





"""simple docstring"""






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	str				     =				(KDPMaDiscreteScheduler,)
				__lowerCamelCase				:	Tuple				     =				10
				def       UpperCAmelCase      (		self    :			Tuple	, **a_    :			List[Any]    )	->	Any:



							'''simple docstring'''



							a__  :	Any		 =			{
							    "num_train_timesteps": 11_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**a_    )
							return config
				def       UpperCAmelCase      (		self    :			int    )	->	Dict:



							'''simple docstring'''



							for timesteps in [10, 50, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=a_    )
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Dict:



							'''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=a_	, beta_end=a_    )
				def       UpperCAmelCase      (		self    :			Tuple    )	->	int:



							'''simple docstring'''



							for schedule in ["linear", "scaled_linear"]:
										self.check_over_configs(beta_schedule=a_    )
				def       UpperCAmelCase      (		self    :			int    )	->	Dict:



							'''simple docstring'''



							for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=a_    )
				def       UpperCAmelCase      (		self    :			Any    )	->	int:



							'''simple docstring'''



							a__  :	List[Any]		 =			self.scheduler_classes[0]
							a__  :	Optional[int]		 =			self.get_scheduler_config(prediction_type="v_prediction"    )
							a__  :	Dict		 =			scheduler_class(**a_    )

							scheduler.set_timesteps(self.num_inference_steps    )

							a__  :	Tuple		 =			self.dummy_model()
							a__  :	Dict		 =			self.dummy_sample_deter * scheduler.init_noise_sigma
							a__  :	List[Any]		 =			sample.to(a_    )

							for i, t in enumerate(scheduler.timesteps    ):
										a__  :	Dict		 =			scheduler.scale_model_input(a_	, a_    )

										a__  :	Optional[Any]		 =			model(a_	, a_    )

										a__  :	List[Any]		 =			scheduler.step(a_	, a_	, a_    )
										a__  :	Union[str, Any]		 =			output.prev_sample

							a__  :	Tuple		 =			torch.sum(torch.abs(a_    )    )
							a__  :	Union[str, Any]		 =			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       UpperCAmelCase      (		self    :			Any    )	->	Tuple:



							'''simple docstring'''



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

							scheduler.set_timesteps(self.num_inference_steps    )

							a__  :	Optional[int]		 =			self.dummy_model()
							a__  :	List[Any]		 =			self.dummy_sample_deter * scheduler.init_noise_sigma
							a__  :	Optional[int]		 =			sample.to(a_    )

							for i, t in enumerate(scheduler.timesteps    ):
										a__  :	Tuple		 =			scheduler.scale_model_input(a_	, a_    )

										a__  :	List[Any]		 =			model(a_	, a_    )

										a__  :	Dict		 =			scheduler.step(a_	, a_	, a_    )
										a__  :	Optional[int]		 =			output.prev_sample

							a__  :	str		 =			torch.sum(torch.abs(a_    )    )
							a__  :	Any		 =			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       UpperCAmelCase      (		self    :			List[Any]    )	->	Any:



							'''simple docstring'''



							if torch_device == "mps":
										return
							a__  :	int		 =			self.scheduler_classes[0]
							a__  :	List[Any]		 =			self.get_scheduler_config()
							a__  :	Optional[Any]		 =			scheduler_class(**a_    )

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

							a__  :	Optional[int]		 =			self.dummy_model()
							a__  :	Union[str, Any]		 =			self.dummy_sample_deter.to(a_    ) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
										a__  :	int		 =			scheduler.scale_model_input(a_	, a_    )

										a__  :	Union[str, Any]		 =			model(a_	, a_    )

										a__  :	str		 =			scheduler.step(a_	, a_	, a_    )
										a__  :	Tuple		 =			output.prev_sample

							a__  :	Optional[Any]		 =			torch.sum(torch.abs(a_    )    )
							a__  :	Union[str, Any]		 =			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 typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase						=			{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase						=			[
		    '''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''InformerForPrediction''',
		    '''InformerModel''',
		    '''InformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_informer import (
				    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    InformerForPrediction,
				    InformerModel,
				    InformerPreTrainedModel,
				)

else:
		import sys

		__UpperCAmelCase						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)