





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			Optional[Any]       =       (KDPMaDiscreteScheduler,)
						__magic_name__:			List[Any]       =       10






						def UpperCAmelCase_				(      self      :      int		,   **_A      :      Any	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Any											=	{
							    'num_train_timesteps': 1100,
							    'beta_start': 0.0_0_0_1,
							    'beta_end': 0.0_2,
							    'beta_schedule': 'linear',
							}

							config.update(**_A	)
							return config






						def UpperCAmelCase_				(      self      :      int	)							->    Optional[int]:







							"""simple docstring"""


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






						def UpperCAmelCase_				(      self      :      Any	)							->    List[str]:







							"""simple docstring"""


							for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		,   [0.0_0_0_2, 0.0_0_2, 0.0_2]	):
								self.check_over_configs(beta_start=_A		,   beta_end=_A	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    str:







							"""simple docstring"""


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






						def UpperCAmelCase_				(      self      :      int	)							->    List[Any]:







							"""simple docstring"""


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






						def UpperCAmelCase_				(      self      :      str	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			List[str]											=	self.scheduler_classes[0]
							snake_case_		:			List[str]											=	self.get_scheduler_config(prediction_type='v_prediction'	)
							snake_case_		:			Any											=	scheduler_class(**_A	)

							scheduler.set_timesteps(self.num_inference_steps	)

							snake_case_		:			List[Any]											=	self.dummy_model()
							snake_case_		:			Tuple											=	self.dummy_sample_deter * scheduler.init_noise_sigma
							snake_case_		:			List[str]											=	sample.to(_A	)

							for i, t in enumerate(scheduler.timesteps	):
								snake_case_		:			Any											=	scheduler.scale_model_input(_A		,   _A	)

								snake_case_		:			Any											=	model(_A		,   _A	)

								snake_case_		:			str											=	scheduler.step(_A		,   _A		,   _A	)
								snake_case_		:			Tuple											=	output.prev_sample

							snake_case_		:			Optional[Any]											=	torch.sum(torch.abs(_A	)	)
							snake_case_		:			str											=	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() - 4.693_4286_5017_0972E-07	) < 1E-2
								assert abs(result_mean.item() - 0.0_0_0_2	) < 1E-3






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    str:







							"""simple docstring"""


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

							scheduler.set_timesteps(self.num_inference_steps	)

							snake_case_		:			str											=	self.dummy_model()
							snake_case_		:			str											=	self.dummy_sample_deter * scheduler.init_noise_sigma
							snake_case_		:			str											=	sample.to(_A	)

							for i, t in enumerate(scheduler.timesteps	):
								snake_case_		:			Dict											=	scheduler.scale_model_input(_A		,   _A	)

								snake_case_		:			str											=	model(_A		,   _A	)

								snake_case_		:			Any											=	scheduler.step(_A		,   _A		,   _A	)
								snake_case_		:			List[Any]											=	output.prev_sample

							snake_case_		:			int											=	torch.sum(torch.abs(_A	)	)
							snake_case_		:			List[Any]											=	torch.mean(torch.abs(_A	)	)

							if torch_device in ["cpu", "mps"]:
								assert abs(result_sum.item() - 2_0.4_1_2_5	) < 1E-2
								assert abs(result_mean.item() - 0.0_2_6_6	) < 1E-3
							else:
								# CUDA
								assert abs(result_sum.item() - 2_0.4_1_2_5	) < 1E-2
								assert abs(result_mean.item() - 0.0_2_6_6	) < 1E-3






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Any:







							"""simple docstring"""


							if torch_device == "mps":
								return
							snake_case_		:			Dict											=	self.scheduler_classes[0]
							snake_case_		:			List[str]											=	self.get_scheduler_config()
							snake_case_		:			Dict											=	scheduler_class(**_A	)

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

							snake_case_		:			Union[str, Any]											=	self.dummy_model()
							snake_case_		:			int											=	self.dummy_sample_deter.to(_A	) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
								snake_case_		:			Any											=	scheduler.scale_model_input(_A		,   _A	)

								snake_case_		:			Optional[Any]											=	model(_A		,   _A	)

								snake_case_		:			int											=	scheduler.step(_A		,   _A		,   _A	)
								snake_case_		:			Union[str, Any]											=	output.prev_sample

							snake_case_		:			Union[str, Any]											=	torch.sum(torch.abs(_A	)	)
							snake_case_		:			Dict											=	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() - 2_0.4_1_2_5	) < 1E-2
								assert abs(result_mean.item() - 0.0_2_6_6	) < 1E-3
							else:
								# CUDA
								assert abs(result_sum.item() - 2_0.4_1_2_5	) < 1E-2
								assert abs(result_mean.item() - 0.0_2_6_6	) < 1E-3








from ....utils import logging


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						def __init__(      self      :      str		,   _A      :      List[Any]		,   _A      :      List[Any]=None		,   _A      :      str=2048	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	config.__dict__
							snake_case_		:			List[str]											=	modal_hidden_size
							if num_labels:
								snake_case_		:			int											=	num_labels

