code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import inspect
  
  from diffusers.utils import is_flax_available
  from diffusers.utils.testing_utils import require_flax
  
  
  if is_flax_available():
  				import jax
  
  
  
  @require_flax
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ):
  			A  ,    A							  =      self.prepare_init_args_and_inputs_for_common()
  
  			A							  =      self.model_class(**_lowerCAmelCase      )
  			A							  =      model.init(inputs_dict["""prng_key"""]      ,      inputs_dict["""sample"""]      )
  			jax.lax.stop_gradient(_lowerCAmelCase      )
  
  			A							  =      model.apply(_lowerCAmelCase      ,      inputs_dict["""sample"""]      )
  
  			if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ):
  				A							  =      output.sample
  
  			self.assertIsNotNone(_lowerCAmelCase      )
  			A							  =      inputs_dict["""sample"""].shape
  			self.assertEqual(output.shape      ,      _lowerCAmelCase      ,      """Input and output shapes do not match"""      )
  
  
  
  
  
  
  
  		def        A     (self						:      int      ):
  			A  ,    A							  =      self.prepare_init_args_and_inputs_for_common()
  
  			A							  =      16
  			A							  =      (16, 32)
  
  			A							  =      self.model_class(**_lowerCAmelCase      )
  			A							  =      model.init(inputs_dict["""prng_key"""]      ,      inputs_dict["""sample"""]      )
  			jax.lax.stop_gradient(_lowerCAmelCase      )
  
  			A							  =      model.apply(_lowerCAmelCase      ,      inputs_dict["""sample"""]      )
  
  			if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ):
  				A							  =      output.sample
  
  			self.assertIsNotNone(_lowerCAmelCase      )
  			A							  =      inputs_dict["""sample"""].shape
  			self.assertEqual(output.shape      ,      _lowerCAmelCase      ,      """Input and output shapes do not match"""      )
  
  
  
  
  
  
  
  		def        A     (self						:      List[str]      ):
  			A							  =      """kwargs""" in inspect.signature(self.model_class.__init__      ).parameters
  			A							  =      len(self.model_class._deprecated_kwargs      ) > 0
  
  			if has_kwarg_in_model_class and not has_deprecated_kwarg:
  				raise ValueError(
  				    F"""{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"""
  				    """ under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"""
  				    """ no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="""
  				    """ [<deprecated_argument>]`"""      )
  
  			if not has_kwarg_in_model_class and has_deprecated_kwarg:
  				raise ValueError(
  				    F"""{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"""
  				    """ under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"""
  				    F""" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"""
  				    """ from `_deprecated_kwargs = [<deprecated_argument>]`"""      )
  
  
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
  
  