


'''simple docstring'''



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


A__						:   List[str]				=    {
    """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__						:   str				=    """hopper-medium-v2"""
      A__						:   str				=    gym.make(env_name)

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

      env.seed(0)
      A__						:   Union[str, Any]				=    env.reset()
      A__						:   int				=    0
      A__						:   Optional[int]				=    0
      A__						:   List[str]				=    1000
      A__						:   Any				=    [obs.copy()]
      try:
            for t in tqdm.tqdm(range(T)):
                  # call the policy
                  A__						:   List[Any]				=    pipeline(obs, planning_horizon=32)

                  # execute action in environment
                  A__		,   A__		,   A__		,   A__						:   Tuple				=    env.step(denorm_actions)
                  A__						:   List[str]				=    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__						:   Any				=    next_observation
      except KeyboardInterrupt:
            pass

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








"""simple docstring"""






from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE							=     {
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MegaForCausalLM',
     'MegaForMaskedLM',
     'MegaForMultipleChoice',
     'MegaForQuestionAnswering',
     'MegaForSequenceClassification',
     'MegaForTokenClassification',
     'MegaModel',
     'MegaPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mega import (
      MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MegaForCausalLM,
      MegaForMaskedLM,
      MegaForMultipleChoice,
      MegaForQuestionAnswering,
      MegaForSequenceClassification,
      MegaForTokenClassification,
      MegaModel,
      MegaPreTrainedModel,
  )

else:
 import sys

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