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        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				Dict    ,		_UpperCamelCase      :				Optional[Any]    ,		_UpperCamelCase      :				int    ,		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				Tuple   )		->					str:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						states.reshape(1    ,		-1    ,		model.config.state_dim   )
     UpperCamelCase__ 						=						actions.reshape(1    ,		-1    ,		model.config.act_dim   )
     UpperCamelCase__ 						=						returns_to_go.reshape(1    ,		-1    ,		1   )
     UpperCamelCase__ 						=						timesteps.reshape(1    ,		-1   )
  
     if model.config.max_length is not None:
        UpperCamelCase__ 						=						states[:, -model.config.max_length :]
        UpperCamelCase__ 						=						actions[:, -model.config.max_length :]
        UpperCamelCase__ 						=						returns_to_go[:, -model.config.max_length :]
        UpperCamelCase__ 						=						timesteps[:, -model.config.max_length :]
  
        # pad all tokens to sequence length
        UpperCamelCase__ 						=						torch.cat(
            [torch.zeros(model.config.max_length - states.shape[1]   ), torch.ones(states.shape[1]   )]   )
        UpperCamelCase__ 						=						attention_mask.to(dtype=torch.long    ,		device=states.device   ).reshape(1    ,		-1   )
        UpperCamelCase__ 						=						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__ 						=						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__ 						=						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__ 						=						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__ 						=						None
  
     UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						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
  
  __lowercase:		List[Any]							=     gym.make("Hopper-v3")
  __lowercase:		Any							=     env.observation_space.shape[0]
  __lowercase:		Union[str, Any]							=     env.action_space.shape[0]
  __lowercase:		List[str]							=     1_000
  __lowercase:		str							=     'cuda'
  __lowercase:		Any							=     1_000.0  # normalization for rewards/returns
  __lowercase:		Tuple							=     3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  __lowercase:		int							=     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,
      ]
  )
  __lowercase:		int							=     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,
      ]
  )
  __lowercase:		Dict							=     torch.from_numpy(state_mean).to(device=device)
  __lowercase:		Tuple							=     torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  __lowercase:		str							=     DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
  __lowercase:		Tuple							=     model.to(device)
  model.eval()
  
  for ep in range(10):
     __lowercase:		int							=     0, 0
     __lowercase:		Optional[Any]							=     env.reset()
     __lowercase:		str							=     torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
     __lowercase:		List[str]							=     torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
     __lowercase:		int							=     torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
     __lowercase:		List[str]							=     torch.zeros(0, device=device, dtype=torch.floataa)
  
     __lowercase:		List[str]							=     torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
     for t in range(max_ep_len):
        env.render()
        # add padding
        __lowercase:		str							=     torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
        __lowercase:		List[str]							=     torch.cat([rewards, torch.zeros(1, device=device)])
  
        __lowercase:		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),
        )
        __lowercase:		str							=     action
        __lowercase:		List[str]							=     action.detach().cpu().numpy()
  
        __lowercase:		Optional[Any]							=     env.step(action)
  
        __lowercase:		str							=     torch.from_numpy(state).to(device=device).reshape(1, state_dim)
        __lowercase:		List[str]							=     torch.cat([states, cur_state], dim=0)
        __lowercase:		Union[str, Any]							=     reward
  
        __lowercase:		List[str]							=     target_return[0, -1] - (reward / scale)
        __lowercase:		str							=     torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
        __lowercase:		str							=     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'''
  
  from __future__ import annotations
  
  import random
  from collections.abc import Iterable
  from typing import Any, Generic, TypeVar
  
  __lowercase:		Optional[Any]							=     TypeVar("T", bound=bool)
  
  
  
  
  
  
  
  class  UpperCAmelCase (       Generic[T]):
  
   def __init__( self :	int,					a_ :	T					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						value
      UpperCamelCase__ 						=						None
      UpperCamelCase__ 						=						None
  
   @property
   def 		lowercase_						( self :	Dict					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self._value
  
   @staticmethod
   def 		lowercase_						( a_ :	RandomizedHeapNode[T] | None,					a_ :	RandomizedHeapNode[T] | None					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not roota:
         return roota
  
      if not roota:
         return roota
  
      if roota.value > roota.value:
         UpperCamelCase__						,		UpperCamelCase__ 						=						roota, roota
  
      if random.choice([True, False]					):
         UpperCamelCase__						,		UpperCamelCase__ 						=						roota.right, roota.left
  
      UpperCamelCase__ 						=						RandomizedHeapNode.merge(roota.left,					a_					)
  
      return roota
  
  
  
  
  
  
  
  class  UpperCAmelCase (       Generic[T]):
  
   def __init__( self :	List[Any],					a_ :	Iterable[T] | None = ()					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						None
  
      if data:
         for item in data:
            self.insert(a_					)
  
   def 		lowercase_						( self :	Any,					a_ :	T					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						RandomizedHeapNode.merge(self._root,					RandomizedHeapNode(a_					)					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.top()
  
      if self._root is None:
         return None
  
      UpperCamelCase__ 						=						RandomizedHeapNode.merge(self._root.left,					self._root.right					)
  
      return result
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not self._root:
         raise IndexError("Can't get top element for the empty heap."					)
      return self._root.value
  
   def 		lowercase_						( self :	Tuple					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						None
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[]
      while self:
         result.append(self.pop()					)
  
      return result
  
   def __bool__( self :	List[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self._root is not None
  
  
  if __name__ == "__main__":
         import doctest
  
         doctest.testmod()