'''simple docstring'''


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


snake_case__					:      Dict				      =  {
    '''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__":
 snake_case__					:      List[Any]				      =  '''hopper-medium-v2'''
 snake_case__					:      str				      =  gym.make(env_name)

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

 env.seed(0)
 snake_case__					:      Union[str, Any]				      =  env.reset()
 snake_case__					:      Tuple				      =  0
 snake_case__					:      Optional[int]				      =  0
 snake_case__					:      Dict				      =  1000
 snake_case__					:      int				      =  [obs.copy()]
 try:
  for t in tqdm.tqdm(range(T)):
   # call the policy
   snake_case__					:      Tuple				      =  pipeline(obs, planning_horizon=32)

   # execute action in environment
   snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__					:      str				      =  env.step(denorm_actions)
   snake_case__					:      Any				      =  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())

   snake_case__					:      int				      =  next_observation
 except KeyboardInterrupt:
  pass

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







'''simple docstring'''


snake_case__					:      str				      =  '''Tobias Carryer'''

from time import time






class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_=int(time()						)						):  # noqa: B008




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				multiplier
       UpperCAmelCase_							:       Dict       =				increment
       UpperCAmelCase_							:       Tuple       =				modulo
       UpperCAmelCase_							:       Dict       =				seed






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				(self.multiplier * self.seed + self.increment) % self.modulo
       return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 snake_case__					:      Any				      =  LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
 while True:
  print(lcg.next_number())






