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


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

     lowerCamelCase__     :		List[Any]        = ValueGuidedRLPipeline.from_pretrained(
         """bglick13/hopper-medium-v2-value-function-hor32""",
         env=env,
     )

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

               # execute action in environment
               lowerCamelCase__   ,    lowerCamelCase__   ,    lowerCamelCase__   ,    lowerCamelCase__     :		List[str]        = env.step(denorm_actions)
               lowerCamelCase__     :		Union[str, 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())

               lowerCamelCase__     :		Tuple        = next_observation
     except KeyboardInterrupt:
          pass

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









import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowercase_     )
class 					lowercase							(			lowercase_     ):
				# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
				__SCREAMING_SNAKE_CASE   :					str			     =     field(default='''text-classification'''      ,    metadata={'''include_in_asdict_even_if_is_default''': True}     )
				__SCREAMING_SNAKE_CASE   :					ClassVar[Features]			     =     Features({'''text''': Value('''string'''     )}     )
				__SCREAMING_SNAKE_CASE   :					ClassVar[Features]			     =     Features({'''labels''': ClassLabel}     )
				__SCREAMING_SNAKE_CASE   :					str			     =     "text"
				__SCREAMING_SNAKE_CASE   :					str			     =     "labels"




				def        a				(   self ,  snake_case			):
											if self.label_column not in features:
																		raise ValueError(F'''Column {self.label_column} is not present in features.'''			)
											if not isinstance(features[self.label_column] ,  snake_case			):
																		raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''			)
											snake_case_     							=       copy.deepcopy(self			)
											snake_case_     							=       self.label_schema.copy()
											snake_case_     							=       features[self.label_column]
											snake_case_     							=       label_schema
											return task_template




				@property
				def        a				(   self			):
											return {
											    self.text_column: "text",
											    self.label_column: "labels",
											}




