






"""simple docstring"""



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


A__	:    Optional[int]			 = logging.get_logger(__name__)

A__	:    Optional[Any]			 = {
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:str             =			"swinv2"

			_UpperCAmelCase		:Optional[Any]             =			{
			    "num_attention_heads": "num_heads",
			    "num_hidden_layers": "num_layers",
			}


			def __init__(				self					:  Tuple       ,	snake_case__					:  List[str]=224       ,	snake_case__					:  List[Any]=4       ,	snake_case__					:  Optional[Any]=3       ,	snake_case__					:  int=96       ,	snake_case__					:  Any=[2, 2, 6, 2]       ,	snake_case__					:  int=[3, 6, 12, 24]       ,	snake_case__					:  Optional[Any]=7       ,	snake_case__					:  Optional[Any]=4.0       ,	snake_case__					:  Optional[int]=True       ,	snake_case__					:  Tuple=0.0       ,	snake_case__					:  int=0.0       ,	snake_case__					:  Dict=0.1       ,	snake_case__					:  Optional[int]="gelu"       ,	snake_case__					:  List[str]=False       ,	snake_case__					:  Dict=0.02       ,	snake_case__					:  Union[str, Any]=1E-5       ,	snake_case__					:  Union[str, Any]=32       ,	**snake_case__					:  Optional[Any]       ,	):
										super().__init__(**snake_case__ )

										lowerCamelCase_	:							List[Any]									=image_size
										lowerCamelCase_	:							Any									=patch_size
										lowerCamelCase_	:							Any									=num_channels
										lowerCamelCase_	:							List[Any]									=embed_dim
										lowerCamelCase_	:							Any									=depths
										lowerCamelCase_	:							Any									=len(snake_case__ )
										lowerCamelCase_	:							Any									=num_heads
										lowerCamelCase_	:							int									=window_size
										lowerCamelCase_	:							List[str]									=mlp_ratio
										lowerCamelCase_	:							Any									=qkv_bias
										lowerCamelCase_	:							List[str]									=hidden_dropout_prob
										lowerCamelCase_	:							Any									=attention_probs_dropout_prob
										lowerCamelCase_	:							int									=drop_path_rate
										lowerCamelCase_	:							List[str]									=hidden_act
										lowerCamelCase_	:							Tuple									=use_absolute_embeddings
										lowerCamelCase_	:							Any									=layer_norm_eps
										lowerCamelCase_	:							List[str]									=initializer_range
										lowerCamelCase_	:							str									=encoder_stride
										# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
										# this indicates the channel dimension after the last stage of the model
										lowerCamelCase_	:							Dict									=int(embed_dim * 2 ** (len(snake_case__ ) - 1) )
										lowerCamelCase_	:							Dict									=(0, 0, 0, 0)











"""simple docstring"""



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


A__	:    int			 = {
    '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__":
						A__	:    str			 = 'hopper-medium-v2'
						A__	:    Dict			 = gym.make(env_name)

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

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

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

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



