
"""simple docstring"""




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


_UpperCAmelCase :			List[Any]              =	logging.get_logger(__name__)

_UpperCAmelCase :			Optional[Any]              =	{
    "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}


class     lowercase (		_SCREAMING_SNAKE_CASE       ):
		__lowercase	:	int     					=    "gpt_neox_japanese"



		def __init__(			self    ,				A_=32_000    ,				A_=2_560    ,				A_=32    ,				A_=32    ,				A_=4    ,				A_="gelu"    ,				A_=1.00    ,				A_=10_000    ,				A_=2_048    ,				A_=0.02    ,				A_=1e-5    ,				A_=True    ,				A_=31_996    ,				A_=31_999    ,				A_=0.1    ,				A_=0.0    ,				**A_    ,				)  -> int:
				"""simple docstring"""
				super().__init__(bos_token_id=A_    ,				eos_token_id=A_    ,				**A_	)
				UpperCamelCase          =					vocab_size
				UpperCamelCase          =					max_position_embeddings
				UpperCamelCase          =					hidden_size
				UpperCamelCase          =					num_hidden_layers
				UpperCamelCase          =					num_attention_heads
				UpperCamelCase          =					intermediate_multiple_size
				UpperCamelCase          =					hidden_act
				UpperCamelCase          =					rotary_pct
				UpperCamelCase          =					rotary_emb_base
				UpperCamelCase          =					initializer_range
				UpperCamelCase          =					layer_norm_eps
				UpperCamelCase          =					use_cache
				UpperCamelCase          =					attention_dropout
				UpperCamelCase          =					hidden_dropout


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


_UpperCAmelCase :			Optional[Any]              =	{
    "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 :			int              =	"hopper-medium-v2"
					_UpperCAmelCase :			Tuple              =	gym.make(env_name)

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

					env.seed(0)
					_UpperCAmelCase :			Optional[Any]              =	env.reset()
					_UpperCAmelCase :			Union[str, Any]              =	0
					_UpperCAmelCase :			Union[str, Any]              =	0
					_UpperCAmelCase :			Dict              =	1_000
					_UpperCAmelCase :			Tuple              =	[obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															_UpperCAmelCase :			int              =	pipeline(obs, planning_horizon=32)

															# execute action in environment
															_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase :			List[Any]              =	env.step(denorm_actions)
															_UpperCAmelCase :			int              =	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 :			Union[str, Any]              =	next_observation
					except KeyboardInterrupt:
										pass

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