


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


__UpperCamelCase       :       int						       =				{
    "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__":
       __UpperCamelCase       :       Optional[int]						       =				"hopper-medium-v2"
       __UpperCamelCase       :       List[Any]						       =				gym.make(env_name)

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

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

                     # execute action in environment
                     __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       :       List[str]						       =				env.step(denorm_actions)
                     __UpperCamelCase       :       Tuple						       =				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())

                     __UpperCamelCase       :       Union[str, Any]						       =				next_observation
       except KeyboardInterrupt:
              pass

       print(f"Total reward: {total_reward}")






from math import factorial, radians

def 			_a  (				SCREAMING_SNAKE_CASE	:				float    , SCREAMING_SNAKE_CASE	:				int = 18    , SCREAMING_SNAKE_CASE	:				int = 10     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

 # Converting from degrees to radians
 UpperCamelCase__					:    int          =   radians(SCREAMING_SNAKE_CASE     )

 UpperCamelCase__					:    Tuple          =   angle_in_radians
 UpperCamelCase__					:    str          =   3
 UpperCamelCase__					:    int          =   -1

 for _ in range(SCREAMING_SNAKE_CASE     ):
  result += (b * (angle_in_radians**a)) / factorial(SCREAMING_SNAKE_CASE     )

  UpperCamelCase__					:    List[Any]          =   -b  # One positive term and the next will be negative and so on...
  a += 2  # Increased by 2 for every term.

 return round(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )


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


