docstr: An integration test that performs autoregressive prediction of state, action and return
  from a sequence of state, actions and returns. Test is performed over two timesteps.
docstr_tok: ["An","integration","test","that","performs","autoregressive","prediction","of","state",",","action","and","return","from","a","sequence","of","state",",","actions","and","returns",".","Test","is","performed","over","two","timesteps","."]
code: def test_autoregressive_prediction(self):
      """
      An integration test that performs autoregressive prediction of state, action and return
      from a sequence of state, actions and returns. Test is performed over two timesteps.
  
      """
  
      NUM_STEPS = 2  # number of steps of autoregressive prediction we will perform
      TARGET_RETURN = (
          10  # defined by the RL environment, may be normalized
      )
      model = DecisionTransformerModel.from_pretrained(
          "edbeeching/decision-transformer-gym-hopper-expert"
      )
      model = model.to(torch_device)
      config = model.config
      torch.manual_seed(0)
      state = torch.randn(1, 1, config.state_dim).to(
          device=torch_device, dtype=torch.float32
      )  # env.reset()
  
      expected_outputs = torch.tensor(
          [
              [0.242793, -0.28693074, 0.8742613],
              [0.67815274, -0.08101085, -0.12952147],
          ],
          device=torch_device,
      )
  
      returns_to_go = torch.tensor(
          TARGET_RETURN, device=torch_device, dtype=torch.float32
      ).reshape(1, 1, 1)
      states = state
      actions = torch.zeros(
          1, 0, config.act_dim, device=torch_device, dtype=torch.float32
      )
      rewards = torch.zeros(
          1, 0, device=torch_device, dtype=torch.float32
      )
      timesteps = torch.tensor(
          0, device=torch_device, dtype=torch.long
      ).reshape(1, 1)
  
      for step in range(NUM_STEPS):
          actions = torch.cat(
              [
                  actions,
                  torch.zeros(
                      1, 1, config.act_dim, device=torch_device
                  ),
              ],
              dim=1,
          )
          rewards = torch.cat(
              [rewards, torch.zeros(1, 1, device=torch_device)], dim=1
          )
  
          attention_mask = torch.ones(1, states.shape[1]).to(
              dtype=torch.long, device=states.device
          )
  
          with torch.no_grad():
              _, action_pred, _ = model(
                  states=states,
                  actions=actions,
                  rewards=rewards,
                  returns_to_go=returns_to_go,
                  timesteps=timesteps,
                  attention_mask=attention_mask,
                  return_dict=False,
              )
  
          self.assertEqual(action_pred.shape, actions.shape)
          self.assertTrue(
              torch.allclose(
                  action_pred[0, -1], expected_outputs[step], atol=1e-4
              )
          )
          state, reward, _, _ = (  # env.step(action)
              torch.randn(1, 1, config.state_dim).to(
                  device=torch_device, dtype=torch.float32
              ),
              1.0,
              False,
              {},
          )
  
          actions[-1] = action_pred[0, -1]
          states = torch.cat([states, state], dim=1)
          pred_return = returns_to_go[0, -1] - reward
          returns_to_go = torch.cat(
              [returns_to_go, pred_return.reshape(1, 1, 1)], dim=1
          )
          timesteps = torch.cat(
              [
                  timesteps,
                  torch.ones(
                      (1, 1), device=torch_device, dtype=torch.long
                  )
                  * (step + 1),
              ],
              dim=1,
          )
  
imports: import inspect
  import unittest
  from transformers import DecisionTransformerConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 1 for calling the DecisionTransformerModelIntegrationTest obj's underlying member method code with example usage: obj.test_autoregressive_prediction() without return types