




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							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)    ->  List[str]:
     # we don't care about the past rewards in this model

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

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

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

     a						,   a						,   a											=   model(
         states=__UpperCamelCase	,      actions=__UpperCamelCase	,      rewards=__UpperCamelCase	,      returns_to_go=__UpperCamelCase	,      timesteps=__UpperCamelCase	,      attention_mask=__UpperCamelCase	,      return_dict=__UpperCamelCase	,      )

     return action_preds[0, -1]


# build the environment

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

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

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

   lowercase__   :    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
      lowercase__   :    str 					=      torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
      lowercase__   :    Any 					=      torch.cat([rewards, torch.zeros(1, device=device)])

      lowercase__   :    Union[str, 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),
      )
      lowercase__   :    int 					=      action
      lowercase__   :    List[str] 					=      action.detach().cpu().numpy()

      lowercase__					,       lowercase__					,       lowercase__					,       lowercase__   :    str 					=      env.step(action)

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

      lowercase__   :    Optional[int] 					=      target_return[0, -1] - (reward / scale)
      lowercase__   :    Any 					=      torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
      lowercase__   :    int 					=      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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase__   :    str 					=      {
    "configuration_rwkv": ["RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP", "RwkvConfig", "RwkvOnnxConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__   :    int 					=      [
       "RWKV_PRETRAINED_MODEL_ARCHIVE_LIST",
       "RwkvForCausalLM",
       "RwkvModel",
       "RwkvPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rwkv import (
          RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
          RwkvForCausalLM,
          RwkvModel,
          RwkvPreTrainedModel,
      )
else:
   import sys

   lowercase__   :    List[Any] 					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

