
import math






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_      )-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      len(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      [0] * (
													    4 * self.N
													)  # approximate the overall size of segment tree with array N
													self.build(1  ,						0  ,						self.N - 1      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> List[Any]:





													'''simple docstring'''






													return idx * 2



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> Any:





													'''simple docstring'''






													return idx * 2 + 1



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> str:  # noqa: E741





													'''simple docstring'''






													if l == r:
																			__UpperCamelCase  	=      A[l]
													else:
																			__UpperCamelCase  	=      (l + r) // 2
																			self.build(self.left(SCREAMING_SNAKE_CASE_      )  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
																			self.build(self.right(SCREAMING_SNAKE_CASE_      )  ,						mid + 1  ,						SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      max(self.st[self.left(SCREAMING_SNAKE_CASE_      )]  ,						self.st[self.right(SCREAMING_SNAKE_CASE_      )]      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> str:





													'''simple docstring'''






													return self.update_recursive(1  ,						0  ,						self.N - 1  ,						a - 1  ,						b - 1  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Any:  # noqa: E741





													'''simple docstring'''






													if r < a or l > b:
																			return True
													if l == r:
																			__UpperCamelCase  	=      val
																			return True
													__UpperCamelCase  	=      (l + r) // 2
													self.update_recursive(self.left(SCREAMING_SNAKE_CASE_      )  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													self.update_recursive(self.right(SCREAMING_SNAKE_CASE_      )  ,						mid + 1  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      max(self.st[self.left(SCREAMING_SNAKE_CASE_      )]  ,						self.st[self.right(SCREAMING_SNAKE_CASE_      )]      )
													return True



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Dict:





													'''simple docstring'''






													return self.query_recursive(1  ,						0  ,						self.N - 1  ,						a - 1  ,						b - 1      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> str:  # noqa: E741





													'''simple docstring'''






													if r < a or l > b:
																			return -math.inf
													if l >= a and r <= b:
																			return self.st[idx]
													__UpperCamelCase  	=      (l + r) // 2
													__UpperCamelCase  	=      self.query_recursive(self.left(SCREAMING_SNAKE_CASE_      )  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      self.query_recursive(self.right(SCREAMING_SNAKE_CASE_      )  ,						mid + 1  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													return max(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      []
													for i in range(1  ,						N + 1      ):
																			show_list += [self.query(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )]
													print(SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
				lowercase__				:				str						=      [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
				lowercase__				:				Optional[Any]						=      1_5
				lowercase__				:				Optional[int]						=      SegmentTree(A)
				print(segt.query(4, 6))
				print(segt.query(7, 1_1))
				print(segt.query(7, 1_2))
				segt.update(1, 3, 1_1_1)
				print(segt.query(1, 1_5))
				segt.update(7, 8, 2_3_5)
				segt.show_data()












'''simple docstring'''







import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	(KDPMaAncestralDiscreteScheduler,)
     _snake_case							   =	10






     def      _UpperCamelCase (self     :						int					,					**lowercase__     :						Optional[int]       )	->							Dict:
           __A		 =   {
               "num_train_timesteps": 1100,
               "beta_start": 0.00_01,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**lowercase__       )
           return config






     def      _UpperCamelCase (self     :						str       )	->							Optional[Any]:
           for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							int:
           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,					[0.00_02, 0.0_02, 0.02]       ):
                 self.check_over_configs(beta_start=lowercase__					,					beta_end=lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							Tuple:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							Any:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

           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(lowercase__       )

           for i, t in enumerate(scheduler.timesteps       ):
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 1_38_49.38_77       ) < 1e-2
           assert abs(result_mean.item() - 18.03_31       ) < 5e-3






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							str:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]       )	->							List[Any]:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config(prediction_type="v_prediction"       )
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter * scheduler.init_noise_sigma
           __A		 =   sample.to(lowercase__       )

           __A		 =   torch.manual_seed(0       )

           for i, t in enumerate(scheduler.timesteps       ):
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 3_28.99_70       ) < 1e-2
           assert abs(result_mean.item() - 0.42_84       ) < 1e-3






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							int:
           if torch_device == "mps":
                 return
           __A		 =   self.scheduler_classes[0]
           __A		 =   self.get_scheduler_config()
           __A		 =   scheduler_class(**lowercase__       )

           scheduler.set_timesteps(self.num_inference_steps					,					device=lowercase__       )
           __A		 =   torch.manual_seed(0       )

           __A		 =   self.dummy_model()
           __A		 =   self.dummy_sample_deter.to(lowercase__       ) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 __A		 =   scheduler.scale_model_input(lowercase__					,					lowercase__       )

                 __A		 =   model(lowercase__					,					lowercase__       )

                 __A		 =   scheduler.step(lowercase__					,					lowercase__					,					lowercase__					,					generator=lowercase__       )
                 __A		 =   output.prev_sample

           __A		 =   torch.sum(torch.abs(lowercase__       )       )
           __A		 =   torch.mean(torch.abs(lowercase__       )       )

           assert abs(result_sum.item() - 1_38_49.38_18       ) < 1e-1
           assert abs(result_mean.item() - 18.03_31       ) < 1e-3


