





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


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

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

					env.seed(0)
					UpperCamelCase__											= env.reset()
					UpperCamelCase__											= 0
					UpperCamelCase__											= 0
					UpperCamelCase__											= 1_000
					UpperCamelCase__											= [obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															UpperCamelCase__											= pipeline(obs, planning_horizon=32)

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

															UpperCamelCase__											= next_observation
					except KeyboardInterrupt:
										pass

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











from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__											= logging.get_logger(__name__)

UpperCamelCase__											= {
    "studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
    "studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json",
}

class        __SCREAMING_SNAKE_CASE					(     _a       ):
							snake_case     :      Optional[int]						     =       """luke"""






							def __init__(						self  , __lowerCAmelCase=50267  , __lowerCAmelCase=500000  , __lowerCAmelCase=768  , __lowerCAmelCase=256  , __lowerCAmelCase=12  , __lowerCAmelCase=12  , __lowerCAmelCase=3072  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=512  , __lowerCAmelCase=2  , __lowerCAmelCase=0.02  , __lowerCAmelCase=1E-12  , __lowerCAmelCase=True  , __lowerCAmelCase=None  , __lowerCAmelCase=1  , __lowerCAmelCase=0  , __lowerCAmelCase=2  , **__lowerCAmelCase  , ):
														super().__init__(pad_token_id=__lowerCAmelCase  , bos_token_id=__lowerCAmelCase  , eos_token_id=__lowerCAmelCase  , **__lowerCAmelCase  )

														UpperCamelCase__				     =			vocab_size
														UpperCamelCase__				     =			entity_vocab_size
														UpperCamelCase__				     =			hidden_size
														UpperCamelCase__				     =			entity_emb_size
														UpperCamelCase__				     =			num_hidden_layers
														UpperCamelCase__				     =			num_attention_heads
														UpperCamelCase__				     =			hidden_act
														UpperCamelCase__				     =			intermediate_size
														UpperCamelCase__				     =			hidden_dropout_prob
														UpperCamelCase__				     =			attention_probs_dropout_prob
														UpperCamelCase__				     =			max_position_embeddings
														UpperCamelCase__				     =			type_vocab_size
														UpperCamelCase__				     =			initializer_range
														UpperCamelCase__				     =			layer_norm_eps
														UpperCamelCase__				     =			use_entity_aware_attention
														UpperCamelCase__				     =			classifier_dropout




