



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


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

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

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

               # execute action in environment
               lowerCAmelCase:      Optional[int]		 =		env.step(denorm_actions)
               lowerCAmelCase:      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())

               lowerCAmelCase:      Tuple		 =		next_observation
     except KeyboardInterrupt:
          pass

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




'''simple docstring'''






class 							a__:







    def __init__(   self				:						Dict		,    __snake_case				:						Optional[int]		,    __snake_case				:						Any		,    __snake_case				:						Tuple			):
      a		:      List[str]       =  name
      a		:      Dict       =  value
      a		:      List[str]       =  weight







    def __repr__(   self				:						int			):
      return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""







    def     lowercase_ (   self				:						Optional[int]			):
      return self.value







    def     lowercase_ (   self				:						List[str]			):
      return self.name







    def     lowercase_ (   self				:						int			):
      return self.weight







    def     lowercase_ (   self				:						List[str]			):
      return self.value / self.weight
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[int]       =  []
  for i in range(len(_A       )       ):
    menu.append(Things(name[i]				,    value[i]				,    weight[i]       )       )
  return menu
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[Any]       =  sorted(_A				,    key=_A				,    reverse=_A       )
  a		:      Optional[int]       =  []
  a     ,			a		:      str       =  0.0, 0.0
  for i in range(len(_A       )       ):
    if (total_cost + items_copy[i].get_weight()) <= max_cost:
      result.append(items_copy[i]       )
      total_cost += items_copy[i].get_weight()
      total_value += items_copy[i].get_value()
  return (result, total_value)
def    lowerCamelCase__ (		):
  pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()