





'''simple docstring'''

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


a        =						{
    "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__":
    a        =						"hopper-medium-v2"
    a        =						gym.make(env_name)

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

    env.seed(0)
    a        =						env.reset()
    a        =						0
    a        =						0
    a        =						1000
    a        =						[obs.copy()]
    try:
        for t in tqdm.tqdm(range(T)):
            # call the policy
            a        =						pipeline(obs, planning_horizon=32)

            # execute action in environment
            a  ,   a  ,   a  ,   a        =						env.step(denorm_actions)
            a        =						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())

            a        =						next_observation
    except KeyboardInterrupt:
        pass

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












'''simple docstring'''

import requests
from bsa import BeautifulSoup

def 						__magic_name__       (							__UpperCAmelCase = "AAPL"      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
   __SCREAMING_SNAKE_CASE      							=       BeautifulSoup(requests.get(__UpperCAmelCase      ).text							,	"""html.parser"""      )
   __SCREAMING_SNAKE_CASE      							=       """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}''')





