






"""simple docstring"""





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(  snake_case_						,       snake_case_						,       snake_case_						,       snake_case_						,       snake_case_						,       snake_case_		):
       """simple docstring"""





       lowercase__:    Optional[Any]        =      states.reshape(1						,       -1						,       model.config.state_dim		)
       lowercase__:    List[Any]        =      actions.reshape(1						,       -1						,       model.config.act_dim		)
       lowercase__:    Dict        =      returns_to_go.reshape(1						,       -1						,       1		)
       lowercase__:    str        =      timesteps.reshape(1						,       -1		)

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

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

       lowercase__  ,     lowercase__  ,     lowercase__:    Optional[int]        =      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

UpperCamelCase			=      gym.make("""Hopper-v3""")
UpperCamelCase			=      env.observation_space.shape[0]
UpperCamelCase			=      env.action_space.shape[0]
UpperCamelCase			=      10_00
UpperCamelCase			=      """cuda"""
UpperCamelCase			=      1_000.0  # normalization for rewards/returns
UpperCamelCase			=      36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase			=      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,
    ]
)
UpperCamelCase			=      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,
    ]
)
UpperCamelCase			=      torch.from_numpy(state_mean).to(device=device)
UpperCamelCase			=      torch.from_numpy(state_std).to(device=device)

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

for ep in range(10):
  UpperCamelCase,      UpperCamelCase			=      0, 0
  UpperCamelCase			=      env.reset()
  UpperCamelCase			=      torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
  UpperCamelCase			=      torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
  UpperCamelCase			=      torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
  UpperCamelCase			=      torch.zeros(0, device=device, dtype=torch.floataa)

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

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

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

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

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














"""simple docstring"""





UpperCamelCase			=      {
    """0""": 1,
    """1""": 1,
    """2""": 2,
    """3""": 6,
    """4""": 24,
    """5""": 1_20,
    """6""": 7_20,
    """7""": 50_40,
    """8""": 4_03_20,
    """9""": 36_28_80,
}

UpperCamelCase			=      {1_45: 1_45}

UpperCamelCase			=      {
    1_45: 0,
    1_69: 3,
    3_63_01: 3,
    14_54: 3,
    8_71: 2,
    4_53_61: 2,
    8_72: 2,
}






def     A(  snake_case_		):
       """simple docstring"""





       if n in CACHE_SUM_DIGIT_FACTORIALS:
              return CACHE_SUM_DIGIT_FACTORIALS[n]
       lowercase__:    Any        =      sum(DIGIT_FACTORIALS[let] for let in str(snake_case_		)		)
       lowercase__:    Optional[Any]        =      ret
       return ret






def     A(  snake_case_						,       snake_case_ = None		):
       """simple docstring"""





       lowercase__:    Optional[int]        =      previous or set()
       if n in CHAIN_LENGTH_CACHE:
              return CHAIN_LENGTH_CACHE[n]
       lowercase__:    Tuple        =      sum_digit_factorials(snake_case_		)
       if next_number in previous:
              lowercase__:    List[Any]        =      0
              return 0
       else:
              previous.add(snake_case_		)
              lowercase__:    Optional[int]        =      1 + chain_length(snake_case_						,       snake_case_		)
              lowercase__:    Any        =      ret
              return ret






def     A(  snake_case_ = 60						,       snake_case_ = 1000000		):
       """simple docstring"""





       return sum(1 for i in range(1						,       snake_case_		) if chain_length(snake_case_		) == num_terms		)


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






