import dataclasses
import shlex
from pathlib import Path

from farconf import parse_cli, update_fns_to_cli

from cleanba.config import Args, sokoban_drc_3_3
from cleanba.convlstm import ConvConfig, ConvLSTMCellConfig, ConvLSTMConfig
from cleanba.environments import random_seed
from cleanba.launcher import FlamingoRun, group_from_fname, launch_jobs

clis = []
for max_grad_norm in [6e-4]:
    for use_relu in [False]:
        for output_activation in ["sigmoid", "tanh"]:
            for projection in ["per-channel", "max", "full", "mean"]:
                for env_seed, learn_seed in [(random_seed(), random_seed()) for _ in range(4)]:

                    def update_fn(config: Args) -> Args:
                        config.eval_envs = {}  # Don't evaluate
                        config.train_env = dataclasses.replace(config.train_env, seed=env_seed)
                        config.local_num_envs = 256
                        config.num_steps = 20
                        config.net = ConvLSTMConfig(
                            n_recurrent=3,
                            repeats_per_step=3,
                            skip_final=True,
                            residual=True,
                            use_relu=use_relu,
                            embed=[ConvConfig(32, (4, 4), (1, 1), "SAME", True)] * 2,
                            recurrent=ConvLSTMCellConfig(
                                ConvConfig(32, (3, 3), (1, 1), "SAME", True),
                                pool_and_inject="horizontal",
                                pool_projection=projection,
                                output_activation=output_activation,
                                fence_pad="valid",
                                forget_bias=1.0,
                            ),
                            head_scale=1.0,
                        )

                        world_size = 1
                        len_actor_device_ids = 1
                        num_actor_threads = 1
                        global_step_multiplier = (
                            config.num_steps * config.local_num_envs * num_actor_threads * len_actor_device_ids * world_size
                        )

                        config.total_timesteps = 12_800_000
                        num_updates = config.total_timesteps // global_step_multiplier
                        assert (
                            num_updates * global_step_multiplier == config.total_timesteps
                        ), f"{config.total_timesteps=} != {num_updates=}*{global_step_multiplier=}"

                        # Evaluate (and save) EVAL_TIMES during training
                        EVAL_TIMES = 16
                        config.eval_frequency = num_updates // EVAL_TIMES

                        config.save_model = True
                        config.base_run_dir = Path("/training/cleanba")

                        config.actor_update_frequency = 1
                        config.actor_update_cutoff = int(1e20)  # disable

                        config.train_epochs = 1
                        config.num_actor_threads = 1
                        config.num_minibatches = 8

                        config.seed = learn_seed
                        config.sync_frequency = int(1e20)

                        logit_l2_coef = 1.5625e-5
                        config.loss = dataclasses.replace(
                            config.loss,
                            vtrace_lambda=0.97,
                            vf_coef=0.25,
                            gamma=0.97,
                            ent_coef=0.001,
                            normalize_advantage=False,
                            logit_l2_coef=logit_l2_coef,
                            weight_l2_coef=logit_l2_coef / 100,
                        )
                        config.base_fan_in = 1
                        config.anneal_lr = False

                        config.optimizer = "adam"
                        config.adam_b1 = 0.9
                        config.rmsprop_decay = 0.999
                        config.learning_rate = 4e-4
                        config.max_grad_norm = max_grad_norm
                        config.rmsprop_eps = 1.5625e-10
                        config.optimizer_yang = False

                        return config

                    cli, _ = update_fns_to_cli(sokoban_drc_3_3, update_fn)
                    print(shlex.join(cli))
                    # Check that parsing doesn't error
                    out = parse_cli(cli, Args)

                    clis.append(cli)

runs: list[FlamingoRun] = []
RUNS_PER_MACHINE = 1
for i in range(0, len(clis), RUNS_PER_MACHINE):
    runs.append(
        FlamingoRun(
            [["python", "-m", "cleanba.cleanba_impala", *clis[i + j]] for j in range(min(RUNS_PER_MACHINE, len(clis) - i))],
            CONTAINER_TAG="df13e85-main",
            CPU=6,
            MEMORY="20G",
            GPU=1,
            PRIORITY="normal-batch",
            XLA_PYTHON_CLIENT_MEM_FRACTION='".95"',
        )
    )


GROUP: str = group_from_fname(__file__)

if __name__ == "__main__":
    launch_jobs(
        runs,
        group=GROUP,
        job_template_path=Path(__file__).parent.parent.parent / "k8s/runner.yaml",
        project="cleanba",
    )
