




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	a     : int	  =					(DDIMScheduler,)
	a     : Union[str, Any]	  =					(("""eta""", 0.0), ("""num_inference_steps""", 5_0))

	def    __a     (					self	,				**lowerCamelCase    ) ->      List[Any]:





								"""simple docstring"""


								lowercase__  :	Union[str, Any]        =							{
								    "num_train_timesteps": 1000,
								    "beta_start": 0.00_01,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								    "clip_sample": True,
								}

								config.update(**lowerCamelCase    )
								return config

	def    __a     (					self	,				**lowerCamelCase    ) ->      Dict:





								"""simple docstring"""


								lowercase__  :	Optional[int]        =							self.scheduler_classes[0]
								lowercase__  :	List[str]        =							self.get_scheduler_config(**lowerCamelCase    )
								lowercase__  :	List[Any]        =							scheduler_class(**lowerCamelCase    )

								lowercase__ ,       lowercase__  :	Union[str, Any]        =							10, 0.0

								lowercase__  :	Union[str, Any]        =							self.dummy_model()
								lowercase__  :	Optional[int]        =							self.dummy_sample_deter

								scheduler.set_timesteps(lowerCamelCase    )

								for t in scheduler.timesteps:
															lowercase__  :	Optional[int]        =							model(lowerCamelCase	,				lowerCamelCase    )
															lowercase__  :	Union[str, Any]        =							scheduler.step(lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase    ).prev_sample

								return sample

	def    __a     (					self    ) ->      Union[str, Any]:





								"""simple docstring"""


								for timesteps in [100, 500, 1000]:
															self.check_over_configs(num_train_timesteps=lowerCamelCase    )

	def    __a     (					self    ) ->      Dict:





								"""simple docstring"""


								for steps_offset in [0, 1]:
															self.check_over_configs(steps_offset=lowerCamelCase    )

								lowercase__  :	int        =							self.scheduler_classes[0]
								lowercase__  :	List[Any]        =							self.get_scheduler_config(steps_offset=1    )
								lowercase__  :	List[str]        =							scheduler_class(**lowerCamelCase    )
								scheduler.set_timesteps(5    )
								assert torch.equal(scheduler.timesteps	,				torch.LongTensor([801, 601, 401, 201, 1]    )    )

	def    __a     (					self    ) ->      List[Any]:





								"""simple docstring"""


								for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]	,				[0.0_02, 0.02, 0.2, 2]    ):
															self.check_over_configs(beta_start=lowerCamelCase	,				beta_end=lowerCamelCase    )

	def    __a     (					self    ) ->      Optional[int]:





								"""simple docstring"""


								for schedule in ["linear", "squaredcos_cap_v2"]:
															self.check_over_configs(beta_schedule=lowerCamelCase    )

	def    __a     (					self    ) ->      Dict:





								"""simple docstring"""


								for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=lowerCamelCase    )

	def    __a     (					self    ) ->      str:





								"""simple docstring"""


								for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=lowerCamelCase    )

	def    __a     (					self    ) ->      Optional[Any]:





								"""simple docstring"""


								for timestep_spacing in ["trailing", "leading"]:
															self.check_over_configs(timestep_spacing=lowerCamelCase    )

	def    __a     (					self    ) ->      Dict:





								"""simple docstring"""


								for rescale_betas_zero_snr in [True, False]:
															self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase    )

	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								self.check_over_configs(thresholding=lowerCamelCase    )
								for threshold in [0.5, 1.0, 2.0]:
															for prediction_type in ["epsilon", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=lowerCamelCase	,				prediction_type=lowerCamelCase	,				sample_max_value=lowerCamelCase	,				)

	def    __a     (					self    ) ->      Any:





								"""simple docstring"""


								for t in [1, 10, 49]:
															self.check_over_forward(time_step=lowerCamelCase    )

	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								for t, num_inference_steps in zip([1, 10, 50]	,				[10, 50, 500]    ):
															self.check_over_forward(time_step=lowerCamelCase	,				num_inference_steps=lowerCamelCase    )

	def    __a     (					self    ) ->      Union[str, Any]:





								"""simple docstring"""


								for t, eta in zip([1, 10, 49]	,				[0.0, 0.5, 1.0]    ):
															self.check_over_forward(time_step=lowerCamelCase	,				eta=lowerCamelCase    )

	def    __a     (					self    ) ->      Dict:





								"""simple docstring"""


								lowercase__  :	Tuple        =							self.scheduler_classes[0]
								lowercase__  :	List[str]        =							self.get_scheduler_config()
								lowercase__  :	Tuple        =							scheduler_class(**lowerCamelCase    )

								assert torch.sum(torch.abs(scheduler._get_variance(0	,				0    ) - 0.0    )    ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(420	,				400    ) - 0.1_47_71    )    ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(980	,				960    ) - 0.3_24_60    )    ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(0	,				0    ) - 0.0    )    ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487	,				486    ) - 0.0_09_79    )    ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999	,				998    ) - 0.02    )    ) < 1E-5

	def    __a     (					self    ) ->      Any:





								"""simple docstring"""


								lowercase__  :	Optional[Any]        =							self.full_loop()

								lowercase__  :	Optional[Any]        =							torch.sum(torch.abs(lowerCamelCase    )    )
								lowercase__  :	List[Any]        =							torch.mean(torch.abs(lowerCamelCase    )    )

								assert abs(result_sum.item() - 1_72.00_67    ) < 1E-2
								assert abs(result_mean.item() - 0.22_39_67    ) < 1E-3

	def    __a     (					self    ) ->      List[str]:





								"""simple docstring"""


								lowercase__  :	Union[str, Any]        =							self.full_loop(prediction_type="v_prediction"    )

								lowercase__  :	List[Any]        =							torch.sum(torch.abs(lowerCamelCase    )    )
								lowercase__  :	Any        =							torch.mean(torch.abs(lowerCamelCase    )    )

								assert abs(result_sum.item() - 52.53_02    ) < 1E-2
								assert abs(result_mean.item() - 0.06_84    ) < 1E-3

	def    __a     (					self    ) ->      Optional[int]:





								"""simple docstring"""


								lowercase__  :	Dict        =							self.full_loop(set_alpha_to_one=lowerCamelCase	,				beta_start=0.01    )
								lowercase__  :	List[Any]        =							torch.sum(torch.abs(lowerCamelCase    )    )
								lowercase__  :	Dict        =							torch.mean(torch.abs(lowerCamelCase    )    )

								assert abs(result_sum.item() - 1_49.82_95    ) < 1E-2
								assert abs(result_mean.item() - 0.19_51    ) < 1E-3







	def    __a     (					self    ) ->      Any:





								"""simple docstring"""


								lowercase__  :	Optional[int]        =							self.full_loop(set_alpha_to_one=lowerCamelCase	,				beta_start=0.01    )
								lowercase__  :	List[Any]        =							torch.sum(torch.abs(lowerCamelCase    )    )
								lowercase__  :	Any        =							torch.mean(torch.abs(lowerCamelCase    )    )

								assert abs(result_sum.item() - 1_49.07_84    ) < 1E-2
								assert abs(result_mean.item() - 0.19_41    ) < 1E-3





def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			list:
							lowercase__  :	Optional[Any]        =							len(SCREAMING_SNAKE_CASE_    )

							# Set the gap value to be decreased by a factor of 1.3
							# after each iteration
							lowercase__  :	Tuple        =							1.3

							# Continue sorting until the gap is 1
							while gap > 1:
														# Decrease the gap value
														lowercase__  :	Tuple        =							int(gap / shrink    )

														# Sort the elements using insertion sort
														for i in range(SCREAMING_SNAKE_CASE_    ,len(SCREAMING_SNAKE_CASE_    )    ):
																					lowercase__  :	int        =							collection[i]
																					lowercase__  :	Union[str, Any]        =							i
																					while j >= gap and collection[j - gap] > temp:
																												lowercase__  :	str        =							collection[j - gap]
																												j -= gap
																					lowercase__  :	List[Any]        =							temp

							return collection


if __name__ == "__main__":
						import doctest

						doctest.testmod()