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		(		lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					# we don't care about the past rewards in this model
  
  					A    =      states.reshape(1    ,							-1    ,							model.config.state_dim   )
  					A    =      actions.reshape(1    ,							-1    ,							model.config.act_dim   )
  					A    =      returns_to_go.reshape(1    ,							-1    ,							1   )
  					A    =      timesteps.reshape(1    ,							-1   )
  
  					if model.config.max_length is not None:
  										A    =      states[:, -model.config.max_length :]
  										A    =      actions[:, -model.config.max_length :]
  										A    =      returns_to_go[:, -model.config.max_length :]
  										A    =      timesteps[:, -model.config.max_length :]
  
  										# pad all tokens to sequence length
  										A    =      torch.cat(
  										    [torch.zeros(model.config.max_length - states.shape[1]   ), torch.ones(states.shape[1]   )]   )
  										A    =      attention_mask.to(dtype=torch.long    ,							device=states.device   ).reshape(1    ,							-1   )
  										A    =      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   )
  										A    =      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   )
  										A    =      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   )
  										A    =      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:
  										A    =      None
  
  					A    ,					A    ,					A    =      model(
  					    states=lowercase__    ,							actions=lowercase__    ,							rewards=lowercase__    ,							returns_to_go=lowercase__    ,							timesteps=lowercase__    ,							attention_mask=lowercase__    ,							return_dict=lowercase__    ,							)
  
  					return action_preds[0, -1]
  
  
  # build the environment
  
  __A  :					Any												=	gym.make('Hopper-v3')
  __A  :					int												=	env.observation_space.shape[0]
  __A  :					List[Any]												=	env.action_space.shape[0]
  __A  :					Tuple												=	10_00
  __A  :					Union[str, Any]												=	'cuda'
  __A  :					Optional[Any]												=	10_00.0  # normalization for rewards/returns
  __A  :					int												=	36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  __A  :					Tuple												=	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  :					Dict												=	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  :					List[str]												=	torch.from_numpy(state_mean).to(device=device)
  __A  :					List[Any]												=	torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  __A  :					Optional[Any]												=	DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
  __A  :					str												=	model.to(device)
  model.eval()
  
  for ep in range(10):
  	__A						,					__A  :					str												=	0, 0
  	__A  :					Union[str, Any]												=	env.reset()
  	__A  :					Tuple												=	torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
  	__A  :					Tuple												=	torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
  	__A  :					List[Any]												=	torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
  	__A  :					str												=	torch.zeros(0, device=device, dtype=torch.floataa)
  
  	__A  :					Dict												=	torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
  	for t in range(max_ep_len):
  		env.render()
  		# add padding
  		__A  :					int												=	torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
  		__A  :					Union[str, Any]												=	torch.cat([rewards, torch.zeros(1, device=device)])
  
  		__A  :					Dict												=	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  :					int												=	action
  		__A  :					str												=	action.detach().cpu().numpy()
  
  		__A						,					__A						,					__A						,					__A  :					Dict												=	env.step(action)
  
  		__A  :					List[Any]												=	torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  		__A  :					Dict												=	torch.cat([states, cur_state], dim=0)
  		__A  :					int												=	reward
  
  		__A  :					Optional[int]												=	target_return[0, -1] - (reward / scale)
  		__A  :					int												=	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  		__A  :					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 functools
  from math import sqrt
  from time import time
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							*lowercase__    ,							**lowercase__   ):
  					"""simple docstring"""
  					A    =      time()
  					A    =      func(*lowercase__    ,							**lowercase__   )
  					A    =      time()
  					if int(end - start   ) > 0:
  										print(F"""{func.__name__} runtime: {(end - start):0.4f} s"""   )
  					else:
  										print(F"""{func.__name__} runtime: {(end - start) * 1_000:0.4f} ms"""   )
  					return output
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if n < 0:
  										raise Exception("n is negative"   )
  					if n == 0:
  										return [0]
  					A    =      [0, 1]
  					for _ in range(n - 1   ):
  										fib.append(fib[-1] + fib[-2]   )
  					return fib
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  
  					def fib_recursive_term(lowercase__   ) -> int:
  										if i < 0:
  															raise Exception("n is negative"   )
  										if i < 2:
  															return i
  										return fib_recursive_term(i - 1   ) + fib_recursive_term(i - 2   )
  
  					if n < 0:
  										raise Exception("n is negative"   )
  					return [fib_recursive_term(lowercase__   ) for i in range(n + 1   )]
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  
  					@functools.cache
  					def fib_recursive_term(lowercase__   ) -> int:
  										if i < 0:
  															raise Exception("n is negative"   )
  										if i < 2:
  															return i
  										return fib_recursive_term(i - 1   ) + fib_recursive_term(i - 2   )
  
  					if n < 0:
  										raise Exception("n is negative"   )
  					return [fib_recursive_term(lowercase__   ) for i in range(n + 1   )]
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if n < 0:
  										raise Exception("n is negative"   )
  					# Cache must be outside recursuive function
  					# other it will reset every time it calls itself.
  					A    =      {0: 0, 1: 1, 2: 1}  # Prefilled cache
  
  					def rec_fn_memoized(lowercase__   ) -> int:
  										if num in cache:
  															return cache[num]
  
  										A    =      rec_fn_memoized(num - 1   ) + rec_fn_memoized(num - 2   )
  										A    =      value
  										return value
  
  					return [rec_fn_memoized(lowercase__   ) for i in range(n + 1   )]
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if n < 0:
  										raise Exception("n is negative"   )
  					if n >= 1_475:
  										raise Exception("n is too large"   )
  					A    =      sqrt(5   )
  					A    =      (1 + sqrt_a) / 2
  					return [round(phi**i / sqrt_a   ) for i in range(n + 1   )]
  
  
  if __name__ == "__main__":
  	__A  :					Optional[Any]												=	30
  	time_func(fib_iterative, num)
  	time_func(fib_recursive, num)  # Around 3s runtime
  	time_func(fib_recursive_cached, num)  # Around 0ms runtime
  	time_func(fib_memoization, num)
  	time_func(fib_binet, num)
  
  
  
  
  
  