"""导入资料结构解析（纯函数，无 IO）。

按 ``material_type`` / ``format`` 把原始资料切片为条目草稿。只做结构解析
（消息数组、段落、字段），语义抽取（档案 / 记忆 / 人格候选）留给后续
模型驱动阶段。领域层禁止 IO，本模块仅产出数据结构。
"""

import json
from dataclasses import dataclass, field
from typing import Any

# 5a 不切片的媒体资料（仅存元数据与授权记录，spec §18 仅预留）。
_NON_TEXT_MATERIAL_TYPES = {"voice_sample"}


@dataclass
class ParsedItemDraft:
    """解析产出的条目草稿；落库前由 service 补 sequence / id。"""

    item_type: str
    role: str
    content: str
    metadata: dict[str, Any] = field(default_factory=dict)


def parse_import(
    material_type: str, fmt: str, *, content: str | None = None
) -> list[ParsedItemDraft]:
    """按格式解析文本资料；媒体资料返回空。"""
    if material_type in _NON_TEXT_MATERIAL_TYPES:
        return []

    if fmt == "json" and content:
        return _parse_json(content)

    if content:
        return _parse_paragraphs(content)

    return []


def _parse_json(content: str) -> list[ParsedItemDraft]:
    try:
        data = json.loads(content)
    except json.JSONDecodeError:
        return _parse_paragraphs(content)

    if (
        isinstance(data, list)
        and data
        and isinstance(data[0], dict)
        and ("role" in data[0] or "content" in data[0])
    ):
        return _parse_messages(data)
    if isinstance(data, dict):
        return _parse_fields(data)
    return _parse_paragraphs(content)


def _parse_messages(messages: list[dict[str, Any]]) -> list[ParsedItemDraft]:
    drafts: list[ParsedItemDraft] = []
    for msg in messages:
        if not isinstance(msg, dict):
            continue
        text = str(msg.get("content", "")).strip()
        if not text:
            continue
        meta = {k: v for k, v in msg.items() if k not in ("role", "content")}
        drafts.append(
            ParsedItemDraft(
                item_type="message",
                role=_normalize_role(str(msg.get("role", "other"))),
                content=text,
                metadata=meta,
            )
        )
    return drafts


def _parse_fields(data: dict[str, Any]) -> list[ParsedItemDraft]:
    drafts: list[ParsedItemDraft] = []
    for key, value in data.items():
        text = str(value).strip()
        if text:
            drafts.append(
                ParsedItemDraft(
                    item_type="profile_field",
                    role="other",
                    content=text,
                    metadata={"field": key},
                )
            )
    return drafts


def _parse_paragraphs(text: str) -> list[ParsedItemDraft]:
    drafts: list[ParsedItemDraft] = []
    for chunk in text.split("\n\n"):
        clean = chunk.strip()
        if clean:
            drafts.append(ParsedItemDraft(item_type="paragraph", role="other", content=clean))
    return drafts


def _normalize_role(role: str) -> str:
    lowered = role.lower()
    if lowered in ("user", "human"):
        return "user"
    if lowered in ("assistant", "ai", "bot"):
        return "assistant"
    if lowered == "system":
        return "system"
    return "other"