



"""simple docstring"""
import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     Any		=	(EulerAncestralDiscreteScheduler,)
					_A						:     Union[str, Any]		=	1_0






					def   lowerCAmelCase_							(			self:				Dict		,	**snake_case:				Union[str, Any]		)  ->      Optional[int]:
											snake_case_					:Any		=     {
											    """num_train_timesteps""": 1_100,
											    """beta_start""": 0.0_0_0_1,
											    """beta_end""": 0.0_2,
											    """beta_schedule""": """linear""",
											}

											config.update(**snake_case		)
											return config






					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      Union[str, Any]:
											for timesteps in [10, 50, 100, 1_000]:
																	self.check_over_configs(num_train_timesteps=snake_case		)






					def   lowerCAmelCase_							(			self:				Any		)  ->      Optional[int]:
											for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		,	[0.0_0_0_2, 0.0_0_2, 0.0_2]		):
																	self.check_over_configs(beta_start=snake_case		,	beta_end=snake_case		)






					def   lowerCAmelCase_							(			self:				int		)  ->      int:
											for schedule in ["linear", "scaled_linear"]:
																	self.check_over_configs(beta_schedule=snake_case		)






					def   lowerCAmelCase_							(			self:				str		)  ->      int:
											for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=snake_case		)






					def   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      List[Any]:
											snake_case_					:str		=     self.scheduler_classes[0]
											snake_case_					:Optional[Any]		=     self.get_scheduler_config()
											snake_case_					:List[str]		=     scheduler_class(**snake_case		)

											scheduler.set_timesteps(self.num_inference_steps		)

											snake_case_					:str		=     torch.manual_seed(0		)

											snake_case_					:Optional[Any]		=     self.dummy_model()
											snake_case_					:Optional[int]		=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											snake_case_					:Optional[int]		=     sample.to(snake_case		)

											for i, t in enumerate(scheduler.timesteps		):
																	snake_case_					:Optional[int]		=     scheduler.scale_model_input(snake_case		,	snake_case		)

																	snake_case_					:List[str]		=     model(snake_case		,	snake_case		)

																	snake_case_					:Optional[int]		=     scheduler.step(snake_case		,	snake_case		,	snake_case		,	generator=snake_case		)
																	snake_case_					:Optional[Any]		=     output.prev_sample

											snake_case_					:Union[str, Any]		=     torch.sum(torch.abs(snake_case		)		)
											snake_case_					:Optional[Any]		=     torch.mean(torch.abs(snake_case		)		)

											assert abs(result_sum.item() - 1_5_2.3_1_9_2		) < 1E-2
											assert abs(result_mean.item() - 0.1_9_8_3		) < 1E-3






					def   lowerCAmelCase_							(			self:				Dict		)  ->      Tuple:
											snake_case_					:Any		=     self.scheduler_classes[0]
											snake_case_					:Any		=     self.get_scheduler_config(prediction_type="""v_prediction"""		)
											snake_case_					:str		=     scheduler_class(**snake_case		)

											scheduler.set_timesteps(self.num_inference_steps		)

											snake_case_					:str		=     torch.manual_seed(0		)

											snake_case_					:List[Any]		=     self.dummy_model()
											snake_case_					:int		=     self.dummy_sample_deter * scheduler.init_noise_sigma
											snake_case_					:Dict		=     sample.to(snake_case		)

											for i, t in enumerate(scheduler.timesteps		):
																	snake_case_					:Tuple		=     scheduler.scale_model_input(snake_case		,	snake_case		)

																	snake_case_					:int		=     model(snake_case		,	snake_case		)

																	snake_case_					:List[str]		=     scheduler.step(snake_case		,	snake_case		,	snake_case		,	generator=snake_case		)
																	snake_case_					:Dict		=     output.prev_sample

											snake_case_					:Any		=     torch.sum(torch.abs(snake_case		)		)
											snake_case_					:Union[str, Any]		=     torch.mean(torch.abs(snake_case		)		)

											assert abs(result_sum.item() - 1_0_8.4_4_3_9		) < 1E-2
											assert abs(result_mean.item() - 0.1_4_1_2		) < 1E-3






					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      Optional[int]:
											snake_case_					:Any		=     self.scheduler_classes[0]
											snake_case_					:Any		=     self.get_scheduler_config()
											snake_case_					:str		=     scheduler_class(**snake_case		)

											scheduler.set_timesteps(self.num_inference_steps		,	device=snake_case		)
											snake_case_					:Any		=     torch.manual_seed(0		)

											snake_case_					:Dict		=     self.dummy_model()
											snake_case_					:List[Any]		=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											snake_case_					:Any		=     sample.to(snake_case		)

											for t in scheduler.timesteps:
																	snake_case_					:Tuple		=     scheduler.scale_model_input(snake_case		,	snake_case		)

																	snake_case_					:Optional[Any]		=     model(snake_case		,	snake_case		)

																	snake_case_					:List[str]		=     scheduler.step(snake_case		,	snake_case		,	snake_case		,	generator=snake_case		)
																	snake_case_					:int		=     output.prev_sample

											snake_case_					:List[Any]		=     torch.sum(torch.abs(snake_case		)		)
											snake_case_					:Dict		=     torch.mean(torch.abs(snake_case		)		)

											assert abs(result_sum.item() - 1_5_2.3_1_9_2		) < 1E-2
											assert abs(result_mean.item() - 0.1_9_8_3		) < 1E-3





"""simple docstring"""
import doctest

import projectq
from projectq.ops import H, Measure
def   A_    (     _lowercase      ):







						'''simple docstring'''


						snake_case_					:Union[str, Any]		=     quantum_engine.allocate_qubit()
						H | qubit
						Measure | qubit
						return int(_lowercase      )


if __name__ == "__main__":
						doctest.testmod()

						# initialises a new quantum backend
						__a           =							projectq.MainEngine()

						# Generate a list of 10 random numbers
						__a           =							[get_random_number(quantum_engine) for _ in range(10)]

						# Flushes the quantum engine from memory
						quantum_engine.flush()

						print("Random numbers", random_numbers_list)
