0), ('''num_inference_steps''', 50))




							def      __a      (    self:						Tuple    ,       **A_:						Optional[int]      ):
										__lowerCamelCase        =			{
										    """num_train_timesteps""": 10_00,
										    """beta_start""": 0.0_001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										    """clip_sample""": True,
										}

										config.update(**A_      )
										return config




							def      __a      (    self:						Optional[Any]    ,       **A_:						Any      ):
										__lowerCamelCase        =			self.scheduler_classes[0]
										__lowerCamelCase        =			self.get_scheduler_config(**A_      )
										__lowerCamelCase        =			scheduler_class(**A_      )

										__lowerCamelCase    ,__lowerCamelCase        =			10, 0.0

										__lowerCamelCase        =			self.dummy_model()
										__lowerCamelCase        =			self.dummy_sample_deter

										scheduler.set_timesteps(A_      )

										for t in scheduler.timesteps:
													__lowerCamelCase        =			model(A_    ,       A_      )
													__lowerCamelCase        =			scheduler.step(A_    ,       A_    ,       A_    ,       A_      ).prev_sample

										return sample




							def      __a      (    self:						List[Any]      ):
										for timesteps in [1_00, 5_00, 10_00]:
													self.check_over_configs(num_train_timesteps=A_      )




							def      __a      (    self:						Tuple      ):
										for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=A_      )

										__lowerCamelCase        =			self.scheduler_classes[0]
										__lowerCamelCase        =			self.get_scheduler_config(steps_offset=1      )
										__lowerCamelCase        =			scheduler_class(**A_      )
										scheduler.set_timesteps(5      )
										assert torch.equal(scheduler.timesteps    ,       torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]      )      )




							def      __a      (    self:						Union[str, Any]      ):
										for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]    ,       [0.002, 0.02, 0.2, 2]      ):
													self.check_over_configs(beta_start=A_    ,       beta_end=A_      )




							def      __a      (    self:						int      ):
										for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=A_      )




							def      __a      (    self:						Any      ):
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=A_      )




							def      __a      (    self:						int      ):
										for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=A_      )




							def      __a      (    self:						int      ):
										for timestep_spacing in ["trailing", "leading"]:
													self.check_over_configs(timestep_spacing=A_      )




							def      __a      (    self:						int      ):
										for rescale_betas_zero_snr in [True, False]:
													self.check_over_configs(rescale_betas_zero_snr=A_      )




							def      __a      (    self:						List[str]      ):
										self.check_over_configs(thresholding=A_      )
										for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(
																    thresholding=A_    ,       prediction_type=A_    ,       sample_max_value=A_    ,       )




							def      __a      (    self:						int      ):
										for t in [1, 10, 49]:
													self.check_over_forward(time_step=A_      )




							def      __a      (    self:						str      ):
										for t, num_inference_steps in zip([1, 10, 50]    ,       [10, 50, 5_00]      ):
													self.check_over_forward(time_step=A_    ,       num_inference_steps=A_      )




							def      __a      (    self:						Optional[int]      ):
										for t, eta in zip([1, 10, 49]    ,       [0.0, 0.5, 1.0]      ):
													self.check_over_forward(time_step=A_    ,       eta=A_      )




							def      __a      (    self:						str      ):
										__lowerCamelCase        =			self.scheduler_classes[0]
										__lowerCamelCase        =			self.get_scheduler_config()
										__lowerCamelCase        =			scheduler_class(**A_      )

										assert torch.sum(torch.abs(scheduler._get_variance(0    ,       0      ) - 0.0      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,       4_00      ) - 0.14_771      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,       9_60      ) - 0.32_460      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(0    ,       0      ) - 0.0      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_87    ,       4_86      ) - 0.00_979      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,       9_98      ) - 0.02      )      ) < 1E-5




							def      __a      (    self:						List[Any]      ):
										__lowerCamelCase        =			self.full_loop()

										__lowerCamelCase        =			torch.sum(torch.abs(A_      )      )
										__lowerCamelCase        =			torch.mean(torch.abs(A_      )      )

										assert abs(result_sum.item() - 172.0_067      ) < 1E-2
										assert abs(result_mean.item() - 0.223_967      ) < 1E-3




							def      __a      (    self:						Tuple      ):
										__lowerCamelCase        =			self.full_loop(prediction_type="""v_prediction"""      )

										__lowerCamelCase        =			torch.sum(torch.abs(A_      )      )
										__lowerCamelCase        =			torch.mean(torch.abs(A_      )      )

										assert abs(result_sum.item() - 52.5_302      ) < 1E-2
										assert abs(result_mean.item() - 0.0_684      ) < 1E-3




							def      __a      (    self:						int      ):
										# We specify different beta, so that the first alpha is 0.99
										__lowerCamelCase        =			self.full_loop(set_alpha_to_one=A_    ,       beta_start=0.01      )
										__lowerCamelCase        =			torch.sum(torch.abs(A_      )      )
										__lowerCamelCase        =			torch.mean(torch.abs(A_      )      )

										assert abs(result_sum.item() - 149.8_295      ) < 1E-2
										assert abs(result_mean.item() - 0.1_951      ) < 1E-3




							def      __a      (    self:						int      ):
										# We specify different beta, so that the first alpha is 0.99
										__lowerCamelCase        =			self.full_loop(set_alpha_to_one=A_    ,       beta_start=0.01      )
										__lowerCamelCase        =			torch.sum(torch.abs(A_      )      )
										__lowerCamelCase        =			torch.mean(torch.abs(A_      )      )

										assert abs(result_sum.item() - 149.0_784      ) < 1E-2
										assert abs(result_mean.item() - 0.1_941      ) < 1E-3








"""simple docstring"""




from __future__ import annotations

import re





def 			a_   (  lowercase__   :list[str]    ):

			def alphanum_key(lowercase__   :int    ):
						return [int(lowercase__    ) if s.isdigit() else s.lower() for s in re.split("""([0-9]+)""", lowercase__    )]

			return sorted(lowercase__, key=lowercase__    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




