"""simple docstring"""



import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__lowercase		:		str														=							{
    "n_samples": 64,
    "horizon": 32,
    "num_inference_steps": 20,
    "n_guide_steps": 2,  # can set to 0 for faster sampling, does not use value network
    "scale_grad_by_std": True,
    "scale": 0.1,
    "eta": 0.0,
    "t_grad_cutoff": 2,
    "device": "cpu",
}


if __name__ == "__main__":
       __lowercase		:		Union[str, Any]														=							"hopper-medium-v2"
       __lowercase		:		str														=							gym.make(env_name)

       __lowercase		:		Any														=							ValueGuidedRLPipeline.from_pretrained(
           "bglick13/hopper-medium-v2-value-function-hor32",
           env=env,
       )

       env.seed(0)
       __lowercase		:		List[Any]														=							env.reset()
       __lowercase		:		Any														=							0
       __lowercase		:		Dict														=							0
       __lowercase		:		Optional[int]														=							1000
       __lowercase		:		int														=							[obs.copy()]
       try:
              for t in tqdm.tqdm(range(T)):
                     # call the policy
                     __lowercase		:		Optional[int]														=							pipeline(obs, planning_horizon=32)

                     # execute action in environment
                     __lowercase							,__lowercase							,__lowercase							,__lowercase		:		List[Any]														=							env.step(denorm_actions)
                     __lowercase		:		Optional[Any]														=							env.get_normalized_score(total_reward)

                     # update return
                     total_reward += reward
                     total_score += score
                     print(
                         F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
                         F""" {total_score}"""
                     )

                     # save observations for rendering
                     rollout.append(next_observation.copy())

                     __lowercase		:		Dict														=							next_observation
       except KeyboardInterrupt:
              pass

       print(F"""Total reward: {total_reward}""")
"""simple docstring"""



import qiskit





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							qiskit.Aer.get_backend('''aer_simulator''')

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

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

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

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


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