




'''simple docstring'''







import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class      lowerCamelCase_							(       __UpperCAmelCase							):




					'''simple docstring'''






					lowercase__    = (HeunDiscreteScheduler,)
					lowercase__    = 1_0






					def 				__SCREAMING_SNAKE_CASE	(    self,  **__UpperCamelCase ):
							_snake_case	       =       {
							    'num_train_timesteps': 1_100,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							}

							config.update(**UpperCAmelCase_ )
							return config






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for timesteps in [10, 50, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=UpperCAmelCase_ )






					def 				__SCREAMING_SNAKE_CASE	(    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=UpperCAmelCase_,  beta_end=UpperCAmelCase_ )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for schedule in ["linear", "scaled_linear", "exp"]:
									self.check_over_configs(beta_schedule=UpperCAmelCase_ )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for clip_sample_range in [1.0, 2.0, 3.0]:
									self.check_over_configs(clip_sample_range=UpperCAmelCase_,  clip_sample=UpperCAmelCase_ )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for prediction_type in ["epsilon", "v_prediction", "sample"]:
									self.check_over_configs(prediction_type=UpperCAmelCase_ )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.scheduler_classes[0]
							_snake_case	       =       self.get_scheduler_config()
							_snake_case	       =       scheduler_class(**UpperCAmelCase_ )

							scheduler.set_timesteps(self.num_inference_steps )

							_snake_case	       =       self.dummy_model()
							_snake_case	       =       self.dummy_sample_deter * scheduler.init_noise_sigma
							_snake_case	       =       sample.to(UpperCAmelCase_ )

							for i, t in enumerate(scheduler.timesteps ):
									_snake_case	       =       scheduler.scale_model_input(UpperCAmelCase_,  UpperCAmelCase_ )

									_snake_case	       =       model(UpperCAmelCase_,  UpperCAmelCase_ )

									_snake_case	       =       scheduler.step(UpperCAmelCase_,  UpperCAmelCase_,  UpperCAmelCase_ )
									_snake_case	       =       output.prev_sample

							_snake_case	       =       torch.sum(torch.abs(UpperCAmelCase_ ) )
							_snake_case	       =       torch.mean(torch.abs(UpperCAmelCase_ ) )

							if torch_device in ["cpu", "mps"]:
									assert abs(result_sum.item() - 0.12_33 ) < 1E-2
									assert abs(result_mean.item() - 0.00_02 ) < 1E-3
							else:
									# CUDA
									assert abs(result_sum.item() - 0.12_33 ) < 1E-2
									assert abs(result_mean.item() - 0.00_02 ) < 1E-3






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.scheduler_classes[0]
							_snake_case	       =       self.get_scheduler_config(prediction_type="v_prediction" )
							_snake_case	       =       scheduler_class(**UpperCAmelCase_ )

							scheduler.set_timesteps(self.num_inference_steps )

							_snake_case	       =       self.dummy_model()
							_snake_case	       =       self.dummy_sample_deter * scheduler.init_noise_sigma
							_snake_case	       =       sample.to(UpperCAmelCase_ )

							for i, t in enumerate(scheduler.timesteps ):
									_snake_case	       =       scheduler.scale_model_input(UpperCAmelCase_,  UpperCAmelCase_ )

									_snake_case	       =       model(UpperCAmelCase_,  UpperCAmelCase_ )

									_snake_case	       =       scheduler.step(UpperCAmelCase_,  UpperCAmelCase_,  UpperCAmelCase_ )
									_snake_case	       =       output.prev_sample

							_snake_case	       =       torch.sum(torch.abs(UpperCAmelCase_ ) )
							_snake_case	       =       torch.mean(torch.abs(UpperCAmelCase_ ) )

							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 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.scheduler_classes[0]
							_snake_case	       =       self.get_scheduler_config()
							_snake_case	       =       scheduler_class(**UpperCAmelCase_ )

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

							_snake_case	       =       self.dummy_model()
							_snake_case	       =       self.dummy_sample_deter.to(UpperCAmelCase_ ) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
									_snake_case	       =       scheduler.scale_model_input(UpperCAmelCase_,  UpperCAmelCase_ )

									_snake_case	       =       model(UpperCAmelCase_,  UpperCAmelCase_ )

									_snake_case	       =       scheduler.step(UpperCAmelCase_,  UpperCAmelCase_,  UpperCAmelCase_ )
									_snake_case	       =       output.prev_sample

							_snake_case	       =       torch.sum(torch.abs(UpperCAmelCase_ ) )
							_snake_case	       =       torch.mean(torch.abs(UpperCAmelCase_ ) )

							if str(UpperCAmelCase_ ).startswith("cpu" ):
									# The following sum varies between 148 and 156 on mps. Why?
									assert abs(result_sum.item() - 0.12_33 ) < 1E-2
									assert abs(result_mean.item() - 0.00_02 ) < 1E-3
							elif str(UpperCAmelCase_ ).startswith("mps" ):
									# Larger tolerance on mps
									assert abs(result_mean.item() - 0.00_02 ) < 1E-2
							else:
									# CUDA
									assert abs(result_sum.item() - 0.12_33 ) < 1E-2
									assert abs(result_mean.item() - 0.00_02 ) < 1E-3






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.scheduler_classes[0]
							_snake_case	       =       self.get_scheduler_config()
							_snake_case	       =       scheduler_class(**UpperCAmelCase_,  use_karras_sigmas=UpperCAmelCase_ )

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

							_snake_case	       =       self.dummy_model()
							_snake_case	       =       self.dummy_sample_deter.to(UpperCAmelCase_ ) * scheduler.init_noise_sigma
							_snake_case	       =       sample.to(UpperCAmelCase_ )

							for t in scheduler.timesteps:
									_snake_case	       =       scheduler.scale_model_input(UpperCAmelCase_,  UpperCAmelCase_ )

									_snake_case	       =       model(UpperCAmelCase_,  UpperCAmelCase_ )

									_snake_case	       =       scheduler.step(UpperCAmelCase_,  UpperCAmelCase_,  UpperCAmelCase_ )
									_snake_case	       =       output.prev_sample

							_snake_case	       =       torch.sum(torch.abs(UpperCAmelCase_ ) )
							_snake_case	       =       torch.mean(torch.abs(UpperCAmelCase_ ) )

							assert abs(result_sum.item() - 0.0_00_15 ) < 1E-2
							assert abs(result_mean.item() - 1.9869554535034695E-07 ) < 1E-2







"""simple docstring"""

from __future__ import annotations
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		assert (
		    isinstance(a_,						a_			) and mask > 0
		), F"""mask needs to be positive integer, your input {mask}"""
		lowerCamelCase     :			Tuple		=    []
		lowerCamelCase     :			Tuple		=    mask

		while submask:
				all_submasks.append(a_			)
				lowerCamelCase     :			Optional[int]		=    (submask - 1) & mask

		return all_submasks


if __name__ == "__main__":
					import doctest

					doctest.testmod()






