


import importlib.metadata
from typing import Union

from packaging.version import Version, parse

from .constants import STR_OPERATION_TO_FUNC


lowercase_	   =     parse(importlib.metadata.version('torch'))
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 if operation not in STR_OPERATION_TO_FUNC.keys():
  raise ValueError(f'`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys()						)}, received {operation}'						)
 __lowerCamelCase		:							str		 =						STR_OPERATION_TO_FUNC[operation]
 if isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							Optional[Any]		 =						parse(importlib.metadata.version(SCREAMING_SNAKE_CASE__						)						)
 return operation(SCREAMING_SNAKE_CASE__      ,	parse(SCREAMING_SNAKE_CASE__						)						)



def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 return compare_versions(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)





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


lowercase_	   =     {
    '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__":
       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_	   =     1_0_0_0
       lowercase_	   =     [obs.copy()]
       try:
              for t in tqdm.tqdm(range(T)):
                     # call the policy
                     lowercase_	   =     pipeline(obs, planning_horizon=3_2)

                     # 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}""")

