



'''simple docstring'''



import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


lowerCAmelCase_     :       str								=						re.compile(R'^(?P<major>\d+)' R'\.(?P<minor>\d+)' R'\.(?P<patch>\d+)$')
@total_ordering
@dataclass
class 	lowerCamelCase_							:
						_lowerCAmelCase     :   str
						_lowerCAmelCase     :   Optional[str]	     =				None
						_lowerCAmelCase     :   Optional[Union[str, int]]	     =				None
						_lowerCAmelCase     :   Optional[Union[str, int]]	     =				None
						_lowerCAmelCase     :   Optional[Union[str, int]]	     =				None







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			_str_to_version_tuple(self.version_str				)







						def __repr__(  self   :      Dict				):

													"""simple docstring"""
													return F"""{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"""







						@property
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													return self.major, self.minor, self.patch







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													if isinstance(lowerCAmelCase__				,    lowerCAmelCase__				):
																				return Version(lowerCAmelCase__				)
													elif isinstance(lowerCAmelCase__				,    lowerCAmelCase__				):
																				return other
													raise TypeError(F"""{other} (type {type(lowerCAmelCase__				)}) cannot be compared to version."""				)







						def __eq__(  self   :      str				,    lowerCAmelCase__   :      Union[str, Any]				):

													"""simple docstring"""
													try:
																				SCREAMING_SNAKE_CASE					:      int     					=			self._validate_operand(lowerCAmelCase__				)
													except (TypeError, ValueError):
																				return False
													else:
																				return self.tuple == other.tuple







						def __lt__(  self   :      Optional[int]				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self._validate_operand(lowerCAmelCase__				)
													return self.tuple < other.tuple







						def __hash__(  self   :      Any				):

													"""simple docstring"""
													return hash(_version_tuple_to_str(self.tuple				)				)







						@classmethod
						def 					__lowercase    (  cls   :      List[str]				,    lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			{f.name for f in dataclasses.fields(cls				)}
													return cls(**{k: v for k, v in dic.items() if k in field_names}				)







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													return self.version_str


def 					UpperCAmelCase   (						A  :				Tuple				):

							'''simple docstring'''





							SCREAMING_SNAKE_CASE					:      List[str]     					=			_VERSION_REG.match(A				)
							if not res:
														raise ValueError(F"""Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits."""				)
							return tuple(int(A				) for v in [res.group('''major'''				), res.group('''minor'''				), res.group('''patch'''				)]				)






def 					UpperCAmelCase   (						A  :				int				):

							'''simple docstring'''





							return ".".join(str(A				) for v in version_tuple				)









'''simple docstring'''



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


lowerCAmelCase_     :       int								=						{
    '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_     :       int								=						'hopper-medium-v2'
							lowerCAmelCase_     :       str								=						gym.make(env_name)

							lowerCAmelCase_     :       Union[str, Any]								=						ValueGuidedRLPipeline.from_pretrained(
							    'bglick13/hopper-medium-v2-value-function-hor32',
							    env=env,
							)

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

																					# execute action in environment
																					lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_     :       Union[str, Any]								=						env.step(denorm_actions)
																					lowerCAmelCase_     :       Optional[int]								=						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_     :       Union[str, Any]								=						next_observation
							except KeyboardInterrupt:
														pass

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




