

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  _snake_case		(snake_case_					:     Union[str, Any]    ,       snake_case_					:     int    ,       snake_case_					:     Tuple    ,       snake_case_					:     Optional[int]    ,       snake_case_					:     Any    ,       snake_case_					:     Optional[int]  )							->		Union[str, Any]:
  # we don't care about the past rewards in this model

  __UpperCAmelCase	:  Dict		=     states.reshape(1    ,       -1    ,       model.config.state_dim  )
  __UpperCAmelCase	:  Any		=     actions.reshape(1    ,       -1    ,       model.config.act_dim  )
  __UpperCAmelCase	:  int		=     returns_to_go.reshape(1    ,       -1    ,       1  )
  __UpperCAmelCase	:  Tuple		=     timesteps.reshape(1    ,       -1  )

  if model.config.max_length is not None:
    __UpperCAmelCase	:  Optional[Any]		=     states[:, -model.config.max_length :]
    __UpperCAmelCase	:  Dict		=     actions[:, -model.config.max_length :]
    __UpperCAmelCase	:  Optional[Any]		=     returns_to_go[:, -model.config.max_length :]
    __UpperCAmelCase	:  List[str]		=     timesteps[:, -model.config.max_length :]

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

  __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  str		=     model(
      states=snake_case_    ,       actions=snake_case_    ,       rewards=snake_case_    ,       returns_to_go=snake_case_    ,       timesteps=snake_case_    ,       attention_mask=snake_case_    ,       return_dict=snake_case_    ,       )

  return action_preds[0, -1]


# build the environment

_SCREAMING_SNAKE_CASE							 =			gym.make('''Hopper-v3''')
_SCREAMING_SNAKE_CASE							 =			env.observation_space.shape[0]
_SCREAMING_SNAKE_CASE							 =			env.action_space.shape[0]
_SCREAMING_SNAKE_CASE							 =			1_0_0_0
_SCREAMING_SNAKE_CASE							 =			'''cuda'''
_SCREAMING_SNAKE_CASE							 =			1_0_0_0.0  # normalization for rewards/returns
_SCREAMING_SNAKE_CASE							 =			3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_SCREAMING_SNAKE_CASE							 =			np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
_SCREAMING_SNAKE_CASE							 =			np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
_SCREAMING_SNAKE_CASE							 =			torch.from_numpy(state_mean).to(device=device)
_SCREAMING_SNAKE_CASE							 =			torch.from_numpy(state_std).to(device=device)

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

for ep in range(1_0):
      _SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE							 =			0, 0
      _SCREAMING_SNAKE_CASE							 =			env.reset()
      _SCREAMING_SNAKE_CASE							 =			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
      _SCREAMING_SNAKE_CASE							 =			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
      _SCREAMING_SNAKE_CASE							 =			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
      _SCREAMING_SNAKE_CASE							 =			torch.zeros(0, device=device, dtype=torch.floataa)

      _SCREAMING_SNAKE_CASE							 =			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							 =			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
            _SCREAMING_SNAKE_CASE							 =			torch.cat([rewards, torch.zeros(1, device=device)])

            _SCREAMING_SNAKE_CASE							 =			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							 =			action
            _SCREAMING_SNAKE_CASE							 =			action.detach().cpu().numpy()

            _SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE							 =			env.step(action)

            _SCREAMING_SNAKE_CASE							 =			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
            _SCREAMING_SNAKE_CASE							 =			torch.cat([states, cur_state], dim=0)
            _SCREAMING_SNAKE_CASE							 =			reward

            _SCREAMING_SNAKE_CASE							 =			target_return[0, -1] - (reward / scale)
            _SCREAMING_SNAKE_CASE							 =			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
            _SCREAMING_SNAKE_CASE							 =			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








def  _snake_case		(snake_case_					:     float  )							->		int:
  if num < 0:
    return -1
  return 1 if num else 0





def  _snake_case		()							->		None:
  assert signum(5  ) == 1
  assert signum(-5  ) == -1
  assert signum(0  ) == 0


if __name__ == "__main__":
      print(signum(1_2))
      print(signum(-1_2))
      print(signum(0))





