#!/usr/bin/env python3
"""Shared utilities for lumen class discovery and fine-tuning scripts."""

from __future__ import annotations

import json
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Iterable

import numpy as np
from matplotlib.path import Path as MplPath

# common.py lives at scripts/finetune/shared/common.py, so repo root is parents[3].
REPO_ROOT = Path(__file__).resolve().parents[3]
if str(REPO_ROOT) not in sys.path:
    sys.path.insert(0, str(REPO_ROOT))

from deepivus.io.dicom import read_dicom
from deepivus.processing.preprocessing import apply_center_black_circle


@dataclass(frozen=True)
class LumenAnnotation:
    """Single manually-annotated lumen polygon for one frame."""

    bank_path: Path
    group: str
    dicom_path: Path
    frame_idx: int
    bifurcation: bool
    lumen_x: np.ndarray
    lumen_y: np.ndarray

    @property
    def sample_id(self) -> str:
        rel_bank = self.bank_path.resolve().relative_to(REPO_ROOT)
        return f"{rel_bank.as_posix()}::{self.frame_idx}"


@dataclass(frozen=True)
class BifurcationAnnotation:
    """Single frame-level bifurcation annotation."""

    bank_path: Path
    group: str
    dicom_path: Path
    frame_idx: int
    bifurcation: bool

    @property
    def sample_id(self) -> str:
        rel_bank = self.bank_path.resolve().relative_to(REPO_ROOT)
        return f"{rel_bank.as_posix()}::{self.frame_idx}"


def _read_jsonl(path: Path) -> list[dict]:
    rows: list[dict] = []
    with path.open("r", encoding="utf-8") as fp:
        for raw in fp:
            line = raw.strip()
            if not line:
                continue
            rows.append(json.loads(line))
    return rows


def _resolve_dicom_path(bank_path: Path, meta: dict) -> Path | None:
    group = str(meta.get("group", bank_path.parent.name))
    dicom_raw = meta.get("dicom_path")
    if dicom_raw:
        dicom_path = Path(str(dicom_raw))
        if not dicom_path.is_absolute():
            dicom_path = (REPO_ROOT / dicom_path).resolve()
        if dicom_path.exists():
            return dicom_path

    fallback = REPO_ROOT / "data" / group / f"{bank_path.stem}.dcm"
    if fallback.exists():
        return fallback.resolve()
    return None


def load_lumen_annotations(frame_bank_root: Path) -> list[LumenAnnotation]:
    """Load all frame-bank lumen annotations with valid polygons."""
    annotations: list[LumenAnnotation] = []
    bank_files = sorted(frame_bank_root.glob("*/*.jsonl"))
    for bank_path in bank_files:
        rows = _read_jsonl(bank_path)
        if not rows:
            continue
        meta = rows[0]
        if meta.get("record_type") != "meta":
            continue

        group = str(meta.get("group", bank_path.parent.name))
        dicom_path = _resolve_dicom_path(bank_path, meta)
        if dicom_path is None:
            continue

        for rec in rows:
            if rec.get("record_type") != "frame":
                continue
            lumen = rec.get("lumen", {})
            xs = np.asarray(lumen.get("x", []), dtype=np.float32)
            ys = np.asarray(lumen.get("y", []), dtype=np.float32)
            if xs.size < 3 or ys.size < 3 or xs.size != ys.size:
                continue
            if not np.all(np.isfinite(xs)) or not np.all(np.isfinite(ys)):
                continue
            annotations.append(
                LumenAnnotation(
                    bank_path=bank_path,
                    group=group,
                    dicom_path=dicom_path,
                    frame_idx=int(rec["frame"]),
                    bifurcation=bool(rec.get("bifurcation", False)),
                    lumen_x=xs,
                    lumen_y=ys,
                )
            )
    return annotations


def load_bifurcation_annotations(frame_bank_root: Path) -> list[BifurcationAnnotation]:
    """Load all frame-bank bifurcation labels, independent of lumen polygon presence."""
    annotations: list[BifurcationAnnotation] = []
    bank_files = sorted(frame_bank_root.glob("*/*.jsonl"))
    for bank_path in bank_files:
        rows = _read_jsonl(bank_path)
        if not rows:
            continue
        meta = rows[0]
        if meta.get("record_type") != "meta":
            continue
        group = str(meta.get("group", bank_path.parent.name))
        dicom_path = _resolve_dicom_path(bank_path, meta)
        if dicom_path is None:
            continue
        for rec in rows:
            if rec.get("record_type") != "frame":
                continue
            bif = rec.get("bifurcation")
            if bif is None:
                continue
            annotations.append(
                BifurcationAnnotation(
                    bank_path=bank_path,
                    group=group,
                    dicom_path=dicom_path,
                    frame_idx=int(rec["frame"]),
                    bifurcation=bool(bif),
                )
            )
    return annotations


def polygon_to_mask(x_coords: np.ndarray, y_coords: np.ndarray, image_shape: tuple[int, int]) -> np.ndarray:
    """Rasterize a polygon in image coordinates to a binary mask."""
    if x_coords.size < 3 or y_coords.size < 3:
        return np.zeros(image_shape, dtype=bool)

    vertices = np.column_stack((x_coords.astype(np.float32), y_coords.astype(np.float32)))
    polygon = MplPath(vertices, closed=True)

    h, w = image_shape
    yy, xx = np.mgrid[0:h, 0:w]
    points = np.column_stack((xx.ravel(), yy.ravel()))
    mask = polygon.contains_points(points, radius=0.5).reshape((h, w))
    return mask


def group_by_dicom(annotations: Iterable[LumenAnnotation]) -> dict[Path, list[LumenAnnotation]]:
    grouped: dict[Path, list[LumenAnnotation]] = {}
    for ann in annotations:
        grouped.setdefault(ann.dicom_path, []).append(ann)
    return grouped


def load_preprocessed_stack(dicom_path: Path, diameter: int) -> np.ndarray:
    """Load one DICOM stack and apply the same preprocessing as pipeline inference."""
    _, images = read_dicom(str(dicom_path))
    return apply_center_black_circle(images, diameter=diameter)


def build_images_and_masks(
    annotations: list[LumenAnnotation],
    diameter: int,
) -> tuple[np.ndarray, np.ndarray, list[LumenAnnotation]]:
    """Materialize image and mask arrays for a list of annotations."""
    images_out: list[np.ndarray] = []
    masks_out: list[np.ndarray] = []
    kept: list[LumenAnnotation] = []

    grouped = group_by_dicom(annotations)
    for dicom_path, ann_list in grouped.items():
        stack = load_preprocessed_stack(dicom_path, diameter=diameter)
        h, w = int(stack.shape[1]), int(stack.shape[2])
        for ann in ann_list:
            if ann.frame_idx < 0 or ann.frame_idx >= int(stack.shape[0]):
                continue
            mask = polygon_to_mask(ann.lumen_x, ann.lumen_y, (h, w))
            if not np.any(mask):
                continue
            images_out.append(stack[ann.frame_idx])
            masks_out.append(mask.astype(np.float32))
            kept.append(ann)

    if not images_out:
        raise RuntimeError("No usable annotations found after loading DICOM frames and rasterizing masks.")

    return np.stack(images_out, axis=0), np.stack(masks_out, axis=0), kept


def stratified_frame_split(
    annotations: list[LumenAnnotation],
    train_fraction: float,
    val_fraction: float,
    test_fraction: float,
    seed: int,
) -> dict[str, list[int]]:
    """Frame-level stratified split by bifurcation label."""
    total = train_fraction + val_fraction + test_fraction
    if total <= 0:
        raise ValueError("Split fractions must sum to a positive value.")
    train_fraction /= total
    val_fraction /= total
    test_fraction /= total

    labels = np.asarray([1 if ann.bifurcation else 0 for ann in annotations], dtype=np.int32)
    indices = np.arange(len(annotations), dtype=np.int64)
    rng = np.random.default_rng(seed)

    train_ids: list[int] = []
    val_ids: list[int] = []
    test_ids: list[int] = []

    for label in (0, 1):
        cls_idx = indices[labels == label]
        rng.shuffle(cls_idx)
        n = len(cls_idx)
        if n == 0:
            continue
        n_train = int(round(n * train_fraction))
        n_val = int(round(n * val_fraction))
        n_test = n - n_train - n_val

        # Keep at least one sample per split when class has enough samples.
        if n >= 3:
            n_train = max(1, n_train)
            n_val = max(1, n_val)
            n_test = max(1, n_test)
            overflow = n_train + n_val + n_test - n
            while overflow > 0:
                if n_train >= n_val and n_train >= n_test and n_train > 1:
                    n_train -= 1
                elif n_val >= n_test and n_val > 1:
                    n_val -= 1
                elif n_test > 1:
                    n_test -= 1
                overflow -= 1

        train_ids.extend(cls_idx[:n_train].tolist())
        val_ids.extend(cls_idx[n_train : n_train + n_val].tolist())
        test_ids.extend(cls_idx[n_train + n_val : n_train + n_val + n_test].tolist())

    rng.shuffle(train_ids)
    rng.shuffle(val_ids)
    rng.shuffle(test_ids)
    return {"train": train_ids, "val": val_ids, "test": test_ids}


def split_summary(annotations: list[LumenAnnotation], split: dict[str, list[int]]) -> dict[str, dict[str, int]]:
    out: dict[str, dict[str, int]] = {}
    for part, ids in split.items():
        bif = sum(1 for i in ids if annotations[i].bifurcation)
        non_bif = len(ids) - bif
        out[part] = {
            "count": len(ids),
            "bifurcation_true": bif,
            "bifurcation_false": non_bif,
        }
    return out


def save_split_json(path: Path, annotations: list[LumenAnnotation], split: dict[str, list[int]], seed: int) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    payload = {
        "seed": int(seed),
        "summary": split_summary(annotations, split),
        "splits": {
            part: [annotations[i].sample_id for i in ids] for part, ids in split.items()
        },
    }
    with path.open("w", encoding="utf-8") as fp:
        json.dump(payload, fp, indent=2)


def load_split_ids(path: Path) -> dict[str, set[str]]:
    with path.open("r", encoding="utf-8") as fp:
        payload = json.load(fp)
    splits = payload.get("splits", {})
    out = {}
    for part in ("train", "val", "test"):
        out[part] = set(str(v) for v in splits.get(part, []))
    return out