"""题目、标签、题目-标签关联。"""
import enum

from sqlalchemy import (
    Boolean,
    ForeignKey,
    Integer,
    String,
    Text,
    UniqueConstraint,
)
from sqlalchemy import Enum as SAEnum
from sqlalchemy.orm import Mapped, mapped_column, relationship
from sqlalchemy.types import JSON

from app.models.base import new_uuid
from app.db import Base, TimestampMixin


class QuestionType(str, enum.Enum):
    # 拍照入库的默认值：先收进来，想练习时再补题型/答案
    unclassified = "unclassified"
    single_choice = "single_choice"
    multiple_choice = "multiple_choice"
    true_false = "true_false"
    fill_blank = "fill_blank"
    short_answer = "short_answer"


# 客观题：有标准答案时可本地精确判分；其余交给 AI 判
OBJECTIVE_TYPES = {
    QuestionType.single_choice,
    QuestionType.multiple_choice,
    QuestionType.true_false,
}


class Tag(Base, TimestampMixin):
    __tablename__ = "tags"
    __table_args__ = (UniqueConstraint("user_id", "name", name="uq_tag_user_name"),)

    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=new_uuid)
    user_id: Mapped[str] = mapped_column(
        ForeignKey("users.id", ondelete="CASCADE"), index=True
    )
    name: Mapped[str] = mapped_column(String(128))

    questions: Mapped[list["Question"]] = relationship(
        secondary="question_tags", back_populates="tags"
    )


class QuestionTag(Base):
    __tablename__ = "question_tags"

    question_id: Mapped[str] = mapped_column(
        ForeignKey("questions.id", ondelete="CASCADE"), primary_key=True
    )
    tag_id: Mapped[str] = mapped_column(
        ForeignKey("tags.id", ondelete="CASCADE"), primary_key=True
    )


class Question(Base, TimestampMixin):
    """一条笔记。

    只有 type 和 stem_markdown 是必填，其中 type 可以是 unclassified，
    所以"拍照识别完直接存"不需要用户补任何东西。
    """

    __tablename__ = "questions"

    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=new_uuid)
    user_id: Mapped[str] = mapped_column(
        ForeignKey("users.id", ondelete="CASCADE"), index=True
    )
    type: Mapped[QuestionType] = mapped_column(
        SAEnum(QuestionType), default=QuestionType.unclassified
    )
    stem_markdown: Mapped[str] = mapped_column(Text)
    # 选项：[{"key": "A", "text_markdown": "..."}]，选择/判断题用
    options: Mapped[list | None] = mapped_column(JSON, nullable=True)
    # 标准答案：选择题 ["A","C"]，填空/简答 ["文本"]
    correct_answer: Mapped[list | None] = mapped_column(JSON, nullable=True)
    # AI 生成的讲解（每次 /ai/explain 会覆写，别往这里写用户内容）
    explanation_markdown: Mapped[str | None] = mapped_column(Text, nullable=True)
    # 用户自己写的笔记 / 错因反思，AI 永不触碰
    my_note_markdown: Mapped[str | None] = mapped_column(Text, nullable=True)
    # 手动标记"待复习"；复习清单 = 这个标记 ∪ 最近一次作答判错
    needs_review: Mapped[bool] = mapped_column(Boolean, default=False, index=True)
    difficulty: Mapped[int | None] = mapped_column(Integer, nullable=True)  # 1-5
    source_image_id: Mapped[str | None] = mapped_column(
        ForeignKey("images.id", ondelete="SET NULL"), nullable=True, index=True
    )
    ocr_engine: Mapped[str | None] = mapped_column(String(32), nullable=True)

    tags: Mapped[list["Tag"]] = relationship(
        secondary="question_tags", back_populates="questions"
    )
    # 便于详情页把原图和笔记一起展示
    source_image: Mapped["Image | None"] = relationship("Image")