





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

     __magic_name__				=       states.reshape(1 ,     -1 ,     model.config.state_dim						)
     __magic_name__				=       actions.reshape(1 ,     -1 ,     model.config.act_dim						)
     __magic_name__				=       returns_to_go.reshape(1 ,     -1 ,     1						)
     __magic_name__				=       timesteps.reshape(1 ,     -1						)

     if model.config.max_length is not None:
          __magic_name__				=       states[:, -model.config.max_length :]
          __magic_name__				=       actions[:, -model.config.max_length :]
          __magic_name__				=       returns_to_go[:, -model.config.max_length :]
          __magic_name__				=       timesteps[:, -model.config.max_length :]

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

     __magic_name__		,  __magic_name__		,  __magic_name__				=       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

a_    :				List[Any]					=			gym.make('Hopper-v3')
a_    :				Optional[int]					=			env.observation_space.shape[0]
a_    :				List[str]					=			env.action_space.shape[0]
a_    :				List[Any]					=			1000
a_    :				int					=			'cuda'
a_    :				str					=			1_000.0  # normalization for rewards/returns
a_    :				Any					=			3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a_    :				int					=			np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
a_    :				List[str]					=			np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
a_    :				Optional[int]					=			torch.from_numpy(state_mean).to(device=device)
a_    :				int					=			torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
a_    :				List[Any]					=			DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
a_    :				Any					=			model.to(device)
model.eval()

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

    a_    :				Union[str, Any]					=			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
    for t in range(max_ep_len):
        env.render()
        # add padding
        a_    :				List[Any]					=			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
        a_    :				Tuple					=			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_    :				Optional[int]					=			action
        a_    :				Optional[int]					=			action.detach().cpu().numpy()

        a_				,       a_				,       a_				,       a_    :				List[str]					=			env.step(action)

        a_    :				Dict					=			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
        a_    :				Tuple					=			torch.cat([states, cur_state], dim=0)
        a_    :				int					=			reward

        a_    :				List[str]					=			target_return[0, -1] - (reward / scale)
        a_    :				Union[str, Any]					=			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






from math import pi




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						float						):
     return degree / (180 / pi)


if __name__ == "__main__":
    from doctest import testmod

    testmod()