'''simple docstring'''




from ...configuration_utils import PretrainedConfig

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						"bert-generation"




      def __init__(       self						:				str						,					A						:				str=50358						,					A						:				int=1024						,					A						:				Optional[Any]=24						,					A						:				Optional[int]=16						,					A						:				str=4096						,					A						:				Tuple="gelu"						,					A						:				str=0.1						,					A						:				Dict=0.1						,					A						:				Tuple=512						,					A						:				Tuple=0.02						,					A						:				Optional[int]=1E-12						,					A						:				Union[str, Any]=0						,					A						:				Any=2						,					A						:				Dict=1						,					A						:				Tuple="absolute"						,					A						:				List[Any]=True						,					**A						:				List[Any]						,					):
             super().__init__(pad_token_id=A						,					bos_token_id=A						,					eos_token_id=A						,					**A       )

             _UpperCAmelCase     :  Optional[int]							      =    vocab_size
             _UpperCAmelCase     :  List[str]							      =    hidden_size
             _UpperCAmelCase     :  int							      =    num_hidden_layers
             _UpperCAmelCase     :  List[str]							      =    num_attention_heads
             _UpperCAmelCase     :  Optional[Any]							      =    hidden_act
             _UpperCAmelCase     :  Tuple							      =    intermediate_size
             _UpperCAmelCase     :  Optional[Any]							      =    hidden_dropout_prob
             _UpperCAmelCase     :  Any							      =    attention_probs_dropout_prob
             _UpperCAmelCase     :  List[str]							      =    max_position_embeddings
             _UpperCAmelCase     :  str							      =    initializer_range
             _UpperCAmelCase     :  List[Any]							      =    layer_norm_eps
             _UpperCAmelCase     :  Optional[int]							      =    position_embedding_type
             _UpperCAmelCase     :  int							      =    use_cache




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_       : Optional[int]         =			'hopper-medium-v2'
	A_       : List[Any]         =			gym.make(env_name)

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

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

			# execute action in environment
			A_       ,			A_       ,			A_       ,			A_       : Dict         =			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_       : int         =			next_observation
	except KeyboardInterrupt:
		pass

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



