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   snake_case_       (							_a     :							List[str] ,					_a     :							Optional[Any] ,					_a     :							List[Any] ,					_a     :							Tuple ,					_a     :							str ,					_a     :							Union[str, Any]			):
        # we don't care about the past rewards in this model
  
        snake_case_						       =							states.reshape(1 ,					-1 ,					model.config.state_dim			)
        snake_case_						       =							actions.reshape(1 ,					-1 ,					model.config.act_dim			)
        snake_case_						       =							returns_to_go.reshape(1 ,					-1 ,					1			)
        snake_case_						       =							timesteps.reshape(1 ,					-1			)
  
        if model.config.max_length is not None:
              snake_case_						       =							states[:, -model.config.max_length :]
              snake_case_						       =							actions[:, -model.config.max_length :]
              snake_case_						       =							returns_to_go[:, -model.config.max_length :]
              snake_case_						       =							timesteps[:, -model.config.max_length :]
  
              # pad all tokens to sequence length
              snake_case_						       =							torch.cat(
                  [torch.zeros(model.config.max_length - states.shape[1]			), torch.ones(states.shape[1]			)]			)
              snake_case_						       =							attention_mask.to(dtype=torch.long ,					device=states.device			).reshape(1 ,					-1			)
              snake_case_						       =							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			)
              snake_case_						       =							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			)
              snake_case_						       =							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			)
              snake_case_						       =							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:
              snake_case_						       =							None
  
        snake_case_       ,	snake_case_       ,	snake_case_						       =							model(
            states=_a ,					actions=_a ,					rewards=_a ,					returns_to_go=_a ,					timesteps=_a ,					attention_mask=_a ,					return_dict=_a ,					)
  
        return action_preds[0, -1]
  
  
  # build the environment
  
  A           =   gym.make("Hopper-v3")
  A           =   env.observation_space.shape[0]
  A           =   env.action_space.shape[0]
  A           =   1_0_0_0
  A           =   "cuda"
  A           =   1_0_0_0.0  # normalization for rewards/returns
  A           =   3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  A           =   np.array(
      [
          1.31_12_79,
          -0.08_46_95_21,
          -0.5_38_27_19,
          -0.07_20_15_76,
          0.04_93_23_66,
          2.1_06_68_56,
          -0.15_01_73_54,
          0.00_87_83_45,
          -0.2_84_81_86,
          -0.18_54_00_96,
          -0.28_46_12_86,
      ]
  )
  A           =   np.array(
      [
          0.17_79_07_51,
          0.05_44_46_21,
          0.21_29_71_39,
          0.14_53_04_19,
          0.6_12_44_44,
          0.85_17_44_65,
          1.4_51_52_52,
          0.6_75_16_96,
          1.53_62_39,
          1.6_16_07_46,
          5.6_07_25_36,
      ]
  )
  A           =   torch.from_numpy(state_mean).to(device=device)
  A           =   torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  A           =   DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
  A           =   model.to(device)
  model.eval()
  
  for ep in range(1_0):
       A    ,     A           =   0, 0
       A           =   env.reset()
       A           =   torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
       A           =   torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
       A           =   torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
       A           =   torch.zeros(0, device=device, dtype=torch.floataa)
  
       A           =   torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
       for t in range(max_ep_len):
            env.render()
            # add padding
            A           =   torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
            A           =   torch.cat([rewards, torch.zeros(1, device=device)])
  
            A           =   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),
            )
            A           =   action
            A           =   action.detach().cpu().numpy()
  
            A    ,     A    ,     A    ,     A           =   env.step(action)
  
            A           =   torch.from_numpy(state).to(device=device).reshape(1, state_dim)
            A           =   torch.cat([states, cur_state], dim=0)
            A           =   reward
  
            A           =   target_return[0, -1] - (reward / scale)
            A           =   torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
            A           =   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: 
  '''simple docstring'''
  
  
  
  
  
  def   snake_case_       (							_a     :							int = 1_000			):
        return sum(i for i in range(_a			) if i % 3 == 0 or i % 5 == 0			)
  
  
  if __name__ == "__main__":
       print(F"""{solution() = }""")
  
  