



'''simple docstring'''




import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	int    					=	(CMStochasticIterativeScheduler,)
			_A   :	Tuple    					=	10

			def UpperCamelCase__      (							self  :				List[str]							,  **lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	{
					    """num_train_timesteps""": 2_0_1,
					    """sigma_min""": 0.0_02,
					    """sigma_max""": 80.0,
					}

					config.update(**lowerCAmelCase__		)
					return config

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	1_0

					__SCREAMING_SNAKE_CASE :	int					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	Any					 =	self.scheduler_classes[0](**lowerCAmelCase__		)

					scheduler.set_timesteps(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[str]					 =	scheduler.timesteps[0]
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	scheduler.timesteps[1]

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dummy_sample
					__SCREAMING_SNAKE_CASE :	Tuple					 =	0.1 * sample

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		).prev_sample
					__SCREAMING_SNAKE_CASE :	Tuple					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		).prev_sample

					self.assertEqual(output_a.shape							,  sample.shape		)
					self.assertEqual(output_a.shape							,  output_a.shape		)

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					for clip_denoised in [True, False]:
							self.check_over_configs(clip_denoised=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	Any					 =	scheduler_class(**lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	1
					scheduler.set_timesteps(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	str					 =	scheduler.timesteps

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.manual_seed(0		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(lowerCAmelCase__		):
							# 1. scale model input
							__SCREAMING_SNAKE_CASE :	Dict					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							# 2. predict noise residual
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							# 3. predict previous sample x_t-1
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  generator=lowerCAmelCase__		).prev_sample

							__SCREAMING_SNAKE_CASE :	str					 =	pred_prev_sample

					__SCREAMING_SNAKE_CASE :	Dict					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					assert abs(result_sum.item() - 1_92.76_14		) < 1E-2
					assert abs(result_mean.item() - 0.25_10		) < 1E-3

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	Any					 =	scheduler_class(**lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Any					 =	[1_0_6, 0]
					scheduler.set_timesteps(timesteps=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	scheduler.timesteps

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.manual_seed(0		)

					__SCREAMING_SNAKE_CASE :	str					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	str					 =	self.dummy_sample_deter * scheduler.init_noise_sigma

					for t in timesteps:
							# 1. scale model input
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							# 2. predict noise residual
							__SCREAMING_SNAKE_CASE :	Dict					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							# 3. predict previous sample x_t-1
							__SCREAMING_SNAKE_CASE :	str					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  generator=lowerCAmelCase__		).prev_sample

							__SCREAMING_SNAKE_CASE :	Any					 =	pred_prev_sample

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					assert abs(result_sum.item() - 3_47.63_57		) < 1E-2
					assert abs(result_mean.item() - 0.45_27		) < 1E-3

			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Any					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	scheduler_class(**lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	[3_9, 3_0, 1_2, 1_5, 0]

					with self.assertRaises(lowerCAmelCase__							,  msg="""`timesteps` must be in descending order."""		):
							scheduler.set_timesteps(timesteps=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	str					 =	scheduler_class(**lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	[3_9, 3_0, 1_2, 1, 0]
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	len(lowerCAmelCase__		)

					with self.assertRaises(lowerCAmelCase__							,  msg="""Can only pass one of `num_inference_steps` or `timesteps`."""		):
							scheduler.set_timesteps(num_inference_steps=lowerCAmelCase__							,  timesteps=lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	scheduler_class(**lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[str]					 =	[scheduler.config.num_train_timesteps]

					with self.assertRaises(
					    lowerCAmelCase__							,  msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""							,  ):
							scheduler.set_timesteps(timesteps=lowerCAmelCase__		)




'''simple docstring'''




import qiskit


def        lowerCAmelCase_     (			_lowerCamelCase:     int = 2      ):
		__SCREAMING_SNAKE_CASE :	Dict					 =	qubits

		# Using Aer's simulator
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	qiskit.Aer.get_backend("""aer_simulator"""      )

		# Creating a Quantum Circuit acting on the q register
		__SCREAMING_SNAKE_CASE :	str					 =	qiskit.QuantumCircuit(_lowerCamelCase ,   _lowerCamelCase      )

		# Adding a H gate on qubit 0 (now q0 in superposition)
		circuit.h(0      )

		for i in range(1 ,   _lowerCamelCase      ):
				# Adding CX (CNOT) gate
				circuit.cx(i - 1 ,   _lowerCamelCase      )

		# Mapping the quantum measurement to the classical bits
		circuit.measure(list(range(_lowerCamelCase      )      ) ,   list(range(_lowerCamelCase      )      )      )

		# Now measuring any one qubit would affect other qubits to collapse
		# their super position and have same state as the measured one.

		# Executing the circuit on the simulator
		__SCREAMING_SNAKE_CASE :	str					 =	qiskit.execute(_lowerCamelCase ,   _lowerCamelCase ,   shots=10_00      )

		return job.result().get_counts(_lowerCamelCase      )


if __name__ == "__main__":
						print(f"Total count for various states are: {quantum_entanglement(3)}")