import dataclasses
import shlex
from pathlib import Path

from farconf import update_fns_to_cli

from cleanba.config import Args, sokoban_resnet
from cleanba.environments import random_seed
from cleanba.launcher import FlamingoRun, group_from_fname, launch_jobs
from cleanba.network import GuezResNetConfig, IdentityNorm

clis = []
# Paper says 200 actors, we'll use 192 so it's evenly divided by 32
n_envs = 256
minibatch_size = 32
assert n_envs % minibatch_size == 0
for env_seed, learn_seed in [(random_seed(), random_seed()) for _ in range(5)]:

    def update_fn(config: Args) -> Args:
        config.net = GuezResNetConfig(yang_init=False, norm=IdentityNorm(), normalize_input=False)
        config.local_num_envs = n_envs
        config.num_steps = 20

        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 = 1_001_472_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 = 100
        config.eval_frequency = num_updates // EVAL_TIMES
        assert config.eval_frequency * EVAL_TIMES * global_step_multiplier > 1e9, "training and saving for insufficiently long"

        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 = (config.local_num_envs * config.num_actor_threads) // minibatch_size

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

        logit_l2_coef = 1.5625e-6  # doesn't seem to matter much from now. May improve stability a tiny bit.
        config.loss = dataclasses.replace(
            config.loss,
            vtrace_lambda=0.97,
            vf_coef=0.25,
            gamma=0.97,
            ent_coef=0.01,
            normalize_advantage=False,
            logit_l2_coef=logit_l2_coef,
            weight_l2_coef=logit_l2_coef / 100,
        )
        config.base_fan_in = 1
        config.anneal_lr = True

        config.optimizer = "adam"
        config.adam_b1 = 0.9
        config.rmsprop_decay = 0.99
        config.learning_rate = 4e-4
        config.max_grad_norm = 2.5e-4
        config.rmsprop_eps = 1.5625e-07
        config.optimizer_yang = False

        return config

    cli, _ = update_fns_to_cli(sokoban_resnet, update_fn)
    print(shlex.join(cli))
    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",
    )
