






'''simple docstring'''

import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import DecisionTransformerModel
     from transformers.models.decision_transformer.modeling_decision_transformer import (
         DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
     )






class 				a_						:

  def __init__(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE=13			,		_SCREAMING_SNAKE_CASE=7			,		_SCREAMING_SNAKE_CASE=6			,		_SCREAMING_SNAKE_CASE=17			,		_SCREAMING_SNAKE_CASE=23			,		_SCREAMING_SNAKE_CASE=11			,		_SCREAMING_SNAKE_CASE=True			,		)  ->      Dict:



        """simple docstring"""






        UpperCamelCase				  =					parent
        UpperCamelCase				  =					batch_size
        UpperCamelCase				  =					seq_length
        UpperCamelCase				  =					act_dim
        UpperCamelCase				  =					state_dim
        UpperCamelCase				  =					hidden_size
        UpperCamelCase				  =					max_length
        UpperCamelCase				  =					is_training

  def   A__						(      self      )  ->      Any:



        """simple docstring"""






        UpperCamelCase				  =					floats_tensor((self.batch_size, self.seq_length, self.state_dim)      )
        UpperCamelCase				  =					floats_tensor((self.batch_size, self.seq_length, self.act_dim)      )
        UpperCamelCase				  =					floats_tensor((self.batch_size, self.seq_length, 1)      )
        UpperCamelCase				  =					floats_tensor((self.batch_size, self.seq_length, 1)      )
        UpperCamelCase				  =					ids_tensor((self.batch_size, self.seq_length)			,		vocab_size=1000      )
        UpperCamelCase				  =					random_attention_mask((self.batch_size, self.seq_length)      )

        UpperCamelCase				  =					self.get_config()

        return (
            config,
            states,
            actions,
            rewards,
            returns_to_go,
            timesteps,
            attention_mask,
        )

  def   A__						(      self      )  ->      Any:



        """simple docstring"""






        return DecisionTransformerConfig(
            batch_size=self.batch_size			,		seq_length=self.seq_length			,		act_dim=self.act_dim			,		state_dim=self.state_dim			,		hidden_size=self.hidden_size			,		max_length=self.max_length			,		)

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		)  ->      Union[str, Any]:



        """simple docstring"""






        UpperCamelCase				  =					DecisionTransformerModel(config=lowerCAmelCase__      )
        model.to(lowerCAmelCase__      )
        model.eval()
        UpperCamelCase				  =					model(lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      )

        self.parent.assertEqual(result.state_preds.shape			,		states.shape      )
        self.parent.assertEqual(result.action_preds.shape			,		actions.shape      )
        self.parent.assertEqual(result.return_preds.shape			,		returns_to_go.shape      )
        self.parent.assertEqual(
            result.last_hidden_state.shape			,		(self.batch_size, self.seq_length * 3, self.hidden_size)      )  # seq length *3 as there are 3 modelities: states, returns and actions


  def   A__						(      self      )  ->      Optional[Any]:



        """simple docstring"""






        UpperCamelCase				  =					self.prepare_config_and_inputs()
        (
            (
            UpperCamelCase
        )  ,(
            UpperCamelCase
        )  ,(
            UpperCamelCase
        )  ,(
            UpperCamelCase
        )  ,(
            UpperCamelCase
        )  ,(
            UpperCamelCase
        )  ,(
            UpperCamelCase
        )  ,
        )				  =					config_and_inputs
        UpperCamelCase				  =					{
            """states""": states,
            """actions""": actions,
            """rewards""": rewards,
            """returns_to_go""": returns_to_go,
            """timesteps""": timesteps,
            """attention_mask""": attention_mask,
        }
        return config, inputs_dict






@require_torch
class 				a_						(			__a	,       __a	,       __a	,       unittest.TestCase				):
  lowercase     		=      (DecisionTransformerModel,) if is_torch_available() else ()
  lowercase     		=      ()
  lowercase     		=      {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

  # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
  lowercase     		=      False

  # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
  lowercase     		=      False
  lowercase     		=      False
  lowercase     		=      False
  lowercase     		=      False
  lowercase     		=      False
  lowercase     		=      False
  lowercase     		=      False
  lowercase     		=      False
  lowercase     		=      False

  def   A__						(      self      )  ->      List[str]:



        """simple docstring"""






        UpperCamelCase				  =					DecisionTransformerModelTester(self      )
        UpperCamelCase				  =					ConfigTester(self			,		config_class=lowerCAmelCase__			,		hidden_size=37      )

  def   A__						(      self      )  ->      Tuple:



        """simple docstring"""






        self.config_tester.run_common_tests()

  def   A__						(      self      )  ->      Dict:



        """simple docstring"""






        UpperCamelCase				  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__      )

  @slow
  def   A__						(      self      )  ->      List[str]:



        """simple docstring"""






        for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase				  =					DecisionTransformerModel.from_pretrained(lowerCAmelCase__      )
              self.assertIsNotNone(lowerCAmelCase__      )


  def   A__						(      self      )  ->      Tuple:



        """simple docstring"""






        UpperCamelCase  ,UpperCamelCase				  =					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase				  =					model_class(lowerCAmelCase__      )
              UpperCamelCase				  =					inspect.signature(model.forward      )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCamelCase				  =					[*signature.parameters.keys()]

              UpperCamelCase				  =					[
                  """states""",
                  """actions""",
                  """rewards""",
                  """returns_to_go""",
                  """timesteps""",
                  """attention_mask""",
              ]

              self.assertListEqual(arg_names[: len(lowerCAmelCase__      )]			,		lowerCAmelCase__      )






@require_torch
class 				a_						(			unittest.TestCase				):

  @slow
  def   A__						(      self      )  ->      Any:



        """simple docstring"""






        UpperCamelCase				  =					2  # number of steps of autoregressive prediction we will perform
        UpperCamelCase				  =					10  # defined by the RL environment, may be normalized
        UpperCamelCase				  =					DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""      )
        UpperCamelCase				  =					model.to(lowerCAmelCase__      )
        UpperCamelCase				  =					model.config
        torch.manual_seed(0      )
        UpperCamelCase				  =					torch.randn(1			,		1			,		config.state_dim      ).to(device=lowerCAmelCase__			,		dtype=torch.floataa      )  # env.reset()

        UpperCamelCase				  =					torch.tensor(
            [[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]]			,		device=lowerCAmelCase__      )

        UpperCamelCase				  =					torch.tensor(lowerCAmelCase__			,		device=lowerCAmelCase__			,		dtype=torch.floataa      ).reshape(1			,		1			,		1      )
        UpperCamelCase				  =					state
        UpperCamelCase				  =					torch.zeros(1			,		0			,		config.act_dim			,		device=lowerCAmelCase__			,		dtype=torch.floataa      )
        UpperCamelCase				  =					torch.zeros(1			,		0			,		device=lowerCAmelCase__			,		dtype=torch.floataa      )
        UpperCamelCase				  =					torch.tensor(0			,		device=lowerCAmelCase__			,		dtype=torch.long      ).reshape(1			,		1      )

        for step in range(lowerCAmelCase__      ):
              UpperCamelCase				  =					torch.cat([actions, torch.zeros(1			,		1			,		config.act_dim			,		device=lowerCAmelCase__      )]			,		dim=1      )
              UpperCamelCase				  =					torch.cat([rewards, torch.zeros(1			,		1			,		device=lowerCAmelCase__      )]			,		dim=1      )

              UpperCamelCase				  =					torch.ones(1			,		states.shape[1]      ).to(dtype=torch.long			,		device=states.device      )

              with torch.no_grad():
                    UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase				  =					model(
                        states=lowerCAmelCase__			,		actions=lowerCAmelCase__			,		rewards=lowerCAmelCase__			,		returns_to_go=lowerCAmelCase__			,		timesteps=lowerCAmelCase__			,		attention_mask=lowerCAmelCase__			,		return_dict=lowerCAmelCase__			,		)

              self.assertEqual(action_pred.shape			,		actions.shape      )
              self.assertTrue(torch.allclose(action_pred[0, -1]			,		expected_outputs[step]			,		atol=1e-4      )      )
              UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase				  =					(  # env.step(action)
                  torch.randn(1			,		1			,		config.state_dim      ).to(device=lowerCAmelCase__			,		dtype=torch.floataa      ),
                  1.0,
                  False,
                  {},
              )

              UpperCamelCase				  =					action_pred[0, -1]
              UpperCamelCase				  =					torch.cat([states, state]			,		dim=1      )
              UpperCamelCase				  =					returns_to_go[0, -1] - reward
              UpperCamelCase				  =					torch.cat([returns_to_go, pred_return.reshape(1			,		1			,		1      )]			,		dim=1      )
              UpperCamelCase				  =					torch.cat(
                  [timesteps, torch.ones((1, 1)			,		device=lowerCAmelCase__			,		dtype=torch.long      ) * (step + 1)]			,		dim=1      )


"""simple docstring"""





from ..utils import DummyObject, requires_backends
class 						SCREAMING_SNAKE_CASE_ (						metaclass=__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		['''keras_nlp''']




		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				requires_backends(self							,  ["""keras_nlp"""])




