

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


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

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

       env.seed(0)
       snake_case_			  = env.reset()
       snake_case_			  = 0
       snake_case_			  = 0
       snake_case_			  = 1_000
       snake_case_			  = [obs.copy()]
       try:
              for t in tqdm.tqdm(range(T)):
                     # call the policy
                     snake_case_			  = pipeline(obs, planning_horizon=32)

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

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




def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     lowercase__    :   str      =       set()

     # Replace all the whitespace in our sentence
     lowercase__    :   Tuple      =       input_str.replace(' '	,     ''			)
     for alpha in input_str:
          if "a" <= alpha.lower() <= "z":
               frequency.add(alpha.lower()			)
     return len(SCREAMING_SNAKE_CASE_			) == 26

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     lowercase__    :   Dict      =       [False] * 26
     for char in input_str:
          if char.islower():
               lowercase__    :   List[Any]      =       True
          elif char.isupper():
               lowercase__    :   Optional[Any]      =       True
     return all(SCREAMING_SNAKE_CASE_			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     return len({char for char in input_str.lower() if char.isalpha()}			) == 26

def 	snake_case__			(    ):



     '''simple docstring'''





     from timeit import timeit

     lowercase__    :   Union[str, Any]      =       'from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
     print(timeit('is_pangram()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     print(timeit('is_pangram_faster()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     print(timeit('is_pangram_fastest()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
     # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()

