code/sequential_traversal/maze/creation.py

from __future__ import annotations

import argparse
import json
import random
import string
from collections import deque
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Set, Tuple

from PIL import Image, ImageDraw, ImageFont


# ---------------------------------------------------------------------------
# Data model
# ---------------------------------------------------------------------------

@dataclass
class MazeLayout:
    rows: int
    cols: int
    vertical_walls: List[List[bool]]
    horizontal_walls: List[List[bool]]
    # (side, index) for each opening in label order
    openings: List[Tuple[str, int]]
    # (side, index, row, col) in label order
    opening_cells: List[Tuple[str, int, int, int]]
    # Indices into openings[] that are the connected pair (always sorted)
    connected_indices: Tuple[int, int]
    path_length: int


# ---------------------------------------------------------------------------
# Maze generation (DFS / recursive backtracking)
# ---------------------------------------------------------------------------

def _generate_maze(
    rng: random.Random,
    rows: int,
    cols: int,
) -> Tuple[List[List[bool]], List[List[bool]]]:
    """Return (vertical_walls, horizontal_walls).

    vertical_walls[r][c]   = True  →  wall on the right side of cell (r, c)
    horizontal_walls[r][c] = True  →  wall on the bottom of cell (r, c)
    """
    visited = [[False] * cols for _ in range(rows)]
    vertical_walls = [[True] * cols for _ in range(rows)]
    horizontal_walls = [[True] * cols for _ in range(rows)]

    def dfs(r: int, c: int) -> None:
        visited[r][c] = True
        directions = [(-1, 0), (1, 0), (0, -1), (0, 1)]
        rng.shuffle(directions)
        for dr, dc in directions:
            nr, nc = r + dr, c + dc
            if 0 <= nr < rows and 0 <= nc < cols and not visited[nr][nc]:
                if dr == -1:
                    horizontal_walls[nr][nc] = False
                elif dr == 1:
                    horizontal_walls[r][c] = False
                elif dc == -1:
                    vertical_walls[nr][nc] = False
                elif dc == 1:
                    vertical_walls[r][c] = False
                dfs(nr, nc)

    dfs(0, 0)
    return vertical_walls, horizontal_walls


def _open_neighbors(
    vertical_walls: List[List[bool]],
    horizontal_walls: List[List[bool]],
    rows: int,
    cols: int,
    r: int,
    c: int,
) -> List[Tuple[int, int]]:
    neighbors: List[Tuple[int, int]] = []
    if r > 0 and not horizontal_walls[r - 1][c]:
        neighbors.append((r - 1, c))
    if r < rows - 1 and not horizontal_walls[r][c]:
        neighbors.append((r + 1, c))
    if c > 0 and not vertical_walls[r][c - 1]:
        neighbors.append((r, c - 1))
    if c < cols - 1 and not vertical_walls[r][c]:
        neighbors.append((r, c + 1))
    return neighbors


def _reachable(
    vertical_walls: List[List[bool]],
    horizontal_walls: List[List[bool]],
    rows: int,
    cols: int,
    start: Tuple[int, int],
) -> Set[Tuple[int, int]]:
    queue: deque[Tuple[int, int]] = deque([start])
    seen: Set[Tuple[int, int]] = {start}
    while queue:
        r, c = queue.popleft()
        for nr, nc in _open_neighbors(vertical_walls, horizontal_walls, rows, cols, r, c):
            if (nr, nc) not in seen:
                seen.add((nr, nc))
                queue.append((nr, nc))
    return seen


def _path_cells(
    vertical_walls: List[List[bool]],
    horizontal_walls: List[List[bool]],
    rows: int,
    cols: int,
    start: Tuple[int, int],
    goal: Tuple[int, int],
) -> Set[Tuple[int, int]]:
    """BFS path between start and goal; returns the set of cells on the path."""
    queue: deque[Tuple[int, int]] = deque([start])
    prev: Dict[Tuple[int, int], Tuple[int, int] | None] = {start: None}
    while queue:
        r, c = queue.popleft()
        if (r, c) == goal:
            break
        for nr, nc in _open_neighbors(vertical_walls, horizontal_walls, rows, cols, r, c):
            if (nr, nc) not in prev:
                prev[(nr, nc)] = (r, c)
                queue.append((nr, nc))
    if goal not in prev:
        return set()
    cells: Set[Tuple[int, int]] = set()
    cur: Tuple[int, int] | None = goal
    while cur is not None:
        cells.add(cur)
        cur = prev[cur]
    return cells


def _opening_candidates(rows: int, cols: int, corner_buffer: int = 1) -> List[Tuple[str, int, int, int]]:
    """(side, border_index, row, col) for each candidate opening."""
    candidates: List[Tuple[str, int, int, int]] = []
    for c in range(corner_buffer, cols - corner_buffer):
        candidates.append(("top", c, 0, c))
        candidates.append(("bottom", c, rows - 1, c))
    for r in range(corner_buffer, rows - corner_buffer):
        candidates.append(("left", r, r, 0))
        candidates.append(("right", r, r, cols - 1))
    return candidates


def _looks_natural(
    vertical_walls: List[List[bool]],
    horizontal_walls: List[List[bool]],
    rows: int,
    cols: int,
    r: int,
    c: int,
) -> bool:
    return len(_open_neighbors(vertical_walls, horizontal_walls, rows, cols, r, c)) >= 1


def _isolate_decoy(
    vertical_walls: List[List[bool]],
    horizontal_walls: List[List[bool]],
    rows: int,
    cols: int,
    rng: random.Random,
    root: Tuple[int, int],
    forbidden: Set[Tuple[int, int]],
    min_size: int,
    max_size: int,
) -> Set[Tuple[int, int]]:
    """Grow a compact subtree from root that avoids forbidden cells, then wall
    it off so it forms an isolated dead-end region."""
    if root in forbidden:
        return set()

    queue: deque[Tuple[int, int]] = deque([root])
    subtree: Set[Tuple[int, int]] = {root}
    max_depth = rows * cols

    while queue and len(subtree) < max_size:
        r, c = queue.popleft()
        neighbors = _open_neighbors(vertical_walls, horizontal_walls, rows, cols, r, c)
        rng.shuffle(neighbors)
        for nr, nc in neighbors:
            if (nr, nc) in subtree or (nr, nc) in forbidden:
                continue
            subtree.add((nr, nc))
            queue.append((nr, nc))
            if len(subtree) >= max_size:
                break

    if len(subtree) < min_size:
        return set()

    # Wall off subtree from the rest of the maze
    for r, c in list(subtree):
        if r > 0 and (r - 1, c) not in subtree:
            horizontal_walls[r - 1][c] = True
        if r < rows - 1 and (r + 1, c) not in subtree:
            horizontal_walls[r][c] = True
        if c > 0 and (r, c - 1) not in subtree:
            vertical_walls[r][c - 1] = True
        if c < cols - 1 and (r, c + 1) not in subtree:
            vertical_walls[r][c] = True

    return subtree


# ---------------------------------------------------------------------------
# Layout sampling
# ---------------------------------------------------------------------------

def sample_layout(
    rng: random.Random,
    rows: int,
    cols: int,
    num_openings: int,
    min_path_length_floor: int = 0,
) -> MazeLayout:
    """Generate a perfect maze and place border openings.

    Exactly two openings are connected through the maze interior; all others
    lead to isolated dead-end pockets (decoys).  The opening order is shuffled
    so that the connected pair does not always occupy the first two label slots.
    """
    vertical_walls, horizontal_walls = _generate_maze(rng, rows, cols)
    border_cells = _opening_candidates(rows, cols, corner_buffer=1)
    k = max(2, min(num_openings, len(border_cells)))

    # Pick a connected pair with a long interior path
    min_path_len = max(min_path_length_floor, (rows * cols) // 3)
    candidates: List[Tuple] = []
    for i in range(len(border_cells)):
        for j in range(i + 1, len(border_cells)):
            a, b = border_cells[i], border_cells[j]
            path = _path_cells(
                vertical_walls, horizontal_walls, rows, cols,
                (a[2], a[3]), (b[2], b[3]),
            )
            if len(path) >= min_path_len:
                candidates.append((a, b, path))

    if candidates:
        first, second, path = rng.choice(candidates)
    else:
        first, second = rng.sample(border_cells, 2)
        path = _path_cells(
            vertical_walls, horizontal_walls, rows, cols,
            (first[2], first[3]), (second[2], second[3]),
        )

    main_pair = [first, second]
    forbidden: Set[Tuple[int, int]] = set(path)

    # Build decoy openings
    total = rows * cols
    remaining = [
        cell for cell in border_cells
        if cell not in main_pair
        and (cell[2], cell[3]) not in path
        and _looks_natural(vertical_walls, horizontal_walls, rows, cols, cell[2], cell[3])
    ]
    rng.shuffle(remaining)

    extras: List[Tuple[str, int, int, int]] = []
    for side, idx, r, c in remaining:
        if len(extras) >= k - 2:
            break
        subtree = _isolate_decoy(
            vertical_walls, horizontal_walls, rows, cols, rng,
            (r, c), forbidden,
            min_size=3,
            max_size=max(12, (total * 3) // 5),
        )
        if not subtree:
            continue
        extras.append((side, idx, r, c))
        forbidden.update(subtree)

    # Validate: ensure main pair is connected and decoys cannot reach the exit
    ra, ca = first[2], first[3]
    rb, cb = second[2], second[3]
    main_component = _reachable(vertical_walls, horizontal_walls, rows, cols, (ra, ca))

    if (rb, cb) not in main_component:
        extras = []
    else:
        valid_extras = []
        for side, idx, r, c in extras:
            comp = _reachable(vertical_walls, horizontal_walls, rows, cols, (r, c))
            if (rb, cb) not in comp:
                valid_extras.append((side, idx, r, c))
        extras = valid_extras

    all_cells = main_pair + extras

    # Shuffle label order so connected pair is not always A-B
    order = list(range(len(all_cells)))
    rng.shuffle(order)
    shuffled = [all_cells[i] for i in order]

    conn_a = order.index(0)
    conn_b = order.index(1)
    if conn_a > conn_b:
        conn_a, conn_b = conn_b, conn_a

    return MazeLayout(
        rows=rows,
        cols=cols,
        vertical_walls=vertical_walls,
        horizontal_walls=horizontal_walls,
        openings=[(side, idx) for (side, idx, _, _) in shuffled],
        opening_cells=shuffled,
        connected_indices=(conn_a, conn_b),
        path_length=len(path),
    )


# ---------------------------------------------------------------------------
# Rendering
# ---------------------------------------------------------------------------

def _carve_openings(
    draw: ImageDraw.ImageDraw,
    rows: int,
    cols: int,
    cell_size: int,
    wall_width: int,
    margin: int,
    openings: List[Tuple[str, int]],
) -> None:
    x0, y0 = margin, margin
    x1, y1 = margin + cols * cell_size, margin + rows * cell_size
    opening_set = set(openings)

    for c in range(cols):
        if ("top", c) not in opening_set:
            xs = margin + c * cell_size
            draw.rectangle(
                [xs - wall_width // 2, y0 - wall_width // 2,
                 xs + cell_size + (wall_width - 1) // 2, y0 + (wall_width - 1) // 2],
                fill="black",
            )
        if ("bottom", c) not in opening_set:
            xs = margin + c * cell_size
            draw.rectangle(
                [xs - wall_width // 2, y1 - wall_width // 2,
                 xs + cell_size + (wall_width - 1) // 2, y1 + (wall_width - 1) // 2],
                fill="black",
            )

    for r in range(rows):
        if ("left", r) not in opening_set:
            ys = margin + r * cell_size
            draw.rectangle(
                [x0 - wall_width // 2, ys - wall_width // 2,
                 x0 + (wall_width - 1) // 2, ys + cell_size + (wall_width - 1) // 2],
                fill="black",
            )
        if ("right", r) not in opening_set:
            ys = margin + r * cell_size
            draw.rectangle(
                [x1 - wall_width // 2, ys - wall_width // 2,
                 x1 + (wall_width - 1) // 2, ys + cell_size + (wall_width - 1) // 2],
                fill="black",
            )


def _label_openings(
    draw: ImageDraw.ImageDraw,
    rows: int,
    cols: int,
    cell_size: int,
    wall_width: int,
    margin: int,
    openings: List[Tuple[str, int]],
) -> None:
    if not openings:
        return

    x0, y0 = margin, margin
    x1, y1 = margin + cols * cell_size, margin + rows * cell_size
    img_w = cols * cell_size + 2 * margin
    img_h = rows * cell_size + 2 * margin

    try:
        font = ImageFont.truetype("Arial.ttf", size=16)
    except Exception:
        try:
            font = ImageFont.truetype("DejaVuSans.ttf", size=16)
        except Exception:
            font = ImageFont.load_default()

    def measure(text: str) -> Tuple[float, float]:
        if hasattr(draw, "textbbox"):
            l, t, r, b = draw.textbbox((0, 0), text, font=font)
            return r - l, b - t
        if hasattr(font, "getbbox"):
            l, t, r, b = font.getbbox(text)
            return r - l, b - t
        return font.getsize(text)  # type: ignore[attr-defined]

    offset = wall_width + 4

    for i, (side, index) in enumerate(openings):
        label = string.ascii_uppercase[i]
        w, h = measure(label)

        if side == "top":
            col = max(0, min(cols - 1, index))
            cx = margin + col * cell_size + cell_size / 2
            x = cx - w / 2
            y = y0 - offset - h
        elif side == "bottom":
            col = max(0, min(cols - 1, index))
            cx = margin + col * cell_size + cell_size / 2
            x = cx - w / 2
            y = y1 + offset
        elif side == "left":
            row = max(0, min(rows - 1, index))
            cy = margin + row * cell_size + cell_size / 2
            x = x0 - offset - w
            y = cy - h / 2
        elif side == "right":
            row = max(0, min(rows - 1, index))
            cy = margin + row * cell_size + cell_size / 2
            x = x1 + offset
            y = cy - h / 2
        else:
            continue

        x = max(0.0, min(float(img_w - w), x))
        y = max(0.0, min(float(img_h - h), y))
        draw.text((x, y), label, fill="black", font=font)


def render_sample(
    out_path: Path,
    layout: MazeLayout,
    cell_size: int,
    wall_width: int,
    margin: int,
) -> None:
    if wall_width % 2 == 0:
        wall_width += 1

    rows, cols = layout.rows, layout.cols
    label_pad = max(16, wall_width * 2)
    draw_margin = margin + label_pad

    width = cols * cell_size + 2 * draw_margin
    height = rows * cell_size + 2 * draw_margin

    img = Image.new("RGB", (width, height), "white")
    draw = ImageDraw.Draw(img)

    _carve_openings(draw, rows, cols, cell_size, wall_width, draw_margin, layout.openings)
    _label_openings(draw, rows, cols, cell_size, wall_width, draw_margin, layout.openings)

    for r in range(rows):
        for c in range(cols):
            cx = draw_margin + c * cell_size
            cy = draw_margin + r * cell_size

            if layout.vertical_walls[r][c] and c != cols - 1:
                x = cx + cell_size
                draw.rectangle(
                    [x - wall_width // 2, cy - wall_width // 2,
                     x + (wall_width - 1) // 2, cy + cell_size + (wall_width - 1) // 2],
                    fill="black",
                )

            if layout.horizontal_walls[r][c] and r != rows - 1:
                y = cy + cell_size
                draw.rectangle(
                    [cx - wall_width // 2, y - wall_width // 2,
                     cx + cell_size + (wall_width - 1) // 2, y + (wall_width - 1) // 2],
                    fill="black",
                )

    img.save(out_path)


# ---------------------------------------------------------------------------
# Annotation building
# ---------------------------------------------------------------------------

def choose_difficulty(rows: int, cols: int, num_openings: int) -> str:
    total = rows * cols
    if total <= 100 and num_openings <= 3:
        return "easy"
    if total <= 256 and num_openings <= 5:
        return "medium"
    return "hard"


def build_record(
    image_name: str,
    layout: MazeLayout,
    cell_size: int,
    wall_width: int,
    margin: int,
) -> Dict[str, object]:
    labels = [string.ascii_uppercase[i] for i in range(len(layout.openings))]
    answer_a = labels[layout.connected_indices[0]]
    answer_b = labels[layout.connected_indices[1]]

    opening_details = []
    for i, (side, idx) in enumerate(layout.openings):
        opening_details.append({
            "label": labels[i],
            "side": side,
            "index": idx,
            "is_connected": i in layout.connected_indices,
        })

    label_list = ", ".join(labels)
    question = (
        f"The image shows a maze with {len(labels)} labeled openings on its border: "
        f"{label_list}. Exactly one pair of openings is connected by a path through "
        f"the maze; all other pairs are blocked by walls. "
        f"Identify the connected pair. "
        f"Trace passages carefully from each opening — a path exists only if you "
        f"can travel from one opening to another through corridors without crossing "
        f"any wall. Report the connected pair in alphabetical order, formatted as "
        f"X-Y (for example, A-C). "
        f"Provide your final answer enclosed in <answer>...</answer> tags."
    )

    return {
        "image": image_name,
        "rows": layout.rows,
        "cols": layout.cols,
        "cell_size": cell_size,
        "wall_width": wall_width,
        "margin": margin,
        "num_openings": len(layout.openings),
        "openings": opening_details,
        "connected_pair": [answer_a, answer_b],
        "question": question,
        "answer": f"{answer_a}-{answer_b}",
        "path_length": layout.path_length,
        "difficulty": choose_difficulty(layout.rows, layout.cols, len(layout.openings)),
    }


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

def ensure_output_dir(root: Path) -> Tuple[Path, Path]:
    root.mkdir(parents=True, exist_ok=True)
    images_dir = root / "images"
    images_dir.mkdir(exist_ok=True)
    return root, images_dir


def generate_dataset(
    rng: random.Random,
    count: int,
    output_dir: Path,
    images_dir: Path,
    min_rows: int,
    max_rows: int,
    min_cols: int,
    max_cols: int,
    cell_size: int,
    wall_width: int,
    margin: int,
    min_openings: int,
    max_openings: int,
    min_path_length_floor: int = 0,
) -> None:
    records: List[Dict[str, object]] = []

    for idx in range(count):
        rows = rng.randint(min_rows, max_rows)
        cols = rng.randint(min_cols, max_cols)
        num_openings = rng.randint(min_openings, max_openings)

        layout = sample_layout(rng, rows, cols, num_openings,
                               min_path_length_floor=min_path_length_floor)

        image_name = f"maze_{idx:05d}.png"
        render_sample(images_dir / image_name, layout, cell_size, wall_width, margin)
        records.append(build_record(f"images/{image_name}", layout, cell_size, wall_width, margin))

    with (output_dir / "annotations.jsonl").open("w", encoding="utf-8") as fh:
        for record in records:
            fh.write(json.dumps(record) + "\n")

    data_json = {
        "task": "maze",
        "category": "sequential_traversal",
        "count": len(records),
        "items": [
            {"image": r["image"], "question": r["question"], "answer": r["answer"]}
            for r in records
        ],
    }
    (output_dir / "data.json").write_text(json.dumps(data_json, indent=2))


# ---------------------------------------------------------------------------
# CLI
# ---------------------------------------------------------------------------

def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Generate a maze puzzle dataset.")
    parser.add_argument("--output-root", type=Path, default=".", help="Dataset root directory.")
    parser.add_argument("--count", type=int, default=36)
    parser.add_argument("--min-rows", type=int, default=8)
    parser.add_argument("--max-rows", type=int, default=20)
    parser.add_argument("--min-cols", type=int, default=8)
    parser.add_argument("--max-cols", type=int, default=20)
    parser.add_argument("--cell-size", type=int, default=32)
    parser.add_argument("--wall-width", type=int, default=5)
    parser.add_argument("--margin", type=int, default=8)
    parser.add_argument("--min-openings", type=int, default=3)
    parser.add_argument("--max-openings", type=int, default=6)
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument("--difficulty", type=int, default=5,
                        help="Integer difficulty >=0; scales maze size, openings, path length.")
    return parser.parse_args()


def main() -> None:
    args = parse_args()
    rng = random.Random(args.seed)
    output_dir, images_dir = ensure_output_dir(args.output_root)

    d = max(0, int(args.difficulty))
    # Formulas (no canvas_scale — canvas self-scales via rows × cols × cell_size):
    #   rows = cols = 10 + 2·d
    #   num_openings = min(10, 3 + d)
    #   min_path_length = 8 + 3·d
    #   wall_thickness_px = max(2, 5 − 0.3·d)
    #   dead_end_count = 3·d  (decoys)
    args.min_rows = args.max_rows = 10 + 2 * d
    args.min_cols = args.max_cols = 10 + 2 * d
    num_openings_val = min(10, 3 + d)
    args.min_openings = num_openings_val
    args.max_openings = num_openings_val
    min_path_length_floor = 8 + 3 * d
    args.wall_width = max(2, int(round(5 - 0.3 * d)))
    dead_end_count = 3 * d  # noqa: F841  (reserved; decoys already grown from extras)

    generate_dataset(
        rng=rng,
        count=args.count,
        output_dir=output_dir,
        images_dir=images_dir,
        min_rows=args.min_rows,
        max_rows=args.max_rows,
        min_cols=args.min_cols,
        max_cols=args.max_cols,
        cell_size=args.cell_size,
        wall_width=args.wall_width,
        margin=args.margin,
        min_openings=args.min_openings,
        max_openings=args.max_openings,
        min_path_length_floor=min_path_length_floor,
    )

    print(f"Saved dataset to {args.output_root}")


if __name__ == "__main__":
    main()