



'''simple docstring'''

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		int        =				(DDIMParallelScheduler,)
							UpperCAmelCase			:		Union[str, Any]        =				(("""eta""", 0.0), ("""num_inference_steps""", 50))


							def    UpperCamelCase     (				self   :						List[str]      ,						**a_   :						Optional[Any]			)			->					int:
										snake_case:    str      							={
										    'num_train_timesteps': 1_0_0_0,
										    'beta_start': 0.0_0_0_1,
										    'beta_end': 0.0_2,
										    'beta_schedule': 'linear',
										    'clip_sample': True,
										}

										config.update(**a_			)
										return config


							def    UpperCamelCase     (				self   :						Dict      ,						**a_   :						Optional[Any]			)			->					List[Any]:
										snake_case:    Any      							=self.scheduler_classes[0]
										snake_case:    int      							=self.get_scheduler_config(**a_			)
										snake_case:    List[str]      							=scheduler_class(**a_			)

										snake_case							,  snake_case:    Optional[int]      							=1_0, 0.0

										snake_case:    Optional[int]      							=self.dummy_model()
										snake_case:    List[Any]      							=self.dummy_sample_deter

										scheduler.set_timesteps(a_			)

										for t in scheduler.timesteps:
													snake_case:    Dict      							=model(a_      ,						a_			)
													snake_case:    int      							=scheduler.step(a_      ,						a_      ,						a_      ,						a_			).prev_sample

										return sample


							def    UpperCamelCase     (				self   :						str			)			->					List[Any]:
										for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=a_			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					Tuple:
										for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=a_			)

										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    Dict      							=self.get_scheduler_config(steps_offset=1			)
										snake_case:    str      							=scheduler_class(**a_			)
										scheduler.set_timesteps(5			)
										assert torch.equal(scheduler.timesteps      ,						torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]			)			)


							def    UpperCamelCase     (				self   :						str			)			->					int:
										for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]      ,						[0.0_0_2, 0.0_2, 0.2, 2]			):
													self.check_over_configs(beta_start=a_      ,						beta_end=a_			)


							def    UpperCamelCase     (				self   :						Any			)			->					Optional[int]:
										for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=a_			)


							def    UpperCamelCase     (				self   :						str			)			->					Dict:
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=a_			)


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					List[str]:
										for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=a_			)


							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[Any]:
										for timestep_spacing in ["trailing", "leading"]:
													self.check_over_configs(timestep_spacing=a_			)


							def    UpperCamelCase     (				self   :						Any			)			->					List[str]:
										for rescale_betas_zero_snr in [True, False]:
													self.check_over_configs(rescale_betas_zero_snr=a_			)


							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[Any]:
										self.check_over_configs(thresholding=a_			)
										for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(
																    thresholding=a_      ,						prediction_type=a_      ,						sample_max_value=a_      ,						)


							def    UpperCamelCase     (				self   :						List[Any]			)			->					Any:
										for t in [1, 1_0, 4_9]:
													self.check_over_forward(time_step=a_			)


							def    UpperCamelCase     (				self   :						Any			)			->					int:
										for t, num_inference_steps in zip([1, 1_0, 5_0]      ,						[1_0, 5_0, 5_0_0]			):
													self.check_over_forward(time_step=a_      ,						num_inference_steps=a_			)


							def    UpperCamelCase     (				self   :						Any			)			->					Optional[int]:
										for t, eta in zip([1, 1_0, 4_9]      ,						[0.0, 0.5, 1.0]			):
													self.check_over_forward(time_step=a_      ,						eta=a_			)


							def    UpperCamelCase     (				self   :						List[str]			)			->					Tuple:
										snake_case:    Dict      							=self.scheduler_classes[0]
										snake_case:    int      							=self.get_scheduler_config()
										snake_case:    Any      							=scheduler_class(**a_			)

										assert torch.sum(torch.abs(scheduler._get_variance(0      ,						0			) - 0.0			)			) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_2_0      ,						4_0_0			) - 0.1_4_7_7_1			)			) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_8_0      ,						9_6_0			) - 0.3_2_4_6_0			)			) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(0      ,						0			) - 0.0			)			) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_8_7      ,						4_8_6			) - 0.0_0_9_7_9			)			) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_9_9      ,						9_9_8			) - 0.0_2			)			) < 1E-5


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Tuple:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    int      							=self.get_scheduler_config()
										snake_case:    Tuple      							=scheduler_class(**a_			)

										snake_case							,  snake_case:    List[str]      							=1_0, 0.0
										scheduler.set_timesteps(a_			)

										snake_case:    Optional[int]      							=self.dummy_model()
										snake_case:    List[Any]      							=self.dummy_sample_deter
										snake_case:    Optional[Any]      							=self.dummy_sample_deter + 0.1
										snake_case:    Union[str, Any]      							=self.dummy_sample_deter - 0.1

										snake_case:    Union[str, Any]      							=samplea.shape[0]
										snake_case:    str      							=torch.stack([samplea, samplea, samplea]      ,						dim=0			)
										snake_case:    Optional[int]      							=torch.arange(a_			)[0:3, None].repeat(1      ,						a_			)

										snake_case:    Dict      							=model(samples.flatten(0      ,						1			)      ,						timesteps.flatten(0      ,						1			)			)
										snake_case:    Dict      							=scheduler.batch_step_no_noise(a_      ,						timesteps.flatten(0      ,						1			)      ,						samples.flatten(0      ,						1			)      ,						a_			)

										snake_case:    str      							=torch.sum(torch.abs(a_			)			)
										snake_case:    List[str]      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 1_1_4_7.7_9_0_4			) < 1E-2
										assert abs(result_mean.item() - 0.4_9_8_2			) < 1E-3


							def    UpperCamelCase     (				self   :						Tuple			)			->					Optional[Any]:
										snake_case:    str      							=self.full_loop()

										snake_case:    List[str]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    List[str]      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 1_7_2.0_0_6_7			) < 1E-2
										assert abs(result_mean.item() - 0.2_2_3_9_6_7			) < 1E-3


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[Any]:
										snake_case:    int      							=self.full_loop(prediction_type='v_prediction'			)

										snake_case:    List[str]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    List[Any]      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 5_2.5_3_0_2			) < 1E-2
										assert abs(result_mean.item() - 0.0_6_8_4			) < 1E-3


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					str:
										# We specify different beta, so that the first alpha is 0.99
										snake_case:    str      							=self.full_loop(set_alpha_to_one=a_      ,						beta_start=0.0_1			)
										snake_case:    Optional[Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Union[str, Any]      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 1_4_9.8_2_9_5			) < 1E-2
										assert abs(result_mean.item() - 0.1_9_5_1			) < 1E-3




							def    UpperCamelCase     (				self   :						Any			)			->					Tuple:
										# We specify different beta, so that the first alpha is 0.99
										snake_case:    Tuple      							=self.full_loop(set_alpha_to_one=a_      ,						beta_start=0.0_1			)
										snake_case:    Union[str, Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    List[str]      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 1_4_9.0_7_8_4			) < 1E-2
										assert abs(result_mean.item() - 0.1_9_4_1			) < 1E-3









'''simple docstring'''

import qiskit







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					qiskit.result.counts.Counts:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=qiskit.Aer.get_backend('aer_simulator'					)

			# Create a Quantum Circuit acting on the q register
			snake_case:    Dict      							=qiskit.QuantumCircuit(__UpperCAmelCase				,							__UpperCAmelCase					)

			# Map the quantum measurement to the classical bits
			circuit.measure([0]				,							[0]					)

			# Execute the circuit on the simulator
			snake_case:    Dict      							=qiskit.execute(__UpperCAmelCase				,							__UpperCAmelCase				,							shots=10_00					)

			# Return the histogram data of the results of the experiment.
			return job.result().get_counts(__UpperCAmelCase					)


if __name__ == "__main__":
	print(F"""Total count for various states are: {single_qubit_measure(1, 1)}""")




