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 _lowercase   (  __A							,__A							,__A							,__A							,__A							,__A       ):
  
  
  
  	'''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
  
  a__						:			List[Any]										=			gym.make('Hopper-v3')
  a__						:			Any										=			env.observation_space.shape[0]
  a__						:			Union[str, Any]										=			env.action_space.shape[0]
  a__						:			List[str]										=			1_0_0_0
  a__						:			str										=			'cuda'
  a__						:			Any										=			1_0_0_0.0  # normalization for rewards/returns
  a__						:			Tuple										=			3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  a__						:			int										=			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__						:			int										=			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__						:			Dict										=			torch.from_numpy(state_mean).to(device=device)
  a__						:			Tuple										=			torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  a__						:			str										=			DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
  a__						:			Tuple										=			model.to(device)
  model.eval()
  
  for ep in range(1_0):
  	a__						:			int										=			0, 0
  	a__						:			Optional[Any]										=			env.reset()
  	a__						:			str										=			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
  	a__						:			List[str]										=			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
  	a__						:			int										=			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
  	a__						:			List[str]										=			torch.zeros(0, device=device, dtype=torch.floataa)
  
  	a__						:			List[str]										=			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
  	for t in range(max_ep_len):
  		env.render()
  		# add padding
  		a__						:			str										=			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
  		a__						:			List[str]										=			torch.cat([rewards, torch.zeros(1, device=device)])
  
  		a__						:			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),
  		)
  		a__						:			str										=			action
  		a__						:			List[str]										=			action.detach().cpu().numpy()
  
  		a__						:			Optional[Any]										=			env.step(action)
  
  		a__						:			str										=			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  		a__						:			List[str]										=			torch.cat([states, cur_state], dim=0)
  		a__						:			Union[str, Any]										=			reward
  
  		a__						:			List[str]										=			target_return[0, -1] - (reward / scale)
  		a__						:			str										=			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  		a__						:			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'''
  
  
  
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  1
   while b > 0:
    if b & 1:
     res *= a
  
    a *= a
    b >>= 1
  
   return res
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  1
   while b > 0:
    if b & 1:
     __UpperCamelCase				      =  ((res % c) * (a % c)) % c
  
    a *= a
    b >>= 1
  
   return res
  
  
  
  
  
  