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


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{
    """facebook/timesformer""": """https://huggingface.co/facebook/timesformer/resolve/main/config.json""",
}





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = '''timesformer'''
				def __init__(						self		,     lowercase=2_2_4		,     lowercase=1_6		,     lowercase=3		,     lowercase=8		,     lowercase=7_6_8		,     lowercase=1_2		,     lowercase=1_2		,     lowercase=3_0_7_2		,     lowercase="gelu"		,     lowercase=0.0		,     lowercase=0.0		,     lowercase=0.02		,     lowercase=1E-6		,     lowercase=True		,     lowercase="divided_space_time"		,     lowercase=0		,     **lowercase		,     ):



						"""simple docstring"""





						super().__init__(**lowercase )

						A_		:    Union[str, Any]         =				image_size
						A_		:    Union[str, Any]         =				patch_size
						A_		:    List[Any]         =				num_channels
						A_		:    Any         =				num_frames

						A_		:    Tuple         =				hidden_size
						A_		:    str         =				num_hidden_layers
						A_		:    str         =				num_attention_heads
						A_		:    str         =				intermediate_size
						A_		:    Union[str, Any]         =				hidden_act
						A_		:    Optional[Any]         =				hidden_dropout_prob
						A_		:    Tuple         =				attention_probs_dropout_prob
						A_		:    Optional[int]         =				initializer_range
						A_		:    Dict         =				layer_norm_eps
						A_		:    Any         =				qkv_bias

						A_		:    Union[str, Any]         =				attention_type
						A_		:    str         =				drop_path_rate







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            =		1000
	_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}""")






