"""Configurable model-object clustering."""

from __future__ import annotations

from dataclasses import dataclass
from typing import Any

import numpy as np
from scipy.ndimage import binary_dilation, label

from .utils import circular_footprint, dilate_fast, km_to_pixels


@dataclass
class ModelCluster:
    cluster_id: int
    mask: np.ndarray
    raw_mask: np.ndarray
    pixel_count: int
    raw_pixel_count: int

    def to_dict(self) -> dict[str, int]:
        return {
            "cluster_id": int(self.cluster_id),
            "pixel_count": int(self.pixel_count),
            "raw_pixel_count": int(self.raw_pixel_count),
        }


class Clusterer:
    def __init__(
        self,
        name: str,
        threshold: float,
        min_cluster_pixels: int = 1,
        pixel_size_km: float = 2.0,
        merge_buffer_km: float = 0.0,
        cluster_mask_expansion_km: float = 0.0,
        connectivity: int = 8,
        buffer_backend: str = "auto",
    ):
        self.name = name
        self.threshold = float(threshold)
        self.min_cluster_pixels = int(min_cluster_pixels)
        self.pixel_size_km = float(pixel_size_km)
        self.merge_radius = km_to_pixels(merge_buffer_km, self.pixel_size_km)
        self.expansion_radius = km_to_pixels(cluster_mask_expansion_km, self.pixel_size_km)
        self.connectivity = int(connectivity)
        self.buffer_backend = str(buffer_backend or "auto")
        self._merge_footprint = circular_footprint(self.merge_radius) if self.merge_radius > 0 else None
        self._expansion_footprint = (
            circular_footprint(self.expansion_radius) if self.expansion_radius > 0 else None
        )

    @property
    def structure(self) -> np.ndarray:
        if self.connectivity == 4:
            return np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=bool)
        return np.ones((3, 3), dtype=bool)

    def cluster(self, data: np.ndarray, valid_mask: np.ndarray | None = None) -> list[ModelCluster]:
        data = np.asarray(data)
        if valid_mask is None:
            valid_mask = np.isfinite(data)
        positive_mask = (data >= self.threshold) & valid_mask & np.isfinite(data)

        if self.name == "connected":
            return self._connected(positive_mask)
        if self.name == "distance_merge":
            return self._distance_merge(positive_mask)
        if self.name == "one_hop_merge":
            return self._one_hop_merge(positive_mask)
        if self.name == "complete_link_merge":
            return self._complete_link_merge(positive_mask)
        if self.name == "mode_like":
            return self._mode_like(positive_mask)
        raise ValueError(f"Unsupported clusterer: {self.name}")

    def _connected_components(self, mask: np.ndarray) -> list[np.ndarray]:
        labeled, n_features = label(mask, structure=self.structure)
        components: list[np.ndarray] = []
        for cid in range(1, n_features + 1):
            component = labeled == cid
            if int(component.sum()) >= self.min_cluster_pixels:
                components.append(component)
        return components

    def _dilate(self, mask: np.ndarray, radius_pixels: int, footprint: np.ndarray | None) -> np.ndarray:
        if radius_pixels <= 0:
            return mask.astype(bool, copy=True)
        backend = "binary" if self.buffer_backend == "binary" and footprint is not None else self.buffer_backend
        return dilate_fast(mask, radius_pixels, backend=backend)

    def _make_cluster(
        self,
        cluster_id: int,
        raw_mask: np.ndarray,
        support_mask: np.ndarray | None = None,
        final_mask: np.ndarray | None = None,
    ) -> ModelCluster | None:
        if int(raw_mask.sum()) < self.min_cluster_pixels:
            return None

        if final_mask is None:
            final_mask = raw_mask.astype(bool, copy=True)
            if self.expansion_radius > 0:
                final_mask = self._dilate(final_mask, self.expansion_radius, self._expansion_footprint)
                if support_mask is not None:
                    final_mask &= support_mask
        else:
            final_mask = final_mask.astype(bool, copy=True)
            if support_mask is not None:
                final_mask &= support_mask

        if int(final_mask.sum()) == 0:
            return None

        return ModelCluster(
            cluster_id=cluster_id,
            mask=final_mask,
            raw_mask=raw_mask.astype(bool, copy=True),
            pixel_count=int(final_mask.sum()),
            raw_pixel_count=int(raw_mask.sum()),
        )

    def _connected(self, positive_mask: np.ndarray) -> list[ModelCluster]:
        clusters: list[ModelCluster] = []
        for raw_mask in self._connected_components(positive_mask):
            cluster = self._make_cluster(len(clusters) + 1, raw_mask, support_mask=None)
            if cluster is not None:
                clusters.append(cluster)
        return clusters

    def _distance_merge(self, positive_mask: np.ndarray) -> list[ModelCluster]:
        components = self._connected_components(positive_mask)
        if not components:
            return []
        if len(components) == 1:
            cluster = self._make_cluster(1, components[0], support_mask=None)
            return [] if cluster is None else [cluster]

        parent = list(range(len(components)))

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

        def union(a: int, b: int) -> None:
            ra, rb = find(a), find(b)
            if ra != rb:
                parent[rb] = ra

        for i, component in enumerate(components):
            buffered = dilate_fast(component, self.merge_radius, backend=self.buffer_backend)
            for j in range(i + 1, len(components)):
                if np.any(buffered & components[j]):
                    union(i, j)

        grouped: dict[int, list[int]] = {}
        for idx in range(len(components)):
            grouped.setdefault(find(idx), []).append(idx)

        clusters: list[ModelCluster] = []
        for member_indices in grouped.values():
            raw_union = np.zeros_like(positive_mask, dtype=bool)
            support = np.zeros_like(positive_mask, dtype=bool)
            for idx in member_indices:
                raw_union |= components[idx]
                support |= dilate_fast(components[idx], self.merge_radius, backend=self.buffer_backend)
            cluster = self._make_cluster(len(clusters) + 1, raw_union, support_mask=support)
            if cluster is not None:
                clusters.append(cluster)
        return clusters

    def _one_hop_merge(self, positive_mask: np.ndarray) -> list[ModelCluster]:
        components = self._connected_components(positive_mask)
        if not components:
            return []
        if len(components) == 1 or self.merge_radius <= 0:
            clusters: list[ModelCluster] = []
            for component in components:
                cluster = self._make_cluster(len(clusters) + 1, component, support_mask=None)
                if cluster is not None:
                    clusters.append(cluster)
            return clusters

        boxes = [self._component_bbox(component) for component in components]
        checked = np.zeros(len(components), dtype=bool)
        clusters: list[ModelCluster] = []
        for seed_idx, component in enumerate(components):
            if checked[seed_idx]:
                continue

            candidate_indices = [
                idx
                for idx in range(len(components))
                if not checked[idx] and self._boxes_within_radius(boxes[seed_idx], boxes[idx], self.merge_radius)
            ]
            member_indices = [
                idx
                for idx in candidate_indices
                if idx == seed_idx
                or self._component_touches_seed_buffer(component, boxes[seed_idx], components[idx], boxes[idx])
            ]

            raw_union = np.zeros_like(positive_mask, dtype=bool)
            support = np.zeros_like(positive_mask, dtype=bool)
            for idx in member_indices:
                checked[idx] = True
                raw_union |= components[idx]
                y0, y1, x0, x1 = self._expanded_bbox(boxes[idx], components[idx].shape, self.merge_radius)
                support_crop = support[y0:y1, x0:x1]
                component_crop = components[idx][y0:y1, x0:x1]
                support_crop |= self._dilate_crop(component_crop, self.merge_radius)

            cluster = self._make_cluster(len(clusters) + 1, raw_union, support_mask=support)
            if cluster is not None:
                clusters.append(cluster)
        return clusters

    @staticmethod
    def _component_bbox(component: np.ndarray) -> tuple[int, int, int, int]:
        ys, xs = np.nonzero(component)
        return int(ys.min()), int(ys.max()) + 1, int(xs.min()), int(xs.max()) + 1

    @staticmethod
    def _expanded_bbox(
        box: tuple[int, int, int, int],
        shape: tuple[int, ...],
        radius: int,
    ) -> tuple[int, int, int, int]:
        y0, y1, x0, x1 = box
        h, w = int(shape[0]), int(shape[1])
        return max(0, y0 - radius), min(h, y1 + radius), max(0, x0 - radius), min(w, x1 + radius)

    @staticmethod
    def _boxes_within_radius(
        a: tuple[int, int, int, int],
        b: tuple[int, int, int, int],
        radius: int,
    ) -> bool:
        ay0, ay1, ax0, ax1 = a
        by0, by1, bx0, bx1 = b
        dy = max(0, by0 - ay1, ay0 - by1)
        dx = max(0, bx0 - ax1, ax0 - bx1)
        return dx * dx + dy * dy <= radius * radius

    def _dilate_crop(self, crop: np.ndarray, radius: int) -> np.ndarray:
        if radius <= 0:
            return crop.astype(bool, copy=True)
        if self.buffer_backend == "binary":
            return binary_dilation(crop.astype(bool), structure=circular_footprint(radius))
        return dilate_fast(crop.astype(bool), radius, backend=self.buffer_backend)

    def _component_touches_seed_buffer(
        self,
        seed: np.ndarray,
        seed_box: tuple[int, int, int, int],
        candidate: np.ndarray,
        candidate_box: tuple[int, int, int, int],
    ) -> bool:
        y0, y1, x0, x1 = self._expanded_bbox(seed_box, seed.shape, self.merge_radius)
        cy0, cy1, cx0, cx1 = candidate_box
        oy0, oy1 = max(y0, cy0), min(y1, cy1)
        ox0, ox1 = max(x0, cx0), min(x1, cx1)
        if oy0 >= oy1 or ox0 >= ox1:
            return False
        seed_support = self._dilate_crop(seed[y0:y1, x0:x1], self.merge_radius)
        return bool(np.any(seed_support[oy0 - y0 : oy1 - y0, ox0 - x0 : ox1 - x0] & candidate[oy0:oy1, ox0:ox1]))

    def _complete_link_merge(self, positive_mask: np.ndarray) -> list[ModelCluster]:
        components = self._connected_components(positive_mask)
        if not components:
            return []
        if len(components) == 1 or self.merge_radius <= 0:
            clusters: list[ModelCluster] = []
            for component in components:
                cluster = self._make_cluster(len(clusters) + 1, component, support_mask=None)
                if cluster is not None:
                    clusters.append(cluster)
            return clusters

        n_components = len(components)
        boxes = [self._component_bbox(component) for component in components]
        close = np.eye(n_components, dtype=bool)
        for i in range(n_components):
            for j in range(i + 1, n_components):
                if not self._boxes_within_radius(boxes[i], boxes[j], self.merge_radius):
                    continue
                is_close = self._component_touches_seed_buffer(components[i], boxes[i], components[j], boxes[j])
                close[i, j] = is_close
                close[j, i] = is_close

        groups: list[list[int]] = [[i] for i in range(n_components)]
        while True:
            best_pair: tuple[int, int] | None = None
            best_size = -1
            for i in range(len(groups)):
                for j in range(i + 1, len(groups)):
                    if not all(close[a, b] for a in groups[i] for b in groups[j]):
                        continue
                    merged_size = len(groups[i]) + len(groups[j])
                    if merged_size > best_size:
                        best_size = merged_size
                        best_pair = (i, j)
            if best_pair is None:
                break

            i, j = best_pair
            groups[i] = groups[i] + groups[j]
            del groups[j]

        clusters: list[ModelCluster] = []
        for member_indices in groups:
            raw_union = np.zeros_like(positive_mask, dtype=bool)
            support = np.zeros_like(positive_mask, dtype=bool)
            for idx in member_indices:
                raw_union |= components[idx]
                y0, y1, x0, x1 = self._expanded_bbox(boxes[idx], components[idx].shape, self.merge_radius)
                support_crop = support[y0:y1, x0:x1]
                component_crop = components[idx][y0:y1, x0:x1]
                support_crop |= self._dilate_crop(component_crop, self.merge_radius)
            cluster = self._make_cluster(len(clusters) + 1, raw_union, support_mask=support)
            if cluster is not None:
                clusters.append(cluster)
        return clusters

    def _mode_like(self, positive_mask: np.ndarray) -> list[ModelCluster]:
        if not np.any(positive_mask):
            return []

        if self.merge_radius > 0:
            support = dilate_fast(positive_mask, self.merge_radius, backend=self.buffer_backend)
        else:
            support = positive_mask.astype(bool, copy=True)

        support_labeled, n_support = label(support, structure=self.structure)
        clusters: list[ModelCluster] = []
        for support_id in range(1, n_support + 1):
            support_mask = support_labeled == support_id
            raw_union = positive_mask & support_mask
            if int(raw_union.sum()) < self.min_cluster_pixels:
                continue
            final_mask = support_mask if self.expansion_radius >= self.merge_radius else None
            cluster = self._make_cluster(
                len(clusters) + 1,
                raw_union,
                support_mask=support_mask,
                final_mask=final_mask,
            )
            if cluster is not None:
                clusters.append(cluster)
        return clusters


def create_clusterer(config: dict[str, Any]) -> Clusterer:
    cluster_config = dict(config.get("clusterer") or {})
    matching_config = config.get("matching") or {}
    performance_config = config.get("performance") or {}
    threshold = cluster_config.get("threshold")
    if threshold is None:
        threshold = (config.get("data_source_thresholds") or {}).get(config["data_source"])
    if threshold is None:
        raise ValueError("clusterer.threshold is null and no data_source_thresholds entry exists")

    return Clusterer(
        name=cluster_config.get("name", "mode_like"),
        threshold=float(threshold),
        min_cluster_pixels=int(cluster_config.get("min_cluster_pixels", 1)),
        pixel_size_km=float(matching_config.get("pixel_size_km", 2.0)),
        merge_buffer_km=float(cluster_config.get("merge_buffer_km", 0.0)),
        cluster_mask_expansion_km=float(cluster_config.get("cluster_mask_expansion_km", 0.0)),
        connectivity=int(cluster_config.get("connectivity", 8)),
        buffer_backend=str(performance_config.get("buffer_backend", "auto")),
    )