"""Train the approved residual map and retain validation-selected checkpoints."""

from __future__ import annotations
import copy, json, time, platform
from pathlib import Path
from dataclasses import dataclass, asdict
import numpy as np
import torch
from pivot.models.pivot import PIVOT
from pivot.models.encoders import build_pert_tensors
from pivot.training.losses import compute_losses
from pivot.evaluation.rewards import rbf_mmd2
from pivot.utils.common import set_seed


@dataclass
class TrainConfig:
    d_pert: int = 64
    hidden: int = 512
    depth: int = 4
    epochs: int = 60
    batch_size: int = 1024
    lr: float = 1e-3
    weight_decay: float = 1e-5
    lam_tan: float = 1.0
    lam_semi: float = 0.5
    lam_reg: float = 1e-4
    lam_dist: float = 0.0
    n_dist_perts: int = 4
    dist_n: int = 64
    grad_clip: float = 5.0
    match: str = "batch"
    rep_mode: str = "gene_op"
    seed: int = 0
    device: str = "cpu"
    threads: int = 4
    endpoint_weight: float = 0.0


def make_model(data, cfg):
    if cfg.rep_mode not in ("gene_op", "gene_only", "op_only", "random_id"):
        raise ValueError("Supported encoders use gene/operation metadata only")
    return PIVOT(
        data.d,
        len(data.genes_vocab),
        len(data.op_vocab),
        len(data.perturbations),
        d_pert=cfg.d_pert,
        hidden=cfg.hidden,
        depth=cfg.depth,
        rep_mode=cfg.rep_mode,
    ).to(cfg.device)


@torch.no_grad()
def validation_loss(model, data, cfg) -> float:
    """Mean condition-level endpoint MSE against validation centroids in PCA space."""
    rng = np.random.default_rng(cfg.seed + 910)
    ctr = data.indices("val", True)
    vid = data.indices("val", False)
    c0 = torch.as_tensor(
        data.emb[rng.choice(ctr, min(128, len(ctr)), replace=False)], device=cfg.device
    )
    model.eval()
    loss = []
    for label in data.labels("val"):
        ids = np.intersect1d(data.pert_to_idx[label], vid)
        g, o, m, pid = build_pert_tensors(data, [label], cfg.device)
        pred = model.endpoint_from_pert(c0, g, o, m, pid).mean(0)
        truth = torch.as_tensor(data.emb[ids].mean(0), device=cfg.device)
        loss.append((pred - truth).square().mean().item())
    return float(np.mean(loss))


def train(data, cfg: TrainConfig, output: str, resume: str | None = None) -> dict:
    """Fit using training cells/controls; write best.pt, last.pt, config and history.

    Checkpoints retain the vocabulary and cache fingerprint. Resume restores
    optimizer, scheduler, NumPy RNG, and Torch RNG state before the next epoch.
    """
    set_seed(cfg.seed)
    torch.set_num_threads(cfg.threads)
    if cfg.device.startswith("cuda") and not torch.cuda.is_available():
        raise RuntimeError("Requested CUDA is unavailable")
    out = Path(output)
    out.mkdir(parents=True, exist_ok=True)
    model = make_model(data, cfg)
    opt = torch.optim.AdamW(
        model.parameters(), lr=cfg.lr, weight_decay=cfg.weight_decay
    )
    sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=cfg.epochs)
    rng = np.random.default_rng(cfg.seed)
    start = 0
    history = []
    best = float("inf")
    if resume:
        ck = torch.load(resume, map_location=cfg.device, weights_only=False)
        if ck["data_meta"] != data.meta or ck["config"] != asdict(cfg):
            raise ValueError("Resume configuration or data mismatch")
        model.load_state_dict(ck["model"])
        opt.load_state_dict(ck["optimizer"])
        sched.load_state_dict(ck["scheduler"])
        rng.bit_generator.state = ck["numpy_rng"]
        torch.set_rng_state(ck["torch_rng"].cpu())
        if ck.get("cuda_rng") is not None and torch.cuda.is_available():
            torch.cuda.set_rng_state_all(ck["cuda_rng"])
        start = ck["epoch"] + 1
        history = ck["history"]
        best = ck["best_val"]
    train_ids = data.indices("train", False)
    ctrl = data.indices("train", True)
    labels = data.obs.perturbation.to_numpy()
    z = torch.as_tensor(data.emb, device=cfg.device)
    groups = {p: train_ids[labels[train_ids] == p] for p in data.labels("train")}
    lam = {"map": 1.0, "tan": cfg.lam_tan, "semi": cfg.lam_semi, "reg": cfg.lam_reg}
    t0 = time.perf_counter()
    for epoch in range(start, cfg.epochs):
        model.train()
        terms = []
        for ids in np.array_split(
            rng.permutation(train_ids), int(np.ceil(len(train_ids) / cfg.batch_size))
        ):
            ci = data.sample_controls(ids, cfg.match, rng, ctrl)
            g, o, m, pid = build_pert_tensors(data, labels[ids], cfg.device)
            e = model.encode(g, o, m, pid)
            total, parts = compute_losses(model.flow, e, z[ci], z[ids], lam)
            if cfg.endpoint_weight:
                le = (model.flow.endpoint(z[ci], e) - z[ids]).square().sum(-1).mean()
                total = total + cfg.endpoint_weight * le
                parts["endpoint"] = le.item()
            if cfg.lam_dist:
                ds = []
                for p in rng.choice(
                    list(groups), min(cfg.n_dist_perts, len(groups)), replace=False
                ):
                    yi = rng.choice(
                        groups[p], min(cfg.dist_n, len(groups[p])), replace=False
                    )
                    xi = data.sample_controls(yi, cfg.match, rng, ctrl)
                    gd, od, md, pd = build_pert_tensors(data, [p], cfg.device)
                    yp = model.endpoint_from_pert(z[xi], gd, od, md, pd)
                    ds.append(rbf_mmd2(yp, z[yi], data.meta["mmd_gamma"]))
                ld = torch.stack(ds).mean()
                total = total + cfg.lam_dist * ld
                parts["dist"] = ld.item()
            opt.zero_grad()
            total.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), cfg.grad_clip)
            opt.step()
            parts["total"] = total.item()
            terms.append(parts)
        sched.step()
        val = validation_loss(model, data, cfg)
        record = {
            "epoch": epoch,
            "validation_mse": val,
            **{k: float(np.mean([t[k] for t in terms])) for k in terms[0]},
        }
        history.append(record)
        improved = val < best
        best = min(best, val)
        ck = {
            "protocol": "split-first-v1",
            "model": model.state_dict(),
            "optimizer": opt.state_dict(),
            "scheduler": sched.state_dict(),
            "epoch": epoch,
            "best_val": best,
            "config": asdict(cfg),
            "data_meta": data.meta,
            "gene_vocab": data.genes_vocab,
            "perturbations": data.perturbations,
            "numpy_rng": rng.bit_generator.state,
            "torch_rng": torch.get_rng_state(),
            "cuda_rng": (
                torch.cuda.get_rng_state_all() if torch.cuda.is_available() else None
            ),
            "history": history,
        }
        torch.save(ck, out / "last.pt")
        if improved:
            torch.save(ck, out / "best.pt")
        print(
            f"epoch {epoch+1}/{cfg.epochs} train={record['total']:.4f} val={val:.4f}",
            flush=True,
        )
    info = {
        "protocol": "split-first-v1",
        "config": asdict(cfg),
        "history": history,
        "duration_s": time.perf_counter() - t0,
        "n_train_cells": len(train_ids),
        "n_parameters": sum(p.numel() for p in model.parameters()),
        "software": {
            "python": platform.python_version(),
            "torch": torch.__version__,
            "numpy": np.__version__,
        },
    }
    (out / "training.json").write_text(json.dumps(info, indent=2))
    return info


def load_checkpoint(path, data, device="cpu"):
    """Load trusted local checkpoint with an exact cache/vocabulary match."""
    ck = torch.load(path, map_location=device, weights_only=False)
    if ck.get("protocol") != "split-first-v1":
        raise ValueError(
            "Historical weights need their original preprocessing and archived loader"
        )
    if ck["data_meta"] != data.meta or ck["gene_vocab"] != data.genes_vocab:
        raise ValueError("Checkpoint and cache do not match")
    cfg = TrainConfig(**ck["config"])
    cfg.device = device
    torch.set_num_threads(cfg.threads)
    model = make_model(data, cfg)
    model.load_state_dict(ck["model"])
    model.eval()
    return model, cfg