


'''simple docstring'''
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						UpperCAmelCase__	:       List[Any]         =     (KDPMaAncestralDiscreteScheduler,)
						UpperCAmelCase__	:       List[str]         =     10
						def 					__A					( self				:					Any	,				**_lowerCamelCase				:					Optional[Any]  )							->					Optional[int]:
										__magic_name__	  = {
										    "num_train_timesteps": 11_00,
										    "beta_start": 0.0_001,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										}

										config.update(**_lowerCamelCase  )
										return config
						def 					__A					( self				:					List[str]  )							->					List[Any]:
										for timesteps in [10, 50, 1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=_lowerCamelCase  )
						def 					__A					( self				:					List[str]  )							->					int:
										for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]	,				[0.0_002, 0.002, 0.02]  ):
														self.check_over_configs(beta_start=_lowerCamelCase	,				beta_end=_lowerCamelCase  )
						def 					__A					( self				:					Optional[Any]  )							->					Dict:
										for schedule in ["linear", "scaled_linear"]:
														self.check_over_configs(beta_schedule=_lowerCamelCase  )
						def 					__A					( self				:					List[str]  )							->					Dict:
										if torch_device == "mps":
														return
										__magic_name__	  = self.scheduler_classes[0]
										__magic_name__	  = self.get_scheduler_config()
										__magic_name__	  = scheduler_class(**_lowerCamelCase  )

										scheduler.set_timesteps(self.num_inference_steps  )

										__magic_name__	  = torch.manual_seed(0  )

										__magic_name__	  = self.dummy_model()
										__magic_name__	  = self.dummy_sample_deter * scheduler.init_noise_sigma
										__magic_name__	  = sample.to(_lowerCamelCase  )

										for i, t in enumerate(scheduler.timesteps  ):
														__magic_name__	  = scheduler.scale_model_input(_lowerCamelCase	,				_lowerCamelCase  )

														__magic_name__	  = model(_lowerCamelCase	,				_lowerCamelCase  )

														__magic_name__	  = scheduler.step(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				generator=_lowerCamelCase  )
														__magic_name__	  = output.prev_sample

										__magic_name__	  = torch.sum(torch.abs(_lowerCamelCase  )  )
										__magic_name__	  = torch.mean(torch.abs(_lowerCamelCase  )  )

										assert abs(result_sum.item() - 1_38_49.38_77  ) < 1e-2
										assert abs(result_mean.item() - 18.0_331  ) < 5e-3
						def 					__A					( self				:					int  )							->					Any:
										for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=_lowerCamelCase  )
						def 					__A					( self				:					Dict  )							->					Dict:
										if torch_device == "mps":
														return
										__magic_name__	  = self.scheduler_classes[0]
										__magic_name__	  = self.get_scheduler_config(prediction_type="v_prediction"  )
										__magic_name__	  = scheduler_class(**_lowerCamelCase  )

										scheduler.set_timesteps(self.num_inference_steps  )

										__magic_name__	  = self.dummy_model()
										__magic_name__	  = self.dummy_sample_deter * scheduler.init_noise_sigma
										__magic_name__	  = sample.to(_lowerCamelCase  )

										__magic_name__	  = torch.manual_seed(0  )

										for i, t in enumerate(scheduler.timesteps  ):
														__magic_name__	  = scheduler.scale_model_input(_lowerCamelCase	,				_lowerCamelCase  )

														__magic_name__	  = model(_lowerCamelCase	,				_lowerCamelCase  )

														__magic_name__	  = scheduler.step(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				generator=_lowerCamelCase  )
														__magic_name__	  = output.prev_sample

										__magic_name__	  = torch.sum(torch.abs(_lowerCamelCase  )  )
										__magic_name__	  = torch.mean(torch.abs(_lowerCamelCase  )  )

										assert abs(result_sum.item() - 328.9_970  ) < 1e-2
										assert abs(result_mean.item() - 0.4_284  ) < 1e-3

						def 					__A					( self				:					Dict  )							->					List[str]:
										if torch_device == "mps":
														return
										__magic_name__	  = self.scheduler_classes[0]
										__magic_name__	  = self.get_scheduler_config()
										__magic_name__	  = scheduler_class(**_lowerCamelCase  )

										scheduler.set_timesteps(self.num_inference_steps	,				device=_lowerCamelCase  )
										__magic_name__	  = torch.manual_seed(0  )

										__magic_name__	  = self.dummy_model()
										__magic_name__	  = self.dummy_sample_deter.to(_lowerCamelCase  ) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
														__magic_name__	  = scheduler.scale_model_input(_lowerCamelCase	,				_lowerCamelCase  )

														__magic_name__	  = model(_lowerCamelCase	,				_lowerCamelCase  )

														__magic_name__	  = scheduler.step(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				generator=_lowerCamelCase  )
														__magic_name__	  = output.prev_sample

										__magic_name__	  = torch.sum(torch.abs(_lowerCamelCase  )  )
										__magic_name__	  = torch.mean(torch.abs(_lowerCamelCase  )  )

										assert abs(result_sum.item() - 1_38_49.38_18  ) < 1e-1
										assert abs(result_mean.item() - 18.0_331  ) < 1e-3







'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


__magic_name__   :   int  							=logging.get_logger(__name__)






class 		UpperCamelCase_		(					A   ):





						"""simple docstring"""




						def __init__( self				:					List[str]	,				*_lowerCamelCase				:					List[str]	,				**_lowerCamelCase				:					Optional[Any]  )							->					None:
										warnings.warn(
										    "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use ConvNextImageProcessor instead."	,				_lowerCamelCase	,				)
										super().__init__(*_lowerCamelCase	,				**_lowerCamelCase  )



