

"""simple docstring"""



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class 					a						(       _lowerCamelCase				):



				"""simple docstring"""



				UpperCAmelCase						 =					(UniPCMultistepScheduler,)
				UpperCAmelCase						 =					(("num_inference_steps", 2_5),)

				def 				UpperCamelCase	(     self: Optional[int]    ,						**UpperCamelCase: Optional[Any]       ):






									"""simple docstring"""


									A__										=  {
									    """num_train_timesteps""": 10_00,
									    """beta_start""": 0.0_001,
									    """beta_end""": 0.02,
									    """beta_schedule""": """linear""",
									    """solver_order""": 2,
									    """solver_type""": """bh2""",
									}

									config.update(**UpperCamelCase       )
									return config

				def 				UpperCamelCase	(     self: Any    ,						UpperCamelCase: Optional[int]=0    ,						**UpperCamelCase: Union[str, Any]       ):






									"""simple docstring"""


									A__										=  dict(self.forward_default_kwargs       )
									A__										=  kwargs.pop("""num_inference_steps"""    ,						UpperCamelCase       )
									A__										=  self.dummy_sample
									A__										=  0.1 * sample
									A__										=  [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
														A__										=  self.get_scheduler_config(**UpperCamelCase       )
														A__										=  scheduler_class(**UpperCamelCase       )
														scheduler.set_timesteps(UpperCamelCase       )
														# copy over dummy past residuals
														A__										=  dummy_past_residuals[: scheduler.config.solver_order]

														with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(UpperCamelCase       )
																			A__										=  scheduler_class.from_pretrained(UpperCamelCase       )
																			new_scheduler.set_timesteps(UpperCamelCase       )
																			# copy over dummy past residuals
																			A__										=  dummy_past_residuals[: new_scheduler.config.solver_order]

														A__			,				A__										=  sample, sample
														for t in range(UpperCamelCase    ,						time_step + scheduler.config.solver_order + 1       ):
																			A__										=  scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase       ).prev_sample
																			A__										=  new_scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase       ).prev_sample

																			assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

				def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: List[str]=0    ,						**UpperCamelCase: List[Any]       ):






									"""simple docstring"""


									A__										=  dict(self.forward_default_kwargs       )
									A__										=  kwargs.pop("""num_inference_steps"""    ,						UpperCamelCase       )
									A__										=  self.dummy_sample
									A__										=  0.1 * sample
									A__										=  [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
														A__										=  self.get_scheduler_config()
														A__										=  scheduler_class(**UpperCamelCase       )
														scheduler.set_timesteps(UpperCamelCase       )

														# copy over dummy past residuals (must be after setting timesteps)
														A__										=  dummy_past_residuals[: scheduler.config.solver_order]

														with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(UpperCamelCase       )
																			A__										=  scheduler_class.from_pretrained(UpperCamelCase       )
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(UpperCamelCase       )

																			# copy over dummy past residual (must be after setting timesteps)
																			A__										=  dummy_past_residuals[: new_scheduler.config.solver_order]

														A__										=  scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase       ).prev_sample
														A__										=  new_scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase       ).prev_sample

														assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

				def 				UpperCamelCase	(     self: List[str]    ,						UpperCamelCase: Optional[Any]=None    ,						**UpperCamelCase: Union[str, Any]       ):






									"""simple docstring"""


									if scheduler is None:
														A__										=  self.scheduler_classes[0]
														A__										=  self.get_scheduler_config(**UpperCamelCase       )
														A__										=  scheduler_class(**UpperCamelCase       )

									A__										=  self.scheduler_classes[0]
									A__										=  self.get_scheduler_config(**UpperCamelCase       )
									A__										=  scheduler_class(**UpperCamelCase       )

									A__										=  10
									A__										=  self.dummy_model()
									A__										=  self.dummy_sample_deter
									scheduler.set_timesteps(UpperCamelCase       )

									for i, t in enumerate(scheduler.timesteps       ):
														A__										=  model(UpperCamelCase    ,						UpperCamelCase       )
														A__										=  scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       ).prev_sample

									return sample

				def 				UpperCamelCase	(     self: List[str]       ):






									"""simple docstring"""


									A__										=  dict(self.forward_default_kwargs       )

									A__										=  kwargs.pop("""num_inference_steps"""    ,						UpperCamelCase       )

									for scheduler_class in self.scheduler_classes:
														A__										=  self.get_scheduler_config()
														A__										=  scheduler_class(**UpperCamelCase       )

														A__										=  self.dummy_sample
														A__										=  0.1 * sample

														if num_inference_steps is not None and hasattr(UpperCamelCase    ,						"""set_timesteps"""       ):
																			scheduler.set_timesteps(UpperCamelCase       )
														elif num_inference_steps is not None and not hasattr(UpperCamelCase    ,						"""set_timesteps"""       ):
																			A__										=  num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														A__										=  [residual + 0.2, residual + 0.15, residual + 0.10]
														A__										=  dummy_past_residuals[: scheduler.config.solver_order]

														A__										=  scheduler.timesteps[5]
														A__										=  scheduler.timesteps[6]

														A__										=  scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase       ).prev_sample
														A__										=  scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase       ).prev_sample

														self.assertEqual(output_a.shape    ,						sample.shape       )
														self.assertEqual(output_a.shape    ,						output_a.shape       )

				def 				UpperCamelCase	(     self: Any       ):






									"""simple docstring"""


									A__										=  UniPCMultistepScheduler(**self.get_scheduler_config()       )
									A__										=  self.full_loop(scheduler=UpperCamelCase       )
									A__										=  torch.mean(torch.abs(UpperCamelCase       )       )

									assert abs(result_mean.item() - 0.2_464       ) < 1e-3

									A__										=  DPMSolverSinglestepScheduler.from_config(scheduler.config       )
									A__										=  DEISMultistepScheduler.from_config(scheduler.config       )
									A__										=  DPMSolverMultistepScheduler.from_config(scheduler.config       )
									A__										=  UniPCMultistepScheduler.from_config(scheduler.config       )

									A__										=  self.full_loop(scheduler=UpperCamelCase       )
									A__										=  torch.mean(torch.abs(UpperCamelCase       )       )

									assert abs(result_mean.item() - 0.2_464       ) < 1e-3

				def 				UpperCamelCase	(     self: Dict       ):






									"""simple docstring"""


									for timesteps in [25, 50, 1_00, 9_99, 10_00]:
														self.check_over_configs(num_train_timesteps=UpperCamelCase       )

				def 				UpperCamelCase	(     self: str       ):






									"""simple docstring"""


									self.check_over_configs(thresholding=UpperCamelCase       )
									for order in [1, 2, 3]:
														for solver_type in ["bh1", "bh2"]:
																			for threshold in [0.5, 1.0, 2.0]:
																								for prediction_type in ["epsilon", "sample"]:
																													self.check_over_configs(
																													    thresholding=UpperCamelCase    ,						prediction_type=UpperCamelCase    ,						sample_max_value=UpperCamelCase    ,						solver_order=UpperCamelCase    ,						solver_type=UpperCamelCase    ,						)

				def 				UpperCamelCase	(     self: str       ):






									"""simple docstring"""


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

				def 				UpperCamelCase	(     self: Tuple       ):






									"""simple docstring"""


									for solver_type in ["bh1", "bh2"]:
														for order in [1, 2, 3]:
																			for prediction_type in ["epsilon", "sample"]:
																								self.check_over_configs(
																								    solver_order=UpperCamelCase    ,						solver_type=UpperCamelCase    ,						prediction_type=UpperCamelCase    ,						)
																								A__										=  self.full_loop(
																								    solver_order=UpperCamelCase    ,						solver_type=UpperCamelCase    ,						prediction_type=UpperCamelCase    ,						)
																								assert not torch.isnan(UpperCamelCase       ).any(), "Samples have nan numbers"

				def 				UpperCamelCase	(     self: Dict       ):






									"""simple docstring"""


									self.check_over_configs(lower_order_final=UpperCamelCase       )
									self.check_over_configs(lower_order_final=UpperCamelCase       )

				def 				UpperCamelCase	(     self: Any       ):






									"""simple docstring"""


									for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
														self.check_over_forward(num_inference_steps=UpperCamelCase    ,						time_step=0       )

				def 				UpperCamelCase	(     self: Optional[Any]       ):






									"""simple docstring"""


									A__										=  self.full_loop()
									A__										=  torch.mean(torch.abs(UpperCamelCase       )       )

									assert abs(result_mean.item() - 0.2_464       ) < 1e-3

				def 				UpperCamelCase	(     self: Union[str, Any]       ):






									"""simple docstring"""


									A__										=  self.full_loop(prediction_type="""v_prediction"""       )
									A__										=  torch.mean(torch.abs(UpperCamelCase       )       )

									assert abs(result_mean.item() - 0.1_014       ) < 1e-3

				def 				UpperCamelCase	(     self: List[str]       ):






									"""simple docstring"""


									A__										=  self.scheduler_classes[0]
									A__										=  self.get_scheduler_config(thresholding=UpperCamelCase    ,						dynamic_thresholding_ratio=0       )
									A__										=  scheduler_class(**UpperCamelCase       )

									A__										=  10
									A__										=  self.dummy_model()
									A__										=  self.dummy_sample_deter.half()
									scheduler.set_timesteps(UpperCamelCase       )

									for i, t in enumerate(scheduler.timesteps       ):
														A__										=  model(UpperCamelCase    ,						UpperCamelCase       )
														A__										=  scheduler.step(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       ).prev_sample

									assert sample.dtype == torch.floataa





				def 				UpperCamelCase	(     self: int    ,						**UpperCamelCase: Tuple       ):






									"""simple docstring"""


									for scheduler_class in self.scheduler_classes:
														A__										=  self.get_scheduler_config(**UpperCamelCase       )
														A__										=  scheduler_class(**UpperCamelCase       )

														scheduler.set_timesteps(scheduler.config.num_train_timesteps       )
														assert len(scheduler.timesteps.unique()       ) == scheduler.num_inference_steps









"""simple docstring"""



from collections import defaultdict





def  _snake_case     (    UpperCAmelCase_  :					int			):
     A__										=  1
     A__										=  True
     for v in tree[start]:
          if v not in visited:
               ret += dfs(UpperCAmelCase_			)
     if ret % 2 == 0:
          cuts.append(UpperCAmelCase_			)
     return ret





def  _snake_case     (    ):
     dfs(1			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_  ,SCREAMING_SNAKE_CASE_					:	Any							  =						1_0, 9
 SCREAMING_SNAKE_CASE_					:	Any							  =						defaultdict(list)
 SCREAMING_SNAKE_CASE_					:	dict[int, bool]							  =						{}
 SCREAMING_SNAKE_CASE_					:	list[int]							  =						[]
 SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						0
 SCREAMING_SNAKE_CASE_					:	Any							  =						[(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (1_0, 8)]
 for u, v in edges:
  tree[u].append(v)
  tree[v].append(u)
 even_tree()
 print(len(cuts) - 1)






