





"""simple docstring"""
import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class  _UpperCAmelCase       (     A__			):
					UpperCamelCase								=							(EulerAncestralDiscreteScheduler,)
					UpperCamelCase								=							1_0


					def      lowerCamelCase	( self      :List[Any]					,	**__UpperCamelCase      :List[str]		):
												A		=  {
												    "num_train_timesteps": 11_00,
												    "beta_start": 0.0_001,
												    "beta_end": 0.02,
												    "beta_schedule": "linear",
												}

												config.update(**_A		)
												return config


					def      lowerCamelCase	( self      :Optional[int]		):






												for timesteps in [10, 50, 1_00, 10_00]:
																			self.check_over_configs(num_train_timesteps=_A		)


					def      lowerCamelCase	( self      :Any		):






												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      lowerCamelCase	( self      :Tuple		):






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


					def      lowerCamelCase	( self      :List[str]		):






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


					def      lowerCamelCase	( self      :Optional[Any]		):
												A		=  self.scheduler_classes[0]
												A		=  self.get_scheduler_config()
												A		=  scheduler_class(**_A		)

												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.cpu()
												A		=  sample.to(_A		)

												for i, t in enumerate(scheduler.timesteps		):
																			A		=  scheduler.scale_model_input(_A					,	_A		)

																			A		=  model(_A					,	_A		)

																			A		=  scheduler.step(_A					,	_A					,	_A					,	generator=_A		)
																			A		=  output.prev_sample

												A		=  torch.sum(torch.abs(_A		)		)
												A		=  torch.mean(torch.abs(_A		)		)

												assert abs(result_sum.item() - 152.3_192		) < 1e-2
												assert abs(result_mean.item() - 0.1_983		) < 1e-3


					def      lowerCamelCase	( self      :Dict		):
												A		=  self.scheduler_classes[0]
												A		=  self.get_scheduler_config(prediction_type="v_prediction"		)
												A		=  scheduler_class(**_A		)

												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(_A		)

												for i, t in enumerate(scheduler.timesteps		):
																			A		=  scheduler.scale_model_input(_A					,	_A		)

																			A		=  model(_A					,	_A		)

																			A		=  scheduler.step(_A					,	_A					,	_A					,	generator=_A		)
																			A		=  output.prev_sample

												A		=  torch.sum(torch.abs(_A		)		)
												A		=  torch.mean(torch.abs(_A		)		)

												assert abs(result_sum.item() - 108.4_439		) < 1e-2
												assert abs(result_mean.item() - 0.1_412		) < 1e-3


					def      lowerCamelCase	( self      :Dict		):
												A		=  self.scheduler_classes[0]
												A		=  self.get_scheduler_config()
												A		=  scheduler_class(**_A		)

												scheduler.set_timesteps(self.num_inference_steps					,	device=_A		)
												A		=  torch.manual_seed(0		)

												A		=  self.dummy_model()
												A		=  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
												A		=  sample.to(_A		)

												for t in scheduler.timesteps:
																			A		=  scheduler.scale_model_input(_A					,	_A		)

																			A		=  model(_A					,	_A		)

																			A		=  scheduler.step(_A					,	_A					,	_A					,	generator=_A		)
																			A		=  output.prev_sample

												A		=  torch.sum(torch.abs(_A		)		)
												A		=  torch.mean(torch.abs(_A		)		)

												assert abs(result_sum.item() - 152.3_192		) < 1e-2
												assert abs(result_mean.item() - 0.1_983		) < 1e-3









"""simple docstring"""
def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							for index, item in enumerate(UpperCamelCase	):
														if item == target:
																					return index
							return -1






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							if not (0 <= high < len(UpperCamelCase	) and 0 <= low < len(UpperCamelCase	)):
														raise Exception("Invalid upper or lower bound!"	)
							if high < low:
														return -1
							if sequence[low] == target:
														return low
							if sequence[high] == target:
														return high
							return rec_linear_search(UpperCamelCase ,  low + 1 ,  high - 1 ,  UpperCamelCase	)


if __name__ == "__main__":
		_snake_case					:		int							=			input('Enter numbers separated by comma:\n').strip()
		_snake_case					:		int							=			[int(item.strip()) for item in user_input.split(',')]

		_snake_case					:		Union[str, Any]							=			int(input('Enter a single number to be found in the list:\n').strip())
		_snake_case					:		List[Any]							=			linear_search(sequence, target)
		if result != -1:
				print(F"""linear_search({sequence}, {target}) = {result}""")
		else:
				print(F"""{target} was not found in {sequence}""")


