text: import pytest
  import copy
  import os
  import mlagents.trainers.tests.mock_brain as mb
  from mlagents.trainers.policy.tf_policy import TFPolicy
  from mlagents.trainers.sac.optimizer_tf import SACOptimizer
  from mlagents.trainers.ppo.optimizer_tf import PPOOptimizer
  from mlagents.trainers.tests.test_simple_rl import PPO_CONFIG, SAC_CONFIG
  from mlagents.trainers.settings import (
      GAILSettings,
      CuriositySettings,
      RewardSignalSettings,
      BehavioralCloningSettings,
      NetworkSettings,
      TrainerType,
      RewardSignalType,
  )
  
  CONTINUOUS_PATH = os.path.dirname(os.path.abspath(__file__)) + "/test.demo"
  DISCRETE_PATH = os.path.dirname(os.path.abspath(__file__)) + "/testdcvis.demo"
  
  
  def ppo_dummy_config():
      return copy.deepcopy(PPO_CONFIG)
  
  
  def sac_dummy_config():
      return copy.deepcopy(SAC_CONFIG)
  
  
  @pytest.fixture
  def gail_dummy_config():
      return {RewardSignalType.GAIL: GAILSettings(demo_path=CONTINUOUS_PATH)}
  
  
  @pytest.fixture
  def curiosity_dummy_config():
      return {RewardSignalType.CURIOSITY: CuriositySettings()}
  
  
  @pytest.fixture
  def extrinsic_dummy_config():
      return {RewardSignalType.EXTRINSIC: RewardSignalSettings()}
  
  
  VECTOR_ACTION_SPACE = 2
  VECTOR_OBS_SPACE = 8
  DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
  BUFFER_INIT_SAMPLES = 20
  BATCH_SIZE = 12
  NUM_AGENTS = 12
  
  
  def create_optimizer_mock(
      trainer_config, reward_signal_config, use_rnn, use_discrete, use_visual
  ):
      mock_specs = mb.setup_test_behavior_specs(
          use_discrete,
          use_visual,
          vector_action_space=DISCRETE_ACTION_SPACE
          if use_discrete
          else VECTOR_ACTION_SPACE,
          vector_obs_space=VECTOR_OBS_SPACE if not use_visual else 0,
      )
      trainer_settings = trainer_config
      trainer_settings.reward_signals = reward_signal_config
      trainer_settings.network_settings.memory = (
          NetworkSettings.MemorySettings(sequence_length=16, memory_size=10)
          if use_rnn
          else None
      )
      policy = TFPolicy(
          0, mock_specs, trainer_settings, "test", False, create_tf_graph=False
      )
      if trainer_settings.trainer_type == TrainerType.SAC:
          optimizer = SACOptimizer(policy, trainer_settings)
      else:
          optimizer = PPOOptimizer(policy, trainer_settings)
      optimizer.policy.initialize()
      return optimizer
  
  
  def reward_signal_eval(optimizer, reward_signal_name):
      buffer = mb.simulate_rollout(BATCH_SIZE, optimizer.policy.behavior_spec)
      # Test evaluate
      rsig_result = optimizer.reward_signals[reward_signal_name].evaluate_batch(buffer)
      assert rsig_result.scaled_reward.shape == (BATCH_SIZE,)
      assert rsig_result.unscaled_reward.shape == (BATCH_SIZE,)
  
  
  def reward_signal_update(optimizer, reward_signal_name):
      buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, optimizer.policy.behavior_spec)
      feed_dict = optimizer.reward_signals[reward_signal_name].prepare_update(
          optimizer.policy, buffer.make_mini_batch(0, 10), 2
      )
      out = optimizer.policy._execute_model(
          feed_dict, optimizer.reward_signals[reward_signal_name].update_dict
      )
      assert type(out) is dict
  
  
  @pytest.mark.parametrize(
      "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
  )
  def test_gail_cc(trainer_config, gail_dummy_config):
      trainer_config.behavioral_cloning = BehavioralCloningSettings(
          demo_path=CONTINUOUS_PATH
      )
      optimizer = create_optimizer_mock(
          trainer_config, gail_dummy_config, False, False, False
      )
      reward_signal_eval(optimizer, "gail")
      reward_signal_update(optimizer, "gail")
  
  
  @pytest.mark.parametrize(
      "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
  )
  def test_gail_dc_visual(trainer_config, gail_dummy_config):
      gail_dummy_config_discrete = {
          RewardSignalType.GAIL: GAILSettings(demo_path=DISCRETE_PATH)
      }
      optimizer = create_optimizer_mock(
          trainer_config, gail_dummy_config_discrete, False, True, True
      )
      reward_signal_eval(optimizer, "gail")
      reward_signal_update(optimizer, "gail")
  
  
  @pytest.mark.parametrize(
      "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
  )
  def test_gail_rnn(trainer_config, gail_dummy_config):
      policy = create_optimizer_mock(
          trainer_config, gail_dummy_config, True, False, False
      )
      reward_signal_eval(policy, "gail")
      reward_signal_update(policy, "gail")
  
  
  @pytest.mark.parametrize(
      "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
  )
  def test_curiosity_cc(trainer_config, curiosity_dummy_config):
      policy = create_optimizer_mock(
          trainer_config, curiosity_dummy_config, False, False, False
      )
      reward_signal_eval(policy, "curiosity")
      reward_signal_update(policy, "curiosity")
  
  
  @pytest.mark.parametrize(
      "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
  )
  def test_curiosity_dc(trainer_config, curiosity_dummy_config):
      policy = create_optimizer_mock(
          trainer_config, curiosity_dummy_config, False, True, False
      )
      reward_signal_eval(policy, "curiosity")
      reward_signal_update(policy, "curiosity")
  
  
  @pytest.mark.parametrize(
      "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
  )
  def test_curiosity_visual(trainer_config, curiosity_dummy_config):
      policy = create_optimizer_mock(
          trainer_config, curiosity_dummy_config, False, False, True
      )
      reward_signal_eval(policy, "curiosity")
      reward_signal_update(policy, "curiosity")
  
  
  @pytest.mark.parametrize(
      "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
  )
  def test_curiosity_rnn(trainer_config, curiosity_dummy_config):
      policy = create_optimizer_mock(
          trainer_config, curiosity_dummy_config, True, False, False
      )
      reward_signal_eval(policy, "curiosity")
      reward_signal_update(policy, "curiosity")
  
  
  @pytest.mark.parametrize(
      "trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
  )
  def test_extrinsic(trainer_config, extrinsic_dummy_config):
      policy = create_optimizer_mock(
          trainer_config, extrinsic_dummy_config, False, False, False
      )
      reward_signal_eval(policy, "extrinsic")
      reward_signal_update(policy, "extrinsic")
  
  
  if __name__ == "__main__":
      pytest.main()
  