






"""simple docstring"""







import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class       SCREAMING_SNAKE_CASE__					(					UpperCamelCase_					):
						_a    						=						(KDPMaDiscreteScheduler,)
						_a    						=						10


						def   __lowercase (							self				:			Union[str, Any]						,						**lowerCAmelCase				:			str					):
										lowerCAmelCase  =				{
										    'num_train_timesteps': 1100,
										    'beta_start': 0.0001,
										    'beta_end': 0.02,
										    'beta_schedule': 'linear',
										}

										config.update(**__a					)
										return config


						def   __lowercase (							self				:			int					):






										for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=__a					)


						def   __lowercase (							self				:			int					):






										for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]						,						[0.0002, 0.002, 0.02]					):
														self.check_over_configs(beta_start=__a						,						beta_end=__a					)


						def   __lowercase (							self				:			Tuple					):






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


						def   __lowercase (							self				:			Optional[int]					):






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


						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				self.scheduler_classes[0]
										lowerCAmelCase  =				self.get_scheduler_config(prediction_type="""v_prediction"""					)
										lowerCAmelCase  =				scheduler_class(**__a					)

										scheduler.set_timesteps(self.num_inference_steps					)

										lowerCAmelCase  =				self.dummy_model()
										lowerCAmelCase  =				self.dummy_sample_deter * scheduler.init_noise_sigma
										lowerCAmelCase  =				sample.to(__a					)

										for i, t in enumerate(scheduler.timesteps					):
														lowerCAmelCase  =				scheduler.scale_model_input(__a						,						__a					)

														lowerCAmelCase  =				model(__a						,						__a					)

														lowerCAmelCase  =				scheduler.step(__a						,						__a						,						__a					)
														lowerCAmelCase  =				output.prev_sample

										lowerCAmelCase  =				torch.sum(torch.abs(__a					)					)
										lowerCAmelCase  =				torch.mean(torch.abs(__a					)					)

										if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 4.6_934e-07					) < 1e-2
														assert abs(result_mean.item() - 6.1_112e-10					) < 1e-3
										else:
														# CUDA
														assert abs(result_sum.item() - 4.693_428_650_170_972e-07					) < 1e-2
														assert abs(result_mean.item() - 0.0002					) < 1e-3


						def   __lowercase (							self				:			Optional[Any]					):






										if torch_device == "mps":
														return
										lowerCAmelCase  =				self.scheduler_classes[0]
										lowerCAmelCase  =				self.get_scheduler_config()
										lowerCAmelCase  =				scheduler_class(**__a					)

										scheduler.set_timesteps(self.num_inference_steps					)

										lowerCAmelCase  =				self.dummy_model()
										lowerCAmelCase  =				self.dummy_sample_deter * scheduler.init_noise_sigma
										lowerCAmelCase  =				sample.to(__a					)

										for i, t in enumerate(scheduler.timesteps					):
														lowerCAmelCase  =				scheduler.scale_model_input(__a						,						__a					)

														lowerCAmelCase  =				model(__a						,						__a					)

														lowerCAmelCase  =				scheduler.step(__a						,						__a						,						__a					)
														lowerCAmelCase  =				output.prev_sample

										lowerCAmelCase  =				torch.sum(torch.abs(__a					)					)
										lowerCAmelCase  =				torch.mean(torch.abs(__a					)					)

										if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 20.4125					) < 1e-2
														assert abs(result_mean.item() - 0.0266					) < 1e-3
										else:
														# CUDA
														assert abs(result_sum.item() - 20.4125					) < 1e-2
														assert abs(result_mean.item() - 0.0266					) < 1e-3




						def   __lowercase (							self				:			str					):






										if torch_device == "mps":
														return
										lowerCAmelCase  =				self.scheduler_classes[0]
										lowerCAmelCase  =				self.get_scheduler_config()
										lowerCAmelCase  =				scheduler_class(**__a					)

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

										lowerCAmelCase  =				self.dummy_model()
										lowerCAmelCase  =				self.dummy_sample_deter.to(__a					) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
														lowerCAmelCase  =				scheduler.scale_model_input(__a						,						__a					)

														lowerCAmelCase  =				model(__a						,						__a					)

														lowerCAmelCase  =				scheduler.step(__a						,						__a						,						__a					)
														lowerCAmelCase  =				output.prev_sample

										lowerCAmelCase  =				torch.sum(torch.abs(__a					)					)
										lowerCAmelCase  =				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() - 20.4125					) < 1e-2
														assert abs(result_mean.item() - 0.0266					) < 1e-3
										else:
														# CUDA
														assert abs(result_sum.item() - 20.4125					) < 1e-2
														assert abs(result_mean.item() - 0.0266					) < 1e-3











"""simple docstring"""







from __future__ import annotations





def 							lowercase					(snake_case__   : list[int]      ,       snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  None:


				'''simple docstring'''






				if (direction == 1 and array[indexa] > array[indexa]) or (
				    direction == 0 and array[indexa] < array[indexa]
				):
								lowerCAmelCase ,						lowerCAmelCase  =				array[indexa], array[indexa]





def 							lowercase					(snake_case__   : list[int]      ,       snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  None:


				'''simple docstring'''






				if length > 1:
								lowerCAmelCase  =				int(length / 2 )
								for i in range(snake_case__      ,       low + middle ):
												comp_and_swap(snake_case__      ,       snake_case__      ,       i + middle      ,       snake_case__ )
								bitonic_merge(snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__ )
								bitonic_merge(snake_case__      ,       low + middle      ,       snake_case__      ,       snake_case__ )





def 							lowercase					(snake_case__   : list[int]      ,       snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  None:


				'''simple docstring'''






				if length > 1:
								lowerCAmelCase  =				int(length / 2 )
								bitonic_sort(snake_case__      ,       snake_case__      ,       snake_case__      ,       1 )
								bitonic_sort(snake_case__      ,       low + middle      ,       snake_case__      ,       0 )
								bitonic_merge(snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__ )


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

		bitonic_sort(unsorted, 0, len(unsorted), 1)
		print('\nSorted array in ascending order is: ', end='')
		print(*unsorted, sep=', ')

		bitonic_merge(unsorted, 0, len(unsorted), 0)
		print('Sorted array in descending order is: ', end='')
		print(*unsorted, sep=', ')





