code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  _lowerCamelCase				:	Union[str, Any]				    =       {'configuration_vilt': ['VILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViltConfig']}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				_lowerCamelCase				:	str				    =       ['ViltFeatureExtractor']
  				_lowerCamelCase				:	Union[str, Any]				    =       ['ViltImageProcessor']
  				_lowerCamelCase				:	Optional[int]				    =       ['ViltProcessor']
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				_lowerCamelCase				:	Union[str, Any]				    =       [
  				    'VILT_PRETRAINED_MODEL_ARCHIVE_LIST',
  				    'ViltForImageAndTextRetrieval',
  				    'ViltForImagesAndTextClassification',
  				    'ViltForTokenClassification',
  				    'ViltForMaskedLM',
  				    'ViltForQuestionAnswering',
  				    'ViltLayer',
  				    'ViltModel',
  				    'ViltPreTrainedModel',
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_vilt import ViltFeatureExtractor
  								from .image_processing_vilt import ViltImageProcessor
  								from .processing_vilt import ViltProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_vilt import (
  								    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    ViltForImageAndTextRetrieval,
  								    ViltForImagesAndTextClassification,
  								    ViltForMaskedLM,
  								    ViltForQuestionAnswering,
  								    ViltForTokenClassification,
  								    ViltLayer,
  								    ViltModel,
  								    ViltPreTrainedModel,
  								)
  
  
  else:
  				import sys
  
  				_lowerCamelCase				:	Optional[int]				    =       _LazyModule(__name__, globals()['__file__'], _import_structure)
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    (KDPMaAncestralDiscreteScheduler,)
  		__lowerCAmelCase 				=    10
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ,      **_lowerCAmelCase						:      List[Any]      ):
  			A							  =      {
  			    """num_train_timesteps""": 1100,
  			    """beta_start""": 0.0_001,
  			    """beta_end""": 0.02,
  			    """beta_schedule""": """linear""",
  			}
  
  			config.update(**_lowerCAmelCase      )
  			return config
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  			for timesteps in [10, 50, 100, 1000]:
  				self.check_over_configs(num_train_timesteps=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[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						:      Tuple      ):
  			for schedule in ["linear", "scaled_linear"]:
  				self.check_over_configs(beta_schedule=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  			if torch_device == "mps":
  				return
  			A							  =      self.scheduler_classes[0]
  			A							  =      self.get_scheduler_config()
  			A							  =      scheduler_class(**_lowerCAmelCase      )
  
  			scheduler.set_timesteps(self.num_inference_steps      )
  
  			A							  =      torch.manual_seed(0      )
  
  			A							  =      self.dummy_model()
  			A							  =      self.dummy_sample_deter * scheduler.init_noise_sigma
  			A							  =      sample.to(_lowerCAmelCase      )
  
  			for i, t in enumerate(scheduler.timesteps      ):
  				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
  				A							  =      output.prev_sample
  
  			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
  			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )
  
  			assert abs(result_sum.item() - 13_849.3_877      ) < 1e-2
  			assert abs(result_mean.item() - 18.0_331      ) < 5e-3
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ):
  			for prediction_type in ["epsilon", "v_prediction"]:
  				self.check_over_configs(prediction_type=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  			if torch_device == "mps":
  				return
  			A							  =      self.scheduler_classes[0]
  			A							  =      self.get_scheduler_config(prediction_type="""v_prediction"""      )
  			A							  =      scheduler_class(**_lowerCAmelCase      )
  
  			scheduler.set_timesteps(self.num_inference_steps      )
  
  			A							  =      self.dummy_model()
  			A							  =      self.dummy_sample_deter * scheduler.init_noise_sigma
  			A							  =      sample.to(_lowerCAmelCase      )
  
  			A							  =      torch.manual_seed(0      )
  
  			for i, t in enumerate(scheduler.timesteps      ):
  				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
  				A							  =      output.prev_sample
  
  			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
  			A							  =      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						:      Optional[int]      ):
  			if torch_device == "mps":
  				return
  			A							  =      self.scheduler_classes[0]
  			A							  =      self.get_scheduler_config()
  			A							  =      scheduler_class(**_lowerCAmelCase      )
  
  			scheduler.set_timesteps(self.num_inference_steps      ,      device=_lowerCAmelCase      )
  			A							  =      torch.manual_seed(0      )
  
  			A							  =      self.dummy_model()
  			A							  =      self.dummy_sample_deter.to(_lowerCAmelCase      ) * scheduler.init_noise_sigma
  
  			for t in scheduler.timesteps:
  				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
  				A							  =      output.prev_sample
  
  			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
  			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )
  
  			assert abs(result_sum.item() - 13_849.3_818      ) < 1e-1
  			assert abs(result_mean.item() - 18.0_331      ) < 1e-3
  
  