
"""simple docstring"""




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


UpperCAmelCase__      :    List[str]    							= {
    'n_samples': 6_4,
    'horizon': 3_2,
    'num_inference_steps': 2_0,
    '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__      :    Any    							= 'hopper-medium-v2'
					UpperCAmelCase__      :    Union[str, Any]    							= gym.make(env_name)

					UpperCAmelCase__      :    List[str]    							= ValueGuidedRLPipeline.from_pretrained(
					    'bglick13/hopper-medium-v2-value-function-hor32',
					    env=env,
					)

					env.seed(0)
					UpperCAmelCase__      :    str    							= env.reset()
					UpperCAmelCase__      :    List[Any]    							= 0
					UpperCAmelCase__      :    Optional[int]    							= 0
					UpperCAmelCase__      :    Optional[Any]    							= 1_0_0_0
					UpperCAmelCase__      :    Union[str, Any]    							= [obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															UpperCAmelCase__      :    Optional[Any]    							= pipeline(obs, planning_horizon=3_2)

															# execute action in environment
															UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__      :    Any    							= env.step(denorm_actions)
															UpperCAmelCase__      :    Dict    							= 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__      :    List[Any]    							= next_observation
					except KeyboardInterrupt:
										pass

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






"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu



@require_cpu
class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.nn.Linear(10   ,       10    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.optim.SGD(model.parameters()   ,       0.1    )
											SCREAMING_SNAKE_CASE__						:       int           =   Accelerator()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   accelerator.prepare(SCREAMING_SNAKE_CASE__    )
											try:
																		pickle.loads(pickle.dumps(SCREAMING_SNAKE_CASE__    )    )
											except Exception as e:
																		self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
											AcceleratorState._reset_state()




