"""Image tiling and multi-tile result merging.

Rationale:
    Whole-page P&ID extraction hits a node-recall ceiling around 50-60%
    on large diagrams because the VLM can only resolve ~50 symbols at
    ~1.15MP vision downsampling. Splitting the image into overlapping
    tiles, extracting each, then merging via bbox-distance deduplication
    lets the model zoom in on smaller regions at full pixel budget.

Coordinate conventions:
    - The VLM sees an individual tile and reports bbox in *normalized*
      [0, 1] coordinates relative to that tile (see `schema.BBox`).
    - `merge_tile_graphs` converts each tile-local normalized bbox into
      global image pixel coordinates and deduplicates across tiles using
      the Euclidean distance between bbox centers.

Overlap:
    Tiles are grown outward by `overlap` fraction of the un-overlapped
    tile size on each internal seam so a symbol that happens to straddle
    a split line appears fully in at least one tile. The dedup step then
    collapses the two detections into one node.
"""

from __future__ import annotations

import base64
import io
from dataclasses import dataclass, field
from pathlib import Path
from typing import Iterable

from PIL import Image

from .schema import BBox, GraphOut


@dataclass
class Tile:
    """One cropped region of the source image, ready to send to the VLM."""

    image: Image.Image
    x0: int  # top-left x in global (full-image) pixel coordinates
    y0: int  # top-left y in global pixel coordinates
    w: int   # tile width in global pixels
    h: int   # tile height in global pixels
    parent_w: int
    parent_h: int
    name: str  # short label, e.g. "r0c0"


def split_image(
    image_path: Path | str,
    rows: int = 2,
    cols: int = 2,
    overlap: float = 0.1,
) -> list[Tile]:
    """Split `image_path` into `rows * cols` overlapping tiles.

    `overlap` is the fractional grow-outward applied to each seam. With
    `overlap=0.1` and 2x2 tiling each tile is 60% wide and 60% tall
    (50% + 10% overlap on the internal edge). Boundary tiles are clipped
    to the image extent so they never exceed the parent dimensions.
    """
    img = Image.open(Path(image_path)).convert("RGB")
    W, H = img.size

    base_tw = W / cols
    base_th = H / rows
    ow = int(round(base_tw * overlap))
    oh = int(round(base_th * overlap))

    tiles: list[Tile] = []
    for r in range(rows):
        for c in range(cols):
            x0 = max(0, int(round(c * base_tw)) - ow)
            y0 = max(0, int(round(r * base_th)) - oh)
            x1 = min(W, int(round((c + 1) * base_tw)) + ow)
            y1 = min(H, int(round((r + 1) * base_th)) + oh)
            tile_img = img.crop((x0, y0, x1, y1))
            tiles.append(
                Tile(
                    image=tile_img,
                    x0=x0,
                    y0=y0,
                    w=x1 - x0,
                    h=y1 - y0,
                    parent_w=W,
                    parent_h=H,
                    name=f"r{r}c{c}",
                )
            )
    return tiles


def tile_to_base64_png(tile: Tile) -> tuple[str, str]:
    """Encode a tile as base64 PNG for the Messages API `source.data` field."""
    buf = io.BytesIO()
    tile.image.save(buf, format="PNG")
    return base64.standard_b64encode(buf.getvalue()).decode("utf-8"), "image/png"


def _tile_bbox_to_global_px(bbox: BBox, tile: Tile) -> tuple[float, float, float, float]:
    """Convert a tile-local normalized bbox to global pixel coordinates."""
    return (
        tile.x0 + bbox.xmin * tile.w,
        tile.y0 + bbox.ymin * tile.h,
        tile.x0 + bbox.xmax * tile.w,
        tile.y0 + bbox.ymax * tile.h,
    )


def _center(bbox_px: tuple[float, float, float, float]) -> tuple[float, float]:
    xmin, ymin, xmax, ymax = bbox_px
    return (xmin + xmax) / 2.0, (ymin + ymax) / 2.0


@dataclass
class _MergedNode:
    id: str
    type: str
    label: str | None
    bbox_px: tuple[float, float, float, float]
    center: tuple[float, float]
    source_tiles: list[str] = field(default_factory=list)


def merge_tile_graphs(
    tile_results: Iterable[tuple[GraphOut, Tile]],
    dedup_px: float = 40.0,
) -> dict:
    """Merge per-tile predictions into one global graph.

    Two nodes are considered the same symbol iff they share the same
    `type` AND their bbox centers are within `dedup_px` pixels in the
    global (un-tiled) image coordinate space. The first occurrence
    wins; later duplicates are absorbed and their tile name is recorded
    on `source_tiles` for debugging.

    Edges are re-wired through the tile-local → global id map. An edge
    whose endpoints map to the same global node is dropped, and
    undirected duplicates across tiles are collapsed.

    Returns a plain dict matching the shape `metrics.evaluate()` expects,
    plus a `directed: False` flag. Extra `bbox` and `source_tiles` fields
    on each node are ignored by the evaluator but useful for visualizing
    predictions later.
    """
    global_nodes: list[_MergedNode] = []
    id_map: dict[tuple[str, str], str] = {}  # (tile.name, tile_local_id) -> global id
    next_id = 1

    tile_results = list(tile_results)

    # --- nodes: dedup by (type, bbox center distance) -------------------
    for graph, tile in tile_results:
        for node in graph.nodes:
            if node.bbox is None:
                # No bbox → can't dedupe across tiles safely. Skip.
                continue
            bbox_px = _tile_bbox_to_global_px(node.bbox, tile)
            cx, cy = _center(bbox_px)

            matched_gid: str | None = None
            for gn in global_nodes:
                if gn.type != node.type:
                    continue
                dx = gn.center[0] - cx
                dy = gn.center[1] - cy
                if (dx * dx + dy * dy) ** 0.5 <= dedup_px:
                    matched_gid = gn.id
                    break

            if matched_gid is None:
                gid = f"n{next_id}"
                next_id += 1
                global_nodes.append(
                    _MergedNode(
                        id=gid,
                        type=node.type,
                        label=node.label,
                        bbox_px=bbox_px,
                        center=(cx, cy),
                        source_tiles=[tile.name],
                    )
                )
                id_map[(tile.name, node.id)] = gid
            else:
                id_map[(tile.name, node.id)] = matched_gid
                for gn in global_nodes:
                    if gn.id == matched_gid:
                        if tile.name not in gn.source_tiles:
                            gn.source_tiles.append(tile.name)
                        break

    # --- edges: remap ids, drop self-loops and duplicates ---------------
    edge_set: set[tuple[str, str]] = set()
    final_edges: list[dict] = []
    for graph, tile in tile_results:
        for edge in graph.edges:
            src = id_map.get((tile.name, edge.source))
            tgt = id_map.get((tile.name, edge.target))
            if src is None or tgt is None:
                continue
            if src == tgt:
                continue
            a, b = sorted((src, tgt))  # undirected canonicalization
            key = (a, b)
            if key in edge_set:
                continue
            edge_set.add(key)
            final_edges.append(
                {
                    "source": a,
                    "target": b,
                    "type": edge.type or "solid",
                    "label": edge.label,
                }
            )

    out_nodes = [
        {
            "id": gn.id,
            "type": gn.type,
            "label": gn.label,
            "bbox": list(gn.bbox_px),
            "source_tiles": gn.source_tiles,
        }
        for gn in global_nodes
    ]

    return {
        "nodes": out_nodes,
        "edges": final_edges,
        "directed": False,
    }


def _inner_seam_lines(tiles: list[Tile]) -> tuple[list[float], list[float]]:
    """Return (vertical_seam_xs, horizontal_seam_ys) for inner tile borders.

    For 2x2 tiling with overlap, each internal seam corresponds to TWO
    pixel x-coordinates: where one tile ends and where the adjacent tile
    begins. Both are returned so the FP filter can match either edge of
    the overlap band.
    """
    if not tiles:
        return [], []
    parent_w = tiles[0].parent_w
    parent_h = tiles[0].parent_h
    vx: set[float] = set()
    hy: set[float] = set()
    for t in tiles:
        if t.x0 > 0:
            vx.add(float(t.x0))
        if t.x0 + t.w < parent_w:
            vx.add(float(t.x0 + t.w))
        if t.y0 > 0:
            hy.add(float(t.y0))
        if t.y0 + t.h < parent_h:
            hy.add(float(t.y0 + t.h))
    return sorted(vx), sorted(hy)


def filter_seam_artifacts(
    merged: dict,
    tiles: list[Tile],
    types: tuple[str, ...] = ("inlet/outlet",),
    seam_threshold: float = 50.0,
    edge_threshold: float = 30.0,
) -> dict:
    """Drop nodes that look like tile-boundary false positives.

    A node is filtered iff ALL of the following hold:
      * its `type` is in `types` (default: inlet/outlet only),
      * its bbox center lies within `seam_threshold` pixels of any inner
        tile seam (vertical OR horizontal), AND
      * its bbox center is NOT within `edge_threshold` pixels of the
        outer image border (real boundary inlet/outlets stay).

    Edges referencing dropped nodes are also removed. The dropped nodes
    are recorded under `seam_filtered` so the report can show what was
    pruned.

    Nodes whose `type` is not in `types`, or that have no `bbox`, are
    passed through untouched.
    """
    if not tiles:
        return merged

    parent_w = tiles[0].parent_w
    parent_h = tiles[0].parent_h
    vx, hy = _inner_seam_lines(tiles)
    types_set = set(types)

    keep_ids: set[str] = set()
    new_nodes: list[dict] = []
    dropped: list[dict] = []

    for n in merged["nodes"]:
        if n.get("type") not in types_set:
            keep_ids.add(n["id"])
            new_nodes.append(n)
            continue

        bbox = n.get("bbox")
        if not bbox or len(bbox) != 4:
            keep_ids.add(n["id"])
            new_nodes.append(n)
            continue

        cx = (bbox[0] + bbox[2]) / 2.0
        cy = (bbox[1] + bbox[3]) / 2.0

        near_outer = (
            cx < edge_threshold
            or cx > parent_w - edge_threshold
            or cy < edge_threshold
            or cy > parent_h - edge_threshold
        )
        near_seam_x = any(abs(cx - x) <= seam_threshold for x in vx)
        near_seam_y = any(abs(cy - y) <= seam_threshold for y in hy)
        near_seam = near_seam_x or near_seam_y

        if near_seam and not near_outer:
            dropped.append({"id": n["id"], "type": n["type"], "bbox": bbox})
            continue

        keep_ids.add(n["id"])
        new_nodes.append(n)

    new_edges = [
        e
        for e in merged["edges"]
        if e["source"] in keep_ids and e["target"] in keep_ids
    ]

    out = dict(merged)
    out["nodes"] = new_nodes
    out["edges"] = new_edges
    out["seam_filtered"] = dropped
    return out