


"""simple docstring"""







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__      				=   1_000
     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}")










'''simple docstring'''




from PIL import Image



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Image:

							def brightness(_lowercase		) -> float:
														return 1_2_8 + level + (c - 1_2_8)

							if not -255.0 <= level <= 255.0:
														raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""		)
							return img.point(_lowercase		)


if __name__ == "__main__":
							# Load image
							with Image.open("""image_data/lena.jpg""") as img:
														# Change brightness to 100
														a   :			Optional[Any]					  =    change_brightness(img, 1_0_0)
														brigt_img.save("""image_data/lena_brightness.png""", format="""png""")

