# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/c51/#c51_jaxpy
import argparse
import os
import random
import time
from distutils.util import strtobool

import flax
import flax.linen as nn
import gymnasium as gym
import jax
import jax.numpy as jnp
import numpy as np
import optax
from flax.training.train_state import TrainState
from stable_baselines3.common.buffers import ReplayBuffer
from torch.utils.tensorboard import SummaryWriter


def parse_args():
    # fmt: off
    parser = argparse.ArgumentParser()
    parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"),
        help="the name of this experiment")
    parser.add_argument("--seed", type=int, default=1,
        help="seed of the experiment")
    parser.add_argument("--track", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
        help="if toggled, this experiment will be tracked with Weights and Biases")
    parser.add_argument("--wandb-project-name", type=str, default="cleanRL",
        help="the wandb's project name")
    parser.add_argument("--wandb-entity", type=str, default=None,
        help="the entity (team) of wandb's project")
    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
        help="whether to capture videos of the agent performances (check out `videos` folder)")
    parser.add_argument("--save-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
        help="whether to save model into the `runs/{run_name}` folder")
    parser.add_argument("--upload-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
        help="whether to upload the saved model to huggingface")
    parser.add_argument("--hf-entity", type=str, default="",
        help="the user or org name of the model repository from the Hugging Face Hub")

    # Algorithm specific arguments
    parser.add_argument("--env-id", type=str, default="CartPole-v1",
        help="the id of the environment")
    parser.add_argument("--total-timesteps", type=int, default=500000,
        help="total timesteps of the experiments")
    parser.add_argument("--learning-rate", type=float, default=2.5e-4,
        help="the learning rate of the optimizer")
    parser.add_argument("--num-envs", type=int, default=1,
        help="the number of parallel game environments")
    parser.add_argument("--n-atoms", type=int, default=101,
        help="the number of atoms")
    parser.add_argument("--v-min", type=float, default=-100,
        help="the return lower bound")
    parser.add_argument("--v-max", type=float, default=100,
        help="the return upper bound")
    parser.add_argument("--buffer-size", type=int, default=10000,
        help="the replay memory buffer size")
    parser.add_argument("--gamma", type=float, default=0.99,
        help="the discount factor gamma")
    parser.add_argument("--target-network-frequency", type=int, default=500,
        help="the timesteps it takes to update the target network")
    parser.add_argument("--batch-size", type=int, default=128,
        help="the batch size of sample from the reply memory")
    parser.add_argument("--start-e", type=float, default=1,
        help="the starting epsilon for exploration")
    parser.add_argument("--end-e", type=float, default=0.05,
        help="the ending epsilon for exploration")
    parser.add_argument("--exploration-fraction", type=float, default=0.5,
        help="the fraction of `total-timesteps` it takes from start-e to go end-e")
    parser.add_argument("--learning-starts", type=int, default=10000,
        help="timestep to start learning")
    parser.add_argument("--train-frequency", type=int, default=10,
        help="the frequency of training")
    args = parser.parse_args()
    # fmt: on
    assert args.num_envs == 1, "vectorized envs are not supported at the moment"

    return args


def make_env(env_id, seed, idx, capture_video, run_name):
    def thunk():
        if capture_video and idx == 0:
            env = gym.make(env_id, render_mode="rgb_array")
            env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
        else:
            env = gym.make(env_id)
        env = gym.wrappers.RecordEpisodeStatistics(env)
        env.action_space.seed(seed)

        return env

    return thunk


# ALGO LOGIC: initialize agent here:
class QNetwork(nn.Module):
    action_dim: int
    n_atoms: int

    @nn.compact
    def __call__(self, x):
        x = nn.Dense(120)(x)
        x = nn.relu(x)
        x = nn.Dense(84)(x)
        x = nn.relu(x)
        x = nn.Dense(self.action_dim * self.n_atoms)(x)
        x = x.reshape((x.shape[0], self.action_dim, self.n_atoms))
        x = nn.softmax(x, axis=-1)  # pmfs
        return x


class TrainState(TrainState):
    target_params: flax.core.FrozenDict
    atoms: jnp.ndarray


def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
    slope = (end_e - start_e) / duration
    return max(slope * t + start_e, end_e)


if __name__ == "__main__":
    import stable_baselines3 as sb3

    if sb3.__version__ < "2.0":
        raise ValueError(
            """Ongoing migration: run the following command to install the new dependencies:

poetry run pip install "stable_baselines3==2.0.0a1"
"""
        )
    args = parse_args()
    run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
    if args.track:
        import wandb

        wandb.init(
            project=args.wandb_project_name,
            entity=args.wandb_entity,
            sync_tensorboard=True,
            config=vars(args),
            name=run_name,
            monitor_gym=True,
            save_code=True,
        )
    writer = SummaryWriter(f"runs/{run_name}")
    writer.add_text(
        "hyperparameters",
        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
    )

    # TRY NOT TO MODIFY: seeding
    random.seed(args.seed)
    np.random.seed(args.seed)
    key = jax.random.PRNGKey(args.seed)
    key, q_key = jax.random.split(key, 2)

    # env setup
    envs = gym.vector.SyncVectorEnv(
        [make_env(args.env_id, args.seed + i, i, args.capture_video, run_name) for i in range(args.num_envs)]
    )
    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"

    obs, _ = envs.reset(seed=args.seed)
    q_network = QNetwork(action_dim=envs.single_action_space.n, n_atoms=args.n_atoms)
    q_state = TrainState.create(
        apply_fn=q_network.apply,
        params=q_network.init(q_key, obs),
        target_params=q_network.init(q_key, obs),
        # directly using jnp.linspace leads to numerical errors
        atoms=jnp.asarray(np.linspace(args.v_min, args.v_max, num=args.n_atoms)),
        tx=optax.adam(learning_rate=args.learning_rate, eps=0.01 / args.batch_size),
    )
    q_network.apply = jax.jit(q_network.apply)
    # This step is not necessary as init called on same observation and key will always lead to same initializations
    q_state = q_state.replace(target_params=optax.incremental_update(q_state.params, q_state.target_params, 1))

    rb = ReplayBuffer(
        args.buffer_size,
        envs.single_observation_space,
        envs.single_action_space,
        "cpu",
        handle_timeout_termination=False,
    )

    @jax.jit
    def update(q_state, observations, actions, next_observations, rewards, dones):
        next_pmfs = q_network.apply(q_state.target_params, next_observations)  # (batch_size, num_actions, num_atoms)
        next_vals = (next_pmfs * q_state.atoms).sum(axis=-1)  # (batch_size, num_actions)
        next_action = jnp.argmax(next_vals, axis=-1)  # (batch_size,)
        next_pmfs = next_pmfs[np.arange(next_pmfs.shape[0]), next_action]
        next_atoms = rewards + args.gamma * q_state.atoms * (1 - dones)
        # projection
        delta_z = q_state.atoms[1] - q_state.atoms[0]
        tz = jnp.clip(next_atoms, a_min=(args.v_min), a_max=(args.v_max))

        b = (tz - args.v_min) / delta_z
        l = jnp.clip(jnp.floor(b), a_min=0, a_max=args.n_atoms - 1)
        u = jnp.clip(jnp.ceil(b), a_min=0, a_max=args.n_atoms - 1)
        # (l == u).astype(jnp.float) handles the case where bj is exactly an integer
        # example bj = 1, then the upper ceiling should be uj= 2, and lj= 1
        d_m_l = (u + (l == u).astype(jnp.float32) - b) * next_pmfs
        d_m_u = (b - l) * next_pmfs
        target_pmfs = jnp.zeros_like(next_pmfs)

        def project_to_bins(i, val):
            val = val.at[i, l[i].astype(jnp.int32)].add(d_m_l[i])
            val = val.at[i, u[i].astype(jnp.int32)].add(d_m_u[i])
            return val

        target_pmfs = jax.lax.fori_loop(0, target_pmfs.shape[0], project_to_bins, target_pmfs)

        def loss(q_params, observations, actions, target_pmfs):
            pmfs = q_network.apply(q_params, observations)
            old_pmfs = pmfs[np.arange(pmfs.shape[0]), actions.squeeze()]

            old_pmfs_l = jnp.clip(old_pmfs, a_min=1e-5, a_max=1 - 1e-5)
            loss = (-(target_pmfs * jnp.log(old_pmfs_l)).sum(-1)).mean()
            return loss, (old_pmfs * q_state.atoms).sum(-1)

        (loss_value, old_values), grads = jax.value_and_grad(loss, has_aux=True)(
            q_state.params, observations, actions, target_pmfs
        )
        q_state = q_state.apply_gradients(grads=grads)
        return loss_value, old_values, q_state

    start_time = time.time()

    # TRY NOT TO MODIFY: start the game
    obs, _ = envs.reset(seed=args.seed)
    for global_step in range(args.total_timesteps):
        # ALGO LOGIC: put action logic here
        epsilon = linear_schedule(args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step)
        if random.random() < epsilon:
            actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)])
        else:
            pmfs = q_network.apply(q_state.params, obs)
            q_vals = (pmfs * q_state.atoms).sum(axis=-1)
            actions = q_vals.argmax(axis=-1)
            actions = jax.device_get(actions)

        # TRY NOT TO MODIFY: execute the game and log data.
        next_obs, rewards, terminated, truncated, infos = envs.step(actions)

        # TRY NOT TO MODIFY: record rewards for plotting purposes
        if "final_info" in infos:
            for info in infos["final_info"]:
                # Skip the envs that are not done
                if "episode" not in info:
                    continue
                print(f"global_step={global_step}, episodic_return={info['episode']['r']}")
                writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step)
                writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step)
                writer.add_scalar("charts/epsilon", epsilon, global_step)

        # TRY NOT TO MODIFY: save data to reply buffer; handle `final_observation`
        real_next_obs = next_obs.copy()
        for idx, d in enumerate(truncated):
            if d:
                real_next_obs[idx] = infos["final_observation"][idx]
        rb.add(obs, real_next_obs, actions, rewards, terminated, infos)

        # TRY NOT TO MODIFY: CRUCIAL step easy to overlook
        obs = next_obs

        # ALGO LOGIC: training.
        if global_step > args.learning_starts and global_step % args.train_frequency == 0:
            data = rb.sample(args.batch_size)
            loss, old_val, q_state = update(
                q_state,
                data.observations.numpy(),
                data.actions.numpy(),
                data.next_observations.numpy(),
                data.rewards.numpy(),
                data.dones.numpy(),
            )

            if global_step % 100 == 0:
                writer.add_scalar("losses/loss", jax.device_get(loss), global_step)
                writer.add_scalar("losses/q_values", jax.device_get(old_val.mean()), global_step)
                print("SPS:", int(global_step / (time.time() - start_time)))
                writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)

            # update the target network
            if global_step % args.target_network_frequency == 0:
                q_state = q_state.replace(target_params=optax.incremental_update(q_state.params, q_state.target_params, 1))

    if args.save_model:
        model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
        model_data = {
            "model_weights": q_state.params,
            "args": vars(args),
        }
        with open(model_path, "wb") as f:
            f.write(flax.serialization.to_bytes(model_data))
        print(f"model saved to {model_path}")
        from cleanrl_utils.evals.c51_jax_eval import evaluate

        episodic_returns = evaluate(
            model_path,
            make_env,
            args.env_id,
            eval_episodes=10,
            run_name=f"{run_name}-eval",
            Model=QNetwork,
            epsilon=0.05,
        )
        for idx, episodic_return in enumerate(episodic_returns):
            writer.add_scalar("eval/episodic_return", episodic_return, idx)

        if args.upload_model:
            from cleanrl_utils.huggingface import push_to_hub

            repo_name = f"{args.env_id}-{args.exp_name}-seed{args.seed}"
            repo_id = f"{args.hf_entity}/{repo_name}" if args.hf_entity else repo_name
            push_to_hub(args, episodic_returns, repo_id, "C51", f"runs/{run_name}", f"videos/{run_name}-eval")

    envs.close()
    writer.close()
