





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						=   1_000
			_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}""")










import string
from math import logaa





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   int:







	'''simple docstring'''







	lowerCamelCase__              =	document.translate(
	    str.maketrans('''''' ,'''''' ,string.punctuation							)							).replace('''\n''' ,''''''							)
	lowerCamelCase__              =	document_without_punctuation.split(''' '''							)  # word tokenization
	return len([word for word in tokenize_document if word.lower() == term.lower()]							)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   tuple[int, int]:







	'''simple docstring'''







	lowerCamelCase__              =	corpus.lower().translate(
	    str.maketrans('''''' ,'''''' ,string.punctuation							)							)  # strip all punctuation and replace it with ''
	lowerCamelCase__              =	corpus_without_punctuation.split('''\n'''							)
	lowerCamelCase__              =	term.lower()
	return (len([doc for doc in docs if term in doc]							), len(__snake_case							))





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case=False							)       ->   float:







	'''simple docstring'''







	if smoothing:
		if n == 0:
			raise ValueError('''log10(0) is undefined.'''							)
		return round(1 + logaa(n / (1 + df)							) ,3							)

	if df == 0:
		raise ZeroDivisionError('''df must be > 0'''							)
	elif n == 0:
		raise ValueError('''log10(0) is undefined.'''							)
	return round(logaa(n / df							) ,3							)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   float:







	'''simple docstring'''







	return round(tf * idf ,3							)



