"""
Optuna hyperparameter search + supervised pretraining + light RL fine-tune.

This script intentionally trains on SYNTHETIC data only (hyperparameter
search does not consume real datasets — that would be wasteful and is
exactly the kind of scarce resource the DatasetRegistry in provenance.py
is meant to protect). Data provenance is fetched via the explicit
get_synthetic_dataset() call, never implicit.

For training against real Well data, use src/run_full.py instead, which
enforces the full contract (hard-fail on missing real data, dataset-reuse
registry, content-addressed atomic checkpoints).
"""
from __future__ import annotations
import os
import sys
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
import optuna
from optuna.trial import Trial
import numpy as np
from tqdm import tqdm

from src.data_real import get_synthetic_dataset
from src.normalization import FieldNormalizer
from src.env import MultiStepPoincareEnv
from src.model import MultiScaleEncoder, HierarchicalHyperbolicPredictor, HyperbolicCritic
from src.physics_losses import combined_physics_loss
from src.provenance import CheckpointStore, hash_dataset, hash_code, validate_trajectory_lengths

WINDOW = 4
SRC_DIR = os.path.dirname(os.path.abspath(__file__))


def get_device():
    return "cuda" if torch.cuda.is_available() else "cpu"


def collate_fields(batch):
    return torch.stack([b["fields"] for b in batch])


def build_normalizer(dataset, max_fit: int = 64) -> FieldNormalizer:
    norm = FieldNormalizer(mode="zscore")
    samples = [dataset[i]["fields"] for i in range(min(len(dataset), max_fit))]
    data = torch.stack(samples, dim=0)
    norm.fit(data)
    print(f"[Norm] fitted {data.shape[0]} trajs | mean={[round(x,4) for x in norm.mean.tolist()]}")
    return norm


def objective(trial: Trial, max_epochs: int = 3, n_samples: int = 128) -> float:
    device = get_device()
    lr = trial.suggest_float("lr", 3e-5, 1.5e-3, log=True)
    c = trial.suggest_float("curvature", 0.3, 1.8)
    hidden = trial.suggest_categorical("hidden", [48, 64, 96])
    batch_size = trial.suggest_categorical("batch_size", [8, 16])
    pred_steps = trial.suggest_int("pred_steps", 2, 4)
    w_phys = trial.suggest_float("w_phys", 1e-4, 5e-2, log=True)

    ds, _provenance = get_synthetic_dataset(max_samples=n_samples, n_steps=14)
    validate_trajectory_lengths(ds, required_length=WINDOW + pred_steps)
    normalizer = build_normalizer(ds, max_fit=40)
    loader = DataLoader(ds, batch_size=batch_size, shuffle=True, collate_fn=collate_fields)

    encoder = MultiScaleEncoder(hidden=hidden, out_dim=8)
    model = HierarchicalHyperbolicPredictor(encoder, c=c, pred_steps=pred_steps, levels=2).to(device)
    opt = torch.optim.Adam(model.parameters(), lr=lr)

    model.train()
    losses = []
    for _ in range(max_epochs):
        ep, n = 0.0, 0
        for batch in loader:
            B, T, C, H, W = batch.shape
            batch = batch.to(device)
            flat = normalizer.transform(batch.view(B * T, C, H, W)).view(B, T, C, H, W)
            x = flat[:, :WINDOW]
            with torch.no_grad():
                tgt = torch.stack([model.encode(flat[:, WINDOW + s]) for s in range(pred_steps)], dim=1)
            pred = model(x)
            loss_h = model.hyperbolic_loss(pred, tgt)
            loss_p = combined_physics_loss(
                flat[:, : WINDOW + pred_steps], w_smooth=w_phys, w_temp=w_phys, w_cons=w_phys * 0.5
            )
            loss = loss_h + loss_p
            if not torch.isfinite(loss):
                raise optuna.TrialPruned(f"non-finite loss: {loss}")
            opt.zero_grad()
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
            opt.step()
            ep += loss.item()
            n += 1
        if n:
            losses.append(ep / n)
    if not losses:
        raise optuna.TrialPruned("no batches were trained on this trial")
    return float(np.mean(losses))


def run_optuna(n_trials: int = 8, timeout: int = 120):
    print("=" * 64)
    print("Optuna - hierarchical Poincare 8D + physics priors (synthetic data)")
    print("=" * 64)
    study = optuna.create_study(direction="minimize")
    study.optimize(lambda t: objective(t), n_trials=n_trials, timeout=timeout)
    print("Best value:", round(study.best_value, 5))
    print("Best params:", study.best_params)
    return study


def supervised_pretrain(study, epochs: int = 5):
    device = get_device()
    params = study.best_params
    print("\n" + "=" * 64)
    print("Supervised multi-step pre-training (best HPs, synthetic data)")
    print("=" * 64)
    ds, provenance = get_synthetic_dataset(max_samples=256, n_steps=14)
    ps = params.get("pred_steps", 3)
    validate_trajectory_lengths(ds, required_length=WINDOW + ps)
    normalizer = build_normalizer(ds, max_fit=64)
    loader = DataLoader(ds, batch_size=params.get("batch_size", 8), shuffle=True, collate_fn=collate_fields)

    encoder = MultiScaleEncoder(hidden=params.get("hidden", 64), out_dim=8)
    model = HierarchicalHyperbolicPredictor(
        encoder, c=params.get("curvature", 1.0), pred_steps=ps, levels=2
    ).to(device)
    opt = torch.optim.Adam(model.parameters(), lr=params.get("lr", 3e-4))
    w_phys = params.get("w_phys", 0.01)

    for epoch in range(epochs):
        total, n = 0.0, 0
        for batch in tqdm(loader, desc=f"Pretrain {epoch+1}/{epochs}"):
            B, T, C, H, W = batch.shape
            batch = batch.to(device)
            flat = normalizer.transform(batch.view(B * T, C, H, W)).view(B, T, C, H, W)
            x = flat[:, :WINDOW]
            with torch.no_grad():
                tgt = torch.stack([model.encode(flat[:, WINDOW + s]) for s in range(ps)], dim=1)
            pred = model(x)
            loss = model.hyperbolic_loss(pred, tgt) + combined_physics_loss(
                flat[:, : WINDOW + ps], w_smooth=w_phys, w_temp=w_phys
            )
            if not torch.isfinite(loss):
                raise RuntimeError(f"[NON_FINITE_LOSS] loss={loss.item()} at epoch {epoch+1}")
            opt.zero_grad()
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
            opt.step()
            total += loss.item()
            n += 1
        print(f"  Epoch {epoch+1} loss: {total/max(n,1):.5f}")

    return model, normalizer, params, ds, provenance


def simple_rl_finetune(model, normalizer, params, episodes: int = 40):
    print("\n" + "=" * 64)
    print("Simple on-policy RL fine-tune (hyperbolic critic, synthetic data)")
    print("=" * 64)
    device = get_device()
    ds, _provenance = get_synthetic_dataset(max_samples=64, n_steps=14)
    env = MultiStepPoincareEnv(
        dataset=ds, normalizer=normalizer, encoder=model.encoder,
        poincare_module=model.poincare, window=WINDOW, horizon=3, device=device
    )
    critic = HyperbolicCritic(c=params.get("curvature", 1.0)).to(device)
    policy_head = torch.nn.Sequential(
        torch.nn.Linear(8, 32), torch.nn.GELU(), torch.nn.Linear(32, 8)
    ).to(device)
    log_std = torch.nn.Parameter(torch.zeros(8, device=device) - 1.2)
    opt_c = torch.optim.Adam(critic.parameters(), lr=1e-3)
    opt_p = torch.optim.Adam(list(policy_head.parameters()) + [log_std], lr=1e-3)

    returns = []
    for ep in range(episodes):
        obs, _ = env.reset()
        logps, rewards, values = [], [], []
        for t in range(4):
            obs_t = torch.as_tensor(obs, device=device).unsqueeze(0)
            with torch.no_grad():
                z_ball = model.poincare.expmap0(obs_t)
            mean = policy_head(obs_t).squeeze(0)
            std = log_std.exp()
            dist = torch.distributions.Normal(mean, std)
            action_raw = dist.rsample()
            action = action_raw.clamp(-1.5, 1.5)
            logp = dist.log_prob(action_raw).sum()
            val = critic(z_ball)
            next_obs, reward, term, trunc, info = env.step(
                action.detach().squeeze(0).cpu().numpy()
            )
            logps.append(logp)
            rewards.append(reward)
            values.append(val)
            obs = next_obs
            if term or trunc:
                break
        R = 0.0
        returns_ep = []
        for r in reversed(rewards):
            R = r + 0.95 * R
            returns_ep.insert(0, R)
        returns_t = torch.tensor(returns_ep, device=device)
        values_t = torch.stack(values).reshape(-1)
        adv = returns_t - values_t.detach()
        loss_p = -(torch.stack(logps) * adv).mean()
        loss_c = F.mse_loss(values_t, returns_t)
        opt_p.zero_grad()
        loss_p.backward()
        opt_p.step()
        opt_c.zero_grad()
        loss_c.backward()
        opt_c.step()
        returns.append(sum(rewards))
        if (ep + 1) % 10 == 0:
            print(f"  RL episode {ep+1}: return={np.mean(returns[-10:]):.3f}")

    print("RL fine-tune finished.")
    return model


if __name__ == "__main__":
    study = run_optuna(n_trials=6, timeout=100)
    model, normalizer, params, ds, provenance = supervised_pretrain(study, epochs=4)
    model = simple_rl_finetune(model, normalizer, params, episodes=30)
    dataset_hash = hash_dataset(ds, sample_cap=64)
    code_hash = hash_code(SRC_DIR)
    store = CheckpointStore(checkpoints_dir="checkpoints")
    result = store.save(
        model_state={"model": model.state_dict()},
        config=params,
        dataset_hash=dataset_hash,
        code_hash=code_hash,
        data_provenance=provenance,
        extra={"normalizer": normalizer.state_dict(), "optuna_best_value": study.best_value},
    )
    print(f"\n[checkpoint] {result['outcome_code']} -> {result['path']}")
    print("Hierarchical + physics + RL search/pretrain completed.")