



'''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










'''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





