






'''simple docstring'''

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


_A				:    str     						={
    "n_samples": 6_4,
    "horizon": 3_2,
    "num_inference_steps": 2_0,
    "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				:    Union[str, Any]     						="hopper-medium-v2"
		_A				:    str     						=gym.make(env_name)

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

		env.seed(0)
		_A				:    List[Any]     						=env.reset()
		_A				:    Any     						=0
		_A				:    Dict     						=0
		_A				:    Optional[int]     						=1_0_0_0
		_A				:    int     						=[obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						_A				:    Optional[int]     						=pipeline(obs, planning_horizon=3_2)

						# execute action in environment
						_A				:    List[Any]     						=env.step(denorm_actions)
						_A				:    Optional[Any]     						=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				:    Dict     						=next_observation
		except KeyboardInterrupt:
				pass

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





'''simple docstring'''

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


_A				:    Dict     						=logging.get_logger(__name__)

_A				:    Dict     						={
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""megatron-bert"""




					def __init__(  self				:	int     ,  UpperCamelCase_				:	int=2_9056     ,  UpperCamelCase_				:	Optional[int]=1024     ,  UpperCamelCase_				:	Optional[Any]=24     ,  UpperCamelCase_				:	List[Any]=16     ,  UpperCamelCase_				:	Optional[int]=4096     ,  UpperCamelCase_				:	Optional[Any]="gelu"     ,  UpperCamelCase_				:	List[Any]=0.1     ,  UpperCamelCase_				:	Union[str, Any]=0.1     ,  UpperCamelCase_				:	int=512     ,  UpperCamelCase_				:	Dict=2     ,  UpperCamelCase_				:	Union[str, Any]=0.02     ,  UpperCamelCase_				:	Any=1E-12     ,  UpperCamelCase_				:	Tuple=0     ,  UpperCamelCase_				:	Optional[int]="absolute"     ,  UpperCamelCase_				:	Optional[Any]=True     ,  **UpperCamelCase_				:	Any     ,  )  ->       List[Any]:



						'''simple docstring'''





						super().__init__(pad_token_id=UpperCamelCase_     ,  **UpperCamelCase_					)

						_lowercase					:       Dict       =		vocab_size
						_lowercase					:       Any       =		hidden_size
						_lowercase					:       Union[str, Any]       =		num_hidden_layers
						_lowercase					:       Dict       =		num_attention_heads
						_lowercase					:       Dict       =		hidden_act
						_lowercase					:       Optional[Any]       =		intermediate_size
						_lowercase					:       Optional[int]       =		hidden_dropout_prob
						_lowercase					:       Optional[Any]       =		attention_probs_dropout_prob
						_lowercase					:       Any       =		max_position_embeddings
						_lowercase					:       str       =		type_vocab_size
						_lowercase					:       Optional[Any]       =		initializer_range
						_lowercase					:       List[str]       =		layer_norm_eps
						_lowercase					:       List[Any]       =		position_embedding_type
						_lowercase					:       Optional[Any]       =		use_cache
