


"""simple docstring"""
import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    {
    '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__":
				__SCREAMING_SNAKE_CASE							:    Any		=    'hopper-medium-v2'
				__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    gym.make(env_name)

				__SCREAMING_SNAKE_CASE							:    int		=    ValueGuidedRLPipeline.from_pretrained(
				    'bglick13/hopper-medium-v2-value-function-hor32',
				    env=env,
				)

				env.seed(0)
				__SCREAMING_SNAKE_CASE							:    Tuple		=    env.reset()
				__SCREAMING_SNAKE_CASE							:    int		=    0
				__SCREAMING_SNAKE_CASE							:    List[Any]		=    0
				__SCREAMING_SNAKE_CASE							:    Optional[int]		=    1_000
				__SCREAMING_SNAKE_CASE							:    Optional[int]		=    [obs.copy()]
				try:
								for t in tqdm.tqdm(range(T)):
												# call the policy
												__SCREAMING_SNAKE_CASE							:    Any		=    pipeline(obs, planning_horizon=32)

												# execute action in environment
												__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    env.step(denorm_actions)
												__SCREAMING_SNAKE_CASE							:    str		=    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())

												__SCREAMING_SNAKE_CASE							:    Optional[int]		=    next_observation
				except KeyboardInterrupt:
								pass

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







"""simple docstring"""
from __future__ import annotations







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				list[int]:
							snake_case_           =		0
							snake_case_           =		len(_SCREAMING_SNAKE_CASE				) - 1

							while i < j:
														if nums[i] + nums[j] == target:
																					return [i, j]
														elif nums[i] + nums[j] < target:
																					snake_case_           =		i + 1
														else:
																					snake_case_           =		j - 1

							return []


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(f"""{two_pointer([2, 7, 11, 15], 9) = }""")



