






from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

A_						:						Dict						      =					TypeVar('T')




class 							_lowerCAmelCase(			Generic[T]   ):





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   Any | T        =							None

         UpperCamelCase_:   int        =							len(_lowerCamelCase				)
         UpperCamelCase_:   list[T]        =							[any_type for _ in range(self.N				)] + arr
         UpperCamelCase_:   str        =							fnc
         self.build()





       def      _a       (   self				):
         for p in range(self.N - 1    ,     0    ,     -1				):
           UpperCamelCase_:   Optional[Any]        =							self.fn(self.st[p * 2]    ,     self.st[p * 2 + 1]				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         p += self.N
         UpperCamelCase_:   str        =							v
         while p > 1:
           UpperCamelCase_:   Union[str, Any]        =							p // 2
           UpperCamelCase_:   Tuple        =							self.fn(self.st[p * 2]    ,     self.st[p * 2 + 1]				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):  # noqa: E741
         UpperCamelCase_ ,UpperCamelCase_:   Optional[Any]        =							l + self.N, r + self.N

         UpperCamelCase_:   T | None        =							None
         while l <= r:
           if l % 2 == 1:
             UpperCamelCase_:   Optional[Any]        =							self.st[l] if res is None else self.fn(_lowerCamelCase    ,     self.st[l]				)
           if r % 2 == 0:
             UpperCamelCase_:   Union[str, Any]        =							self.st[r] if res is None else self.fn(_lowerCamelCase    ,     self.st[r]				)
           UpperCamelCase_ ,UpperCamelCase_:   Tuple        =							(l + 1) // 2, (r - 1) // 2
         return res


if __name__ == "__main__":
   from functools import reduce

   A_						:						Dict						      =					[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

   A_						:						Optional[int]						      =					{
       0: 7,
       1: 2,
       2: 6,
       3: -14,
       4: 5,
       5: 4,
       6: 7,
       7: -10,
       8: 9,
       9: 10,
       10: 12,
       11: 1,
   }

   A_						:						str						      =					SegmentTree(test_array, min)
   A_						:						Tuple						      =					SegmentTree(test_array, max)
   A_						:						int						      =					SegmentTree(test_array, lambda a, b: a + b)

   def     snake_case   ()			->							None:
     for i in range(len(UpperCAmelCase__    )    ):
       for j in range(UpperCAmelCase__      ,       len(UpperCAmelCase__    )    ):
         UpperCamelCase_:   List[Any]        =							reduce(UpperCAmelCase__      ,       test_array[i : j + 1]    )
         UpperCamelCase_:   Any        =							reduce(UpperCAmelCase__      ,       test_array[i : j + 1]    )
         UpperCamelCase_:   Optional[int]        =							reduce(lambda UpperCAmelCase__      ,       UpperCAmelCase__    : a + b      ,       test_array[i : j + 1]    )
         assert min_range == min_segment_tree.query(UpperCAmelCase__      ,       UpperCAmelCase__    )
         assert max_range == max_segment_tree.query(UpperCAmelCase__      ,       UpperCAmelCase__    )
         assert sum_range == sum_segment_tree.query(UpperCAmelCase__      ,       UpperCAmelCase__    )

   test_all_segments()

   for index, value in test_updates.items():
      A_						:						Any						      =					value
      min_segment_tree.update(index, value)
      max_segment_tree.update(index, value)
      sum_segment_tree.update(index, value)
      test_all_segments()






import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class 						UpperCamelCase_  (  snake_case__							):
					_a      :						Union[str, Any]						    =					(DPMSolverSDEScheduler,)
					_a      :						List[Any]						    =					1_0



					def   __a     (		self     :     Any       ,      **lowerCamelCase     :     str						):
							lowerCamelCase_	:     Any 						=      {
							    'num_train_timesteps': 11_00,
							    'beta_start': 0.0_001,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'noise_sampler_seed': 0,
							}

							config.update(**lowerCamelCase						)
							return config



					def   __a     (		self     :     Tuple						):
							for timesteps in [10, 50, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase						)



					def   __a     (		self     :     int						):
							for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]       ,      [0.0_002, 0.002, 0.02]						):
									self.check_over_configs(beta_start=lowerCamelCase       ,      beta_end=lowerCamelCase						)



					def   __a     (		self     :     Any						):
							for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=lowerCamelCase						)



					def   __a     (		self     :     Optional[int]						):
							for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=lowerCamelCase						)



					def   __a     (		self     :     Optional[int]						):
							lowerCamelCase_	:     int 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     str 						=      self.get_scheduler_config()
							lowerCamelCase_	:     Optional[Any] 						=      scheduler_class(**lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							lowerCamelCase_	:     Optional[Any] 						=      self.dummy_model()
							lowerCamelCase_	:     Optional[Any] 						=      self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase_	:     Union[str, Any] 						=      sample.to(lowerCamelCase						)

							for i, t in enumerate(scheduler.timesteps						):
									lowerCamelCase_	:     str 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Tuple 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Optional[int] 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Tuple 						=      output.prev_sample

							lowerCamelCase_	:     List[str] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     Optional[Any] 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 167.47_821_044_921_875						) < 1E-2
									assert abs(result_mean.item() - 0.2_178_705_964_565_277						) < 1E-3
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 171.59_352_111_816_406						) < 1E-2
									assert abs(result_mean.item() - 0.22_342_906_892_299_652						) < 1E-3
							else:
									assert abs(result_sum.item() - 162.52_383_422_851_562						) < 1E-2
									assert abs(result_mean.item() - 0.211_619_570_851_326						) < 1E-3



					def   __a     (		self     :     Tuple						):
							lowerCamelCase_	:     List[str] 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     Any 						=      self.get_scheduler_config(prediction_type='v_prediction'						)
							lowerCamelCase_	:     Tuple 						=      scheduler_class(**lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							lowerCamelCase_	:     Any 						=      self.dummy_model()
							lowerCamelCase_	:     Tuple 						=      self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase_	:     List[Any] 						=      sample.to(lowerCamelCase						)

							for i, t in enumerate(scheduler.timesteps						):
									lowerCamelCase_	:     Dict 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Optional[int] 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Union[str, Any] 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Dict 						=      output.prev_sample

							lowerCamelCase_	:     Optional[Any] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     List[Any] 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 124.77_149_200_439_453						) < 1E-2
									assert abs(result_mean.item() - 0.16_226_289_014_816_284						) < 1E-3
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 128.1_663_360_595_703						) < 1E-2
									assert abs(result_mean.item() - 0.16_688_326_001_167_297						) < 1E-3
							else:
									assert abs(result_sum.item() - 119.8_487_548_828_125						) < 1E-2
									assert abs(result_mean.item() - 0.1_560_530_662_536_621						) < 1E-3



					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     Union[str, Any] 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     List[Any] 						=      self.get_scheduler_config()
							lowerCamelCase_	:     List[str] 						=      scheduler_class(**lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps       ,      device=lowerCamelCase						)

							lowerCamelCase_	:     Dict 						=      self.dummy_model()
							lowerCamelCase_	:     Dict 						=      self.dummy_sample_deter.to(lowerCamelCase						) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
									lowerCamelCase_	:     Tuple 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     List[str] 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     int 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Optional[int] 						=      output.prev_sample

							lowerCamelCase_	:     List[Any] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     str 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 167.46_957_397_460_938						) < 1E-2
									assert abs(result_mean.item() - 0.21_805_934_607_982_635						) < 1E-3
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 171.59_353_637_695_312						) < 1E-2
									assert abs(result_mean.item() - 0.22_342_908_382_415_771						) < 1E-3
							else:
									assert abs(result_sum.item() - 162.52_383_422_851_562						) < 1E-2
									assert abs(result_mean.item() - 0.211_619_570_851_326						) < 1E-3





					def   __a     (		self     :     Any						):
							lowerCamelCase_	:     Dict 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     List[Any] 						=      self.get_scheduler_config()
							lowerCamelCase_	:     Union[str, Any] 						=      scheduler_class(**lowerCamelCase       ,      use_karras_sigmas=lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps       ,      device=lowerCamelCase						)

							lowerCamelCase_	:     int 						=      self.dummy_model()
							lowerCamelCase_	:     str 						=      self.dummy_sample_deter.to(lowerCamelCase						) * scheduler.init_noise_sigma
							lowerCamelCase_	:     Dict 						=      sample.to(lowerCamelCase						)

							for t in scheduler.timesteps:
									lowerCamelCase_	:     Optional[Any] 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Dict 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Dict 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Any 						=      output.prev_sample

							lowerCamelCase_	:     Optional[int] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     Union[str, Any] 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 176.66_974_135_742_188						) < 1E-2
									assert abs(result_mean.item() - 0.23_003_872_730_981_811						) < 1E-2
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 177.63_653_564_453_125						) < 1E-2
									assert abs(result_mean.item() - 0.23_003_872_730_981_811						) < 1E-2
							else:
									assert abs(result_sum.item() - 170.3_135_223_388_672						) < 1E-2
									assert abs(result_mean.item() - 0.23_003_872_730_981_811						) < 1E-2
