"""左右匹配器（§6.2）：硬约束 + 代价升序贪心一一配对；接口注入。

默认 GreedyMatcher 为纯函数（§4 构件表）；调用方可通过
StereoRanger(matcher=...) 注入匈牙利等替换实现而不动调用方（§10.3）。

硬约束（任一不满足直接拒绝配对，不进代价排序）：类别一致、|Δy| ≤ τ_y、
框高相对差 ≤ τ_h。视差域 [D_min, D_max] 不参与配对：越界属有效性门
（§6.2 已回填，与 §5 失败路径表/§6.3/§6.4/§2.3 一致）。
"""

from dataclasses import dataclass
from typing import List, Protocol, Sequence

from .config import StereoConfig
from .sampling import SamplePoint


@dataclass(frozen=True)
class MatchPair:
    """一一配对结果：左右下标 + 匹配代价（§6.2）。"""

    left_index: int
    right_index: int
    cost: float


class Matcher(Protocol):
    """匹配器接口（§4：接口注入，默认贪心）。"""

    def match(
        self,
        left: Sequence[SamplePoint],
        right: Sequence[SamplePoint],
        config: StereoConfig,
    ) -> List[MatchPair]:
        ...


class GreedyMatcher:
    """硬约束过滤 + 代价升序贪心：cost < 1 才接受，一一配对。"""

    def match(
        self,
        left: Sequence[SamplePoint],
        right: Sequence[SamplePoint],
        config: StereoConfig,
    ) -> List[MatchPair]:
        tau_y = config.tau_y_px
        tau_h = config.tau_h_ratio
        candidates = []
        for i, point_l in enumerate(left):
            if not point_l.finite:
                continue
            for j, point_r in enumerate(right):
                if not point_r.finite:
                    continue
                if point_l.box.class_id != point_r.box.class_id:
                    continue
                dy = abs(point_l.center_y - point_r.center_y)
                if dy > tau_y:
                    continue
                height_max = max(point_l.box.height, point_r.box.height)
                if height_max <= 0.0:
                    continue
                dh_ratio = abs(point_l.box.height - point_r.box.height) / height_max
                if dh_ratio > tau_h:
                    continue
                cost = dy / tau_y + dh_ratio / tau_h
                if cost >= 1.0:
                    continue
                candidates.append((cost, i, j))
        candidates.sort(key=lambda item: (item[0], item[1], item[2]))
        used_left = set()
        used_right = set()
        pairs: List[MatchPair] = []
        for cost, i, j in candidates:
            if i in used_left or j in used_right:
                continue
            pairs.append(MatchPair(left_index=i, right_index=j, cost=cost))
            used_left.add(i)
            used_right.add(j)
        return pairs