"""SQLite database layer for paper caching.

Provides a lightweight persistence layer using SQLAlchemy with SQLite.
Papers are cached locally so repeated queries are fast and the corpus
grows over time for trend detection and citation analysis.
"""

import json
import os
import threading
from datetime import datetime, timezone
from pathlib import Path

from sqlalchemy import (
    Column,
    DateTime,
    Float,
    Integer,
    String,
    Text,
    UniqueConstraint,
    create_engine,
    event,
    tuple_,
)
from sqlalchemy.orm import Session, declarative_base, sessionmaker

Base = declarative_base()

_CACHE_DIR = Path(os.environ.get(
    "RESEARCH_MCP_CACHE_DIR",
    os.path.expanduser("~/.research-papers-mcp"),
))


class Paper(Base):
    __tablename__ = "papers"
    __table_args__ = (
        UniqueConstraint("source", "source_id", name="uq_source_paper"),
    )

    id = Column(Integer, primary_key=True)
    title = Column(String, nullable=False)
    abstract = Column(Text)
    authors = Column(String)
    publication_date = Column(DateTime)
    source = Column(String, nullable=False)
    source_id = Column(String, nullable=False)
    url = Column(String)
    doi = Column(String)
    topics_json = Column(Text, default="[]")
    citation_count = Column(Integer)
    influential_citation_count = Column(Integer)
    impact_score = Column(Float)
    created_at = Column(DateTime, default=lambda: datetime.now(timezone.utc))
    updated_at = Column(DateTime, default=lambda: datetime.now(timezone.utc),
                        onupdate=lambda: datetime.now(timezone.utc))

    @property
    def topics(self):
        """Topic names only, normalised from either on-disk schema shape."""
        from .topics.schema import to_names
        try:
            raw = json.loads(self.topics_json or "[]")
        except (json.JSONDecodeError, TypeError):
            return []
        return to_names(raw)

    @property
    def topic_entries(self):
        """Full topic entries: ``[{"name", "confidence", "source"}, ...]``."""
        from .topics.schema import to_entries
        try:
            raw = json.loads(self.topics_json or "[]")
        except (json.JSONDecodeError, TypeError):
            return []
        return to_entries(raw)

    @topics.setter
    def topics(self, value):
        self.topics_json = json.dumps(value or [])

    def to_dict(self, compact: bool = False):
        d = {
            "id": self.id,
            "title": self.title,
            "authors": self.authors,
            "publication_date": (
                self.publication_date.isoformat() if self.publication_date else None
            ),
            "source": self.source,
            "url": self.url,
            "topics": self.topics,
            "citation_count": self.citation_count,
            "impact_score": self.impact_score,
        }
        if not compact:
            d["abstract"] = self.abstract
            d["source_id"] = self.source_id
            d["doi"] = self.doi
            d["influential_citation_count"] = self.influential_citation_count
            d["updated_at"] = (
                self.updated_at.isoformat() if self.updated_at else None
            )
        return d


_engine = None
_SessionLocal = None
_init_lock = threading.Lock()


def _enable_wal(dbapi_conn, connection_record):
    cursor = dbapi_conn.cursor()
    cursor.execute("PRAGMA journal_mode=WAL")
    cursor.close()


def get_engine():
    global _engine
    if _engine is None:
        with _init_lock:
            if _engine is None:
                _CACHE_DIR.mkdir(parents=True, exist_ok=True)
                db_path = _CACHE_DIR / "papers.db"
                _engine = create_engine(
                    f"sqlite:///{db_path}",
                    connect_args={"check_same_thread": False},
                )
                event.listen(_engine, "connect", _enable_wal)
                Base.metadata.create_all(_engine)
    return _engine


def get_session() -> Session:
    global _SessionLocal
    if _SessionLocal is None:
        with _init_lock:
            if _SessionLocal is None:
                _SessionLocal = sessionmaker(bind=get_engine())
    return _SessionLocal()


def upsert_papers(papers: list[dict]) -> int:
    """Insert papers, skipping duplicates. Returns count of new papers."""
    if not papers:
        return 0
    session = get_session()
    new_count = 0
    try:
        # Batch-fetch existing papers to avoid N+1 queries
        keys = [(p["source"], p["source_id"]) for p in papers]
        existing_rows = (
            session.query(Paper)
            .filter(
                tuple_(Paper.source, Paper.source_id).in_(keys)
            )
            .all()
        )
        existing_map = {(r.source, r.source_id): r for r in existing_rows}

        for p in papers:
            key = (p["source"], p["source_id"])
            existing = existing_map.get(key)
            if existing:
                updated = False
                if p.get("citation_count") is not None:
                    existing.citation_count = p["citation_count"]
                    updated = True
                if p.get("influential_citation_count") is not None:
                    existing.influential_citation_count = p["influential_citation_count"]
                    updated = True
                if p.get("impact_score") is not None:
                    existing.impact_score = p["impact_score"]
                    updated = True
                if p.get("abstract") and not existing.abstract:
                    existing.abstract = p["abstract"]
                    updated = True
                if updated:
                    existing.updated_at = datetime.now(timezone.utc)
                continue

            paper = Paper(
                title=p["title"],
                abstract=p.get("abstract"),
                authors=p.get("authors"),
                publication_date=p.get("publication_date"),
                source=p["source"],
                source_id=p["source_id"],
                url=p.get("url"),
                doi=p.get("doi"),
                citation_count=p.get("citation_count"),
                influential_citation_count=p.get("influential_citation_count"),
                impact_score=p.get("impact_score"),
            )
            paper.topics = p.get("topics", [])
            session.add(paper)
            new_count += 1
        session.commit()
    except Exception:
        session.rollback()
        raise
    finally:
        session.close()
    return new_count