






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













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()





