

'''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__      		=			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}')




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase__      		=			{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__      		=			[
     '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ViTMAEForPreTraining''',
     '''ViTMAELayer''',
     '''ViTMAEModel''',
     '''ViTMAEPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__      		=			[
     '''TFViTMAEForPreTraining''',
     '''TFViTMAEModel''',
     '''TFViTMAEPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_mae import (
      VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTMAEForPreTraining,
      ViTMAELayer,
      ViTMAEModel,
      ViTMAEPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
 import sys

 lowerCAmelCase__      		=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

