import os
import time
import logging
from typing import List

import numpy as np
import psycopg2
import psycopg2.extras
from psycopg2.extras import execute_values
from sentence_transformers import SentenceTransformer
import torch

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

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"),
)

EMB_MODEL = os.environ.get(
    "EMB_MODEL",
    "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
)
EMB_BATCH = int(os.environ.get("EMB_BATCH", "128"))
SLEEP_IDLE = float(os.environ.get("EMB_SLEEP_IDLE", "5.0"))
EMB_LANGS = [s.strip() for s in os.environ.get("EMB_LANGS", "es").split(",") if s.strip()]
DEVICE_ENV = os.environ.get("DEVICE", "auto").lower()
EMB_LIMIT = int(os.environ.get("EMB_LIMIT", "1000"))


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


def ensure_schema(conn):
    with conn.cursor() as cur:
        cur.execute(
            """
        CREATE TABLE IF NOT EXISTS traduccion_embeddings (
          id SERIAL PRIMARY KEY,
          traduccion_id INT NOT NULL REFERENCES traducciones(id) ON DELETE CASCADE,
          model TEXT NOT NULL,
          dim INT NOT NULL,
          embedding DOUBLE PRECISION[] NOT NULL,
          created_at TIMESTAMP DEFAULT NOW(),
          UNIQUE (traduccion_id, model)
        );
        """
        )
        cur.execute("CREATE INDEX IF NOT EXISTS idx_tr_emb_model ON traduccion_embeddings(model);")
        cur.execute("CREATE INDEX IF NOT EXISTS idx_tr_emb_traduccion_id ON traduccion_embeddings(traduccion_id);")
    conn.commit()


def fetch_batch_pending(conn) -> List[psycopg2.extras.DictRow]:
    with conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
        cur.execute(
            """
            SELECT t.id          AS traduccion_id,
                   t.lang_to     AS lang_to,
                   COALESCE(NULLIF(t.titulo_trad, ''), '')  AS titulo_trad,
                   COALESCE(NULLIF(t.resumen_trad,''), '')  AS resumen_trad,
                   n.id          AS noticia_id
            FROM traducciones t
            JOIN noticias n ON n.id = t.noticia_id
            LEFT JOIN traduccion_embeddings e
              ON e.traduccion_id = t.id AND e.model = %s
            WHERE t.status = 'done'
              AND t.lang_to = ANY(%s)
              AND e.traduccion_id IS NULL
            ORDER BY t.id
            LIMIT %s
        """,
            (EMB_MODEL, EMB_LANGS, EMB_LIMIT),
        )
        return cur.fetchall()


def texts_from_rows(rows: List[psycopg2.extras.DictRow]) -> List[str]:
    texts: List[str] = []
    for r in rows:
        title = (r["titulo_trad"] or "").strip()
        body = (r["resumen_trad"] or "").strip()
        if title and body:
            texts.append(f"{title}\n{body}")
        else:
            texts.append(title or body or "")
    return texts


def upsert_embeddings(conn, rows, embs: np.ndarray, model_name: str):
    if embs.size == 0 or not rows:
        return

    dim = int(embs.shape[1])

    data = [
        (
            int(r["traduccion_id"]),
            model_name,
            dim,
            [float(x) for x in e],
        )
        for r, e in zip(rows, embs)
    ]

    with conn.cursor() as cur:
        execute_values(
            cur,
            """
            INSERT INTO traduccion_embeddings (traduccion_id, model, dim, embedding)
            VALUES %s
            ON CONFLICT (traduccion_id, model) DO UPDATE
            SET embedding  = EXCLUDED.embedding,
                dim        = EXCLUDED.dim,
                created_at = NOW()
            """,
            data,
        )
    conn.commit()


def main():
    log.info("Arrancando embeddings_worker para TRADUCCIONES")
    log.info(
        "Modelo: %s | Batch: %s | Idiomas: %s | DEVICE env: %s",
        EMB_MODEL,
        EMB_BATCH,
        ",".join(EMB_LANGS),
        DEVICE_ENV,
    )

    if DEVICE_ENV == "auto":
        device = "cuda" if torch.cuda.is_available() else "cpu"
    else:
        device = DEVICE_ENV

    log.info("Usando dispositivo: %s", device)

    model = SentenceTransformer(EMB_MODEL, device=device)

    while True:
        try:
            with get_conn() as conn:
                ensure_schema(conn)

                rows = fetch_batch_pending(conn)
                if not rows:
                    time.sleep(SLEEP_IDLE)
                    continue

                texts = texts_from_rows(rows)
                embs = model.encode(
                    texts,
                    batch_size=EMB_BATCH,
                    convert_to_numpy=True,
                    show_progress_bar=False,
                    normalize_embeddings=True,
                )

                upsert_embeddings(conn, rows, embs, EMB_MODEL)
                log.info("Embeddings upserted: %d", len(rows))

        except Exception as e:
            log.exception("Error en embeddings_worker: %s", e)
            time.sleep(SLEEP_IDLE)


if __name__ == "__main__":
    main()