from __future__ import annotations

import argparse
import json
import os
from pathlib import Path
from typing import Any

import numpy as np
import pandas as pd
import torch
from PIL import Image, ImageDraw
from torch.utils.data import DataLoader, Subset
from tqdm import tqdm

from src.data.dataset import MonogramSegmentationDataset, collate_batch
from src.losses import SegmentationLoss
from src.metrics import binary_metrics
from src.models import make_model


def _wandb_config(args: argparse.Namespace) -> dict[str, Any]:
    return {
        key: str(value) if isinstance(value, Path) else value
        for key, value in vars(args).items()
        if key != "wandb_tags"
    }


def _start_wandb(args: argparse.Namespace):
    if not args.wandb:
        return None
    wandb_root = Path(args.output_dir) / "wandb"
    wandb_root.mkdir(parents=True, exist_ok=True)
    os.environ.setdefault("WANDB_DIR", str(wandb_root))
    os.environ.setdefault("WANDB_CACHE_DIR", str(wandb_root / "cache"))
    os.environ.setdefault("WANDB_CONFIG_DIR", str(wandb_root / "config"))
    import wandb

    run = wandb.init(
        project=args.wandb_project,
        entity=args.wandb_entity or None,
        name=args.wandb_name or None,
        group=args.wandb_group or None,
        mode=args.wandb_mode,
        tags=[tag.strip() for tag in args.wandb_tags.split(",") if tag.strip()],
        config=_wandb_config(args),
        dir=str(Path(args.output_dir)),
    )
    wandb.define_metric("epoch")
    wandb.define_metric("*", step_metric="epoch")
    return run


def _log_artifact(run: Any, path: Path, name: str, artifact_type: str) -> None:
    if run is None or not path.exists():
        return
    import wandb

    artifact = wandb.Artifact(name=name, type=artifact_type)
    artifact.add_file(str(path))
    run.log_artifact(artifact)


def _tensor_rgb(image: torch.Tensor) -> np.ndarray:
    return ((image.detach().cpu().clamp(-1, 1) * 0.5 + 0.5).permute(1, 2, 0).numpy() * 255).astype("uint8")


def _tensor_mask(mask: torch.Tensor) -> np.ndarray:
    return (mask.detach().cpu().clamp(0, 1).squeeze(0).numpy() * 255).astype("uint8")


def _overlay(image: np.ndarray, mask: np.ndarray, color: tuple[int, int, int]) -> np.ndarray:
    color_arr = np.zeros_like(image)
    color_arr[..., 0] = color[0]
    color_arr[..., 1] = color[1]
    color_arr[..., 2] = color[2]
    return np.where(mask[..., None] > 0, (0.55 * image + 0.45 * color_arr).astype("uint8"), image)


@torch.no_grad()
def _save_validation_preview(
    model: torch.nn.Module,
    loader: DataLoader,
    device: torch.device,
    threshold: float,
    out_dir: Path,
    epoch: int,
    max_images: int,
) -> Path | None:
    if max_images <= 0:
        return None
    model.eval()
    try:
        batch = next(iter(loader))
    except StopIteration:
        model.train()
        return None
    image = batch["image"].to(device)
    logits = model(image)
    pred = (torch.sigmoid(logits) >= threshold).float()
    count = min(max_images, image.size(0))
    tile_w = int(image.shape[-1])
    tile_h = int(image.shape[-2])
    label_h = 26
    rows: list[Image.Image] = []
    for idx in range(count):
        rgb = _tensor_rgb(batch["image"][idx])
        gt = _tensor_mask(batch["mask"][idx])
        pred_mask = _tensor_mask(pred[idx])
        panels = [
            Image.fromarray(rgb),
            Image.fromarray(gt).convert("RGB"),
            Image.fromarray(pred_mask).convert("RGB"),
            Image.fromarray(_overlay(rgb, pred_mask, (255, 0, 0))),
        ]
        row = Image.new("RGB", (tile_w * len(panels), tile_h + label_h), "white")
        for panel_idx, panel in enumerate(panels):
            row.paste(panel.resize((tile_w, tile_h)), (panel_idx * tile_w, 0))
        draw = ImageDraw.Draw(row)
        caption = f"{batch['sample_id'][idx]} | {batch['collection'][idx]} | {batch['quality_label'][idx]}"
        draw.text((4, tile_h + 6), caption, fill=(0, 0, 0))
        rows.append(row)
    preview_dir = out_dir / "previews"
    preview_dir.mkdir(parents=True, exist_ok=True)
    sheet = Image.new("RGB", (tile_w * 4, (tile_h + label_h) * len(rows)), "white")
    for idx, row in enumerate(rows):
        sheet.paste(row, (0, idx * (tile_h + label_h)))
    path = preview_dir / f"val_epoch_{epoch:04d}.jpg"
    sheet.save(path, quality=95)
    model.train()
    return path


def _device(name: str) -> torch.device:
    if name == "cuda" and not torch.cuda.is_available():
        return torch.device("cpu")
    return torch.device(name)


def _loader(
    csv_path: Path,
    batch_size: int,
    image_size: int,
    augment: bool,
    limit: int | None,
    shuffle: bool,
    num_workers: int,
) -> DataLoader:
    frame = pd.read_csv(csv_path)
    dataset = MonogramSegmentationDataset(frame, image_size=image_size, augment=augment)
    if limit is not None:
        dataset = Subset(dataset, list(range(min(limit, len(dataset)))))
    return DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=shuffle,
        num_workers=num_workers,
        pin_memory=torch.cuda.is_available(),
        collate_fn=collate_batch,
    )


@torch.no_grad()
def validate(model: torch.nn.Module, loader: DataLoader, device: torch.device, threshold: float) -> dict[str, float]:
    model.eval()
    rows: list[dict[str, float]] = []
    for batch in loader:
        image = batch["image"].to(device)
        mask = batch["mask"].to(device)
        rows.extend(binary_metrics(model(image), mask, threshold))
    model.train()
    if not rows:
        return {}
    return {key: float(sum(row[key] for row in rows) / len(rows)) for key in rows[0]}


def train(args: argparse.Namespace) -> Path:
    device = _device(args.device)
    train_loader = _loader(Path(args.train_csv), args.batch_size, args.image_size, True, args.limit_train, True, args.num_workers)
    val_loader = _loader(Path(args.val_csv), args.batch_size, args.image_size, False, args.limit_val, False, args.num_workers)
    model_kwargs: dict[str, Any] = {}
    if args.model == "unet":
        model_kwargs["encoder_name"] = args.encoder_name
        model_kwargs["encoder_weights"] = None if args.no_pretrained else args.encoder_weights
    elif args.model == "segformer":
        model_kwargs["model_name"] = args.segformer_model_name
        model_kwargs["pretrained"] = args.segformer_pretrained
        model_kwargs["freeze_encoder"] = args.segformer_freeze_encoder
        model_kwargs["unfreeze_last_n_blocks"] = args.segformer_unfreeze_last_n_blocks
    model = make_model(args.model, **model_kwargs).to(device)
    loss_fn = SegmentationLoss(args.bce_weight, args.dice_weight, args.cldice_weight)
    optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
    out_dir = Path(args.output_dir)
    out_dir.mkdir(parents=True, exist_ok=True)
    run = _start_wandb(args)
    best_iou = -1.0
    history: list[dict[str, float | int]] = []
    try:
        for epoch in range(1, args.epochs + 1):
            totals: dict[str, float] = {}
            steps = 0
            for batch in tqdm(train_loader, desc=f"epoch {epoch}/{args.epochs}"):
                image = batch["image"].to(device)
                mask = batch["mask"].to(device)
                optimizer.zero_grad(set_to_none=True)
                loss, loss_parts = loss_fn(model(image), mask)
                loss.backward()
                optimizer.step()
                for key, value in loss_parts.items():
                    totals[key] = totals.get(key, 0.0) + float(value.item())
                steps += 1
            train_row = {f"train_{key}": value / max(steps, 1) for key, value in totals.items()}
            val_row = {f"val_{key}": value for key, value in validate(model, val_loader, device, args.threshold).items()}
            row = {"epoch": epoch, **train_row, **val_row}
            history.append(row)
            (out_dir / "history.json").write_text(json.dumps(history, indent=2))
            if run is not None:
                log_row = dict(row)
                preview_path = None
                if args.wandb_log_samples and (epoch == 1 or epoch % args.wandb_sample_every == 0 or epoch == args.epochs):
                    preview_path = _save_validation_preview(
                        model,
                        val_loader,
                        device,
                        args.threshold,
                        out_dir,
                        epoch,
                        args.wandb_sample_count,
                    )
                if preview_path is not None:
                    import wandb

                    log_row["val_examples"] = wandb.Image(str(preview_path), caption=f"epoch {epoch}: image | gt | pred | overlay")
                run.log(log_row)
            if val_row.get("val_iou", -1.0) > best_iou:
                best_iou = float(val_row["val_iou"])
                torch.save({"model": model.state_dict(), "args": vars(args), "epoch": epoch}, out_dir / "best.pt")
                if run is not None:
                    run.summary["best_val_iou"] = best_iou
                    run.summary["best_epoch"] = epoch
        torch.save({"model": model.state_dict(), "args": vars(args), "epoch": args.epochs}, out_dir / "last.pt")
        if args.wandb_artifacts:
            artifact_base = args.wandb_artifact_name or f"{args.model}-{Path(args.output_dir).name}"
            _log_artifact(run, out_dir / "best.pt", f"{artifact_base}-best", "model")
            _log_artifact(run, out_dir / "last.pt", f"{artifact_base}-last", "model")
            _log_artifact(run, out_dir / "history.json", f"{artifact_base}-history", "training-history")
    finally:
        if run is not None:
            run.finish()
    return out_dir / "best.pt"


def main() -> None:
    parser = argparse.ArgumentParser(description="Train a monogram stroke segmentation model.")
    parser.add_argument("--model", choices=["unet", "segformer"], default="unet")
    parser.add_argument("--train-csv", default="splits/e3_full_data/train.csv")
    parser.add_argument("--val-csv", default="splits/e3_full_data/val.csv")
    parser.add_argument("--output-dir", default="outputs/train")
    parser.add_argument("--image-size", type=int, default=512)
    parser.add_argument("--batch-size", type=int, default=4)
    parser.add_argument("--num-workers", type=int, default=4)
    parser.add_argument("--epochs", type=int, default=20)
    parser.add_argument("--lr", type=float, default=1e-4)
    parser.add_argument("--weight-decay", type=float, default=1e-4)
    parser.add_argument("--threshold", type=float, default=0.5)
    parser.add_argument("--device", default="cuda")
    parser.add_argument("--limit-train", type=int)
    parser.add_argument("--limit-val", type=int)
    parser.add_argument("--no-pretrained", action="store_true")
    parser.add_argument("--encoder-name", default="resnet34")
    parser.add_argument("--encoder-weights", default="imagenet")
    parser.add_argument("--segformer-model-name", default="nvidia/mit-b2")
    parser.add_argument("--segformer-pretrained", action=argparse.BooleanOptionalAction, default=True)
    parser.add_argument("--segformer-freeze-encoder", action=argparse.BooleanOptionalAction, default=False)
    parser.add_argument("--segformer-unfreeze-last-n-blocks", type=int, default=2)
    parser.add_argument("--bce-weight", type=float, default=1.0)
    parser.add_argument("--dice-weight", type=float, default=1.0)
    parser.add_argument("--cldice-weight", type=float, default=0.5)
    parser.add_argument("--wandb", action="store_true", help="Enable Weights & Biases logging.")
    parser.add_argument("--wandb-project", default="segment-monograms")
    parser.add_argument("--wandb-entity", default="")
    parser.add_argument("--wandb-name", default="")
    parser.add_argument("--wandb-group", default="")
    parser.add_argument("--wandb-mode", choices=["online", "offline", "disabled"], default="online")
    parser.add_argument("--wandb-tags", default="")
    parser.add_argument("--wandb-artifacts", action="store_true", help="Upload best.pt, last.pt, and history.json as W&B artifacts.")
    parser.add_argument("--wandb-artifact-name", default="")
    parser.add_argument("--wandb-log-samples", action="store_true", help="Log validation image/mask/prediction previews to W&B.")
    parser.add_argument("--wandb-sample-every", type=int, default=5)
    parser.add_argument("--wandb-sample-count", type=int, default=4)
    args = parser.parse_args()
    print(train(args))


if __name__ == "__main__":
    main()