



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


UpperCAmelCase_		 =					{
    """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_		 =					"""hopper-medium-v2"""
   UpperCAmelCase_		 =					gym.make(env_name)

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

   env.seed(0)
   UpperCAmelCase_		 =					env.reset()
   UpperCAmelCase_		 =					0
   UpperCAmelCase_		 =					0
   UpperCAmelCase_		 =					1_0_0_0
   UpperCAmelCase_		 =					[obs.copy()]
   try:
      for t in tqdm.tqdm(range(T)):
         # call the policy
         UpperCAmelCase_		 =					pipeline(obs, planning_horizon=3_2)

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

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








import requests
from bsa import BeautifulSoup

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str = "AAPL"				)  ->     str:
   _A      =       F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
   _A      =       BeautifulSoup(requests.get(_snake_case				).text	,   '''html.parser'''				)
   _A      =       '''My(6px) Pos(r) smartphone_Mt(6px)'''
   return soup.find('''div'''	,   class_=class_				).find('''span'''				).text


if __name__ == "__main__":
   for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
      print(f'Current {symbol:<4} stock price is {stock_price(symbol):>8}')



