"""simple docstring"""







import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class      _lowercase						(				__lowerCamelCase					):
				lowerCamelCase__									=    (HeunDiscreteScheduler,)
				lowerCamelCase__									=    1_0

				def     A	(       self    :      str    ,      **_lowercase    :      Optional[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						{
										    """num_train_timesteps""": 1_1_0_0,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										}

										config.update(**_lowercase       )
										return config

				def     A	(       self    :      Any       ):


										'''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    :      Optional[int]       ):


										'''simple docstring'''
										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=_lowercase    ,      beta_end=_lowercase       )

				def     A	(       self    :      Any       ):


										'''simple docstring'''
										for schedule in ["linear", "scaled_linear", "exp"]:
																self.check_over_configs(beta_schedule=_lowercase       )

				def     A	(       self    :      Union[str, Any]       ):


										'''simple docstring'''
										for clip_sample_range in [1.0, 2.0, 3.0]:
																self.check_over_configs(clip_sample_range=_lowercase    ,      clip_sample=_lowercase       )

				def     A	(       self    :      Dict       ):


										'''simple docstring'''
										for prediction_type in ["epsilon", "v_prediction", "sample"]:
																self.check_over_configs(prediction_type=_lowercase       )

				def     A	(       self    :      Optional[int]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.scheduler_classes[0]
										UpperCAmelCase__   		=						self.get_scheduler_config()
										UpperCAmelCase__   		=						scheduler_class(**_lowercase       )

										scheduler.set_timesteps(self.num_inference_steps       )

										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       )
																UpperCAmelCase__   		=						output.prev_sample

										UpperCAmelCase__   		=						torch.sum(torch.abs(_lowercase       )       )
										UpperCAmelCase__   		=						torch.mean(torch.abs(_lowercase       )       )

										if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 0.1233       ) < 1e-2
																assert abs(result_mean.item() - 0.0002       ) < 1e-3
										else:
																# CUDA
																assert abs(result_sum.item() - 0.1233       ) < 1e-2
																assert abs(result_mean.item() - 0.0002       ) < 1e-3

				def     A	(       self    :      Tuple       ):


										'''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__   		=						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       )
																UpperCAmelCase__   		=						output.prev_sample

										UpperCAmelCase__   		=						torch.sum(torch.abs(_lowercase       )       )
										UpperCAmelCase__   		=						torch.mean(torch.abs(_lowercase       )       )

										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() - 0.0002       ) < 1e-3

				def     A	(       self    :      Dict       ):


										'''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__   		=						self.dummy_model()
										UpperCAmelCase__   		=						self.dummy_sample_deter.to(_lowercase       ) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
																UpperCAmelCase__   		=						scheduler.scale_model_input(_lowercase    ,      _lowercase       )

																UpperCAmelCase__   		=						model(_lowercase    ,      _lowercase       )

																UpperCAmelCase__   		=						scheduler.step(_lowercase    ,      _lowercase    ,      _lowercase       )
																UpperCAmelCase__   		=						output.prev_sample

										UpperCAmelCase__   		=						torch.sum(torch.abs(_lowercase       )       )
										UpperCAmelCase__   		=						torch.mean(torch.abs(_lowercase       )       )

										if str(_lowercase       ).startswith("""cpu"""       ):
																# The following sum varies between 148 and 156 on mps. Why?
																assert abs(result_sum.item() - 0.1233       ) < 1e-2
																assert abs(result_mean.item() - 0.0002       ) < 1e-3
										elif str(_lowercase       ).startswith("""mps"""       ):
																# Larger tolerance on mps
																assert abs(result_mean.item() - 0.0002       ) < 1e-2
										else:
																# CUDA
																assert abs(result_sum.item() - 0.1233       ) < 1e-2
																assert abs(result_mean.item() - 0.0002       ) < 1e-3

				def     A	(       self    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.scheduler_classes[0]
										UpperCAmelCase__   		=						self.get_scheduler_config()
										UpperCAmelCase__   		=						scheduler_class(**_lowercase    ,      use_karras_sigmas=_lowercase       )

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

										UpperCAmelCase__   		=						self.dummy_model()
										UpperCAmelCase__   		=						self.dummy_sample_deter.to(_lowercase       ) * scheduler.init_noise_sigma
										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       )
																UpperCAmelCase__   		=						output.prev_sample

										UpperCAmelCase__   		=						torch.sum(torch.abs(_lowercase       )       )
										UpperCAmelCase__   		=						torch.mean(torch.abs(_lowercase       )       )

										assert abs(result_sum.item() - 0.0_0015       ) < 1e-2
										assert abs(result_mean.item() - 1.9869554535034695e-07       ) < 1e-2


"""simple docstring"""







from __future__ import annotations


def 					a__ (						UpperCAmelCase__    :     int     )->		list[int]:
						assert (
						    isinstance(UpperCAmelCase__						,     UpperCAmelCase__     ) and mask > 0
						), f'mask needs to be positive integer, your input {mask}'
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						mask

						while submask:
												all_submasks.append(UpperCAmelCase__     )
												UpperCAmelCase__   		=						(submask - 1) & mask

						return all_submasks


if __name__ == "__main__":
						import doctest

						doctest.testmod()

