






'''simple docstring'''


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			__a				:				int         =       (EulerAncestralDiscreteScheduler,)
			__a				:				int         =       10






			def 					A   ( self				:    int			,   **lowercase				:    Optional[Any]      )				->     Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			{
									    """num_train_timesteps""": 1_1_0_0,
									    """beta_start""": 0.0_0_0_1,
									    """beta_end""": 0.0_2,
									    """beta_schedule""": """linear""",
									}

									config.update(**lowercase      )
									return config






			def 					A   ( self				:    Any      )				->     str:

									'''simple docstring'''







									for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=lowercase      )






			def 					A   ( self				:    Any      )				->     Any:

									'''simple docstring'''







									for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]			,   [0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
															self.check_over_configs(beta_start=lowercase			,   beta_end=lowercase      )






			def 					A   ( self				:    Optional[int]      )				->     List[str]:

									'''simple docstring'''







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






			def 					A   ( self				:    Optional[int]      )				->     Union[str, Any]:

									'''simple docstring'''







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






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

									'''simple docstring'''







									UpperCamelCase__       =			self.scheduler_classes[0]
									UpperCamelCase__       =			self.get_scheduler_config()
									UpperCamelCase__       =			scheduler_class(**lowercase      )

									scheduler.set_timesteps(self.num_inference_steps      )

									UpperCamelCase__       =			torch.manual_seed(0      )

									UpperCamelCase__       =			self.dummy_model()
									UpperCamelCase__       =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									UpperCamelCase__       =			sample.to(lowercase      )

									for i, t in enumerate(scheduler.timesteps      ):
															UpperCamelCase__       =			scheduler.scale_model_input(lowercase			,   lowercase      )

															UpperCamelCase__       =			model(lowercase			,   lowercase      )

															UpperCamelCase__       =			scheduler.step(lowercase			,   lowercase			,   lowercase			,   generator=lowercase      )
															UpperCamelCase__       =			output.prev_sample

									UpperCamelCase__       =			torch.sum(torch.abs(lowercase      )      )
									UpperCamelCase__       =			torch.mean(torch.abs(lowercase      )      )

									assert abs(result_sum.item() - 1_5_2.3_1_9_2      ) < 1e-2
									assert abs(result_mean.item() - 0.1_9_8_3      ) < 1e-3






			def 					A   ( self				:    Tuple      )				->     Optional[int]:

									'''simple docstring'''







									UpperCamelCase__       =			self.scheduler_classes[0]
									UpperCamelCase__       =			self.get_scheduler_config(prediction_type="""v_prediction"""      )
									UpperCamelCase__       =			scheduler_class(**lowercase      )

									scheduler.set_timesteps(self.num_inference_steps      )

									UpperCamelCase__       =			torch.manual_seed(0      )

									UpperCamelCase__       =			self.dummy_model()
									UpperCamelCase__       =			self.dummy_sample_deter * scheduler.init_noise_sigma
									UpperCamelCase__       =			sample.to(lowercase      )

									for i, t in enumerate(scheduler.timesteps      ):
															UpperCamelCase__       =			scheduler.scale_model_input(lowercase			,   lowercase      )

															UpperCamelCase__       =			model(lowercase			,   lowercase      )

															UpperCamelCase__       =			scheduler.step(lowercase			,   lowercase			,   lowercase			,   generator=lowercase      )
															UpperCamelCase__       =			output.prev_sample

									UpperCamelCase__       =			torch.sum(torch.abs(lowercase      )      )
									UpperCamelCase__       =			torch.mean(torch.abs(lowercase      )      )

									assert abs(result_sum.item() - 1_0_8.4_4_3_9      ) < 1e-2
									assert abs(result_mean.item() - 0.1_4_1_2      ) < 1e-3







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

									'''simple docstring'''







									UpperCamelCase__       =			self.scheduler_classes[0]
									UpperCamelCase__       =			self.get_scheduler_config()
									UpperCamelCase__       =			scheduler_class(**lowercase      )

									scheduler.set_timesteps(self.num_inference_steps			,   device=lowercase      )
									UpperCamelCase__       =			torch.manual_seed(0      )

									UpperCamelCase__       =			self.dummy_model()
									UpperCamelCase__       =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									UpperCamelCase__       =			sample.to(lowercase      )

									for t in scheduler.timesteps:
															UpperCamelCase__       =			scheduler.scale_model_input(lowercase			,   lowercase      )

															UpperCamelCase__       =			model(lowercase			,   lowercase      )

															UpperCamelCase__       =			scheduler.step(lowercase			,   lowercase			,   lowercase			,   generator=lowercase      )
															UpperCamelCase__       =			output.prev_sample

									UpperCamelCase__       =			torch.sum(torch.abs(lowercase      )      )
									UpperCamelCase__       =			torch.mean(torch.abs(lowercase      )      )

									assert abs(result_sum.item() - 1_5_2.3_1_9_2      ) < 1e-2
									assert abs(result_mean.item() - 0.1_9_8_3      ) < 1e-3








'''simple docstring'''


def 						__magic_name__(			_A       ):
						'''simple docstring'''





						UpperCamelCase__       =			len(_A       )

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

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

												# Sort the elements using insertion sort
												for i in range(_A    ,   len(_A       )       ):
																		UpperCamelCase__       =			collection[i]
																		UpperCamelCase__       =			i
																		while j >= gap and collection[j - gap] > temp:
																								UpperCamelCase__       =			collection[j - gap]
																								j -= gap
																		UpperCamelCase__       =			temp

						return collection


if __name__ == "__main__":
					import doctest

					doctest.testmod()
