






import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 							__A	(     _SCREAMING_SNAKE_CASE					):





				"""simple docstring"""







				UpperCamelCase__					: Optional[int]     					=(KDPMaAncestralDiscreteScheduler,)
				UpperCamelCase__					: int     					=1_0


				def  __lowercase				(					self				,    **lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   List[str]									={
									    'num_train_timesteps': 1100,
									    'beta_start': 0.0_001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									}

									config.update(**A_       )
									return config


				def  __lowercase				(					self       ):




									"""simple docstring"""

									for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=A_       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

									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=A_				,    beta_end=A_       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

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


				def  __lowercase				(					self       ):




									"""simple docstring"""

									if torch_device == "mps":
														return
									__UpperCamelCase :   Any									=self.scheduler_classes[0]
									__UpperCamelCase :   int									=self.get_scheduler_config()
									__UpperCamelCase :   int									=scheduler_class(**A_       )

									scheduler.set_timesteps(self.num_inference_steps       )

									__UpperCamelCase :   int									=torch.manual_seed(0       )

									__UpperCamelCase :   List[str]									=self.dummy_model()
									__UpperCamelCase :   Any									=self.dummy_sample_deter * scheduler.init_noise_sigma
									__UpperCamelCase :   List[Any]									=sample.to(A_       )

									for i, t in enumerate(scheduler.timesteps       ):
														__UpperCamelCase :   Any									=scheduler.scale_model_input(A_				,    A_       )

														__UpperCamelCase :   List[str]									=model(A_				,    A_       )

														__UpperCamelCase :   Optional[int]									=scheduler.step(A_				,    A_				,    A_				,    generator=A_       )
														__UpperCamelCase :   Optional[int]									=output.prev_sample

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

									assert abs(result_sum.item() - 13849.3877       ) < 1E-2
									assert abs(result_mean.item() - 18.0_331       ) < 5E-3


				def  __lowercase				(					self       ):




									"""simple docstring"""

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


				def  __lowercase				(					self       ):




									"""simple docstring"""

									if torch_device == "mps":
														return
									__UpperCamelCase :   List[Any]									=self.scheduler_classes[0]
									__UpperCamelCase :   str									=self.get_scheduler_config(prediction_type='v_prediction'       )
									__UpperCamelCase :   str									=scheduler_class(**A_       )

									scheduler.set_timesteps(self.num_inference_steps       )

									__UpperCamelCase :   str									=self.dummy_model()
									__UpperCamelCase :   Any									=self.dummy_sample_deter * scheduler.init_noise_sigma
									__UpperCamelCase :   Dict									=sample.to(A_       )

									__UpperCamelCase :   str									=torch.manual_seed(0       )

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

														__UpperCamelCase :   Any									=model(A_				,    A_       )

														__UpperCamelCase :   Any									=scheduler.step(A_				,    A_				,    A_				,    generator=A_       )
														__UpperCamelCase :   Optional[Any]									=output.prev_sample

									__UpperCamelCase :   Dict									=torch.sum(torch.abs(A_       )       )
									__UpperCamelCase :   int									=torch.mean(torch.abs(A_       )       )

									assert abs(result_sum.item() - 328.9_970       ) < 1E-2
									assert abs(result_mean.item() - 0.4_284       ) < 1E-3







				def  __lowercase				(					self       ):




									"""simple docstring"""

									if torch_device == "mps":
														return
									__UpperCamelCase :   Tuple									=self.scheduler_classes[0]
									__UpperCamelCase :   Union[str, Any]									=self.get_scheduler_config()
									__UpperCamelCase :   Union[str, Any]									=scheduler_class(**A_       )

									scheduler.set_timesteps(self.num_inference_steps				,    device=A_       )
									__UpperCamelCase :   List[str]									=torch.manual_seed(0       )

									__UpperCamelCase :   List[Any]									=self.dummy_model()
									__UpperCamelCase :   List[str]									=self.dummy_sample_deter.to(A_       ) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
														__UpperCamelCase :   int									=scheduler.scale_model_input(A_				,    A_       )

														__UpperCamelCase :   List[Any]									=model(A_				,    A_       )

														__UpperCamelCase :   List[str]									=scheduler.step(A_				,    A_				,    A_				,    generator=A_       )
														__UpperCamelCase :   str									=output.prev_sample

									__UpperCamelCase :   Any									=torch.sum(torch.abs(A_       )       )
									__UpperCamelCase :   List[str]									=torch.mean(torch.abs(A_       )       )

									assert abs(result_sum.item() - 13849.3818       ) < 1E-1
									assert abs(result_mean.item() - 18.0_331       ) < 1E-3



from __future__ import annotations


def  A			(   lowercase       ,			lowercase					)  ->    int:
		'''simple docstring'''

		UpperCamelCase      ,    UpperCamelCase            =	0, len(lowercase					) - 1
		while start_ind <= end_ind:
				if array[start_ind] == search_item:
						return start_ind
				elif array[end_ind] == search_item:
						return end_ind
				else:
						start_ind += 1
						end_ind -= 1
    # returns -1 if search_item is not found in array
		return -1


if __name__ == "__main__":
					print(double_linear_search(list(range(100)), 40))
