





"""simple docstring"""






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	Tuple				     =				(DDIMParallelScheduler,)
				__lowerCamelCase				:	str				     =				(("eta", 0.0), ("num_inference_steps", 50))
				def       UpperCAmelCase      (		self    :			str	, **a_    :			Optional[int]    )	->	List[Any]:



							'''simple docstring'''



							a__  :	str		 =			{
							    "num_train_timesteps": 10_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "clip_sample": True,
							}

							config.update(**a_    )
							return config
				def       UpperCAmelCase      (		self    :			Optional[Any]	, **a_    :			Union[str, Any]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	Tuple		 =			self.scheduler_classes[0]
							a__  :	List[Any]		 =			self.get_scheduler_config(**a_    )
							a__  :	Any		 =			scheduler_class(**a_    )

							a__ ,	a__  :	List[str]		 =			10, 0.0

							a__  :	Optional[int]		 =			self.dummy_model()
							a__  :	Any		 =			self.dummy_sample_deter

							scheduler.set_timesteps(a_    )

							for t in scheduler.timesteps:
										a__  :	Optional[int]		 =			model(a_	, a_    )
										a__  :	List[Any]		 =			scheduler.step(a_	, a_	, a_	, a_    ).prev_sample

							return sample
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	int:



							'''simple docstring'''



							for timesteps in [1_00, 5_00, 10_00]:
										self.check_over_configs(num_train_timesteps=a_    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[Any]:



							'''simple docstring'''



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

							a__  :	Dict		 =			self.scheduler_classes[0]
							a__  :	Optional[int]		 =			self.get_scheduler_config(steps_offset=1    )
							a__  :	int		 =			scheduler_class(**a_    )
							scheduler.set_timesteps(5    )
							assert torch.equal(scheduler.timesteps	, torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]    )    )
				def       UpperCAmelCase      (		self    :			int    )	->	List[str]:



							'''simple docstring'''



							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]	, [0.002, 0.02, 0.2, 2]    ):
										self.check_over_configs(beta_start=a_	, beta_end=a_    )
				def       UpperCAmelCase      (		self    :			str    )	->	Dict:



							'''simple docstring'''



							for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=a_    )
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	str:



							'''simple docstring'''



							for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=a_    )
				def       UpperCAmelCase      (		self    :			Any    )	->	Tuple:



							'''simple docstring'''



							for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=a_    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	Optional[int]:



							'''simple docstring'''



							for timestep_spacing in ["trailing", "leading"]:
										self.check_over_configs(timestep_spacing=a_    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	List[str]:



							'''simple docstring'''



							for rescale_betas_zero_snr in [True, False]:
										self.check_over_configs(rescale_betas_zero_snr=a_    )
				def       UpperCAmelCase      (		self    :			str    )	->	Any:



							'''simple docstring'''



							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       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Union[str, Any]:



							'''simple docstring'''



							for t in [1, 10, 49]:
										self.check_over_forward(time_step=a_    )
				def       UpperCAmelCase      (		self    :			int    )	->	List[str]:



							'''simple docstring'''



							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       UpperCAmelCase      (		self    :			Optional[int]    )	->	Any:



							'''simple docstring'''



							for t, eta in zip([1, 10, 49]	, [0.0, 0.5, 1.0]    ):
										self.check_over_forward(time_step=a_	, eta=a_    )
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	List[str]:



							'''simple docstring'''



							a__  :	List[Any]		 =			self.scheduler_classes[0]
							a__  :	List[str]		 =			self.get_scheduler_config()
							a__  :	str		 =			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    ) - 0.1_4771    )    ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_80	, 9_60    ) - 0.3_2460    )    ) < 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    ) - 0.0_0979    )    ) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99	, 9_98    ) - 0.02    )    ) < 1E-5
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Tuple:



							'''simple docstring'''



							a__  :	int		 =			self.scheduler_classes[0]
							a__  :	Tuple		 =			self.get_scheduler_config()
							a__  :	Optional[int]		 =			scheduler_class(**a_    )

							a__ ,	a__  :	int		 =			10, 0.0
							scheduler.set_timesteps(a_    )

							a__  :	List[Any]		 =			self.dummy_model()
							a__  :	int		 =			self.dummy_sample_deter
							a__  :	Dict		 =			self.dummy_sample_deter + 0.1
							a__  :	str		 =			self.dummy_sample_deter - 0.1

							a__  :	Optional[Any]		 =			samplea.shape[0]
							a__  :	List[str]		 =			torch.stack([samplea, samplea, samplea]	, dim=0    )
							a__  :	Union[str, Any]		 =			torch.arange(a_    )[0:3, None].repeat(1	, a_    )

							a__  :	str		 =			model(samples.flatten(0	, 1    )	, timesteps.flatten(0	, 1    )    )
							a__  :	Tuple		 =			scheduler.batch_step_no_noise(a_	, timesteps.flatten(0	, 1    )	, samples.flatten(0	, 1    )	, a_    )

							a__  :	Optional[int]		 =			torch.sum(torch.abs(a_    )    )
							a__  :	Optional[int]		 =			torch.mean(torch.abs(a_    )    )

							assert abs(result_sum.item() - 1147.7904    ) < 1E-2
							assert abs(result_mean.item() - 0.4982    ) < 1E-3
				def       UpperCAmelCase      (		self    :			Dict    )	->	Tuple:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			self.full_loop()

							a__  :	int		 =			torch.sum(torch.abs(a_    )    )
							a__  :	List[str]		 =			torch.mean(torch.abs(a_    )    )

							assert abs(result_sum.item() - 172.0067    ) < 1E-2
							assert abs(result_mean.item() - 0.22_3967    ) < 1E-3
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Dict:



							'''simple docstring'''



							a__  :	int		 =			self.full_loop(prediction_type="v_prediction"    )

							a__  :	Union[str, Any]		 =			torch.sum(torch.abs(a_    )    )
							a__  :	str		 =			torch.mean(torch.abs(a_    )    )

							assert abs(result_sum.item() - 52.5302    ) < 1E-2
							assert abs(result_mean.item() - 0.0684    ) < 1E-3
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	str:



							'''simple docstring'''



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

							assert abs(result_sum.item() - 149.8295    ) < 1E-2
							assert abs(result_mean.item() - 0.1951    ) < 1E-3






				def       UpperCAmelCase      (		self    :			str    )	->	List[Any]:



							'''simple docstring'''



							a__  :	Optional[int]		 =			self.full_loop(set_alpha_to_one=a_	, beta_start=0.01    )
							a__  :	str		 =			torch.sum(torch.abs(a_    )    )
							a__  :	Optional[int]		 =			torch.mean(torch.abs(a_    )    )

							assert abs(result_sum.item() - 149.0784    ) < 1E-2
							assert abs(result_mean.item() - 0.1941    ) < 1E-3






"""simple docstring"""






def      lowercase__ (					lowerCAmelCase__   :			str			)    ->			list[int]:



			'''simple docstring'''
			a__  :	List[str]		 =			[0 for i in range(len(lowerCAmelCase__			)			)]

			# initialize interval's left pointer and right pointer
			a__ ,	a__  :	int		 =			0, 0

			for i in range(1							,			len(lowerCAmelCase__			)			):
						# case when current index is inside the interval
						if i <= right_pointer:
									a__  :	List[Any]		 =			min(right_pointer - i + 1							,			z_result[i - left_pointer]			)
									a__  :	List[str]		 =			min_edge

						while go_next(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			):
									z_result[i] += 1

						# if new index's result gives us more right interval,
						# we've to update left_pointer and right_pointer
						if i + z_result[i] - 1 > right_pointer:
									a__ ,	a__  :	str		 =			i, i + z_result[i] - 1

			return z_result






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			list[int]							,			lowerCAmelCase__   :			str			)    ->			bool:



			'''simple docstring'''
			return i + z_result[i] < len(lowerCAmelCase__			) and s[z_result[i]] == s[i + z_result[i]]






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			str			)    ->			int:



			'''simple docstring'''
			a__  :	List[Any]		 =			0
			# concatenate 'pattern' and 'input_str' and call z_function
			# with concatenated string
			a__  :	List[str]		 =			z_function(pattern + input_str			)

			for val in z_result:
						# if value is greater then length of the pattern string
						# that means this index is starting position of substring
						# which is equal to pattern string
						if val >= len(lowerCAmelCase__			):
									answer += 1

			return answer


if __name__ == "__main__":
		import doctest

		doctest.testmod()