code: 
  
  
  
  
  
  
  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_       :      Dict     =10
  						def 					UpperCAmelCase		(       self					,       **SCREAMING_SNAKE_CASE_				)       ->   List[Any]:
  										UpperCamelCase :int										=     {
  										    '''num_train_timesteps''': 1100,
  										    '''beta_start''': 0.0001,
  										    '''beta_end''': 0.02,
  										    '''beta_schedule''': '''linear''',
  										}
  
  										config.update(**_lowerCAmelCase				)
  										return config
  						def 					UpperCAmelCase		(       self				)       ->   Optional[Any]:
  										for timesteps in [10, 50, 100, 1000]:
  														self.check_over_configs(num_train_timesteps=_lowerCAmelCase				)
  						def 					UpperCAmelCase		(       self				)       ->   Optional[Any]:
  										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=_lowerCAmelCase					,       beta_end=_lowerCAmelCase				)
  						def 					UpperCAmelCase		(       self				)       ->   List[Any]:
  										for schedule in ["linear", "scaled_linear"]:
  														self.check_over_configs(beta_schedule=_lowerCAmelCase				)
  						def 					UpperCAmelCase		(       self				)       ->   Tuple:
  										if torch_device == "mps":
  														return
  										UpperCamelCase :Dict										=     self.scheduler_classes[0]
  										UpperCamelCase :Tuple										=     self.get_scheduler_config()
  										UpperCamelCase :Dict										=     scheduler_class(**_lowerCAmelCase				)
  
  										scheduler.set_timesteps(self.num_inference_steps				)
  
  										UpperCamelCase :Optional[int]										=     torch.manual_seed(0				)
  
  										UpperCamelCase :Union[str, Any]										=     self.dummy_model()
  										UpperCamelCase :Union[str, Any]										=     self.dummy_sample_deter * scheduler.init_noise_sigma
  										UpperCamelCase :Optional[int]										=     sample.to(_lowerCAmelCase				)
  
  										for i, t in enumerate(scheduler.timesteps				):
  														UpperCamelCase :str										=     scheduler.scale_model_input(_lowerCAmelCase					,       _lowerCAmelCase				)
  
  														UpperCamelCase :int										=     model(_lowerCAmelCase					,       _lowerCAmelCase				)
  
  														UpperCamelCase :Optional[Any]										=     scheduler.step(_lowerCAmelCase					,       _lowerCAmelCase					,       _lowerCAmelCase					,       generator=_lowerCAmelCase				)
  														UpperCamelCase :int										=     output.prev_sample
  
  										UpperCamelCase :Union[str, Any]										=     torch.sum(torch.abs(_lowerCAmelCase				)				)
  										UpperCamelCase :List[Any]										=     torch.mean(torch.abs(_lowerCAmelCase				)				)
  
  										assert abs(result_sum.item() - 1_3849.3877				) < 1e-2
  										assert abs(result_mean.item() - 18.0331				) < 5e-3
  						def 					UpperCAmelCase		(       self				)       ->   Any:
  										for prediction_type in ["epsilon", "v_prediction"]:
  														self.check_over_configs(prediction_type=_lowerCAmelCase				)
  						def 					UpperCAmelCase		(       self				)       ->   int:
  										if torch_device == "mps":
  														return
  										UpperCamelCase :Optional[Any]										=     self.scheduler_classes[0]
  										UpperCamelCase :List[str]										=     self.get_scheduler_config(prediction_type='''v_prediction'''				)
  										UpperCamelCase :Any										=     scheduler_class(**_lowerCAmelCase				)
  
  										scheduler.set_timesteps(self.num_inference_steps				)
  
  										UpperCamelCase :Union[str, Any]										=     self.dummy_model()
  										UpperCamelCase :Any										=     self.dummy_sample_deter * scheduler.init_noise_sigma
  										UpperCamelCase :Dict										=     sample.to(_lowerCAmelCase				)
  
  										UpperCamelCase :Optional[Any]										=     torch.manual_seed(0				)
  
  										for i, t in enumerate(scheduler.timesteps				):
  														UpperCamelCase :List[Any]										=     scheduler.scale_model_input(_lowerCAmelCase					,       _lowerCAmelCase				)
  
  														UpperCamelCase :Tuple										=     model(_lowerCAmelCase					,       _lowerCAmelCase				)
  
  														UpperCamelCase :Any										=     scheduler.step(_lowerCAmelCase					,       _lowerCAmelCase					,       _lowerCAmelCase					,       generator=_lowerCAmelCase				)
  														UpperCamelCase :List[str]										=     output.prev_sample
  
  										UpperCamelCase :Dict										=     torch.sum(torch.abs(_lowerCAmelCase				)				)
  										UpperCamelCase :Optional[int]										=     torch.mean(torch.abs(_lowerCAmelCase				)				)
  
  										assert abs(result_sum.item() - 328.9970				) < 1e-2
  										assert abs(result_mean.item() - 0.4284				) < 1e-3
  
  
  
  
  						def 					UpperCAmelCase		(       self				)       ->   List[str]:
  										if torch_device == "mps":
  														return
  										UpperCamelCase :List[Any]										=     self.scheduler_classes[0]
  										UpperCamelCase :Optional[Any]										=     self.get_scheduler_config()
  										UpperCamelCase :List[str]										=     scheduler_class(**_lowerCAmelCase				)
  
  										scheduler.set_timesteps(self.num_inference_steps					,       device=_lowerCAmelCase				)
  										UpperCamelCase :Optional[int]										=     torch.manual_seed(0				)
  
  										UpperCamelCase :Tuple										=     self.dummy_model()
  										UpperCamelCase :Union[str, Any]										=     self.dummy_sample_deter.to(_lowerCAmelCase				) * scheduler.init_noise_sigma
  
  										for t in scheduler.timesteps:
  														UpperCamelCase :int										=     scheduler.scale_model_input(_lowerCAmelCase					,       _lowerCAmelCase				)
  
  														UpperCamelCase :Any										=     model(_lowerCAmelCase					,       _lowerCAmelCase				)
  
  														UpperCamelCase :Tuple										=     scheduler.step(_lowerCAmelCase					,       _lowerCAmelCase					,       _lowerCAmelCase					,       generator=_lowerCAmelCase				)
  														UpperCamelCase :Dict										=     output.prev_sample
  
  										UpperCamelCase :Optional[Any]										=     torch.sum(torch.abs(_lowerCAmelCase				)				)
  										UpperCamelCase :int										=     torch.mean(torch.abs(_lowerCAmelCase				)				)
  
  										assert abs(result_sum.item() - 1_3849.3818				) < 1e-1
  										assert abs(result_mean.item() - 18.0331				) < 1e-3
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  
  
  
  
  
  
  class     UpperCAmelCase_		:
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
            UpperCamelCase :Tuple										=     value
            UpperCamelCase :Node | None										=     None
            UpperCamelCase :Node | None										=     None
  
  
  
  
  
  
  
  class     UpperCAmelCase_		:
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      int
        def __init__(       self				)       ->   None:
            UpperCamelCase :List[Any]										=     0
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   None:
            if node is None:
                return
  
            if path_sum == self.target:
                self.paths += 1
  
            if node.left:
                self.depth_first_search(node.left					,       path_sum + node.left.value				)
            if node.right:
                self.depth_first_search(node.right					,       path_sum + node.right.value				)
  
  
  
  
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None				)       ->   int:
            if node is None:
                return 0
            if target is not None:
                UpperCamelCase :Tuple										=     target
  
            self.depth_first_search(SCREAMING_SNAKE_CASE_					,       node.value				)
            self.path_sum(node.left				)
            self.path_sum(node.right				)
  
            return self.paths
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
  
  
  
  
  