
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 (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 # we don't care about the past rewards in this model

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

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

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

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

 return action_preds[0, -1]


# build the environment

_lowerCamelCase        =gym.make("""Hopper-v3""")
_lowerCamelCase        =env.observation_space.shape[0]
_lowerCamelCase        =env.action_space.shape[0]
_lowerCamelCase        =1_0_0_0
_lowerCamelCase        ="""cuda"""
_lowerCamelCase        =1_0_0_0.0  # normalization for rewards/returns
_lowerCamelCase        =3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCamelCase        =np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
_lowerCamelCase        =np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
_lowerCamelCase        =torch.from_numpy(state_mean).to(device=device)
_lowerCamelCase        =torch.from_numpy(state_std).to(device=device)

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

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

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

    _lowerCamelCase        =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),
    )
    _lowerCamelCase        =action
    _lowerCamelCase        =action.detach().cpu().numpy()

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

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

    _lowerCamelCase        =target_return[0, -1] - (reward / scale)
    _lowerCamelCase        =torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    _lowerCamelCase        =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    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Optional[int]          =				[1 / first_term]
 lowerCamelCase		:     Any          =				1 / first_term
 for _ in range(number_of_terms - 1							):
  first_term += common_difference
  arithmetic_progression.append(lowerCamelCase							)
 lowerCamelCase		:     Optional[Any]          =				[1 / step for step in arithmetic_progression]
 return sum(lowerCamelCase							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(sum_of_harmonic_progression(1 / 2, 2, 2))




