



import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class      UpperCAmelCase_						(     UpperCamelCase_ ):

						'''simple docstring'''




						UpperCamelCase__    :				Optional[int]       						=						(KDPMaAncestralDiscreteScheduler,)
						UpperCamelCase__    :				List[str]       						=						10






						def  _A			(					self   ,							**_A	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      {
													    'num_train_timesteps': 1_100,
													    'beta_start': 0.0_0_0_1,
													    'beta_end': 0.0_2,
													    'beta_schedule': 'linear',
													}

													config.update(**_A	)
													return config






						def  _A			(					self	):







													'''simple docstring'''

													for timesteps in [10, 50, 100, 1_000]:
																				self.check_over_configs(num_train_timesteps=_A	)






						def  _A			(					self	):







													'''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	):







													'''simple docstring'''

													for schedule in ["linear", "scaled_linear"]:
																				self.check_over_configs(beta_schedule=_A	)






						def  _A			(					self	):







													'''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	):







													'''simple docstring'''

													for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(prediction_type=_A	)






						def  _A			(					self	):







													'''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	):







													'''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











from __future__ import annotations

class      UpperCAmelCase_						:

						'''simple docstring'''




						def __init__(					self   ,							_A	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      value
													__SCREAMING_SNAKE_CASE								=      None
													__SCREAMING_SNAKE_CASE								=      None







class      UpperCAmelCase_						:

						'''simple docstring'''




						UpperCamelCase__    :				int






						def __init__(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      0






						def  _A			(					self   ,							_A   ,							_A	):







													'''simple docstring'''

													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  _A			(					self   ,							_A   ,							_A = None	):







													'''simple docstring'''

													if node is None:
																				return 0
													if target is not None:
																				__SCREAMING_SNAKE_CASE								=      target

													self.depth_first_search(_A   ,							node.value	)
													self.path_sum(node.left	)
													self.path_sum(node.right	)

													return self.paths


if __name__ == "__main__":
			import doctest

			doctest.testmod()






