



"""simple docstring"""





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


lowercase  							=					{
    '''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  							=					'''hopper-medium-v2'''
						lowercase  							=					gym.make(env_name)

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

						env.seed(0)
						lowercase  							=					env.reset()
						lowercase  							=					0
						lowercase  							=					0
						lowercase  							=					10_00
						lowercase  							=					[obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		lowercase  							=					pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		lowercase							,       lowercase							,       lowercase							,       lowercase  							=					env.step(denorm_actions)
																		lowercase  							=					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  							=					next_observation
						except KeyboardInterrupt:
												pass

						print(F'''Total reward: {total_reward}''')







"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


lowercase  							=					logging.get_logger(__name__)




class 			lowercase__			(      A    ):
							'''simple docstring'''







							def __init__(  self	,       *snake_case	,       **snake_case						)			->							None:
									warnings.warn(
									    'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use YolosImageProcessor instead.'	,       snake_case	,       )
									super().__init__(*snake_case	,       **snake_case						)


