import unittest

import ray
from ray.rllib.algorithms.ppo import PPOConfig
from ray.rllib.policy.dynamic_tf_policy_v2 import DynamicTFPolicyV2
from ray.rllib.policy.eager_tf_policy_v2 import EagerTFPolicyV2
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
from ray.rllib.utils.test_utils import check


class TestPolicy(unittest.TestCase):
    @classmethod
    def setUpClass(cls) -> None:
        ray.init()

    @classmethod
    def tearDownClass(cls) -> None:
        ray.shutdown()

    def test_policy_get_and_set_state(self):
        config = (
            PPOConfig()
            .environment("CartPole-v1")
            .api_stack(
                enable_env_runner_and_connector_v2=False,
                enable_rl_module_and_learner=False,
            )
        )
        algo = config.build()
        policy = algo.get_policy()
        state1 = policy.get_state()
        algo.train()
        state2 = policy.get_state()
        check(state1["global_timestep"], state2["global_timestep"], false=True)

        # Reset policy to its original state and compare.
        policy.set_state(state1)
        state3 = policy.get_state()
        # Make sure everything is the same.
        check(state1["_exploration_state"], state3["_exploration_state"])
        check(state1["global_timestep"], state3["global_timestep"])
        check(state1["weights"], state3["weights"])

        # Create a new Policy only from state (which could be part of an algorithm's
        # checkpoint). This would allow users to restore a policy w/o having access
        # to the original code (e.g. the config, policy class used, etc..).
        if isinstance(policy, (EagerTFPolicyV2, DynamicTFPolicyV2, TorchPolicyV2)):
            policy_restored_from_scratch = Policy.from_state(state3)
            state4 = policy_restored_from_scratch.get_state()
            check(state3["_exploration_state"], state4["_exploration_state"])
            check(state3["global_timestep"], state4["global_timestep"])
            # For tf static graph, the new model has different layer names
            # (as it gets written into the same graph as the old one).
            check(state3["weights"], state4["weights"])


if __name__ == "__main__":
    import sys

    import pytest

    sys.exit(pytest.main(["-v", __file__]))