"""This script is a test that fails when MAML-TRPO performance is too low."""
import pytest
try:
    # pylint: disable=unused-import
    import mujoco_py  # noqa: F401
except ImportError:
    pytest.skip('To use mujoco-based features, please install garage[mujoco].',
                allow_module_level=True)
except Exception:  # pylint: disable=broad-except
    pytest.skip(
        'Skipping tests, failed to import mujoco. Do you have a '
        'valid mujoco key installed?',
        allow_module_level=True)
import torch

from garage.envs import GarageEnv, normalize
from garage.envs.mujoco import HalfCheetahDirEnv
from garage.experiment import deterministic, LocalRunner
from garage.torch.algos import MAMLPPO
from garage.torch.policies import GaussianMLPPolicy
from garage.torch.value_functions import GaussianMLPValueFunction
from tests.fixtures import snapshot_config


@pytest.mark.mujoco
class TestMAMLPPO:
    """Test class for MAML-PPO."""

    def setup_method(self):
        """Setup method which is called before every test."""
        self.env = GarageEnv(
            normalize(HalfCheetahDirEnv(), expected_action_scale=10.))
        self.policy = GaussianMLPPolicy(
            env_spec=self.env.spec,
            hidden_sizes=(64, 64),
            hidden_nonlinearity=torch.tanh,
            output_nonlinearity=None,
        )
        self.value_function = GaussianMLPValueFunction(env_spec=self.env.spec,
                                                       hidden_sizes=(32, 32))

    def teardown_method(self):
        """Teardown method which is called after every test."""
        self.env.close()

    def test_ppo_pendulum(self):
        """Test PPO with Pendulum environment."""
        deterministic.set_seed(0)

        rollouts_per_task = 5
        max_path_length = 100

        runner = LocalRunner(snapshot_config)
        algo = MAMLPPO(env=self.env,
                       policy=self.policy,
                       value_function=self.value_function,
                       max_path_length=max_path_length,
                       meta_batch_size=5,
                       discount=0.99,
                       gae_lambda=1.,
                       inner_lr=0.1,
                       num_grad_updates=1)

        runner.setup(algo, self.env)
        last_avg_ret = runner.train(n_epochs=10,
                                    batch_size=rollouts_per_task *
                                    max_path_length)

        assert last_avg_ret > -5
