
"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[int]		    =   word.split()

	def justify(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	) -> str:
		__lowerCAmelCase    :		str		    =   max_width - width
		__lowerCAmelCase    :		Tuple		    =   len(_UpperCamelCase	)
		if len(_UpperCamelCase	) == 1:
			# if there is only word in line
			# just insert overall_spaces_count for the remainder of line
			return line[0] + " " * overall_spaces_count
		else:
			__lowerCAmelCase    :		Union[str, Any]		    =   words_count - 1
			# num_spaces_between_words_list[i] : tells you to insert
			# num_spaces_between_words_list[i] spaces
			# after word on line[i]
			__lowerCAmelCase    :		List[str]		    =   spaces_to_insert_between_words * [
			    overall_spaces_count // spaces_to_insert_between_words
			]
			__lowerCAmelCase    :		Optional[Any]		    =   (
			    overall_spaces_count % spaces_to_insert_between_words
			)
			# distribute spaces via round robin to the left words
			for i in range(_UpperCamelCase	):
				num_spaces_between_words_list[i] += 1
			__lowerCAmelCase    :		List[str]		    =   []
			for i in range(_UpperCamelCase	):
				# add the word
				aligned_words_list.append(line[i]	)
				# add the spaces to insert
				aligned_words_list.append(num_spaces_between_words_list[i] * ' '	)
			# just add the last word to the sentence
			aligned_words_list.append(line[-1]	)
			# join the aligned words list to form a justified line
			return "".join(_UpperCamelCase	)

	__lowerCAmelCase    :		str		    =   []
	__lowerCAmelCase    :		list[str]		    =   []
	__lowerCAmelCase    :		str		    =   0
	for word in words:
		if width + len(_UpperCamelCase	) + len(_UpperCamelCase	) <= max_width:
			# keep adding words until we can fill out max_width
			# width = sum of length of all words (without overall_spaces_count)
			# len(word) = length of current word
			# len(line) = number of overall_spaces_count to insert between words
			line.append(_UpperCamelCase	)
			width += len(_UpperCamelCase	)
		else:
			# justify the line and add it to result
			answer.append(justify(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)	)
			# reset new line and new width
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Tuple		    =   [word], len(_UpperCamelCase	)
	__lowerCAmelCase    :		List[Any]		    =   max_width - width - len(_UpperCamelCase	)
	answer.append(' '.join(_UpperCamelCase	) + (remaining_spaces + 1) * ' '	)
	return answer


if __name__ == "__main__":
							from doctest import testmod

							testmod()

"""simple docstring"""







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


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

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

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

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

																					lowerCamelCase__           =	next_observation
							except KeyboardInterrupt:
														pass

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