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 = []
drc_n_n = 3

num_envs = 32
gamma = 0.97
vtrace_lambda = 0.97
min_episode_steps = 120 * 3 // 4

# for num_envs in [32, 64]:
# for vtrace_lambda in [0.97, 1.0, 0.5]:
# for min_episode_steps in [120 * 3 // 4, 115, 120, 0]:
for gamma in [0.97, 0.99, 0.95, 0.7]:
    for env_seed, learn_seed in [(random_seed(), random_seed()) for _ in range(5)]:

        def update_fn(config: Args) -> Args:
            config.train_env = dataclasses.replace(config.train_env, seed=env_seed, min_episode_steps=min_episode_steps)
            config.local_num_envs = num_envs
            config.num_steps = 20
            config.net = ConvLSTMConfig(
                n_recurrent=drc_n_n,
                repeats_per_step=drc_n_n,
                skip_final=True,
                residual=False,
                use_relu=False,
                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="tanh",
                    fence_pad="valid",
                    forget_bias=0.0,
                ),
                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 = 20_000_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 = 8
            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 = config.local_num_envs // 32

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

            logit_l2_coef = 1.5625e-5
            config.loss = dataclasses.replace(
                config.loss,
                vtrace_lambda=vtrace_lambda,
                vf_coef=0.25,
                gamma=gamma,
                ent_coef=0.001,
                normalize_advantage=False,
                logit_l2_coef=logit_l2_coef,
                weight_l2_coef=logit_l2_coef / 100,
                vf_loss_type="square",
                advantage_multiplier="one",
            )
            config.base_fan_in = 1
            config.anneal_lr = False  # Keep the high learning rate all the way

            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

            config.eval_envs["valid_medium"].steps_to_think = [0, 2, 4, 8, 12, 16, 24, 32]

            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, "normal-batch"))

runs: list[FlamingoRun] = []
RUNS_PER_MACHINE = 1
for i in range(0, len(clis), RUNS_PER_MACHINE):
    this_run_clis = [
        ["python", "-m", "cleanba.cleanba_impala", *clis[i + j][0]] for j in range(min(RUNS_PER_MACHINE, len(clis) - i))
    ]
    priority = clis[i][1]
    print("With priority", priority)
    runs.append(
        FlamingoRun(
            this_run_clis,
            CONTAINER_TAG="5d26ad1-main",
            CPU=6,
            MEMORY="20G",
            GPU=1,
            PRIORITY=priority,
            XLA_PYTHON_CLIENT_MEM_FRACTION='".95"',
        )
    )


GROUP: str = group_from_fname(__file__, "one")

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