



'''simple docstring'''



from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 	lowerCamelCase_							(					snake_case_			):







						def __init__(  self   :      Tuple				,    lowerCAmelCase__   :      NestedDataStructureLike[PathLike]				,    lowerCAmelCase__   :      Optional[NamedSplit] = None				,    lowerCAmelCase__   :      Optional[Features] = None				,    lowerCAmelCase__   :      str = None				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      Optional[int] = None				,    **lowerCAmelCase__   :      List[str]				,    ):

													"""simple docstring"""
													super().__init__(
													    lowerCAmelCase__				,    split=lowerCAmelCase__				,    features=lowerCAmelCase__				,    cache_dir=lowerCAmelCase__				,    keep_in_memory=lowerCAmelCase__				,    streaming=lowerCAmelCase__				,    num_proc=lowerCAmelCase__				,    **lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      Tuple     					=			path_or_paths if isinstance(lowerCAmelCase__				,    lowerCAmelCase__				) else {self.split: path_or_paths}
													SCREAMING_SNAKE_CASE					:      Tuple     					=			Text(
													    cache_dir=lowerCAmelCase__				,    data_files=lowerCAmelCase__				,    features=lowerCAmelCase__				,    **lowerCAmelCase__				,    )







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													# Build iterable dataset
													if self.streaming:
																				SCREAMING_SNAKE_CASE					:      Any     					=			self.builder.as_streaming_dataset(split=self.split				)
													# Build regular (map-style) dataset
													else:
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			None
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			None
																				SCREAMING_SNAKE_CASE					:      str     					=			None
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			None

																				self.builder.download_and_prepare(
																				    download_config=lowerCAmelCase__				,    download_mode=lowerCAmelCase__				,    verification_mode=lowerCAmelCase__				,    base_path=lowerCAmelCase__				,    num_proc=self.num_proc				,    )
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			self.builder.as_dataset(
																				    split=self.split				,    verification_mode=lowerCAmelCase__				,    in_memory=self.keep_in_memory				)
													return dataset









'''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}')




