#!/usr/bin/env python3

import datetime
import os
from collections.abc import Sequence

from jsonargparse import CLI

from examples.mujoco.mujoco_env import MujocoEnvFactory
from tianshou.highlevel.agent import DefaultAutoAlphaFactory, SACAgentFactory, SACConfig
from tianshou.highlevel.experiment import (
    RLExperiment,
    RLExperimentConfig,
)
from tianshou.highlevel.config import RLSamplingConfig
from tianshou.highlevel.logger import DefaultLoggerFactory
from tianshou.highlevel.module import (
    ContinuousActorProbFactory,
    ContinuousNetCriticFactory,
)
from tianshou.highlevel.optim import AdamOptimizerFactory


def main(
    experiment_config: RLExperimentConfig,
    task: str = "Ant-v3",
    buffer_size: int = 1000000,
    hidden_sizes: Sequence[int] = (256, 256),
    actor_lr: float = 1e-3,
    critic_lr: float = 1e-3,
    gamma: float = 0.99,
    tau: float = 0.005,
    alpha: float = 0.2,
    auto_alpha: bool = False,
    alpha_lr: float = 3e-4,
    start_timesteps: int = 10000,
    epoch: int = 200,
    step_per_epoch: int = 5000,
    step_per_collect: int = 1,
    update_per_step: int = 1,
    n_step: int = 1,
    batch_size: int = 256,
    training_num: int = 1,
    test_num: int = 10,
):
    now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
    log_name = os.path.join(task, "sac", str(experiment_config.seed), now)
    logger_factory = DefaultLoggerFactory()

    sampling_config = RLSamplingConfig(
        num_epochs=epoch,
        step_per_epoch=step_per_epoch,
        num_train_envs=training_num,
        num_test_envs=test_num,
        buffer_size=buffer_size,
        batch_size=batch_size,
        step_per_collect=step_per_collect,
        update_per_step=update_per_step,
        start_timesteps=start_timesteps,
        start_timesteps_random=True,
    )

    env_factory = MujocoEnvFactory(task, experiment_config.seed, sampling_config)

    if auto_alpha:
        alpha = DefaultAutoAlphaFactory(lr=alpha_lr)
    sac_config = SACConfig(
        tau=tau,
        gamma=gamma,
        alpha=alpha,
        estimation_step=n_step,
        actor_lr=actor_lr,
        critic1_lr=critic_lr,
        critic2_lr=critic_lr,
    )
    actor_factory = ContinuousActorProbFactory(hidden_sizes, conditioned_sigma=True)
    critic_factory = ContinuousNetCriticFactory(hidden_sizes)
    optim_factory = AdamOptimizerFactory()
    agent_factory = SACAgentFactory(
        sac_config,
        sampling_config,
        actor_factory,
        critic_factory,
        critic_factory,
        optim_factory,
    )

    experiment = RLExperiment(experiment_config, env_factory, logger_factory, agent_factory)

    experiment.run(log_name)


if __name__ == "__main__":
    CLI(main)