



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


__snake_case						: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__":
 __snake_case						:Union[str, Any]      		=     '''hopper-medium-v2'''
 __snake_case						:Optional[int]      		=     gym.make(env_name)

 __snake_case						:Optional[Any]      		=     ValueGuidedRLPipeline.from_pretrained(
     '''bglick13/hopper-medium-v2-value-function-hor32''',
     env=env,
 )

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

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

   __snake_case						:Tuple      		=     next_observation
 except KeyboardInterrupt:
  pass

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










'''simple docstring'''




from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCamelCase__       :	Dict    = [
    '''python''',
    '''tqdm''',
    '''regex''',
    '''requests''',
    '''packaging''',
    '''filelock''',
    '''numpy''',
    '''tokenizers''',
    '''huggingface-hub''',
    '''safetensors''',
    '''accelerate''',
    '''pyyaml''',
]

for pkg in pkgs_to_check_at_runtime:
						if pkg in deps:
												if pkg == "tokenizers":
																		# must be loaded here, or else tqdm check may fail
																		from .utils import is_tokenizers_available

																		if not is_tokenizers_available():
																								continue  # not required, check version only if installed
												elif pkg == "accelerate":
																		# must be loaded here, or else tqdm check may fail
																		from .utils import is_accelerate_available

																		# Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
																		# Transformers with PyTorch
																		if not is_accelerate_available():
																								continue  # not required, check version only if installed

												require_version_core(deps[pkg])
						else:
												raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[Any]       ,      _lowerCamelCase:   Optional[int]=None					):
		require_version(deps[pkg]       ,      _lowerCamelCase					)