import os
import time
import math
import logging
from typing import List, Tuple

import psycopg2
import psycopg2.extras

logging.basicConfig(
    level=logging.INFO,
    format='[related] %(asctime)s %(levelname)s: %(message)s'
)

DB = dict(
    host=os.environ.get("DB_HOST", "localhost"),
    port=int(os.environ.get("DB_PORT", 5432)),
    dbname=os.environ.get("DB_NAME", "rss"),
    user=os.environ.get("DB_USER", "rss"),
    password=os.environ.get("DB_PASS", "x"),
)

TOPK         = int(os.environ.get("RELATED_TOPK", 10))
BATCH_IDS    = int(os.environ.get("RELATED_BATCH_IDS", 200))
BATCH_SIM    = int(os.environ.get("RELATED_BATCH_SIM", 2000))
SLEEP_IDLE   = float(os.environ.get("RELATED_SLEEP", 10))
MIN_SCORE    = float(os.environ.get("RELATED_MIN_SCORE", 0.0))
WINDOW_HOURS = int(os.environ.get("RELATED_WINDOW_H", 0))


def get_conn():
    return psycopg2.connect(**DB)


def _fetch_all_embeddings(cur):
    if WINDOW_HOURS > 0:
        cur.execute(
            """
            SELECT e.traduccion_id, e.vec
            FROM embeddings e
            JOIN traducciones t ON t.id = e.traduccion_id
            JOIN noticias n ON n.id = t.noticia_id
            WHERE n.fecha >= NOW() - INTERVAL %s
            """,
            (f"{WINDOW_HOURS} hours",),
        )
    else:
        cur.execute("SELECT traduccion_id, vec FROM embeddings")

    rows = cur.fetchall()
    if not rows:
        return [], [], []

    ids = []
    vecs = []
    norms = []
    for tr_id, v in rows:
        if v is None:
            v = []
        nrm = math.sqrt(sum(((x or 0.0) * (x or 0.0)) for x in v)) or 1e-8
        ids.append(tr_id)
        vecs.append(v)
        norms.append(nrm)
    return ids, vecs, norms


def _fetch_pending_ids(cur, limit) -> List[int]:
    cur.execute(
        """
        SELECT e.traduccion_id
        FROM embeddings e
        LEFT JOIN related_noticias r ON r.traduccion_id = e.traduccion_id
        GROUP BY e.traduccion_id
        HAVING COUNT(r.related_traduccion_id) = 0
        ORDER BY e.traduccion_id DESC
        LIMIT %s;
        """,
        (limit,),
    )
    return [r[0] for r in cur.fetchall()]


def _cosine_with_norms(a, b, na, nb):
    num = 0.0
    for x, y in zip(a, b):
        xv = x or 0.0
        yv = y or 0.0
        num += xv * yv
    denom = na * nb
    if denom <= 0.0:
        return 0.0
    return num / denom


def _topk_for_one(
    idx: int,
    ids_all: List[int],
    vecs_all: List[List[float]],
    norms_all: List[float],
    pool_indices: List[int],
    K: int,
) -> List[Tuple[int, float]]:
    me_vec = vecs_all[idx]
    me_norm = norms_all[idx]

    out: List[Tuple[int, float]] = []
    for j in pool_indices:
        if j == idx:
            continue
        s = _cosine_with_norms(me_vec, vecs_all[j], me_norm, norms_all[j])
        out.append((ids_all[j], s))

    out.sort(key=lambda t: t[1], reverse=True)
    if MIN_SCORE > 0.0:
        out = [p for p in out if p[1] >= MIN_SCORE]
    return out[:K]


def _insert_related(cur, tr_id: int, pairs: List[Tuple[int, float]]):
    if not pairs:
        return
    psycopg2.extras.execute_values(
        cur,
        """
        INSERT INTO related_noticias (traduccion_id, related_traduccion_id, score)
        VALUES %s
        ON CONFLICT (traduccion_id, related_traduccion_id)
        DO UPDATE SET score = EXCLUDED.score
        """,
        [(tr_id, rid, float(score)) for (rid, score) in pairs],
    )


def build_for_ids(conn, target_ids: List[int]) -> int:
    with conn.cursor() as cur:
        ids_all, vecs_all, norms_all = _fetch_all_embeddings(cur)
    if not ids_all:
        return 0

    pos = {tid: i for i, tid in enumerate(ids_all)}
    n = len(ids_all)
    processed = 0

    with conn.cursor() as cur:
        for tr_id in target_ids:
            if tr_id not in pos:
                continue
            i = pos[tr_id]

            top: List[Tuple[int, float]] = []
            for start in range(0, n, BATCH_SIM):
                block = list(range(start, min(start + BATCH_SIM, n)))
                candidates = _topk_for_one(i, ids_all, vecs_all, norms_all, block, TOPK)
                top += candidates
                top.sort(key=lambda t: t[1], reverse=True)
                if len(top) > TOPK:
                    top = top[:TOPK]

            _insert_related(cur, tr_id, top)
            processed += 1

        conn.commit()
    return processed


def main():
    logging.info(
        "Iniciando related_worker (TOPK=%s, BATCH_IDS=%s, BATCH_SIM=%s, MIN_SCORE=%.3f, WINDOW_H=%s)",
        TOPK,
        BATCH_IDS,
        BATCH_SIM,
        MIN_SCORE,
        WINDOW_HOURS,
    )
    while True:
        try:
            with get_conn() as conn, conn.cursor() as cur:
                todo = _fetch_pending_ids(cur, BATCH_IDS)

            if not todo:
                time.sleep(SLEEP_IDLE)
                continue

            with get_conn() as conn:
                done = build_for_ids(conn, todo)
                logging.info("Relacionadas generadas/actualizadas para %d traducciones.", done)

        except Exception:
            logging.exception("Error en related_worker")
            time.sleep(SLEEP_IDLE)


if __name__ == "__main__":
    main()