

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


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

_UpperCAmelCase						:  int									=			{
    """Visual-Attention-Network/van-base""": (
        """https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
    ),
}







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     '''van'''




				def __init__(   self ,  snake_case=224 ,  snake_case=3 ,  snake_case=[7, 3, 3, 3] ,  snake_case=[4, 2, 2, 2] ,  snake_case=[64, 128, 320, 512] ,  snake_case=[3, 3, 12, 3] ,  snake_case=[8, 8, 4, 4] ,  snake_case="gelu" ,  snake_case=0.02 ,  snake_case=1e-6 ,  snake_case=1e-2 ,  snake_case=0.0 ,  snake_case=0.0 ,  **snake_case ,  ):
											super().__init__(**snake_case			)
											snake_case_     							=       image_size
											snake_case_     							=       num_channels
											snake_case_     							=       patch_sizes
											snake_case_     							=       strides
											snake_case_     							=       hidden_sizes
											snake_case_     							=       depths
											snake_case_     							=       mlp_ratios
											snake_case_     							=       hidden_act
											snake_case_     							=       initializer_range
											snake_case_     							=       layer_norm_eps
											snake_case_     							=       layer_scale_init_value
											snake_case_     							=       drop_path_rate
											snake_case_     							=       dropout_rate







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


_UpperCAmelCase						:  List[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						:  List[str]									=			"""hopper-medium-v2"""
					_UpperCAmelCase						:  Any									=			gym.make(env_name)

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

					env.seed(0)
					_UpperCAmelCase						:  Tuple									=			env.reset()
					_UpperCAmelCase						:  str									=			0
					_UpperCAmelCase						:  Union[str, Any]									=			0
					_UpperCAmelCase						:  List[str]									=			1000
					_UpperCAmelCase						:  Optional[int]									=			[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						:  str									=			env.step(denorm_actions)
															_UpperCAmelCase						:  Tuple									=			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						:  Dict									=			next_observation
					except KeyboardInterrupt:
										pass

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




