





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


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

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

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

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

												snake_case_							=			next_observation
				except KeyboardInterrupt:
								pass

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








from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class        snake_case_		(			_A						,					_A):







		@register_to_config
		def __init__(		self			,     __lowercase = 7_6_8			,     )				->   List[Any]:
				super().__init__()

				lowerCamelCase   : str								=nn.Parameter(torch.zeros(1			,     __lowercase   )   )
				lowerCamelCase   : Union[str, Any]								=nn.Parameter(torch.ones(1			,     __lowercase   )   )







		def 			__lowercase   (		self			,     __lowercase = None			,     __lowercase = None			,     )				->   List[str]:
				lowerCamelCase   : Tuple								=nn.Parameter(self.mean.to(__lowercase   ).to(__lowercase   )   )
				lowerCamelCase   : Dict								=nn.Parameter(self.std.to(__lowercase   ).to(__lowercase   )   )
				return self







		def 			__lowercase   (		self			,     __lowercase   )				->   Optional[Any]:
				lowerCamelCase   : Any								=(embeds - self.mean) * 1.0 / self.std
				return embeds







		def 			__lowercase   (		self			,     __lowercase   )				->   Optional[int]:
				lowerCamelCase   : Any								=(embeds * self.std) + self.mean
				return embeds

