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

import numpy as np
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))
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)


# ---------------------------------------------------------
# Cargar embeddings SOLO de traducciones en español (lang_to='es')
# ---------------------------------------------------------
def _fetch_all_embeddings(cur):
    base_sql = """
        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 t.lang_to = 'es'
    """

    params = []
    if WINDOW_HOURS > 0:
        base_sql += " AND n.fecha >= NOW() - INTERVAL %s"
        params.append(f"{WINDOW_HOURS} hours")

    cur.execute(base_sql, params)

    rows = cur.fetchall()
    if not rows:
        return [], None

    ids = []
    vecs = []

    for tid, v in rows:
        if v is None:
            continue
        ids.append(tid)
        vecs.append(v)

    if not ids:
        return [], None

    # Convertimos a matriz numpy
    mat = np.array(vecs, dtype=np.float32)

    # Normalizamos (evita división por 0)
    norms = np.linalg.norm(mat, axis=1, keepdims=True)
    norms[norms == 0] = 1e-8
    mat = mat / norms

    return ids, mat


# ---------------------------------------------------------
# Obtiene IDs pendientes
# ---------------------------------------------------------
def _fetch_pending_ids(cur, limit) -> List[int]:
    cur.execute(
        """
        SELECT e.traduccion_id
        FROM embeddings e
        JOIN traducciones t ON t.id = e.traduccion_id
        LEFT JOIN related_noticias r
               ON r.traduccion_id = e.traduccion_id
        WHERE t.lang_to = 'es'
        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()]


# ---------------------------------------------------------
# TOP-K usando NumPy (súper rápido)
# ---------------------------------------------------------
def _topk_numpy(
    idx: int,
    ids_all: List[int],
    mat: np.ndarray,
    K: int
) -> List[Tuple[int, float]]:

    # vector de la noticia central
    q = mat[idx]  # (dim,)

    # similitudes coseno: dot product (matriz · vector)
    sims = np.dot(mat, q)

    # eliminar self-match
    sims[idx] = -999.0

    # filtramos por score mínimo
    if MIN_SCORE > 0:
        mask = sims >= MIN_SCORE
        sims = np.where(mask, sims, -999.0)

    # obtenemos los índices top-k (mucho más rápido que ordenar todo)
    if K >= len(sims):
        top_idx = np.argsort(-sims)
    else:
        part = np.argpartition(-sims, K)[:K]
        top_idx = part[np.argsort(-sims[part])]

    out = [(ids_all[j], float(sims[j])) for j in top_idx[:K]]
    return out


# ---------------------------------------------------------
# Inserta en la tabla related_noticias
# ---------------------------------------------------------
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, score) for (rid, score) in pairs],
    )


# ---------------------------------------------------------
# Procesar IDs objetivo
# ---------------------------------------------------------
def build_for_ids(conn, target_ids: List[int]) -> int:
    with conn.cursor() as cur:
        ids_all, mat = _fetch_all_embeddings(cur)

    if not ids_all or mat is None:
        return 0

    # Mapa ID → index
    pos = {tid: i for i, tid in enumerate(ids_all)}
    processed = 0

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

            idx = pos[tr_id]
            pairs = _topk_numpy(idx, ids_all, mat, TOPK)
            _insert_related(cur, tr_id, pairs)
            processed += 1

        conn.commit()

    return processed


# ---------------------------------------------------------
# MAIN
# ---------------------------------------------------------
def main():
    logging.info(
        "Iniciando related_worker (TOPK=%s, BATCH_IDS=%s, MIN_SCORE=%.3f, WINDOW_H=%s)",
        TOPK,
        BATCH_IDS,
        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()