
'''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_  (    __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	Tuple			)					->							List[Any]:





  """simple docstring"""

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

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

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

  __A				,       __A				,       __A		:  Dict       				=		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

A__ :		Union[str, Any]					=gym.make('Hopper-v3')
A__ :		List[str]					=env.observation_space.shape[0]
A__ :		Union[str, Any]					=env.action_space.shape[0]
A__ :		Dict					=1_000
A__ :		Optional[int]					='cuda'
A__ :		Tuple					=1_0_0_0.0  # normalization for rewards/returns
A__ :		Any					=3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
A__ :		Any					=np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
A__ :		str					=np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
A__ :		int					=torch.from_numpy(state_mean).to(device=device)
A__ :		Dict					=torch.from_numpy(state_std).to(device=device)

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

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

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

          A__ :		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),
          )
          A__ :		Union[str, Any]					=action
          A__ :		str					=action.detach().cpu().numpy()

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

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

          A__ :		str					=target_return[0, -1] - (reward / scale)
          A__ :		Optional[int]					=torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
          A__ :		Union[str, 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






"""simple docstring"""






from ..utils import DummyObject, requires_backends
class 	_lowerCAmelCase    (      metaclass=__snake_case       ):
							__lowerCAmelCase					:    Optional[Any]     =  ['''tensorflow_text''']

							def __init__( self       :    Optional[int] ,		*a       :    int ,		**a       :    str    )   ->    str:

									"""simple docstring"""
									requires_backends(self ,		['''tensorflow_text''']    )