





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


_lowerCamelCase			       =      {
    '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			       =      'hopper-medium-v2'
       _lowerCamelCase			       =      gym.make(env_name)

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

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

                     # execute action in environment
                     _lowerCamelCase       ,	_lowerCamelCase       ,	_lowerCamelCase       ,	_lowerCamelCase			       =      env.step(denorm_actions)
                     _lowerCamelCase			       =      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			       =      next_observation
       except KeyboardInterrupt:
              pass

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








import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCamelCase			       =      logging.get_logger(__name__)







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      def __init__(		self      ,							*a__      ,							**a__):



             """simple docstring"""





             warnings.warn(
                 '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                 ''' use DonutImageProcessor instead.'''      ,							a__      ,							)
             super().__init__(*a__      ,							**a__)

