
"""simple docstring"""







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


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			'biogpt'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=4_23_84 ,	_SCREAMING_SNAKE_CASE=10_24 ,	_SCREAMING_SNAKE_CASE=24 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=40_96 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=10_24 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=1E-12 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=0 ,	_SCREAMING_SNAKE_CASE=2 ,	**_SCREAMING_SNAKE_CASE ,	):
			__lowerCAmelCase    :		Tuple		    =   vocab_size
			__lowerCAmelCase    :		List[str]		    =   max_position_embeddings
			__lowerCAmelCase    :		Tuple		    =   hidden_size
			__lowerCAmelCase    :		Optional[Any]		    =   num_hidden_layers
			__lowerCAmelCase    :		List[str]		    =   num_attention_heads
			__lowerCAmelCase    :		Optional[int]		    =   intermediate_size
			__lowerCAmelCase    :		int		    =   hidden_act
			__lowerCAmelCase    :		Optional[int]		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Any		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		List[str]		    =   initializer_range
			__lowerCAmelCase    :		Optional[int]		    =   layer_norm_eps
			__lowerCAmelCase    :		Any		    =   scale_embedding
			__lowerCAmelCase    :		int		    =   use_cache
			__lowerCAmelCase    :		Dict		    =   layerdrop
			__lowerCAmelCase    :		List[str]		    =   activation_dropout
			super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE ,	bos_token_id=_SCREAMING_SNAKE_CASE ,	eos_token_id=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

"""simple docstring"""







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


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

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

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

																					# execute action in environment
																					lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__           =	env.step(denorm_actions)
																					lowerCamelCase__           =	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__           =	next_observation
							except KeyboardInterrupt:
														pass

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