text: import pytest
  from unittest import mock
  import yaml
  
  from mlagents.tf_utils import tf
  
  
  from mlagents.trainers.sac.trainer import SACTrainer
  from mlagents.trainers.sac.optimizer import SACOptimizer
  from mlagents.trainers.policy.nn_policy import NNPolicy
  from mlagents.trainers.agent_processor import AgentManagerQueue
  from mlagents.trainers.tests import mock_brain as mb
  from mlagents.trainers.tests.mock_brain import make_brain_parameters
  from mlagents.trainers.tests.test_trajectory import make_fake_trajectory
  from mlagents.trainers.exception import UnityTrainerException
  
  
  @pytest.fixture
  def dummy_config():
      return yaml.safe_load(
          """
          trainer: sac
          batch_size: 32
          buffer_size: 10240
          buffer_init_steps: 0
          hidden_units: 32
          init_entcoef: 0.1
          learning_rate: 3.0e-4
          max_steps: 1024
          memory_size: 10
          normalize: true
          num_update: 1
          train_interval: 1
          num_layers: 1
          time_horizon: 64
          sequence_length: 16
          summary_freq: 1000
          tau: 0.005
          use_recurrent: false
          curiosity_enc_size: 128
          demo_path: None
          vis_encode_type: simple
          reward_signals:
              extrinsic:
                  strength: 1.0
                  gamma: 0.99
          """
      )
  
  
  VECTOR_ACTION_SPACE = [2]
  VECTOR_OBS_SPACE = 8
  DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
  BUFFER_INIT_SAMPLES = 64
  NUM_AGENTS = 12
  
  
  def create_sac_optimizer_mock(dummy_config, use_rnn, use_discrete, use_visual):
      mock_brain = mb.setup_mock_brain(
          use_discrete,
          use_visual,
          vector_action_space=VECTOR_ACTION_SPACE,
          vector_obs_space=VECTOR_OBS_SPACE,
          discrete_action_space=DISCRETE_ACTION_SPACE,
      )
  
      trainer_parameters = dummy_config
      model_path = "testmodel"
      trainer_parameters["model_path"] = model_path
      trainer_parameters["keep_checkpoints"] = 3
      trainer_parameters["use_recurrent"] = use_rnn
      policy = NNPolicy(
          0, mock_brain, trainer_parameters, False, False, create_tf_graph=False
      )
      optimizer = SACOptimizer(policy, trainer_parameters)
      return optimizer
  
  
  @pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
  @pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
  @pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
  def test_sac_optimizer_update(dummy_config, rnn, visual, discrete):
      # Test evaluate
      tf.reset_default_graph()
      optimizer = create_sac_optimizer_mock(
          dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
      )
      # Test update
      update_buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, optimizer.policy.brain)
      # Mock out reward signal eval
      update_buffer["extrinsic_rewards"] = update_buffer["environment_rewards"]
      optimizer.update(
          update_buffer,
          num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
      )
  
  
  @pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
  def test_sac_update_reward_signals(dummy_config, discrete):
      # Test evaluate
      tf.reset_default_graph()
      # Add a Curiosity module
      dummy_config["reward_signals"]["curiosity"] = {}
      dummy_config["reward_signals"]["curiosity"]["strength"] = 1.0
      dummy_config["reward_signals"]["curiosity"]["gamma"] = 0.99
      dummy_config["reward_signals"]["curiosity"]["encoding_size"] = 128
      optimizer = create_sac_optimizer_mock(
          dummy_config, use_rnn=False, use_discrete=discrete, use_visual=False
      )
  
      # Test update, while removing PPO-specific buffer elements.
      update_buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, optimizer.policy.brain)
  
      # Mock out reward signal eval
      update_buffer["extrinsic_rewards"] = update_buffer["environment_rewards"]
      update_buffer["curiosity_rewards"] = update_buffer["environment_rewards"]
      optimizer.update_reward_signals(
          {"curiosity": update_buffer}, num_sequences=update_buffer.num_experiences
      )
  
  
  def test_sac_save_load_buffer(tmpdir, dummy_config):
      mock_brain = mb.setup_mock_brain(
          False,
          False,
          vector_action_space=VECTOR_ACTION_SPACE,
          vector_obs_space=VECTOR_OBS_SPACE,
          discrete_action_space=DISCRETE_ACTION_SPACE,
      )
      trainer_params = dummy_config
      trainer_params["summary_path"] = str(tmpdir)
      trainer_params["model_path"] = str(tmpdir)
      trainer_params["save_replay_buffer"] = True
      trainer = SACTrainer(mock_brain.brain_name, 1, trainer_params, True, False, 0, 0)
      policy = trainer.create_policy(mock_brain.brain_name, mock_brain)
      trainer.add_policy(mock_brain.brain_name, policy)
  
      trainer.update_buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, policy.brain)
      buffer_len = trainer.update_buffer.num_experiences
      trainer.save_model(mock_brain.brain_name)
  
      # Wipe Trainer and try to load
      trainer2 = SACTrainer(mock_brain.brain_name, 1, trainer_params, True, True, 0, 0)
  
      policy = trainer2.create_policy(mock_brain.brain_name, mock_brain)
      trainer2.add_policy(mock_brain.brain_name, policy)
      assert trainer2.update_buffer.num_experiences == buffer_len
  
  
  @mock.patch("mlagents.trainers.sac.trainer.SACOptimizer")
  def test_add_get_policy(sac_optimizer, dummy_config):
      brain_params = make_brain_parameters(
          discrete_action=False, visual_inputs=0, vec_obs_size=6
      )
      mock_optimizer = mock.Mock()
      mock_optimizer.reward_signals = {}
      sac_optimizer.return_value = mock_optimizer
  
      dummy_config["summary_path"] = "./summaries/test_trainer_summary"
      dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
      trainer = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0")
      policy = mock.Mock(spec=NNPolicy)
      policy.get_current_step.return_value = 2000
  
      trainer.add_policy(brain_params.brain_name, policy)
      assert trainer.get_policy(brain_params.brain_name) == policy
  
      # Make sure the summary steps were loaded properly
      assert trainer.get_step == 2000
      assert trainer.next_summary_step > 2000
  
      # Test incorrect class of policy
      policy = mock.Mock()
      with pytest.raises(RuntimeError):
          trainer.add_policy(brain_params, policy)
  
  
  def test_process_trajectory(dummy_config):
      brain_params = make_brain_parameters(
          discrete_action=False, visual_inputs=0, vec_obs_size=6
      )
      dummy_config["summary_path"] = "./summaries/test_trainer_summary"
      dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
      trainer = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0")
      policy = trainer.create_policy(brain_params.brain_name, brain_params)
      trainer.add_policy(brain_params.brain_name, policy)
  
      trajectory_queue = AgentManagerQueue("testbrain")
      trainer.subscribe_trajectory_queue(trajectory_queue)
  
      trajectory = make_fake_trajectory(
          length=15,
          max_step_complete=True,
          vec_obs_size=6,
          num_vis_obs=0,
          action_space=[2],
      )
      trajectory_queue.put(trajectory)
      trainer.advance()
  
      # Check that trainer put trajectory in update buffer
      assert trainer.update_buffer.num_experiences == 15
  
      # Check that the stats are being collected as episode isn't complete
      for reward in trainer.collected_rewards.values():
          for agent in reward.values():
              assert agent > 0
  
      # Add a terminal trajectory
      trajectory = make_fake_trajectory(
          length=15,
          max_step_complete=False,
          vec_obs_size=6,
          num_vis_obs=0,
          action_space=[2],
      )
      trajectory_queue.put(trajectory)
      trainer.advance()
  
      # Check that the stats are reset as episode is finished
      for reward in trainer.collected_rewards.values():
          for agent in reward.values():
              assert agent == 0
      assert trainer.stats_reporter.get_stats_summaries("Policy/Extrinsic Reward").num > 0
      # Assert we're not just using the default values
      assert (
          trainer.stats_reporter.get_stats_summaries("Policy/Extrinsic Reward").mean > 0
      )
  
  
  def test_bad_config(dummy_config):
      brain_params = make_brain_parameters(
          discrete_action=False, visual_inputs=0, vec_obs_size=6
      )
      # Test that we throw an error if we have sequence length greater than batch size
      dummy_config["sequence_length"] = 64
      dummy_config["batch_size"] = 32
      dummy_config["use_recurrent"] = True
      dummy_config["summary_path"] = "./summaries/test_trainer_summary"
      dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
      with pytest.raises(UnityTrainerException):
          _ = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0")
  
  
  if __name__ == "__main__":
      pytest.main()
  