import pytest
from rlberry.envs import gym_make
from rlberry.agents.torch.dqn import DQNAgent
from rlberry.agents.torch.utils.training import model_factory
from rlberry.manager import ExperimentManager
import os
import pathlib

import tempfile


@pytest.mark.parametrize(
    "use_double_dqn, use_prioritized_replay", [(False, False), (True, True)]
)
def test_dqn_agent(use_double_dqn, use_prioritized_replay):
    env = gym_make("CartPole-v1")
    agent = DQNAgent(
        env,
        learning_starts=5,
        eval_interval=75,
        train_interval=2,
        gradient_steps=-1,
        use_double_dqn=use_double_dqn,
        use_prioritized_replay=use_prioritized_replay,
    )
    agent.fit(budget=500)

    model_configs = {
        "type": "MultiLayerPerceptron",
        "layer_sizes": (5, 5),
        "reshape": False,
    }

    def mlp(env, **kwargs):
        """
        Returns a default Q value network.
        """
        kwargs["in_size"] = env.observation_space.shape[0]
        kwargs["out_size"] = env.action_space.n
        return model_factory(**kwargs)

    new_agent = DQNAgent(
        env, q_net_constructor=mlp, q_net_kwargs=model_configs, learning_starts=100
    )
    new_agent.fit(budget=2000)


def test_dqn_classic_env():
    env = gym_make("CartPole-v1")
    agent = DQNAgent(
        env,
        learning_starts=5,
        eval_interval=75,
        train_interval=2,
        gradient_steps=-1,
        use_double_dqn=True,
        use_prioritized_replay=True,
    )
    agent.fit(budget=200)

    with tempfile.TemporaryDirectory() as tmpdirname:
        saving_path = tmpdirname + "/agent_test_dqn_classic_env.pickle"

        # test the save function
        agent.save(saving_path)
        assert os.path.exists(saving_path)

        # test the loading function
        test_load_env = gym_make("CartPole-v1")
        loaded_agent = DQNAgent.load(saving_path, **dict(env=test_load_env))
        assert loaded_agent

        # test the agent
        observation, info = test_load_env.reset()
        for tt in range(100):
            action = loaded_agent.policy(observation)
            next_observation, reward, terminated, truncated, info = test_load_env.step(
                action
            )
            done = terminated or truncated
            if done:
                next_observation, info = test_load_env.reset()
            observation = next_observation


def METHOD_NAME():
    # saving_path = "rlberry/agents/torch/tests/agentmanager_test_dqn_classic_env"

    with tempfile.TemporaryDirectory() as tmpdirname:
        saving_path = tmpdirname + "/agentmanager_test_dqn_classic_env"

        test_experiment_manager = ExperimentManager(
            DQNAgent,  # The Agent class.
            (
                gym_make,
                dict(
                    id="CartPole-v1",
                ),
            ),  # The Environment to solve.
            init_kwargs=dict(  # Where to put the agent's hyperparameters
                learning_starts=5,
                eval_interval=75,
                train_interval=2,
                gradient_steps=-1,
                use_double_dqn=True,
                use_prioritized_replay=True,
                chunk_size=1,
            ),
            fit_budget=200,  # The number of interactions between the agent and the environment during training.
            eval_kwargs=dict(
                eval_horizon=50
            ),  # The number of interactions between the agent and the environment during evaluations.
            n_fit=1,  # The number of agents to train. Usually, it is good to do more than 1 because the training is stochastic.
            agent_name="test_dqn_classic_env",  # The agent's name.
            output_dir=saving_path,
        )

        test_experiment_manager.fit(budget=200)

        # test the save function
        test_experiment_manager.save()
        assert os.path.exists(saving_path)

        # test the loading function
        test_load_env = gym_make("CartPole-v1")
        path_to_load = next(pathlib.Path(saving_path).glob("**/*.pickle"))
        loaded_experiment_manager = ExperimentManager.load(path_to_load)
        assert loaded_experiment_manager

        # test the agent
        state, info = test_load_env.reset()
        for tt in range(50):
            action = loaded_experiment_manager.get_agent_instances()[0].policy(state)
            next_s, _, terminated, truncated, test = test_load_env.step(action)
            done = terminated or truncated
            if done:
                break
            state = next_s