






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


SCREAMING_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__":
     SCREAMING_SNAKE_CASE__     =       'hopper-medium-v2'
     SCREAMING_SNAKE_CASE__     =       gym.make(env_name)

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

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

               # execute action in environment
               SCREAMING_SNAKE_CASE__   ,				SCREAMING_SNAKE_CASE__   ,				SCREAMING_SNAKE_CASE__   ,				SCREAMING_SNAKE_CASE__     =       env.step(denorm_actions)
               SCREAMING_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())

               SCREAMING_SNAKE_CASE__     =       next_observation
     except KeyboardInterrupt:
          pass

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








'''simple docstring'''

import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE__     =       'scheduler_config.json'






class     a_						(      lowerCamelCase    ):
  lowercase							=				1
  lowercase							=				2
  lowercase							=				3
  lowercase							=				4
  lowercase							=				5
  lowercase							=				6
  lowercase							=				7
  lowercase							=				8
  lowercase							=				9
  lowercase							=				10
  lowercase							=				11
  lowercase							=				12
  lowercase							=				13
  lowercase							=				14






@dataclass
class     a_						(      lowerCamelCase    ):
  lowercase							=				42






class     a_						:
  lowercase							=				SCHEDULER_CONFIG_NAME
  lowercase							=				[]
  lowercase							=				True

  @classmethod
  def   A__						(				cls      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE=False      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase      = cls.load_config(
            pretrained_model_name_or_path=_SCREAMING_SNAKE_CASE      ,		subfolder=_SCREAMING_SNAKE_CASE      ,		return_unused_kwargs=_SCREAMING_SNAKE_CASE      ,		return_commit_hash=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)
        return cls.from_config(_SCREAMING_SNAKE_CASE      ,		return_unused_kwargs=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = False      ,		**_SCREAMING_SNAKE_CASE				)  ->				Dict:



        """simple docstring"""






        self.save_config(save_directory=_SCREAMING_SNAKE_CASE      ,		push_to_hub=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        return self._get_compatibles()


  @classmethod
  def   A__						(				cls				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = list(set([cls.__name__] + cls._compatibles				)				)
        UpperCamelCase      = importlib.import_module(__name__.split("""."""				)[0]				)
        UpperCamelCase      = [
            getattr(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				) for c in compatible_classes_str if hasattr(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        ]
        return compatible_classes
