from __future__ import annotations

from typing import Any

from ..schemas import TaskBreakdownBusinessObject, TaskBreakdownPayload
from .task_persistence_core import SkillTagResolution, resolve_skill_tag_ids_by_names
from .task_persistence_core.shared import AsyncSession


def normalize_task_breakdown_payload(raw_payload: Any) -> TaskBreakdownPayload:
    """将模型结构化输出归一为任务拆解载荷。"""

    if isinstance(raw_payload, TaskBreakdownPayload):
        return raw_payload
    if not isinstance(raw_payload, dict):
        raise ValueError("task_breakdown_payload must be a JSON object")
    return TaskBreakdownPayload.model_validate(raw_payload)


def build_task_breakdown_ai_payload(payload: TaskBreakdownPayload) -> dict[str, Any]:
    """将结构化拆解载荷保存为 JSON 负载。"""

    return payload.model_dump(mode="json")


async def resolve_task_breakdown_skill_tag_ids(
    db: AsyncSession,
    *,
    tenant_id: int,
    task: TaskBreakdownBusinessObject,
) -> tuple[list[int], SkillTagResolution]:
    """将任务里的技能名称和已有 skill_tag_id 合并成最终 ID 列表。"""

    resolution = await resolve_skill_tag_ids_by_names(
        db,
        tenant_id=tenant_id,
        skill_names=task.required_skill_names,
    )
    merged_ids: list[int] = []
    seen: set[int] = set()
    for raw in list(task.required_skill_tag_ids or []) + list(resolution.matched_ids or []):
        try:
            skill_id = int(raw)
        except (TypeError, ValueError):
            continue
        if skill_id <= 0 or skill_id in seen:
            continue
        seen.add(skill_id)
        merged_ids.append(skill_id)
    return merged_ids, resolution


def should_auto_assign_breakdown_task(
    *,
    task: TaskBreakdownBusinessObject,
    top_match_score: float | None,
    top_workload_rate: float | None,
    confidence_threshold: float = 0.85,
    match_score_threshold: float = 75.0,
    workload_threshold: float = 70.0,
) -> bool:
    """判断 AI 拆解任务是否可自动分配负责人。"""

    confidence = float(task.confidence or 0.0)
    if confidence < confidence_threshold:
        return False
    if str(task.risk_level or "").strip().lower() not in {"low"}:
        return False
    if top_match_score is None or top_workload_rate is None:
        return False
    if top_match_score < match_score_threshold:
        return False
    if top_workload_rate >= workload_threshold:
        return False
    return True


__all__ = [
    "normalize_task_breakdown_payload",
    "build_task_breakdown_ai_payload",
    "resolve_task_breakdown_skill_tag_ids",
    "should_auto_assign_breakdown_task",
]