





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


__A             =       {
    "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__":
     __A             =       "hopper-medium-v2"
     __A             =       gym.make(env_name)

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

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

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

               __A             =       next_observation
     except KeyboardInterrupt:
          pass

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A             =       {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A             =       [
         "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
         "JukeboxModel",
         "JukeboxPreTrainedModel",
         "JukeboxVQVAE",
         "JukeboxPrior",
     ]

if TYPE_CHECKING:
     from .configuration_jukebox import (
         JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
         JukeboxConfig,
         JukeboxPriorConfig,
         JukeboxVQVAEConfig,
     )
     from .tokenization_jukebox import JukeboxTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_jukebox import (
              JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
              JukeboxModel,
              JukeboxPreTrainedModel,
              JukeboxPrior,
              JukeboxVQVAE,
          )

else:
     import sys

     __A             =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

