code: 
  
  
  
  """simple docstring"""
  
  
  
  
  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		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Optional[int]		,						SCREAMING_SNAKE_CASE__			:		str		,						SCREAMING_SNAKE_CASE__			:		Dict		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		Optional[Any]		)						->     Optional[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			int              =					states.reshape(1		,						-1		,						model.config.state_dim		)
       _UpperCAmelCase    :			List[str]              =					actions.reshape(1		,						-1		,						model.config.act_dim		)
       _UpperCAmelCase    :			List[str]              =					returns_to_go.reshape(1		,						-1		,						1		)
       _UpperCAmelCase    :			Optional[Any]              =					timesteps.reshape(1		,						-1		)
  
       if model.config.max_length is not None:
            _UpperCAmelCase    :			Union[str, Any]              =					states[:, -model.config.max_length :]
            _UpperCAmelCase    :			List[Any]              =					actions[:, -model.config.max_length :]
            _UpperCAmelCase    :			List[Any]              =					returns_to_go[:, -model.config.max_length :]
            _UpperCAmelCase    :			Optional[int]              =					timesteps[:, -model.config.max_length :]
  
            # pad all tokens to sequence length
            _UpperCAmelCase    :			Any              =					torch.cat(
                [torch.zeros(model.config.max_length - states.shape[1]		), torch.ones(states.shape[1]		)]		)
            _UpperCAmelCase    :			Union[str, Any]              =					attention_mask.to(dtype=torch.long		,						device=states.device		).reshape(1		,						-1		)
            _UpperCAmelCase    :			Dict              =					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		)
            _UpperCAmelCase    :			Any              =					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		)
            _UpperCAmelCase    :			Optional[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		)
            _UpperCAmelCase    :			Optional[int]              =					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:
            _UpperCAmelCase    :			Dict              =					None
  
       _UpperCAmelCase    :			Union[str, Any]              =					model(
           states=_SCREAMING_SNAKE_CASE		,						actions=_SCREAMING_SNAKE_CASE		,						rewards=_SCREAMING_SNAKE_CASE		,						returns_to_go=_SCREAMING_SNAKE_CASE		,						timesteps=_SCREAMING_SNAKE_CASE		,						attention_mask=_SCREAMING_SNAKE_CASE		,						return_dict=_SCREAMING_SNAKE_CASE		,						)
  
       return action_preds[0, -1]
  
  
  # build the environment
  
  _lowerCAmelCase       :      Any							=							gym.make("Hopper-v3")
  _lowerCAmelCase       :      Union[str, Any]							=							env.observation_space.shape[0]
  _lowerCAmelCase       :      List[Any]							=							env.action_space.shape[0]
  _lowerCAmelCase       :      Dict							=							10_00
  _lowerCAmelCase       :      Tuple							=							'cuda'
  _lowerCAmelCase       :      int							=							10_00.0  # normalization for rewards/returns
  _lowerCAmelCase       :      Optional[Any]							=							36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  _lowerCAmelCase       :      List[str]							=							np.array(
      [
          1.3_1_1_2_7_9,
          -0.0_8_4_6_9_5_2_1,
          -0.5_3_8_2_7_1_9,
          -0.0_7_2_0_1_5_7_6,
          0.0_4_9_3_2_3_6_6,
          2.1_0_6_6_8_5_6,
          -0.1_5_0_1_7_3_5_4,
          0.0_0_8_7_8_3_4_5,
          -0.2_8_4_8_1_8_6,
          -0.1_8_5_4_0_0_9_6,
          -0.2_8_4_6_1_2_8_6,
      ]
  )
  _lowerCAmelCase       :      Tuple							=							np.array(
      [
          0.1_7_7_9_0_7_5_1,
          0.0_5_4_4_4_6_2_1,
          0.2_1_2_9_7_1_3_9,
          0.1_4_5_3_0_4_1_9,
          0.6_1_2_4_4_4_4,
          0.8_5_1_7_4_4_6_5,
          1.4_5_1_5_2_5_2,
          0.6_7_5_1_6_9_6,
          1.5_3_6_2_3_9,
          1.6_1_6_0_7_4_6,
          5.6_0_7_2_5_3_6,
      ]
  )
  _lowerCAmelCase       :      Union[str, Any]							=							torch.from_numpy(state_mean).to(device=device)
  _lowerCAmelCase       :      int							=							torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  _lowerCAmelCase       :      List[Any]							=							DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
  _lowerCAmelCase       :      Any							=							model.to(device)
  model.eval()
  
  for ep in range(10):
   _lowerCAmelCase       :      Any							=							0, 0
   _lowerCAmelCase       :      int							=							env.reset()
   _lowerCAmelCase       :      List[str]							=							torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
   _lowerCAmelCase       :      List[Any]							=							torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
   _lowerCAmelCase       :      Tuple							=							torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
   _lowerCAmelCase       :      Dict							=							torch.zeros(0, device=device, dtype=torch.floataa)
  
   _lowerCAmelCase       :      Tuple							=							torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
   for t in range(max_ep_len):
    env.render()
    # add padding
    _lowerCAmelCase       :      Tuple							=							torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
    _lowerCAmelCase       :      Optional[int]							=							torch.cat([rewards, torch.zeros(1, device=device)])
  
    _lowerCAmelCase       :      Tuple							=							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),
    )
    _lowerCAmelCase       :      List[str]							=							action
    _lowerCAmelCase       :      Optional[int]							=							action.detach().cpu().numpy()
  
    _lowerCAmelCase       :      Union[str, Any]							=							env.step(action)
  
    _lowerCAmelCase       :      int							=							torch.from_numpy(state).to(device=device).reshape(1, state_dim)
    _lowerCAmelCase       :      List[str]							=							torch.cat([states, cur_state], dim=0)
    _lowerCAmelCase       :      int							=							reward
  
    _lowerCAmelCase       :      Optional[Any]							=							target_return[0, -1] - (reward / scale)
    _lowerCAmelCase       :      Dict							=							torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    _lowerCAmelCase       :      Union[str, Any]							=							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"""
  
  
  
  
  import sys
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		list[int]       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       if not numbers:
            return 0
       if target == 0 and target in numbers:
            return 0
       if not isinstance(SCREAMING_SNAKE_CASE__		,						(list, tuple)       ) or not all(
           isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ) for number in numbers       ):
            raise ValueError("numbers must be an iterable of integers"       )
  
       _UpperCAmelCase    :			List[Any]              =					0
       _UpperCAmelCase    :			str              =					sys.maxsize
  
       while right < len(SCREAMING_SNAKE_CASE__       ):
            curr_sum += numbers[right]
            while curr_sum >= target and left <= right:
                 _UpperCAmelCase    :			Any              =					min(SCREAMING_SNAKE_CASE__		,						right - left + 1       )
                 curr_sum -= numbers[left]
                 left += 1
            right += 1
  
       return 0 if min_len == sys.maxsize else min_len
  