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 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a	,	__a	,	__a )       ->			Union[str, Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: List[str]	     =states.reshape(1	,	-1	,	model.config.state_dim )
  							SCREAMING_SNAKE_CASE			: Dict	     =actions.reshape(1	,	-1	,	model.config.act_dim )
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =returns_to_go.reshape(1	,	-1	,	1 )
  							SCREAMING_SNAKE_CASE			: str	     =timesteps.reshape(1	,	-1 )
  
  							if model.config.max_length is not None:
  														SCREAMING_SNAKE_CASE			: str	     =states[:, -model.config.max_length :]
  														SCREAMING_SNAKE_CASE			: Dict	     =actions[:, -model.config.max_length :]
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =returns_to_go[:, -model.config.max_length :]
  														SCREAMING_SNAKE_CASE			: int	     =timesteps[:, -model.config.max_length :]
  
  														# pad all tokens to sequence length
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =torch.cat(
  														    [torch.zeros(model.config.max_length - states.shape[1] ), torch.ones(states.shape[1] )] )
  														SCREAMING_SNAKE_CASE			: Any	     =attention_mask.to(dtype=torch.long	,	device=states.device ).reshape(1	,	-1 )
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =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 )
  														SCREAMING_SNAKE_CASE			: Optional[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 )
  														SCREAMING_SNAKE_CASE			: 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 )
  														SCREAMING_SNAKE_CASE			: Tuple	     =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:
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =None
  
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Optional[int]	     =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
  
  _A							    =	gym.make("""Hopper-v3""")
  _A							    =	env.observation_space.shape[0]
  _A							    =	env.action_space.shape[0]
  _A							    =	1000
  _A							    =	"""cuda"""
  _A							    =	1000.0  # normalization for rewards/returns
  _A							    =	3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  _A							    =	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,
      ]
  )
  _A							    =	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,
      ]
  )
  _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(10):
  							_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: 
  
  
  
  
  import sys
  
  sys.set_int_max_str_digits(0)  # type: ignore
  def 						lowerCAmelCase_       (	__a )       ->			bool:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =[0] * 11
  							SCREAMING_SNAKE_CASE			: str	     =[0] * 11
  
  							# mark last 9 numbers
  							for _ in range(9 ):
  														SCREAMING_SNAKE_CASE			: List[str]	     =1
  														SCREAMING_SNAKE_CASE			: List[Any]	     =number // 10
  							# flag
  							SCREAMING_SNAKE_CASE			: str	     =True
  
  							# check last 9 numbers for pandigitality
  
  							for x in range(9 ):
  														if not check_last[x + 1]:
  																					SCREAMING_SNAKE_CASE			: Dict	     =False
  							if not f:
  														return f
  
  							# mark first 9 numbers
  							SCREAMING_SNAKE_CASE			: Dict	     =int(str(__a )[:9] )
  
  							for _ in range(9 ):
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =1
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =number // 10
  
  							# check first 9 numbers for pandigitality
  
  							for x in range(9 ):
  														if not check_front[x + 1]:
  																					SCREAMING_SNAKE_CASE			: Any	     =False
  							return f
  def 						lowerCAmelCase_       (	__a )       ->			bool:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Any	     =[0] * 11
  
  							# mark last 9 numbers
  							for _ in range(9 ):
  														SCREAMING_SNAKE_CASE			: int	     =1
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =number // 10
  							# flag
  							SCREAMING_SNAKE_CASE			: Any	     =True
  
  							# check last 9 numbers for pandigitality
  
  							for x in range(9 ):
  														if not check_last[x + 1]:
  																					SCREAMING_SNAKE_CASE			: Any	     =False
  							return f
  def 						lowerCAmelCase_       (	)       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: int	     =1
  							SCREAMING_SNAKE_CASE			: List[Any]	     =1
  							SCREAMING_SNAKE_CASE			: List[Any]	     =2
  							# temporary Fibonacci numbers
  
  							SCREAMING_SNAKE_CASE			: Dict	     =1
  							SCREAMING_SNAKE_CASE			: Dict	     =1
  							SCREAMING_SNAKE_CASE			: Tuple	     =2
  							# temporary Fibonacci numbers mod 1e9
  
  							# mod m=1e9, done for fast optimisation
  							SCREAMING_SNAKE_CASE			: str	     =[0] * 1000000
  							SCREAMING_SNAKE_CASE			: Any	     =1000000000
  
  							for x in range(1000000 ):
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =(aa + ba) % m
  														SCREAMING_SNAKE_CASE			: int	     =ba % m
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =ca % m
  														if checka(__a ):
  																					SCREAMING_SNAKE_CASE			: List[Any]	     =1
  
  							for x in range(1000000 ):
  														SCREAMING_SNAKE_CASE			: Tuple	     =a + b
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =b
  														SCREAMING_SNAKE_CASE			: List[str]	     =c
  														# perform check only if in tocheck
  														if tocheck[x + 3] and check(__a ):
  																					return x + 3  # first 2 already done
  							return -1
  
  
  if __name__ == "__main__":
  							print(F'''{solution() = }''')
  
  
  
  
  
  
  