




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 	lowerCAmelCase_	(     lowerCamelCase:				str   , lowerCamelCase:				Optional[int]   , lowerCamelCase:				Optional[Any]   , lowerCamelCase:				List[str]   , lowerCamelCase:				int   , lowerCamelCase:				int  )    ->	Optional[Any]:


      '''simple docstring'''




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

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

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

      lowerCamelCase__     ,						lowerCamelCase__     ,						lowerCamelCase__					     = 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

__SCREAMING_SNAKE_CASE			:      Any      			=gym.make("""Hopper-v3""")
__SCREAMING_SNAKE_CASE			:      Dict      			=env.observation_space.shape[0]
__SCREAMING_SNAKE_CASE			:      Union[str, Any]      			=env.action_space.shape[0]
__SCREAMING_SNAKE_CASE			:      Dict      			=1_000
__SCREAMING_SNAKE_CASE			:      Dict      			="""cuda"""
__SCREAMING_SNAKE_CASE			:      Optional[Any]      			=1000.0  # normalization for rewards/returns
__SCREAMING_SNAKE_CASE			:      Tuple      			=3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__SCREAMING_SNAKE_CASE			:      Optional[Any]      			=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			:      Tuple      			=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			:      Dict      			=torch.from_numpy(state_mean).to(device=device)
__SCREAMING_SNAKE_CASE			:      Optional[int]      			=torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
__SCREAMING_SNAKE_CASE			:      Tuple      			=DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-medium""")
__SCREAMING_SNAKE_CASE			:      List[str]      			=model.to(device)
model.eval()

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

      __SCREAMING_SNAKE_CASE			:      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
            __SCREAMING_SNAKE_CASE			:      str      			=torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
            __SCREAMING_SNAKE_CASE			:      Optional[int]      			=torch.cat([rewards, torch.zeros(1, device=device)])

            __SCREAMING_SNAKE_CASE			:      Optional[int]      			=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			:      str      			=action
            __SCREAMING_SNAKE_CASE			:      Tuple      			=action.detach().cpu().numpy()

            __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			:      Tuple      			=env.step(action)

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

            __SCREAMING_SNAKE_CASE			:      Dict      			=target_return[0, -1] - (reward / scale)
            __SCREAMING_SNAKE_CASE			:      List[Any]      			=torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
            __SCREAMING_SNAKE_CASE			:      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 	lowerCAmelCase_	(     lowerCamelCase:				int  )    ->	int:


      '''simple docstring'''




      return n * (2 * n - 1)

def 	lowerCAmelCase_	(     lowerCamelCase:				int  )    ->	bool:


      '''simple docstring'''




      lowerCamelCase__					     = (1 + 24 * n) ** 0.5
      return ((1 + root) / 6) % 1 == 0

def 	lowerCAmelCase_	(     lowerCamelCase:				int = 1_44  )    ->	int:


      '''simple docstring'''




      lowerCamelCase__					     = start
      lowerCamelCase__					     = hexagonal_num(lowerCamelCase  )
      while not is_pentagonal(lowerCamelCase  ):
            n += 1
            lowerCamelCase__					     = hexagonal_num(lowerCamelCase  )
      return num


if __name__ == "__main__":
      print(F"""{solution()} = """)