from __future__ import annotations

import argparse
import json
import math
import random
from pathlib import Path
from typing import Dict, List, Tuple

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np


Cell = Tuple[int, int]


# ---------------------------------------------------------------------------
# Geometry helpers
# ---------------------------------------------------------------------------


def _cross(ox: float, oy: float, px: float, py: float, qx: float, qy: float) -> float:
    return (px - ox) * (qy - oy) - (py - oy) * (qx - ox)


def segments_intersect_properly(
    ax: float, ay: float, bx: float, by: float,
    cx: float, cy: float, dx: float, dy: float,
) -> bool:
    """True if segment AB *properly* crosses segment CD (shared endpoints don't count)."""
    d1 = _cross(cx, cy, dx, dy, ax, ay)
    d2 = _cross(cx, cy, dx, dy, bx, by)
    d3 = _cross(ax, ay, bx, by, cx, cy)
    d4 = _cross(ax, ay, bx, by, dx, dy)
    if ((d1 > 0 and d2 < 0) or (d1 < 0 and d2 > 0)) and \
       ((d3 > 0 and d4 < 0) or (d3 < 0 and d4 > 0)):
        return True
    return False


def point_seg_dist(px: float, py: float, ax: float, ay: float, bx: float, by: float) -> float:
    dx = bx - ax
    dy = by - ay
    len_sq = dx * dx + dy * dy
    if len_sq < 1e-12:
        return math.hypot(px - ax, py - ay)
    t = max(0.0, min(1.0, ((px - ax) * dx + (py - ay) * dy) / len_sq))
    return math.hypot(px - (ax + t * dx), py - (ay + t * dy))


# ---------------------------------------------------------------------------
# Union-Find
# ---------------------------------------------------------------------------


class UnionFind:
    def __init__(self, n: int) -> None:
        self.parent = list(range(n))
        self.rank = [0] * n
        self.num_sets = n

    def find(self, x: int) -> int:
        while self.parent[x] != x:
            self.parent[x] = self.parent[self.parent[x]]
            x = self.parent[x]
        return x

    def union(self, a: int, b: int) -> bool:
        ra, rb = self.find(a), self.find(b)
        if ra == rb:
            return False
        if self.rank[ra] < self.rank[rb]:
            ra, rb = rb, ra
        self.parent[rb] = ra
        if self.rank[ra] == self.rank[rb]:
            self.rank[ra] += 1
        self.num_sets -= 1
        return True


# ---------------------------------------------------------------------------
# Graph construction — planar, no-dot-crossing edge set
# ---------------------------------------------------------------------------


def place_dots(
    rng: random.Random,
    grid_rows: int,
    grid_cols: int,
    num_dots: int,
    min_gap: float,
    border_margin: int = 5,
    max_attempts: int = 8000,
) -> List[Cell]:
    cells: List[Cell] = []
    lo_r, hi_r = border_margin, grid_rows - border_margin
    lo_c, hi_c = border_margin, grid_cols - border_margin
    for _ in range(max_attempts):
        if len(cells) == num_dots:
            break
        r = rng.randint(lo_r, hi_r - 1)
        c = rng.randint(lo_c, hi_c - 1)
        if all(math.hypot(r - er, c - ec) >= min_gap for er, ec in cells):
            cells.append((r, c))
    return cells


def build_planar_edge_set(
    dots: List[Cell],
    dot_radius: float,
    max_edge_len: float,
) -> List[Tuple[int, int, float]]:
    n = len(dots)
    candidates: List[Tuple[float, int, int]] = []
    for i in range(n):
        ri, ci = dots[i]
        for j in range(i + 1, n):
            rj, cj = dots[j]
            d = math.hypot(ri - rj, ci - cj)
            if d > max_edge_len:
                continue
            clear = True
            for k in range(n):
                if k == i or k == j:
                    continue
                if point_seg_dist(dots[k][0], dots[k][1], ri, ci, rj, cj) < dot_radius + 0.8:
                    clear = False
                    break
            if clear:
                candidates.append((d, i, j))
    candidates.sort()

    accepted: List[Tuple[int, int, float]] = []
    seg_coords: List[Tuple[float, float, float, float]] = []
    for dist, i, j in candidates:
        ri, ci = dots[i]
        rj, cj = dots[j]
        crosses = False
        for ax, ay, bx, by in seg_coords:
            if (ri == ax and ci == ay) or (ri == bx and ci == by) or \
               (rj == ax and cj == ay) or (rj == bx and cj == by):
                continue
            if segments_intersect_properly(ri, ci, rj, cj, ax, ay, bx, by):
                crosses = True
                break
        if not crosses:
            accepted.append((i, j, dist))
            seg_coords.append((float(ri), float(ci), float(rj), float(cj)))

    return accepted


# ---------------------------------------------------------------------------
# Graph construction for bounded faces
# ---------------------------------------------------------------------------


def build_graph_with_faces(
    rng: random.Random,
    n: int,
    planar_edges: List[Tuple[int, int, float]],
    target_faces: int,
) -> Tuple[List[Tuple[int, int]], int, int] | None:
    """Build a planar graph targeting a specific number of bounded faces.

    Strategy:
      1. Build a spanning forest from shuffled edges (connecting as many dots
         as possible into one component).
      2. Each additional intra-component edge beyond the spanning forest creates
         exactly one new bounded face (Euler: F_bounded = E - V + C).
      3. Add extra edges until we reach the target face count.

    Returns (edges, bounded_faces, num_components) or None if infeasible.
    """
    uf = UnionFind(n)

    # Shuffle edges, biased toward shorter ones
    mid = len(planar_edges) // 2
    short = list(planar_edges[:mid])
    long = list(planar_edges[mid:])
    rng.shuffle(short)
    rng.shuffle(long)
    shuffled = short + long

    # Phase 1: build spanning forest (connect everything into one component ideally)
    tree_edges: List[Tuple[int, int]] = []
    extra_edges: List[Tuple[int, int]] = []

    for i, j, d in shuffled:
        if uf.find(i) != uf.find(j):
            uf.union(i, j)
            tree_edges.append((i, j))
        else:
            extra_edges.append((i, j))

    num_components = uf.num_sets

    # Phase 2: add extra edges to create faces
    # bounded_faces = E - V + C = (tree + extra) - V + C
    # spanning forest has V - C edges, so faces = num_extra_added
    rng.shuffle(extra_edges)

    if len(extra_edges) < target_faces:
        return None

    selected_edges = tree_edges + extra_edges[:target_faces]
    bounded_faces = target_faces

    return selected_edges, bounded_faces, num_components


# ---------------------------------------------------------------------------
# Instance sampling
# ---------------------------------------------------------------------------

QUESTION = (
    "How many distinct enclosed regions (bounded faces) are visible in this image? "
    "An enclosed region is a maximal area that is fully surrounded by the drawn "
    "line segments on every side, with no opening to the outside background. The "
    "unbounded outside area does not count as an enclosed region. Each enclosed "
    "region should be counted exactly once, regardless of its shape. Count every "
    "enclosed region in the entire image and report the total as a positive integer. "
    "Provide your final answer enclosed in <answer>...</answer> tags."
)


def sample_instance(
    rng: random.Random,
    width: int,
    height: int,
    grid_rows: int,
    grid_cols: int,
    min_faces: int,
    max_faces: int,
    num_dots_min: int,
    num_dots_max: int,
    min_gap: float,
    dot_radius: float,
    max_edge_len: float,
) -> Dict[str, object] | None:
    target_faces = rng.randint(min_faces, max_faces)
    num_dots = rng.randint(num_dots_min, num_dots_max)

    dots = place_dots(rng, grid_rows, grid_cols, num_dots, min_gap)
    if len(dots) < 10:
        return None

    planar_edges = build_planar_edge_set(dots, dot_radius, max_edge_len)

    result = build_graph_with_faces(rng, len(dots), planar_edges, target_faces)
    if result is None:
        return None

    edges, bounded_faces, num_components = result

    if bounded_faces < min_faces:
        return None

    margin = int(min(width, height) * 0.10)
    square_size = min(width, height) - 2 * margin
    square_left = (width - square_size) / 2.0
    square_top = (height - square_size) / 2.0

    return {
        "width": width,
        "height": height,
        "grid_rows": grid_rows,
        "grid_cols": grid_cols,
        "square_left": round(square_left, 2),
        "square_top": round(square_top, 2),
        "square_size": round(square_size, 2),
        "num_dots": len(dots),
        "num_edges": len(edges),
        "num_components": num_components,
        "question": QUESTION,
        "answer": bounded_faces,
        "dots": [[r, c] for r, c in dots],
        "edges": [[i, j] for i, j in edges],
        "dot_radius": dot_radius,
    }


# ---------------------------------------------------------------------------
# Rendering (matplotlib — smooth anti-aliased output)
# ---------------------------------------------------------------------------

LINE_COLOR = "#2f2f2f"
DOT_COLOR = "#1d1916"


def render_instance(out_path: Path, record: Dict[str, object], noise_seed: int = 0) -> None:
    width = int(record["width"])
    height = int(record["height"])
    grid_rows = int(record["grid_rows"])
    grid_cols = int(record["grid_cols"])
    square_left = float(record["square_left"])
    square_top = float(record["square_top"])
    square_size = float(record["square_size"])
    dots: List[List[int]] = record["dots"]  # type: ignore[assignment]
    edges: List[List[int]] = record["edges"]  # type: ignore[assignment]
    dot_radius_grid = float(record["dot_radius"])

    cell_w = square_size / grid_cols
    cell_h = square_size / grid_rows

    def to_pixel(r: float, c: float) -> Tuple[float, float]:
        px = square_left + (c + 0.5) * cell_w
        py = square_top + (r + 0.5) * cell_h
        return px, py

    pixel_dot_radius = dot_radius_grid * min(cell_w, cell_h) * 0.5
    edge_thickness = max(1.5, pixel_dot_radius * 0.3)

    fig = plt.figure(figsize=(width / 100, height / 100), dpi=100)
    ax = fig.add_axes([0, 0, 1, 1])
    ax.set_xlim(0, width)
    ax.set_ylim(height, 0)
    ax.axis("off")
    ax.set_facecolor("#f8f6f0")

    # Subtle noise background
    nrng = np.random.default_rng(noise_seed)
    noise = nrng.normal(0.0, 1.0, size=(height, width))
    noise = (noise - noise.min()) / max(noise.max() - noise.min(), 1e-6)
    ax.imshow(noise, cmap="Greys", alpha=0.05, extent=(0, width, height, 0),
              interpolation="bilinear")

    # White square background
    ax.fill_between(
        [square_left, square_left + square_size],
        [square_top, square_top],
        [square_top + square_size, square_top + square_size],
        color="#fffdf8", zorder=0.5,
    )

    # Border
    bx = [square_left, square_left + square_size, square_left + square_size, square_left, square_left]
    by = [square_top, square_top, square_top + square_size, square_top + square_size, square_top]
    ax.plot(bx, by, color="#2d2720", linewidth=2.0, solid_capstyle="round", zorder=1.0)

    # Plain (v4_plain): solid edges. No dashed-line anti-shortcut.
    for i, j in edges:
        px1, py1 = to_pixel(dots[i][0], dots[i][1])
        px2, py2 = to_pixel(dots[j][0], dots[j][1])
        ax.plot([px1, px2], [py1, py2],
                color=LINE_COLOR, linewidth=edge_thickness,
                solid_capstyle="round", alpha=0.92, zorder=2.0)

    # Dots on top
    for r, c in dots:
        px, py = to_pixel(r, c)
        circle = plt.Circle((px, py), pixel_dot_radius, color=DOT_COLOR, zorder=3.0)
        ax.add_patch(circle)

    fig.savefig(out_path, dpi=100, bbox_inches="tight", pad_inches=0)
    plt.close(fig)


# ---------------------------------------------------------------------------
# Dataset generation
# ---------------------------------------------------------------------------


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--output-root", type=Path, required=True)
    parser.add_argument("--count", type=int, default=30)
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument("--width", type=int, default=1024)
    parser.add_argument("--height", type=int, default=1024)
    parser.add_argument("--grid-rows", type=int, default=100)
    parser.add_argument("--grid-cols", type=int, default=100)
    parser.add_argument("--min-faces", type=int, default=4)
    parser.add_argument("--max-faces", type=int, default=15)
    parser.add_argument("--num-dots-min", type=int, default=60)
    parser.add_argument("--num-dots-max", type=int, default=120)
    parser.add_argument("--min-gap", type=float, default=5.0)
    parser.add_argument("--dot-radius", type=float, default=1.5)
    parser.add_argument("--max-edge-len", type=float, default=25.0)
    parser.add_argument("--difficulty", type=int, default=5,
                        help="Integer difficulty >=0; scales faces and dot count.")
    args = parser.parse_args()

    d = max(0, int(args.difficulty))
    # Difficulty scaling per spec
    args.min_faces = 5
    args.max_faces = 5 + 2 * d
    args.num_dots_min = 10 * d
    args.num_dots_max = 20 + 10 * d
    base_max_edge_len = args.max_edge_len
    args.max_edge_len = base_max_edge_len / (1.0 + 0.08 * d)

    # Canvas scaling based on num_dots_max growth
    N_d = 20 + 10 * d
    N_0 = 20
    s = math.sqrt(max(1.0, N_d / N_0))
    args.width = int(round(args.width * s))
    args.height = int(round(args.height * s))

    out_root: Path = args.output_root
    img_dir = out_root / "images"
    img_dir.mkdir(parents=True, exist_ok=True)
    ann_path = out_root / "annotations.jsonl"

    rng = random.Random(args.seed)
    records = []
    # Force evenly-spaced answers across [min_faces, max_faces].
    if args.count > 1:
        forced_targets = [
            int(round(args.min_faces + i * (args.max_faces - args.min_faces) / (args.count - 1)))
            for i in range(args.count)
        ]
    else:
        forced_targets = [args.min_faces]
    print(f"forced face counts: {forced_targets}")
    with ann_path.open("w") as f:
        for i in range(args.count):
            sub_seed = rng.randint(0, 2**31 - 1)
            tgt = forced_targets[i]
            for _ in range(2000):
                record = sample_instance(
                    rng=rng,
                    width=args.width,
                    height=args.height,
                    grid_rows=args.grid_rows,
                    grid_cols=args.grid_cols,
                    min_faces=tgt,
                    max_faces=tgt,
                    num_dots_min=args.num_dots_min,
                    num_dots_max=args.num_dots_max,
                    min_gap=args.min_gap,
                    dot_radius=args.dot_radius,
                    max_edge_len=args.max_edge_len,
                )
                if record is not None and record.get("answer") == tgt:
                    break
            else:
                print(f"  [{i+1}/{args.count}] SKIP (failed to generate)")
                continue

            name = f"bounded_faces_counting_{i:05d}.png"
            render_instance(img_dir / name, record, noise_seed=sub_seed)
            print(f"  [{i+1}/{args.count}] faces={record['answer']} dots={record['num_dots']} edges={record['num_edges']}")

            rec = {
                "image": f"images/{name}",
                "question": QUESTION,
                "answer": record["answer"],
                "metadata": {
                    "bounded_faces": record["answer"],
                    "num_dots": record["num_dots"],
                    "num_edges": record["num_edges"],
                    "num_components": record["num_components"],
                    "seed": sub_seed,
                },
            }
            f.write(json.dumps(rec) + "\n")
            records.append(rec)

    data_json = {
        "task": "bounded_faces_counting",
        "category": "distributed_scanning",
        "count": len(records),
        "items": records,
    }
    (out_root / "data.json").write_text(json.dumps(data_json, indent=2))
    print(f"Saved to {out_root}")


if __name__ == "__main__":
    main()