code: 
  
  
  import gym
  import numpy as np
  import torch
  from mujoco_py import GlfwContext
  
  from transformers import DecisionTransformerModel
  
  
  GlfwContext(offscreen=True)  # Create a window to init GLFW.
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							str:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Any           =			states.reshape(1    ,     -1    ,     model.config.state_dim			)
         _lowercase					:				List[Any]           =			actions.reshape(1    ,     -1    ,     model.config.act_dim			)
         _lowercase					:				int           =			returns_to_go.reshape(1    ,     -1    ,     1			)
         _lowercase					:				List[Any]           =			timesteps.reshape(1    ,     -1			)
  
         if model.config.max_length is not None:
                _lowercase					:				Optional[int]           =			states[:, -model.config.max_length :]
                _lowercase					:				str           =			actions[:, -model.config.max_length :]
                _lowercase					:				int           =			returns_to_go[:, -model.config.max_length :]
                _lowercase					:				List[str]           =			timesteps[:, -model.config.max_length :]
  
                # pad all tokens to sequence length
                _lowercase					:				Optional[Any]           =			torch.cat(
                    [torch.zeros(model.config.max_length - states.shape[1]			), torch.ones(states.shape[1]			)]			)
                _lowercase					:				List[Any]           =			attention_mask.to(dtype=torch.long    ,     device=states.device			).reshape(1    ,     -1			)
                _lowercase					:				List[str]           =			torch.cat(
                    [
                        torch.zeros(
                            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)    ,     device=states.device    ,     ),
                        states,
                    ]    ,     dim=1    ,     ).to(dtype=torch.floataa			)
                _lowercase					:				int           =			torch.cat(
                    [
                        torch.zeros(
                            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)    ,     device=actions.device    ,     ),
                        actions,
                    ]    ,     dim=1    ,     ).to(dtype=torch.floataa			)
                _lowercase					:				List[Any]           =			torch.cat(
                    [
                        torch.zeros(
                            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)    ,     device=returns_to_go.device    ,     ),
                        returns_to_go,
                    ]    ,     dim=1    ,     ).to(dtype=torch.floataa			)
                _lowercase					:				Dict           =			torch.cat(
                    [
                        torch.zeros(
                            (timesteps.shape[0], model.config.max_length - timesteps.shape[1])    ,     device=timesteps.device			),
                        timesteps,
                    ]    ,     dim=1    ,     ).to(dtype=torch.long			)
         else:
                _lowercase					:				Dict           =			None
  
         _lowercase       ,	_lowercase       ,	_lowercase					:				List[str]           =			model(
             states=UpperCAmelCase_    ,     actions=UpperCAmelCase_    ,     rewards=UpperCAmelCase_    ,     returns_to_go=UpperCAmelCase_    ,     timesteps=UpperCAmelCase_    ,     attention_mask=UpperCAmelCase_    ,     return_dict=UpperCAmelCase_    ,     )
  
         return action_preds[0, -1]
  
  
  # build the environment
  
  UpperCamelCase__				       =			gym.make('Hopper-v3')
  UpperCamelCase__				       =			env.observation_space.shape[0]
  UpperCamelCase__				       =			env.action_space.shape[0]
  UpperCamelCase__				       =			1_000
  UpperCamelCase__				       =			'cuda'
  UpperCamelCase__				       =			1_000.0  # normalization for rewards/returns
  UpperCamelCase__				       =			3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  UpperCamelCase__				       =			np.array(
      [
          1.31_1279,
          -0.0846_9521,
          -0.538_2719,
          -0.0720_1576,
          0.0493_2366,
          2.106_6856,
          -0.1501_7354,
          0.0087_8345,
          -0.284_8186,
          -0.1854_0096,
          -0.2846_1286,
      ]
  )
  UpperCamelCase__				       =			np.array(
      [
          0.1779_0751,
          0.0544_4621,
          0.2129_7139,
          0.1453_0419,
          0.612_4444,
          0.8517_4465,
          1.451_5252,
          0.675_1696,
          1.53_6239,
          1.616_0746,
          5.607_2536,
      ]
  )
  UpperCamelCase__				       =			torch.from_numpy(state_mean).to(device=device)
  UpperCamelCase__				       =			torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  UpperCamelCase__				       =			DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
  UpperCamelCase__				       =			model.to(device)
  model.eval()
  
  for ep in range(10):
     UpperCamelCase__				       =			0, 0
     UpperCamelCase__				       =			env.reset()
     UpperCamelCase__				       =			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
     UpperCamelCase__				       =			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
     UpperCamelCase__				       =			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
     UpperCamelCase__				       =			torch.zeros(0, device=device, dtype=torch.floataa)
  
     UpperCamelCase__				       =			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
     for t in range(max_ep_len):
        env.render()
        # add padding
        UpperCamelCase__				       =			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
        UpperCamelCase__				       =			torch.cat([rewards, torch.zeros(1, device=device)])
  
        UpperCamelCase__				       =			get_action(
            model,
            (states.to(dtype=torch.floataa) - state_mean) / state_std,
            actions.to(dtype=torch.floataa),
            rewards.to(dtype=torch.floataa),
            target_return.to(dtype=torch.floataa),
            timesteps.to(dtype=torch.long),
        )
        UpperCamelCase__				       =			action
        UpperCamelCase__				       =			action.detach().cpu().numpy()
  
        UpperCamelCase__				       =			env.step(action)
  
        UpperCamelCase__				       =			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
        UpperCamelCase__				       =			torch.cat([states, cur_state], dim=0)
        UpperCamelCase__				       =			reward
  
        UpperCamelCase__				       =			target_return[0, -1] - (reward / scale)
        UpperCamelCase__				       =			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
        UpperCamelCase__				       =			torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)
  
        episode_return += reward
        episode_length += 1
  
        if done:
           break
style_context: 
  
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  UpperCamelCase__				       =			logging.get_logger(__name__)
  
  UpperCamelCase__				       =			{
      'facebook/sam-vit-huge': 'https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json',
      'facebook/sam-vit-large': 'https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json',
      'facebook/sam-vit-base': 'https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json',
  }
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       List[Any]	,      UpperCamelCase       :       Optional[Any]=2_56	,      UpperCamelCase       :       str=10_24	,      UpperCamelCase       :       Any=16	,      UpperCamelCase       :       Optional[Any]=16	,      UpperCamelCase       :       Union[str, Any]=4	,      UpperCamelCase       :       str="gelu"	,      UpperCamelCase       :       Dict=1E-6	,      **UpperCamelCase       :       Tuple	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__(**UpperCamelCase		)
              _lowercase					:				Union[str, Any]           =			hidden_size
              _lowercase					:				Optional[Any]           =			image_size
              _lowercase					:				Tuple           =			patch_size
              _lowercase					:				List[str]           =			image_size // patch_size
              _lowercase					:				Optional[Any]           =			mask_input_channels
              _lowercase					:				str           =			num_point_embeddings
              _lowercase					:				List[Any]           =			hidden_act
              _lowercase					:				Union[str, Any]           =			layer_norm_eps
  
  
  
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Optional[int]	,      UpperCamelCase       :       str=2_56	,      UpperCamelCase       :       int="relu"	,      UpperCamelCase       :       Any=20_48	,      UpperCamelCase       :       List[Any]=2	,      UpperCamelCase       :       Union[str, Any]=8	,      UpperCamelCase       :       List[str]=2	,      UpperCamelCase       :       Any=3	,      UpperCamelCase       :       List[Any]=3	,      UpperCamelCase       :       int=2_56	,      UpperCamelCase       :       List[str]=1E-6	,      **UpperCamelCase       :       Tuple	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__(**UpperCamelCase		)
              _lowercase					:				List[Any]           =			hidden_size
              _lowercase					:				Optional[int]           =			hidden_act
              _lowercase					:				Union[str, Any]           =			mlp_dim
              _lowercase					:				Optional[int]           =			num_hidden_layers
              _lowercase					:				List[str]           =			num_attention_heads
              _lowercase					:				Dict           =			attention_downsample_rate
              _lowercase					:				Optional[Any]           =			num_multimask_outputs
              _lowercase					:				Optional[Any]           =			iou_head_depth
              _lowercase					:				Dict           =			iou_head_hidden_dim
              _lowercase					:				List[str]           =			layer_norm_eps
  
  
  
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       List[str]	,      UpperCamelCase       :       Union[str, Any]=7_68	,      UpperCamelCase       :       Optional[Any]=2_56	,      UpperCamelCase       :       Optional[Any]=12	,      UpperCamelCase       :       Dict=12	,      UpperCamelCase       :       Dict=3	,      UpperCamelCase       :       Optional[int]=10_24	,      UpperCamelCase       :       Any=16	,      UpperCamelCase       :       Union[str, Any]="gelu"	,      UpperCamelCase       :       int=1E-06	,      UpperCamelCase       :       Tuple=0.0	,      UpperCamelCase       :       List[str]=1E-10	,      UpperCamelCase       :       List[Any]=True	,      UpperCamelCase       :       Any=4.0	,      UpperCamelCase       :       str=True	,      UpperCamelCase       :       Optional[int]=True	,      UpperCamelCase       :       Optional[int]=14	,      UpperCamelCase       :       Any=[2, 5, 8, 11]	,      UpperCamelCase       :       Tuple=1_28	,      UpperCamelCase       :       str=None	,      **UpperCamelCase       :       int	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__(**UpperCamelCase		)
  
              _lowercase					:				Union[str, Any]           =			hidden_size
              _lowercase					:				Optional[int]           =			output_channels
              _lowercase					:				Dict           =			num_hidden_layers
              _lowercase					:				str           =			num_attention_heads
              _lowercase					:				Dict           =			num_channels
              _lowercase					:				List[Any]           =			image_size
              _lowercase					:				int           =			patch_size
              _lowercase					:				Optional[Any]           =			hidden_act
              _lowercase					:				Any           =			layer_norm_eps
              _lowercase					:				Any           =			attention_dropout
              _lowercase					:				Optional[int]           =			initializer_range
              _lowercase					:				List[str]           =			qkv_bias
              _lowercase					:				Optional[Any]           =			mlp_ratio
              _lowercase					:				Optional[Any]           =			use_abs_pos
              _lowercase					:				List[Any]           =			use_rel_pos
              _lowercase					:				str           =			window_size
              _lowercase					:				Tuple           =			global_attn_indexes
              _lowercase					:				List[Any]           =			num_pos_feats
              _lowercase					:				int           =			int(hidden_size * mlp_ratio		) if mlp_dim is None else mlp_dim
  
  
  
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  '''sam'''
       UpperCAmelCase_         =  True
  
  
  
  
       def __init__( self       :       Tuple	,      UpperCamelCase       :       Tuple=None	,      UpperCamelCase       :       Any=None	,      UpperCamelCase       :       Tuple=None	,      UpperCamelCase       :       Dict=0.02	,      **UpperCamelCase       :       Union[str, Any]	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__(**UpperCamelCase		)
              _lowercase					:				Optional[int]           =			vision_config if vision_config is not None else {}
              _lowercase					:				Any           =			prompt_encoder_config if prompt_encoder_config is not None else {}
              _lowercase					:				Optional[Any]           =			mask_decoder_config if mask_decoder_config is not None else {}
  
              if isinstance(UpperCamelCase	,      UpperCamelCase		):
                     _lowercase					:				Dict           =			vision_config.to_dict()
              if isinstance(UpperCamelCase	,      UpperCamelCase		):
                     _lowercase					:				List[str]           =			prompt_encoder_config.to_dict()
              if isinstance(UpperCamelCase	,      UpperCamelCase		):
                     _lowercase					:				Optional[int]           =			mask_decoder_config.to_dict()
  
              _lowercase					:				Union[str, Any]           =			SamVisionConfig(**UpperCamelCase		)
              _lowercase					:				Optional[Any]           =			SamPromptEncoderConfig(**UpperCamelCase		)
              _lowercase					:				List[Any]           =			SamMaskDecoderConfig(**UpperCamelCase		)
              _lowercase					:				Union[str, Any]           =			initializer_range
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[Any]           =			copy.deepcopy(self.__dict__		)
              _lowercase					:				int           =			self.vision_config.to_dict()
              _lowercase					:				Optional[int]           =			self.prompt_encoder_config.to_dict()
              _lowercase					:				List[Any]           =			self.mask_decoder_config.to_dict()
              _lowercase					:				List[Any]           =			self.__class__.model_type
              return output