






"""simple docstring"""



from __future__ import annotations




def     lowerCamelCase_					(	__A	:		list[list[int]]      ,						__A	:		int      ,						__A	:		int      ,						__A	:		int | float      ):
					lowerCamelCase__					,  lowerCamelCase__					=m - 1, 0
					while i >= 0 and j < n:
										if key == mat[i][j]:
															print(f"Key {key} found at row- {i + 1} column- {j + 1}"      )
															return
										if key < mat[i][j]:
															i -= 1
										else:
															j += 1
					print(f"Key {key} not found"      )




def     lowerCamelCase_					(	):
					lowerCamelCase__					=[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
					lowerCamelCase__					=int(input('Enter the element to be searched:'      )      )
					print(__A      )
					search_in_a_sorted_matrix(__A      ,						len(__A      )      ,						len(mat[0]      )      ,						__A      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()







"""simple docstring"""



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 							__lowercase(							_UpperCamelCase	):
						lowerCAmelCase_		:				Dict=							(EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_		:				int=							10






						def 		lowerCAmelCase (       self     ,    **_SCREAMING_SNAKE_CASE)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					={
											    'num_train_timesteps': 11_00,
											    'beta_start': 0.0001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**_SCREAMING_SNAKE_CASE)
											return config






						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self)      ->	Tuple:




											"""simple docstring"""





											for beta_start, beta_end in zip([0.00001, 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 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self)      ->	Dict:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config()
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps)

											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for i, t in enumerate(scheduler.timesteps):
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 152.3192) < 1E-2
											assert abs(result_mean.item() - 0.1983) < 1E-3






						def 		lowerCAmelCase (       self)      ->	str:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config(prediction_type='v_prediction')
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps)

											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for i, t in enumerate(scheduler.timesteps):
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 108.4439) < 1E-2
											assert abs(result_mean.item() - 0.1412) < 1E-3






						def 		lowerCAmelCase (       self)      ->	Any:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config()
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps     ,    device=_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for t in scheduler.timesteps:
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 152.3192) < 1E-2
											assert abs(result_mean.item() - 0.1983) < 1E-3