






'''simple docstring'''







import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				(KDPMaDiscreteScheduler,)
			snake_case__  =				10






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple  ,						**__SCREAMING_SNAKE_CASE      :			Optional[int]					)			->			int:
							a_		:  List[Any]											=      {
							    '''num_train_timesteps''': 1100,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							}

							config.update(**__SCREAMING_SNAKE_CASE					)
							return config






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			List[Any]:
							for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Tuple:
							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=__SCREAMING_SNAKE_CASE  ,						beta_end=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			Any:
							for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			Union[str, Any]:
							for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Any:
							a_		:  Tuple											=      self.scheduler_classes[0]
							a_		:  List[Any]											=      self.get_scheduler_config(prediction_type='''v_prediction'''					)
							a_		:  int											=      scheduler_class(**__SCREAMING_SNAKE_CASE					)

							scheduler.set_timesteps(self.num_inference_steps					)

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

							for i, t in enumerate(scheduler.timesteps					):
											a_		:  Union[str, Any]											=      scheduler.scale_model_input(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

											a_		:  Optional[int]											=      model(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

											a_		:  int											=      scheduler.step(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
											a_		:  int											=      output.prev_sample

							a_		:  Tuple											=      torch.sum(torch.abs(__SCREAMING_SNAKE_CASE					)					)
							a_		:  Optional[Any]											=      torch.mean(torch.abs(__SCREAMING_SNAKE_CASE					)					)

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6_934e-07					) < 1e-2
											assert abs(result_mean.item() - 6.1_112e-10					) < 1e-3
							else:
											# CUDA
											assert abs(result_sum.item() - 4.693_428_650_170_972e-07					) < 1e-2
											assert abs(result_mean.item() - 0.0002					) < 1e-3






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Any:
							if torch_device == "mps":
											return
							a_		:  List[str]											=      self.scheduler_classes[0]
							a_		:  Any											=      self.get_scheduler_config()
							a_		:  List[Any]											=      scheduler_class(**__SCREAMING_SNAKE_CASE					)

							scheduler.set_timesteps(self.num_inference_steps					)

							a_		:  List[str]											=      self.dummy_model()
							a_		:  Tuple											=      self.dummy_sample_deter * scheduler.init_noise_sigma
							a_		:  Union[str, Any]											=      sample.to(__SCREAMING_SNAKE_CASE					)

							for i, t in enumerate(scheduler.timesteps					):
											a_		:  Any											=      scheduler.scale_model_input(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

											a_		:  Union[str, Any]											=      model(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

											a_		:  Dict											=      scheduler.step(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
											a_		:  Optional[Any]											=      output.prev_sample

							a_		:  List[str]											=      torch.sum(torch.abs(__SCREAMING_SNAKE_CASE					)					)
							a_		:  Union[str, Any]											=      torch.mean(torch.abs(__SCREAMING_SNAKE_CASE					)					)

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 20.4125					) < 1e-2
											assert abs(result_mean.item() - 0.0266					) < 1e-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125					) < 1e-2
											assert abs(result_mean.item() - 0.0266					) < 1e-3







			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Optional[Any]:
							if torch_device == "mps":
											return
							a_		:  Dict											=      self.scheduler_classes[0]
							a_		:  Tuple											=      self.get_scheduler_config()
							a_		:  Optional[Any]											=      scheduler_class(**__SCREAMING_SNAKE_CASE					)

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

							a_		:  Union[str, Any]											=      self.dummy_model()
							a_		:  Optional[int]											=      self.dummy_sample_deter.to(__SCREAMING_SNAKE_CASE					) * scheduler.init_noise_sigma

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

											a_		:  List[str]											=      model(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

											a_		:  int											=      scheduler.step(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
											a_		:  Tuple											=      output.prev_sample

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

							if str(__SCREAMING_SNAKE_CASE					).startswith('''cpu'''					):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 20.4125					) < 1e-2
											assert abs(result_mean.item() - 0.0266					) < 1e-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125					) < 1e-2
											assert abs(result_mean.item() - 0.0266					) < 1e-3












'''simple docstring'''







from __future__ import annotations





def 		_UpperCAmelCase   (							__A							:   list[int]      ):
				a_		:  int											=      len(__A      ) // 2

				# choose the middle 3 elements
				a_		:  Dict											=      lst[m - 1 : m + 2]

				# if middle element is peak
				if three[1] > three[0] and three[1] > three[2]:
								return three[1]

				# if increasing, recurse on right
				elif three[0] < three[2]:
								if len(lst[:m]      ) == 2:
												m -= 1
								return peak(lst[m:]      )

				# decreasing
				else:
								if len(lst[:m]      ) == 2:
												m += 1
								return peak(lst[:m]      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




