




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   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				str:
       # we don't care about the past rewards in this model

       lowerCamelCase     :   Dict    				=      states.reshape(1				,-1				,model.config.state_dim					)
       lowerCamelCase     :   Optional[Any]    				=      actions.reshape(1				,-1				,model.config.act_dim					)
       lowerCamelCase     :   List[str]    				=      returns_to_go.reshape(1				,-1				,1					)
       lowerCamelCase     :   List[Any]    				=      timesteps.reshape(1				,-1					)

       if model.config.max_length is not None:
              lowerCamelCase     :   str    				=      states[:, -model.config.max_length :]
              lowerCamelCase     :   int    				=      actions[:, -model.config.max_length :]
              lowerCamelCase     :   Union[str, Any]    				=      returns_to_go[:, -model.config.max_length :]
              lowerCamelCase     :   Optional[Any]    				=      timesteps[:, -model.config.max_length :]

              # pad all tokens to sequence length
              lowerCamelCase     :   str    				=      torch.cat(
                  [torch.zeros(model.config.max_length - states.shape[1]					), torch.ones(states.shape[1]					)]					)
              lowerCamelCase     :   Optional[Any]    				=      attention_mask.to(dtype=torch.long				,device=states.device					).reshape(1				,-1					)
              lowerCamelCase     :   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					)
              lowerCamelCase     :   str    				=      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					)
              lowerCamelCase     :   Tuple    				=      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					)
              lowerCamelCase     :   Optional[int]    				=      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:
              lowerCamelCase     :   int    				=      None

       lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase     :   Optional[Any]    				=      model(
           states=_SCREAMING_SNAKE_CASE				,actions=_SCREAMING_SNAKE_CASE				,rewards=_SCREAMING_SNAKE_CASE				,returns_to_go=_SCREAMING_SNAKE_CASE				,timesteps=_SCREAMING_SNAKE_CASE				,attention_mask=_SCREAMING_SNAKE_CASE				,return_dict=_SCREAMING_SNAKE_CASE				,)

       return action_preds[0, -1]


# build the environment

SCREAMING_SNAKE_CASE__ :   int						=     gym.make('Hopper-v3')
SCREAMING_SNAKE_CASE__ :   Optional[int]						=     env.observation_space.shape[0]
SCREAMING_SNAKE_CASE__ :   Optional[Any]						=     env.action_space.shape[0]
SCREAMING_SNAKE_CASE__ :   List[str]						=     1000
SCREAMING_SNAKE_CASE__ :   Tuple						=     'cuda'
SCREAMING_SNAKE_CASE__ :   Dict						=     1000.0  # normalization for rewards/returns
SCREAMING_SNAKE_CASE__ :   List[Any]						=     3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
SCREAMING_SNAKE_CASE__ :   str						=     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,
    ]
)
SCREAMING_SNAKE_CASE__ :   List[str]						=     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,
    ]
)
SCREAMING_SNAKE_CASE__ :   int						=     torch.from_numpy(state_mean).to(device=device)
SCREAMING_SNAKE_CASE__ :   Any						=     torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
SCREAMING_SNAKE_CASE__ :   int						=     DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
SCREAMING_SNAKE_CASE__ :   Tuple						=     model.to(device)
model.eval()

for ep in range(10):
       SCREAMING_SNAKE_CASE__						,						SCREAMING_SNAKE_CASE__ :   Tuple						=     0, 0
       SCREAMING_SNAKE_CASE__ :   str						=     env.reset()
       SCREAMING_SNAKE_CASE__ :   str						=     torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
       SCREAMING_SNAKE_CASE__ :   Tuple						=     torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
       SCREAMING_SNAKE_CASE__ :   Any						=     torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
       SCREAMING_SNAKE_CASE__ :   int						=     torch.zeros(0, device=device, dtype=torch.floataa)

       SCREAMING_SNAKE_CASE__ :   Dict						=     torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
       for t in range(max_ep_len):
              env.render()
              # add padding
              SCREAMING_SNAKE_CASE__ :   Any						=     torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
              SCREAMING_SNAKE_CASE__ :   Dict						=     torch.cat([rewards, torch.zeros(1, device=device)])

              SCREAMING_SNAKE_CASE__ :   List[Any]						=     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),
              )
              SCREAMING_SNAKE_CASE__ :   Tuple						=     action
              SCREAMING_SNAKE_CASE__ :   str						=     action.detach().cpu().numpy()

              SCREAMING_SNAKE_CASE__						,						SCREAMING_SNAKE_CASE__						,						SCREAMING_SNAKE_CASE__						,						SCREAMING_SNAKE_CASE__ :   Union[str, Any]						=     env.step(action)

              SCREAMING_SNAKE_CASE__ :   int						=     torch.from_numpy(state).to(device=device).reshape(1, state_dim)
              SCREAMING_SNAKE_CASE__ :   Optional[Any]						=     torch.cat([states, cur_state], dim=0)
              SCREAMING_SNAKE_CASE__ :   Optional[int]						=     reward

              SCREAMING_SNAKE_CASE__ :   Optional[Any]						=     target_return[0, -1] - (reward / scale)
              SCREAMING_SNAKE_CASE__ :   Optional[int]						=     torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
              SCREAMING_SNAKE_CASE__ :   Optional[int]						=     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












SCREAMING_SNAKE_CASE__ :   str						=     {
    '+': lambda x, y: x + y,
    '-': lambda x, y: x - y,
    '*': lambda x, y: x * y,
    '/': lambda x, y: x / y,
}

def   A   (   _SCREAMING_SNAKE_CASE					)			->				List[str]:
       return c.isdigit()

def   A   (   _SCREAMING_SNAKE_CASE					)			->				Optional[Any]:
       lowerCamelCase     :   Tuple    				=      []

       # iterate over the string in reverse order
       for c in expression.split()[::-1]:
              # push operand to stack
              if is_operand(_SCREAMING_SNAKE_CASE					):
                     stack.append(int(_SCREAMING_SNAKE_CASE					)					)

              else:
                     # pop values from stack can calculate the result
                     # push the result onto the stack again
                     lowerCamelCase     :   str    				=      stack.pop()
                     lowerCamelCase     :   Optional[int]    				=      stack.pop()
                     stack.append(calc[c](_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)					)

       return stack.pop()


# Driver code
if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__ :   Optional[int]						=     '+ 9 * 2 6'
       print(evaluate(test_expression))

       SCREAMING_SNAKE_CASE__ :   Union[str, Any]						=     '/ * 10 2 + 4 1 '
       print(evaluate(test_expression))






