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 vf_coef in [0.25, 0.0625]:
    for env_seed, learn_seed in [(random_seed(), random_seed()) for _ in range(5)]:

        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,
                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="per-channel",
                    output_activation="sigmoid",
                    fence_pad="valid",
                    forget_bias=1.0,
                ),
                head_scale=5.0,
            )

            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 = 5_120_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=vf_coef,
                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 = 1.5e-4
            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__, "3")

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