






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 				lowerCAmelCase_	(		SCREAMING_SNAKE_CASE__			):
				_UpperCamelCase					:       int							       = (KDPMaDiscreteScheduler,)
				_UpperCamelCase					:       List[Any]							       = 10


				def 						__a					(					self ,						**_lowerCAmelCase      ):
											_lowercase							:   Dict 							=   {
											    'num_train_timesteps': 1_1_0_0,
											    'beta_start': 0.00_01,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**snake_case__      )
											return config


				def 						__a					(					self      ):

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


				def 						__a					(					self      ):

											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=snake_case__ ,						beta_end=snake_case__      )


				def 						__a					(					self      ):

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


				def 						__a					(					self      ):

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


				def 						__a					(					self      ):
											_lowercase							:   Union[str, Any] 							=   self.scheduler_classes[0]
											_lowercase							:   List[Any] 							=   self.get_scheduler_config(prediction_type='v_prediction'      )
											_lowercase							:   Optional[Any] 							=   scheduler_class(**snake_case__      )

											scheduler.set_timesteps(self.num_inference_steps      )

											_lowercase							:   Dict 							=   self.dummy_model()
											_lowercase							:   List[str] 							=   self.dummy_sample_deter * scheduler.init_noise_sigma
											_lowercase							:   Any 							=   sample.to(snake_case__      )

											for i, t in enumerate(scheduler.timesteps      ):
																		_lowercase							:   Optional[Any] 							=   scheduler.scale_model_input(snake_case__ ,						snake_case__      )

																		_lowercase							:   Dict 							=   model(snake_case__ ,						snake_case__      )

																		_lowercase							:   Optional[Any] 							=   scheduler.step(snake_case__ ,						snake_case__ ,						snake_case__      )
																		_lowercase							:   int 							=   output.prev_sample

											_lowercase							:   Dict 							=   torch.sum(torch.abs(snake_case__      )      )
											_lowercase							:   Dict 							=   torch.mean(torch.abs(snake_case__      )      )

											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() - 4.693_4286_5017_0972E-07      ) < 1E-2
																		assert abs(result_mean.item() - 0.00_02      ) < 1E-3


				def 						__a					(					self      ):

											if torch_device == "mps":
																		return
											_lowercase							:   List[Any] 							=   self.scheduler_classes[0]
											_lowercase							:   Union[str, Any] 							=   self.get_scheduler_config()
											_lowercase							:   List[str] 							=   scheduler_class(**snake_case__      )

											scheduler.set_timesteps(self.num_inference_steps      )

											_lowercase							:   Dict 							=   self.dummy_model()
											_lowercase							:   Any 							=   self.dummy_sample_deter * scheduler.init_noise_sigma
											_lowercase							:   Tuple 							=   sample.to(snake_case__      )

											for i, t in enumerate(scheduler.timesteps      ):
																		_lowercase							:   Any 							=   scheduler.scale_model_input(snake_case__ ,						snake_case__      )

																		_lowercase							:   Dict 							=   model(snake_case__ ,						snake_case__      )

																		_lowercase							:   str 							=   scheduler.step(snake_case__ ,						snake_case__ ,						snake_case__      )
																		_lowercase							:   Dict 							=   output.prev_sample

											_lowercase							:   Union[str, Any] 							=   torch.sum(torch.abs(snake_case__      )      )
											_lowercase							:   Tuple 							=   torch.mean(torch.abs(snake_case__      )      )

											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 						__a					(					self      ):

											if torch_device == "mps":
																		return
											_lowercase							:   Optional[int] 							=   self.scheduler_classes[0]
											_lowercase							:   Optional[Any] 							=   self.get_scheduler_config()
											_lowercase							:   str 							=   scheduler_class(**snake_case__      )

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

											_lowercase							:   int 							=   self.dummy_model()
											_lowercase							:   Optional[Any] 							=   self.dummy_sample_deter.to(snake_case__      ) * scheduler.init_noise_sigma

											for t in scheduler.timesteps:
																		_lowercase							:   Optional[Any] 							=   scheduler.scale_model_input(snake_case__ ,						snake_case__      )

																		_lowercase							:   str 							=   model(snake_case__ ,						snake_case__      )

																		_lowercase							:   Any 							=   scheduler.step(snake_case__ ,						snake_case__ ,						snake_case__      )
																		_lowercase							:   str 							=   output.prev_sample

											_lowercase							:   Optional[int] 							=   torch.sum(torch.abs(snake_case__      )      )
											_lowercase							:   str 							=   torch.mean(torch.abs(snake_case__      )      )

											if str(snake_case__      ).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 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							[1]
		for i in range(2    ,						lowerCAmelCase ):
				factorials.append(factorials[-1] * i )
		assert 0 <= k < factorials[-1] * n, "k out of bounds"

		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							list(range(lowerCAmelCase ) )

		# Find permutation
		while factorials:
				SCREAMING_SNAKE_CASE_					      =							factorials.pop()
				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							divmod(lowerCAmelCase    ,						lowerCAmelCase )
				permutation.append(elements[number] )
				elements.remove(elements[number] )
		permutation.append(elements[0] )

		return permutation


if __name__ == "__main__":
						import doctest

						doctest.testmod()