#  Copyright (c) Meta Platforms, Inc. and affiliates.
#
#  This source code is licensed under the license found in the
#  LICENSE file in the root directory of this source tree.
#

import os
from pathlib import Path

from benchmarl.algorithms import MappoConfig
from benchmarl.environments import VmasTask
from benchmarl.experiment import Experiment, ExperimentConfig
from benchmarl.hydra_config import reload_experiment_from_file
from benchmarl.models.mlp import MlpConfig

if __name__ == "__main__":

    experiment_config = ExperimentConfig.get_from_yaml()
    # Save the experiment in the current folder
    experiment_config.save_folder = Path(os.path.dirname(os.path.realpath(__file__)))
    # Checkpoint at every iteration
    experiment_config.checkpoint_interval = (
        experiment_config.on_policy_collected_frames_per_batch
    )
    # Run 3 iterations
    experiment_config.max_n_iters = 3

    task = VmasTask.BALANCE.get_from_yaml()
    algorithm_config = MappoConfig.get_from_yaml()
    model_config = MlpConfig.get_from_yaml()
    critic_model_config = MlpConfig.get_from_yaml()
    experiment = Experiment(
        task=task,
        algorithm_config=algorithm_config,
        model_config=model_config,
        critic_model_config=critic_model_config,
        seed=0,
        config=experiment_config,
    )
    experiment.run()

    # Now we tell it where to resume from
    restored_experiment = reload_experiment_from_file(
        (
            experiment.folder_name
            / "checkpoints"
            / f"checkpoint_{experiment_config.checkpoint_interval}.pt"
        )
    )  # Restore from first checkpoint

    # We keep the same configuration
    restored_experiment.run()

    # We can also evaluate
    restored_experiment.evaluate()