






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


lowerCamelCase	:							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__":
 lowerCamelCase	:							List[str]				='''hopper-medium-v2'''
 lowerCamelCase	:							int				=gym.make(env_name)

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

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

   # execute action in environment
   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase	:							List[Any]				=env.step(denorm_actions)
   lowerCamelCase	:							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())

   lowerCamelCase	:							Optional[int]				=next_observation
 except KeyboardInterrupt:
  pass

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


lowerCamelCase	:							int				={
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ASTForAudioClassification''',
     '''ASTModel''',
     '''ASTPreTrainedModel''',
 ]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Optional[int]				=['''ASTFeatureExtractor''']

if TYPE_CHECKING:
 from .configuration_audio_spectrogram_transformer import (
     AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ASTConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_audio_spectrogram_transformer import (
      AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      ASTForAudioClassification,
      ASTModel,
      ASTPreTrainedModel,
  )

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
 import sys

 lowerCamelCase	:							Optional[int]				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)