


import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def     UpperCAmelCase    (					_lowercase   :      float = np.pi / 2 ,      _lowercase   :      float = np.pi / 2 ,      _lowercase   :      float = np.pi / 2  )	->   int:


	"""simple docstring"""




	lowerCAmelCase_			       = QuantumRegister(3 ,      '''qr'''  )  # Define the number of quantum bits
	lowerCAmelCase_			       = ClassicalRegister(1 ,      '''cr'''  )  # Define the number of classical bits

	lowerCAmelCase_			       = QuantumCircuit(UpperCamelCase__ ,      UpperCamelCase__  )  # Define the quantum circuit.

	# Build the circuit
	quantum_circuit.u(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      0  )  # Quantum State to teleport
	quantum_circuit.h(1  )  # add hadamard gate
	quantum_circuit.cx(
	    1 ,      2  )  # add control gate with qubit 1 as control and 2 as target.
	quantum_circuit.cx(0 ,      1  )
	quantum_circuit.h(0  )
	quantum_circuit.cz(0 ,      2  )  # add control z gate.
	quantum_circuit.cx(1 ,      2  )

	quantum_circuit.measure([2] ,      [0]  )  # measure the qubit.

	# Simulate the circuit using qasm simulator
	lowerCAmelCase_			       = Aer.get_backend('''aer_simulator'''  )
	lowerCAmelCase_			       = execute(UpperCamelCase__ ,      UpperCamelCase__ ,      shots=1_0_0_0  )

	return job.result().get_counts(UpperCamelCase__  )


if __name__ == "__main__":
		print(
		    'Total count for teleported state is: '
		    f"""{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}"""
		)



"""simple docstring"""



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class       __a	(   __snake_case       ):
				lowerCamelCase    :					Union[str, Any] 					=(LMSDiscreteScheduler,)
				lowerCamelCase    :					int 					=10







				def     lowerCamelCase_    (   self			,  **UpperCAmelCase      ):

					'''simple docstring'''






					lowerCAmelCase_			       = {
					    '''num_train_timesteps''': 1100,
					    '''beta_start''': 0.0_0_0_1,
					    '''beta_end''': 0.0_2,
					    '''beta_schedule''': '''linear''',
					}

					config.update(**UpperCAmelCase      )
					return config







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for timesteps in [10, 50, 100, 1000]:
						self.check_over_configs(num_train_timesteps=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					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=UpperCAmelCase			,  beta_end=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






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







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






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







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for t in [0, 500, 800]:
						self.check_over_forward(time_step=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 1_0_0_6.3_8_8      ) < 1E-2
					assert abs(result_mean.item() - 1.3_1      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config(prediction_type='''v_prediction'''      )
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 0.0_0_1_7      ) < 1E-2
					assert abs(result_mean.item() - 2.2_6_7_6E-0_6      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps			,  device=UpperCAmelCase      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					lowerCAmelCase_			       = sample.to(UpperCAmelCase      )

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 1_0_0_6.3_8_8      ) < 1E-2
					assert abs(result_mean.item() - 1.3_1      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase			,  use_karras_sigmas=UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps			,  device=UpperCAmelCase      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter.to(UpperCAmelCase      ) * scheduler.init_noise_sigma
					lowerCAmelCase_			       = sample.to(UpperCAmelCase      )

					for t in scheduler.timesteps:
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 3_8_1_2.9_9_2_7      ) < 2E-2
					assert abs(result_mean.item() - 4.9_6_4_8      ) < 1E-3