
import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     (KDPMaDiscreteScheduler,)
	snake_case_					    =     10






	def 				UpperCamelCase_  (       self					: Optional[int]							,**A					: int ):
					__A   			= {
					    "num_train_timesteps": 11_00,
					    "beta_start": 0.00_01,
					    "beta_end": 0.02,
					    "beta_schedule": "linear",
					}

					config.update(**A )
					return config






	def 				UpperCamelCase_  (       self					: Dict ):
					for timesteps in [10, 50, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=A )






	def 				UpperCamelCase_  (       self					: Dict ):
					for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]							,[0.00_02, 0.0_02, 0.02] ):
									self.check_over_configs(beta_start=A							,beta_end=A )






	def 				UpperCamelCase_  (       self					: Tuple ):
					for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=A )






	def 				UpperCamelCase_  (       self					: int ):
					for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=A )






	def 				UpperCamelCase_  (       self					: str ):
					__A   			= self.scheduler_classes[0]
					__A   			= self.get_scheduler_config(prediction_type="v_prediction" )
					__A   			= scheduler_class(**A )

					scheduler.set_timesteps(self.num_inference_steps )

					__A   			= self.dummy_model()
					__A   			= self.dummy_sample_deter * scheduler.init_noise_sigma
					__A   			= sample.to(A )

					for i, t in enumerate(scheduler.timesteps ):
									__A   			= scheduler.scale_model_input(A							,A )

									__A   			= model(A							,A )

									__A   			= scheduler.step(A							,A							,A )
									__A   			= output.prev_sample

					__A   			= torch.sum(torch.abs(A ) )
					__A   			= torch.mean(torch.abs(A ) )

					if torch_device in ["cpu", "mps"]:
									assert abs(result_sum.item() - 4.6934E-07 ) < 1E-2
									assert abs(result_mean.item() - 6.1112E-10 ) < 1E-3
					else:
									# CUDA
									assert abs(result_sum.item() - 4.693428650170972E-07 ) < 1E-2
									assert abs(result_mean.item() - 0.00_02 ) < 1E-3






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					if torch_device == "mps":
									return
					__A   			= self.scheduler_classes[0]
					__A   			= self.get_scheduler_config()
					__A   			= scheduler_class(**A )

					scheduler.set_timesteps(self.num_inference_steps )

					__A   			= self.dummy_model()
					__A   			= self.dummy_sample_deter * scheduler.init_noise_sigma
					__A   			= sample.to(A )

					for i, t in enumerate(scheduler.timesteps ):
									__A   			= scheduler.scale_model_input(A							,A )

									__A   			= model(A							,A )

									__A   			= scheduler.step(A							,A							,A )
									__A   			= output.prev_sample

					__A   			= torch.sum(torch.abs(A ) )
					__A   			= torch.mean(torch.abs(A ) )

					if torch_device in ["cpu", "mps"]:
									assert abs(result_sum.item() - 20.41_25 ) < 1E-2
									assert abs(result_mean.item() - 0.02_66 ) < 1E-3
					else:
									# CUDA
									assert abs(result_sum.item() - 20.41_25 ) < 1E-2
									assert abs(result_mean.item() - 0.02_66 ) < 1E-3







	def 				UpperCamelCase_  (       self					: Dict ):
					if torch_device == "mps":
									return
					__A   			= self.scheduler_classes[0]
					__A   			= self.get_scheduler_config()
					__A   			= scheduler_class(**A )

					scheduler.set_timesteps(self.num_inference_steps							,device=A )

					__A   			= self.dummy_model()
					__A   			= self.dummy_sample_deter.to(A ) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
									__A   			= scheduler.scale_model_input(A							,A )

									__A   			= model(A							,A )

									__A   			= scheduler.step(A							,A							,A )
									__A   			= output.prev_sample

					__A   			= torch.sum(torch.abs(A ) )
					__A   			= torch.mean(torch.abs(A ) )

					if str(A ).startswith("cpu" ):
									# The following sum varies between 148 and 156 on mps. Why?
									assert abs(result_sum.item() - 20.41_25 ) < 1E-2
									assert abs(result_mean.item() - 0.02_66 ) < 1E-3
					else:
									# CUDA
									assert abs(result_sum.item() - 20.41_25 ) < 1E-2
									assert abs(result_mean.item() - 0.02_66 ) < 1E-3


def 						UpperCAmelCase			(	a_						)		->	list:







				"""simple docstring"""



				if len(a_						) <= 1:
								return lst

				__A   			= 1

				while i < len(a_						):
								if lst[i - 1] <= lst[i]:
												i += 1
								else:
												__A			,							__A   			= lst[i], lst[i - 1]
												i -= 1
												if i == 0:
																__A   			= 1

				return lst


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:List[Any]							 =				input('Enter numbers separated by a comma:\n').strip()
					SCREAMING_SNAKE_CASE							:List[Any]							 =				[int(item) for item in user_input.split(',')]
					print(gnome_sort(unsorted))
