

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    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Optional[int]  ,				_SCREAMING_SNAKE_CASE   :						Dict  ,				_SCREAMING_SNAKE_CASE   :						List[str]  ,				_SCREAMING_SNAKE_CASE   :						List[Any]  ,				_SCREAMING_SNAKE_CASE   :						str  ,				_SCREAMING_SNAKE_CASE   :						Optional[Any]    ):





    """simple docstring"""


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

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

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

    SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  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

UpperCamelCase__					: Optional[Any]								=      gym.make("Hopper-v3")
UpperCamelCase__					: Tuple								=      env.observation_space.shape[0]
UpperCamelCase__					: Any								=      env.action_space.shape[0]
UpperCamelCase__					: Optional[Any]								=      1_000
UpperCamelCase__					: Optional[Any]								=      "cuda"
UpperCamelCase__					: Tuple								=      1_000.0  # normalization for rewards/returns
UpperCamelCase__					: Tuple								=      3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase__					: 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,
    ]
)
UpperCamelCase__					: Optional[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,
    ]
)
UpperCamelCase__					: Union[str, Any]								=      torch.from_numpy(state_mean).to(device=device)
UpperCamelCase__					: List[str]								=      torch.from_numpy(state_std).to(device=device)

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

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

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

              UpperCamelCase__					: 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),
              )
              UpperCamelCase__					: Optional[Any]								=      action
              UpperCamelCase__					: Optional[int]								=      action.detach().cpu().numpy()

              UpperCamelCase__					: int								=      env.step(action)

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

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





    """simple docstring"""


    if power < 0:
        return -1
    base %= mod
    SCREAMING_SNAKE_CASE_													=  1

    while power > 0:
        if power & 1:
            SCREAMING_SNAKE_CASE_													=  (result * base) % mod
        SCREAMING_SNAKE_CASE_													=  power >> 1
        SCREAMING_SNAKE_CASE_													=  (base * base) % mod

    return result


def    _UpperCAmelCase    (					):





    """simple docstring"""


    print(modular_exponential(3	,				200	,				13    )    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       main()


