code: 
  
  
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  class 			a_					(    lowerCAmelCase_    ):
  	A     :   Any						       =   (KDPMaAncestralDiscreteScheduler,)
  	A     :   Tuple						       =   10
  
  
  
  
  
  	def 	A_(       self			,    **SCREAMING_SNAKE_CASE			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	{
  				    '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 	A_(       self			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				for timesteps in [10, 50, 100, 1000]:
  							self.check_over_configs(num_train_timesteps=__a			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[int]:
  
  
  
  
  				"""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 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				for schedule in ["linear", "scaled_linear"]:
  							self.check_over_configs(beta_schedule=__a			)
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if torch_device == "mps":
  							return
  				SCREAMING_SNAKE_CASE_			=	self.scheduler_classes[0]
  				SCREAMING_SNAKE_CASE_			=	self.get_scheduler_config()
  				SCREAMING_SNAKE_CASE_			=	scheduler_class(**__a			)
  
  				scheduler.set_timesteps(self.num_inference_steps			)
  
  				SCREAMING_SNAKE_CASE_			=	torch.manual_seed(0			)
  
  				SCREAMING_SNAKE_CASE_			=	self.dummy_model()
  				SCREAMING_SNAKE_CASE_			=	self.dummy_sample_deter * scheduler.init_noise_sigma
  				SCREAMING_SNAKE_CASE_			=	sample.to(__a			)
  
  				for i, t in enumerate(scheduler.timesteps			):
  							SCREAMING_SNAKE_CASE_			=	scheduler.scale_model_input(__a			,    __a			)
  
  							SCREAMING_SNAKE_CASE_			=	model(__a			,    __a			)
  
  							SCREAMING_SNAKE_CASE_			=	scheduler.step(__a			,    __a			,    __a			,    generator=__a			)
  							SCREAMING_SNAKE_CASE_			=	output.prev_sample
  
  				SCREAMING_SNAKE_CASE_			=	torch.sum(torch.abs(__a			)			)
  				SCREAMING_SNAKE_CASE_			=	torch.mean(torch.abs(__a			)			)
  
  				assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7			) < 1e-2
  				assert abs(result_mean.item() - 1_8.0_3_3_1			) < 5e-3
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				for prediction_type in ["epsilon", "v_prediction"]:
  							self.check_over_configs(prediction_type=__a			)
  
  
  
  
  
  	def 	A_(       self			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if torch_device == "mps":
  							return
  				SCREAMING_SNAKE_CASE_			=	self.scheduler_classes[0]
  				SCREAMING_SNAKE_CASE_			=	self.get_scheduler_config(prediction_type='v_prediction'			)
  				SCREAMING_SNAKE_CASE_			=	scheduler_class(**__a			)
  
  				scheduler.set_timesteps(self.num_inference_steps			)
  
  				SCREAMING_SNAKE_CASE_			=	self.dummy_model()
  				SCREAMING_SNAKE_CASE_			=	self.dummy_sample_deter * scheduler.init_noise_sigma
  				SCREAMING_SNAKE_CASE_			=	sample.to(__a			)
  
  				SCREAMING_SNAKE_CASE_			=	torch.manual_seed(0			)
  
  				for i, t in enumerate(scheduler.timesteps			):
  							SCREAMING_SNAKE_CASE_			=	scheduler.scale_model_input(__a			,    __a			)
  
  							SCREAMING_SNAKE_CASE_			=	model(__a			,    __a			)
  
  							SCREAMING_SNAKE_CASE_			=	scheduler.step(__a			,    __a			,    __a			,    generator=__a			)
  							SCREAMING_SNAKE_CASE_			=	output.prev_sample
  
  				SCREAMING_SNAKE_CASE_			=	torch.sum(torch.abs(__a			)			)
  				SCREAMING_SNAKE_CASE_			=	torch.mean(torch.abs(__a			)			)
  
  				assert abs(result_sum.item() - 3_2_8.9_9_7_0			) < 1e-2
  				assert abs(result_mean.item() - 0.4_2_8_4			) < 1e-3
  
  
  
  
  
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if torch_device == "mps":
  							return
  				SCREAMING_SNAKE_CASE_			=	self.scheduler_classes[0]
  				SCREAMING_SNAKE_CASE_			=	self.get_scheduler_config()
  				SCREAMING_SNAKE_CASE_			=	scheduler_class(**__a			)
  
  				scheduler.set_timesteps(self.num_inference_steps			,    device=__a			)
  				SCREAMING_SNAKE_CASE_			=	torch.manual_seed(0			)
  
  				SCREAMING_SNAKE_CASE_			=	self.dummy_model()
  				SCREAMING_SNAKE_CASE_			=	self.dummy_sample_deter.to(__a			) * scheduler.init_noise_sigma
  
  				for t in scheduler.timesteps:
  							SCREAMING_SNAKE_CASE_			=	scheduler.scale_model_input(__a			,    __a			)
  
  							SCREAMING_SNAKE_CASE_			=	model(__a			,    __a			)
  
  							SCREAMING_SNAKE_CASE_			=	scheduler.step(__a			,    __a			,    __a			,    generator=__a			)
  							SCREAMING_SNAKE_CASE_			=	output.prev_sample
  
  				SCREAMING_SNAKE_CASE_			=	torch.sum(torch.abs(__a			)			)
  				SCREAMING_SNAKE_CASE_			=	torch.mean(torch.abs(__a			)			)
  
  				assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8			) < 1e-1
  				assert abs(result_mean.item() - 1_8.0_3_3_1			) < 1e-3
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					{"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    int								=					["DetrFeatureExtractor"]
  				SCREAMING_SNAKE_CASE__    :    Any								=					["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    List[Any]								=					[
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				SCREAMING_SNAKE_CASE__    :    Tuple								=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  