"""bioai.training.train_pinn -- train the DegradationPINN on synthetic fate data.

We do not have real environmental fate measurements for dsRNA (collecting them
requires a multi-week field trial with LC-MS). For the hackathon we therefore
generate SYNTHETIC (features, half-life) pairs from a hand-coded physical
heuristic -- higher temperature, UV, salinity, and humidity all increase the
degradation rate ``k``; higher GC content and length slightly stabilise the
duplex -- and use them to teach the PINN the right qualitative behaviour.
The physics-consistency loss on ``C(t) = C0 * exp(-k * t)`` then ensures the
network respects the ODE even at feature combos it has never seen.

The PINN is consumed by the ranker to penalise candidates with a half-life
under 6 hours, so the synthetic training just needs to produce a network
that says "too short" for hot/sunny/wet conditions and "long enough" for
cool/dry conditions. The MSE + physics loss combo does exactly that.

CLI::

    python -m bioai.training.train_pinn --epochs 100
"""

from __future__ import annotations

import argparse
import math
import sys
from pathlib import Path
from typing import List

import numpy as np
import torch
import torch.nn as nn

from ..models.pinn_fate import DegradationPINN, PINN_FEATURE_NAMES
from ..models.sirna_cnn import resolve_device

# Portable checkpoint path (resolved from bioai.paths)
from bioai.paths import PINN_CHECKPOINT as CHECKPOINT_PATH  # noqa: E402


# --------------------------------------------------------------------------- #
# Synthetic fate generator
# --------------------------------------------------------------------------- #
def generate_synthetic_fate_data(
    n_samples: int = 1024,
    seed: int = 13,
) -> tuple[np.ndarray, np.ndarray]:
    """Generate ``(features, half_life_hours)`` pairs.

    Features (8-dim) follow realistic ranges; the half-life is derived from
    a hand-coded rate that captures the qualitative physics (Arrhenius-style
    temperature dependence, UV photocatalysis, salinity-driven hydrolysis,
    GC-stabilisation, length-stabilisation). This is a *teaching signal*,
    not a measurement -- see module docstring for the rationale.
    """
    rng = np.random.default_rng(seed)
    # Realistic ranges per feature
    temp = rng.uniform(10.0, 40.0, n_samples)         # Celsius
    pH = rng.uniform(5.0, 9.0, n_samples)
    uv = rng.uniform(0.0, 12.0, n_samples)             # UV index
    gc = rng.uniform(0.3, 0.7, n_samples)              # fraction
    length = rng.uniform(50.0, 500.0, n_samples)       # nt
    sal = rng.uniform(0.0, 35.0, n_samples)            # ppt
    clay = rng.uniform(0.0, 60.0, n_samples)           # %
    hum = rng.uniform(10.0, 100.0, n_samples)          # %

    features = np.stack([temp, pH, uv, gc, length, sal, clay, hum], axis=1).astype(np.float32)

    # Hand-coded rate (1/hours). Each term contributes multiplicatively.
    # Reference: dsRNA in soil literature reports half-lives of 1-72 hours
    # depending on conditions; we target that range.
    arrhenius = np.exp((temp - 25.0) / 12.0)          # Q10-style
    uv_factor = 1.0 + 0.15 * uv                       # UV photocatalysis
    sal_factor = 1.0 + 0.03 * sal                     # salinity hydrolysis
    hum_factor = 1.0 + 0.005 * (hum - 50.0)           # humidity mild effect
    gc_stabiliser = 1.0 / (0.5 + gc)                  # high GC -> slower
    len_stabiliser = 200.0 / length                   # long duplex -> slower
    base_rate = 0.10                                   # 1/hours at reference
    k = base_rate * arrhenius * uv_factor * sal_factor * hum_factor * gc_stabiliser * len_stabiliser
    # Add small noise so the PINN can't just memorise the heuristic.
    k = k * rng.uniform(0.9, 1.1, n_samples)
    half_life = np.log(2.0) / k
    return features, half_life.astype(np.float32)


# --------------------------------------------------------------------------- #
# Training
# --------------------------------------------------------------------------- #
def train(
    epochs: int = 100,
    batch_size: int = 64,
    lr: float = 1e-3,
    device: str = "auto",
    n_samples: int = 1024,
    physics_weight: float = 0.5,
    checkpoint_path: Path | None = None,
) -> str:
    device_t = resolve_device(device)
    print(f"[train_pinn] device = {device_t}")

    features_np, hl_np = generate_synthetic_fate_data(n_samples=n_samples)
    # Convert half-life to rate (the PINN output) for the supervised MSE.
    k_np = (np.log(2.0) / hl_np).astype(np.float32)
    # Hold out 20% for validation.
    n_val = max(1, int(0.2 * len(features_np)))
    rng = np.random.default_rng(42)
    perm = rng.permutation(len(features_np))
    val_idx, train_idx = perm[:n_val], perm[n_val:]
    feat_tr = torch.tensor(features_np[train_idx], dtype=torch.float32, device=device_t)
    k_tr = torch.tensor(k_np[train_idx], dtype=torch.float32, device=device_t).view(-1, 1)
    feat_val = torch.tensor(features_np[val_idx], dtype=torch.float32, device=device_t)
    k_val = torch.tensor(k_np[val_idx], dtype=torch.float32, device=device_t).view(-1, 1)
    print(f"[train_pinn] {len(train_idx)} train / {len(val_idx)} val synthetic samples")

    model = DegradationPINN(feature_dim=8).to(device_t)
    optimizer = torch.optim.Adam(model.parameters(), lr=lr)

    checkpoint_path = checkpoint_path or CHECKPOINT_PATH
    checkpoint_path.parent.mkdir(parents=True, exist_ok=True)

    # Time grid for physics-consistency loss (hours).
    t_grid = torch.linspace(0.0, 24.0, 13, device=device_t)  # every 2 hours
    C0 = 1.0

    best_val_mse = float("inf")
    for epoch in range(1, epochs + 1):
        model.train()
        # Mini-batch gradient descent over the training set.
        perm_t = torch.randperm(len(feat_tr), device=device_t)
        total_supervised = 0.0
        total_physics = 0.0
        n_batches = 0
        for i in range(0, len(feat_tr), batch_size):
            idx = perm_t[i:i + batch_size]
            f_b = feat_tr[idx]
            k_b = k_tr[idx]
            optimizer.zero_grad()
            # Supervised MSE on rate.
            k_pred = model.predict_rate(f_b)
            loss_sup = nn.functional.mse_loss(k_pred, k_b)
            # Physics consistency: generate a target trajectory from the
            # GROUND-TRUTH rate and ask the PINN to reproduce it from
            # features alone. This forces k_pred to match k_b *via* the ODE.
            C_target = C0 * torch.exp(-k_b * t_grid.unsqueeze(0))   # (B, T)
            C_pred = model.predict_concentration(C0, t_grid, f_b)   # (B, T)
            loss_phys = nn.functional.mse_loss(C_pred, C_target)
            loss = loss_sup + physics_weight * loss_phys
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
            optimizer.step()
            total_supervised += loss_sup.item()
            total_physics += loss_phys.item()
            n_batches += 1

        # Validation
        model.eval()
        with torch.no_grad():
            k_val_pred = model.predict_rate(feat_val)
            val_mse = nn.functional.mse_loss(k_val_pred, k_val).item()
            hl_pred = model.half_life(feat_val).cpu().numpy().reshape(-1)
            hl_true = (math.log(2.0) / k_val.cpu().numpy().reshape(-1))

        if epoch % 10 == 0 or epoch == 1:
            print(
                f"Epoch {epoch:3d}/{epochs}: "
                f"sup_loss={total_supervised / max(1, n_batches):.6f}  "
                f"phys_loss={total_physics / max(1, n_batches):.6f}  "
                f"val_mse_k={val_mse:.6f}  "
                f"val_hl_mean_pred={hl_pred.mean():.2f}h  "
                f"val_hl_mean_true={hl_true.mean():.2f}h"
            )

        if val_mse < best_val_mse:
            best_val_mse = val_mse
            torch.save(model.state_dict(), checkpoint_path)

    print(f"[train_pinn] done. best_val_mse_k={best_val_mse:.6f}")
    print(f"[train_pinn] checkpoint: {checkpoint_path}")
    return str(checkpoint_path)


# --------------------------------------------------------------------------- #
# CLI
# --------------------------------------------------------------------------- #
def main(argv: List[str] | None = None) -> int:
    p = argparse.ArgumentParser(description="Train the DegradationPINN on synthetic fate data.")
    p.add_argument("--epochs", type=int, default=100)
    p.add_argument("--batch-size", type=int, default=64)
    p.add_argument("--lr", type=float, default=1e-3)
    p.add_argument("--device", type=str, default="auto", choices=["auto", "cpu", "cuda"])
    p.add_argument("--n-samples", type=int, default=1024)
    p.add_argument("--checkpoint", type=str, default=str(CHECKPOINT_PATH))
    args = p.parse_args(argv)

    train(
        epochs=args.epochs,
        batch_size=args.batch_size,
        lr=args.lr,
        device=args.device,
        n_samples=args.n_samples,
        checkpoint_path=Path(args.checkpoint),
    )
    return 0


if __name__ == "__main__":
    sys.exit(main())