
'''simple docstring'''


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


__lowercase							:   List[Any]					=     {
    '''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__":
					__lowercase							:   str					=     '''hopper-medium-v2'''
					__lowercase							:   Optional[int]					=     gym.make(env_name)

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

					env.seed(0)
					__lowercase							:   Optional[int]					=     env.reset()
					__lowercase							:   Any					=     0
					__lowercase							:   str					=     0
					__lowercase							:   List[str]					=     1000
					__lowercase							:   List[str]					=     [obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															__lowercase							:   Union[str, Any]					=     pipeline(obs, planning_horizon=32)

															# execute action in environment
															__lowercase							:   Tuple					=     env.step(denorm_actions)
															__lowercase							:   Any					=     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())

															__lowercase							:   int					=     next_observation
					except KeyboardInterrupt:
										pass

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




'''simple docstring'''


from collections import namedtuple

__lowercase							:   List[Any]					=     namedtuple('''from_to''', '''from_ to''')

__lowercase							:   Optional[Any]					=     {
    '''cubicmeter''': from_to(1, 1),
    '''litre''': from_to(0.0_01, 1000),
    '''kilolitre''': from_to(1, 1),
    '''gallon''': from_to(0.0_04_54, 2_64.1_72),
    '''cubicyard''': from_to(0.7_64_55, 1.3_07_95),
    '''cubicfoot''': from_to(0.0_28, 35.31_47),
    '''cup''': from_to(0.0_00_23_65_88, 42_26.75),
}




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							float:
					'''simple docstring'''

					if from_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					if to_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
					import doctest

					doctest.testmod()


