





"""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 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





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

  if model.config.max_length is not None:
    A_    :			Tuple									=    states[:, -model.config.max_length :]
    A_    :			Dict									=    actions[:, -model.config.max_length :]
    A_    :			str									=    returns_to_go[:, -model.config.max_length :]
    A_    :			Any									=    timesteps[:, -model.config.max_length :]

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

  A_		,			A_		,			A_    :			Any									=    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

lowerCamelCase_	:		int         =   gym.make('Hopper-v3')
lowerCamelCase_	:		Optional[int]         =   env.observation_space.shape[0]
lowerCamelCase_	:		Tuple         =   env.action_space.shape[0]
lowerCamelCase_	:		List[str]         =   10_00
lowerCamelCase_	:		Optional[Any]         =   'cuda'
lowerCamelCase_	:		str         =   1000.0  # normalization for rewards/returns
lowerCamelCase_	:		Tuple         =   36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
lowerCamelCase_	:		Optional[Any]         =   np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
lowerCamelCase_	:		Tuple         =   np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
lowerCamelCase_	:		List[Any]         =   torch.from_numpy(state_mean).to(device=device)
lowerCamelCase_	:		int         =   torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
lowerCamelCase_	:		str         =   DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
lowerCamelCase_	:		Tuple         =   model.to(device)
model.eval()

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

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

      lowerCamelCase_	:		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),
      )
      lowerCamelCase_	:		Dict         =   action
      lowerCamelCase_	:		Tuple         =   action.detach().cpu().numpy()

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

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

      lowerCamelCase_	:		Tuple         =   target_return[0, -1] - (reward / scale)
      lowerCamelCase_	:		List[Any]         =   torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
      lowerCamelCase_	:		Optional[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






"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


lowerCamelCase_	:		List[str]         =   logging.get_logger(__name__)

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def __init__(	self   , *snake_case_   , **snake_case_    ):

        """simple docstring"""

        warnings.warn(
            'The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
            ' Please use DetrImageProcessor instead.'   , snake_case_   , )
        super().__init__(*snake_case_   , **snake_case_    )