671 lines
20 KiB
Python
671 lines
20 KiB
Python
import os
|
|
import time
|
|
import logging
|
|
import contextlib
|
|
import re
|
|
from typing import List, Optional
|
|
|
|
import psycopg2
|
|
import psycopg2.extras
|
|
from psycopg2.extras import execute_values
|
|
|
|
import torch
|
|
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
|
|
from langdetect import detect, DetectorFactory
|
|
|
|
DetectorFactory.seed = 0
|
|
|
|
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")
|
|
LOG = logging.getLogger(__name__)
|
|
|
|
DB_CONFIG = {
|
|
"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"),
|
|
}
|
|
|
|
|
|
def _env_list(name: str, *fallbacks: str, default: str = "es") -> List[str]:
|
|
raw = None
|
|
for key in (name, *fallbacks):
|
|
raw = os.environ.get(key)
|
|
if raw:
|
|
break
|
|
raw = raw if raw is not None else default
|
|
return [s.strip() for s in raw.split(",") if s and s.strip()]
|
|
|
|
|
|
def _env_int(name: str, *fallbacks: str, default: int = 8) -> int:
|
|
for key in (name, *fallbacks):
|
|
val = os.environ.get(key)
|
|
if val:
|
|
try:
|
|
return int(val)
|
|
except ValueError:
|
|
pass
|
|
return default
|
|
|
|
|
|
def _env_float(name: str, *fallbacks: str, default: float = 5.0) -> float:
|
|
for key in (name, *fallbacks):
|
|
val = os.environ.get(key)
|
|
if val:
|
|
try:
|
|
return float(val)
|
|
except ValueError:
|
|
pass
|
|
return default
|
|
|
|
|
|
def _env_str(name: str, *fallbacks: str, default: Optional[str] = None) -> Optional[str]:
|
|
for key in (name, *fallbacks):
|
|
val = os.environ.get(key)
|
|
if val:
|
|
return val
|
|
return default
|
|
|
|
|
|
def _env_bool(name: str, default: bool = False) -> bool:
|
|
val = os.environ.get(name)
|
|
if val is None:
|
|
return default
|
|
return str(val).strip().lower() in ("1", "true", "yes", "y", "on")
|
|
|
|
|
|
TARGET_LANGS = _env_list("TARGET_LANGS", "TRANSLATE_TO", default="es")
|
|
BATCH_SIZE = _env_int("BATCH", "TRANSLATOR_BATCH", "TRANSLATE_BATCH", default=8)
|
|
ENQUEUE_MAX = _env_int("ENQUEUE", "TRANSLATOR_ENQUEUE", "TRANSLATE_ENQUEUE", default=200)
|
|
SLEEP_IDLE = _env_float("SLEEP_IDLE", "TRANSLATOR_SLEEP_IDLE", "TRANSLATE_SLEEP_IDLE", default=5.0)
|
|
DEVICE_CFG = (_env_str("DEVICE", default="auto") or "auto").lower()
|
|
|
|
MAX_SRC_TOKENS = _env_int("MAX_SRC_TOKENS", default=512)
|
|
MAX_NEW_TOKENS = _env_int("MAX_NEW_TOKENS", default=256)
|
|
|
|
|
|
def _beams_from_env():
|
|
nb_global = os.environ.get("NUM_BEAMS")
|
|
has_title = os.environ.get("NUM_BEAMS_TITLE") is not None
|
|
has_body = os.environ.get("NUM_BEAMS_BODY") is not None
|
|
if nb_global and not has_title and not has_body:
|
|
try:
|
|
v = max(1, int(nb_global))
|
|
return v, v
|
|
except ValueError:
|
|
pass
|
|
return _env_int("NUM_BEAMS_TITLE", default=2), _env_int("NUM_BEAMS_BODY", default=1)
|
|
|
|
|
|
NUM_BEAMS_TITLE, NUM_BEAMS_BODY = _beams_from_env()
|
|
|
|
UNIVERSAL_MODEL = _env_str("UNIVERSAL_MODEL", default="facebook/nllb-200-distilled-600M")
|
|
|
|
CHUNK_BY_SENTENCES = _env_bool("CHUNK_BY_SENTENCES", default=True)
|
|
CHUNK_MAX_TOKENS = _env_int("CHUNK_MAX_TOKENS", default=900)
|
|
CHUNK_OVERLAP_SENTS = _env_int("CHUNK_OVERLAP_SENTS", default=0)
|
|
|
|
_ABBR = ("Sr", "Sra", "Dr", "Dra", "Ing", "Lic", "pág", "etc")
|
|
_ABBR_MARK = "§"
|
|
|
|
_SENT_SPLIT_RE = re.compile(
|
|
r'(?<=[\.!\?…])\s+(?=["“\(\[A-ZÁÉÍÓÚÑÄÖÜ0-9])|(?:\n{2,})'
|
|
)
|
|
|
|
NLLB_LANG = {
|
|
"es": "spa_Latn",
|
|
"en": "eng_Latn",
|
|
"fr": "fra_Latn",
|
|
"de": "deu_Latn",
|
|
"it": "ita_Latn",
|
|
"pt": "por_Latn",
|
|
"nl": "nld_Latn",
|
|
"sv": "swe_Latn",
|
|
"da": "dan_Latn",
|
|
"fi": "fin_Latn",
|
|
"no": "nob_Latn",
|
|
"nb": "nob_Latn",
|
|
"nn": "nno_Latn",
|
|
"pl": "pol_Latn",
|
|
"cs": "ces_Latn",
|
|
"sk": "slk_Latn",
|
|
"sl": "slv_Latn",
|
|
"hu": "hun_Latn",
|
|
"ro": "ron_Latn",
|
|
"bg": "bul_Cyrl",
|
|
"el": "ell_Grek",
|
|
"ru": "rus_Cyrl",
|
|
"uk": "ukr_Cyrl",
|
|
"hr": "hrv_Latn",
|
|
"sr": "srp_Cyrl",
|
|
"bs": "bos_Latn",
|
|
"tr": "tur_Latn",
|
|
"ar": "arb_Arab",
|
|
"fa": "pes_Arab",
|
|
"he": "heb_Hebr",
|
|
"zh": "zho_Hans",
|
|
"ja": "jpn_Jpan",
|
|
"ko": "kor_Hang",
|
|
"vi": "vie_Latn",
|
|
"th": "tha_Thai",
|
|
"id": "ind_Latn",
|
|
"ms": "zsm_Latn",
|
|
"pt-br": "por_Latn",
|
|
"pt-pt": "por_Latn",
|
|
}
|
|
|
|
|
|
def _protect_abbrev(text: str) -> str:
|
|
t = re.sub(r"\b([A-ZÁÉÍÓÚÑÄÖÜ])\.", r"\1" + _ABBR_MARK, text)
|
|
pat = r"\b(?:" + "|".join(map(re.escape, _ABBR)) + r")\."
|
|
t = re.sub(pat, lambda m: m.group(0)[:-1] + _ABBR_MARK, t, flags=re.IGNORECASE)
|
|
return t
|
|
|
|
|
|
def _restore_abbrev(text: str) -> str:
|
|
return text.replace(_ABBR_MARK, ".")
|
|
|
|
|
|
def split_into_sentences(text: str) -> List[str]:
|
|
text = (text or "").strip()
|
|
if not text:
|
|
return []
|
|
protected = _protect_abbrev(text)
|
|
parts = [p.strip() for p in _SENT_SPLIT_RE.split(protected) if p and p.strip()]
|
|
parts = [_restore_abbrev(p) for p in parts]
|
|
merged: List[str] = []
|
|
for p in parts:
|
|
if merged and len(p) < 40:
|
|
merged[-1] = merged[-1] + " " + p
|
|
else:
|
|
merged.append(p)
|
|
return merged
|
|
|
|
|
|
def map_to_nllb(code: Optional[str]) -> Optional[str]:
|
|
if not code:
|
|
return None
|
|
code = code.strip().lower()
|
|
if code in NLLB_LANG:
|
|
return NLLB_LANG[code]
|
|
return f"{code}_Latn"
|
|
|
|
|
|
def normalize_lang(code: Optional[str], default: Optional[str] = None) -> Optional[str]:
|
|
if not code:
|
|
return default
|
|
code = code.strip().lower()
|
|
return code if code else default
|
|
|
|
|
|
def get_conn():
|
|
return psycopg2.connect(**DB_CONFIG)
|
|
|
|
|
|
def ensure_indexes(conn):
|
|
with conn.cursor() as cur:
|
|
cur.execute(
|
|
"""
|
|
CREATE INDEX IF NOT EXISTS traducciones_lang_to_status_idx
|
|
ON traducciones (lang_to, status);
|
|
CREATE INDEX IF NOT EXISTS traducciones_status_idx
|
|
ON traducciones (status);
|
|
"""
|
|
)
|
|
conn.commit()
|
|
|
|
|
|
def ensure_pending(conn, lang_to: str, enqueue_limit: int):
|
|
with conn.cursor() as cur:
|
|
cur.execute(
|
|
"""
|
|
INSERT INTO traducciones (noticia_id, lang_from, lang_to, status)
|
|
SELECT sub.id, NULL, %s, 'pending'
|
|
FROM (
|
|
SELECT n.id
|
|
FROM noticias n
|
|
LEFT JOIN traducciones t
|
|
ON t.noticia_id = n.id AND t.lang_to = %s
|
|
WHERE t.id IS NULL
|
|
ORDER BY n.fecha DESC NULLS LAST, n.id
|
|
LIMIT %s
|
|
) AS sub;
|
|
""",
|
|
(lang_to, lang_to, enqueue_limit),
|
|
)
|
|
conn.commit()
|
|
|
|
|
|
def fetch_pending_batch(conn, lang_to: str, batch_size: int):
|
|
with conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
|
|
cur.execute(
|
|
"""
|
|
SELECT t.id AS tr_id, t.noticia_id, t.lang_from, t.lang_to,
|
|
n.titulo, n.resumen
|
|
FROM traducciones t
|
|
JOIN noticias n ON n.id = t.noticia_id
|
|
WHERE t.lang_to = %s AND t.status = 'pending'
|
|
ORDER BY t.id
|
|
LIMIT %s;
|
|
""",
|
|
(lang_to, batch_size),
|
|
)
|
|
rows = cur.fetchall()
|
|
if rows:
|
|
ids = [r["tr_id"] for r in rows]
|
|
with conn.cursor() as cur2:
|
|
cur2.execute("UPDATE traducciones SET status='processing' WHERE id = ANY(%s)", (ids,))
|
|
conn.commit()
|
|
return rows
|
|
|
|
|
|
def detect_lang(text1: str, text2: str) -> Optional[str]:
|
|
txt = (text1 or "").strip() or (text2 or "").strip()
|
|
if not txt:
|
|
return None
|
|
try:
|
|
return detect(txt)
|
|
except Exception:
|
|
return None
|
|
|
|
|
|
_TOKENIZER: Optional[AutoTokenizer] = None
|
|
_MODEL: Optional[AutoModelForSeq2SeqLM] = None
|
|
_DEVICE: Optional[torch.device] = None
|
|
_CUDA_FAILS: int = 0
|
|
_CUDA_DISABLED: bool = False
|
|
|
|
|
|
def _resolve_device() -> torch.device:
|
|
global _CUDA_DISABLED
|
|
if _CUDA_DISABLED:
|
|
return torch.device("cpu")
|
|
if DEVICE_CFG == "cpu":
|
|
return torch.device("cpu")
|
|
if DEVICE_CFG == "cuda":
|
|
return torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
|
return torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
|
|
|
|
|
def _is_cuda_mem_error(exc: Exception) -> bool:
|
|
s = str(exc)
|
|
return ("CUDA out of memory" in s) or ("CUDACachingAllocator" in s) or ("expandable_segment" in s)
|
|
|
|
|
|
def _free_cuda():
|
|
if torch.cuda.is_available():
|
|
try:
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
except Exception:
|
|
pass
|
|
|
|
|
|
def _load_model_on(device: torch.device):
|
|
global _TOKENIZER, _MODEL, _DEVICE
|
|
dtype = torch.float16 if device.type == "cuda" else torch.float32
|
|
|
|
LOG.info("Cargando modelo universal %s (device=%s, dtype=%s)", UNIVERSAL_MODEL, device, dtype)
|
|
tok = AutoTokenizer.from_pretrained(UNIVERSAL_MODEL)
|
|
mdl = AutoModelForSeq2SeqLM.from_pretrained(
|
|
UNIVERSAL_MODEL,
|
|
torch_dtype=dtype,
|
|
low_cpu_mem_usage=True,
|
|
)
|
|
|
|
try:
|
|
mdl.config.use_cache = False
|
|
except Exception:
|
|
pass
|
|
|
|
mdl.to(device)
|
|
mdl.eval()
|
|
|
|
_TOKENIZER, _MODEL, _DEVICE = tok, mdl, device
|
|
|
|
|
|
def get_universal_components():
|
|
global _TOKENIZER, _MODEL, _DEVICE, _CUDA_FAILS, _CUDA_DISABLED
|
|
|
|
if _MODEL is not None and _DEVICE is not None:
|
|
return _TOKENIZER, _MODEL, _DEVICE
|
|
|
|
dev = _resolve_device()
|
|
try:
|
|
_load_model_on(dev)
|
|
return _TOKENIZER, _MODEL, _DEVICE
|
|
except Exception as e:
|
|
LOG.warning("Fallo cargando modelo en %s: %s", dev, e)
|
|
if dev.type == "cuda" and _is_cuda_mem_error(e):
|
|
_CUDA_FAILS += 1
|
|
_CUDA_DISABLED = True
|
|
_free_cuda()
|
|
LOG.warning("Deshabilitando CUDA y reintentando en CPU (fallos CUDA=%d)", _CUDA_FAILS)
|
|
_load_model_on(torch.device("cpu"))
|
|
return _TOKENIZER, _MODEL, _DEVICE
|
|
_load_model_on(torch.device("cpu"))
|
|
return _TOKENIZER, _MODEL, _DEVICE
|
|
|
|
|
|
def _safe_src_len(tokenizer) -> int:
|
|
model_max = getattr(tokenizer, "model_max_length", 1024) or 1024
|
|
return min(MAX_SRC_TOKENS, int(model_max) - 16)
|
|
|
|
|
|
def _token_chunks(tokenizer, text: str, max_tokens: int) -> List[str]:
|
|
if not text:
|
|
return []
|
|
ids = tokenizer.encode(text, add_special_tokens=False)
|
|
if len(ids) <= max_tokens:
|
|
return [text]
|
|
chunks = []
|
|
for i in range(0, len(ids), max_tokens):
|
|
sub = ids[i : i + max_tokens]
|
|
piece = tokenizer.decode(sub, skip_special_tokens=True, clean_up_tokenization_spaces=True)
|
|
if piece.strip():
|
|
chunks.append(piece.strip())
|
|
return chunks
|
|
|
|
|
|
def _norm(s: str) -> str:
|
|
import re as _re
|
|
|
|
return _re.sub(r"\W+", "", (s or "").lower()).strip()
|
|
|
|
|
|
def _forced_bos_id(tokenizer: AutoTokenizer, model: AutoModelForSeq2SeqLM, tgt_code: str) -> int:
|
|
try:
|
|
mapping = getattr(tokenizer, "lang_code_to_id", None)
|
|
if isinstance(mapping, dict):
|
|
tid = mapping.get(tgt_code)
|
|
if isinstance(tid, int):
|
|
return tid
|
|
except Exception:
|
|
pass
|
|
|
|
try:
|
|
mapping = getattr(getattr(model, "config", None), "lang_code_to_id", None)
|
|
if isinstance(mapping, dict):
|
|
tid = mapping.get(tgt_code)
|
|
if isinstance(tid, int):
|
|
return tid
|
|
except Exception:
|
|
pass
|
|
|
|
try:
|
|
tid = tokenizer.convert_tokens_to_ids(tgt_code)
|
|
if isinstance(tid, int) and tid not in (-1, getattr(tokenizer, "unk_token_id", -1)):
|
|
return tid
|
|
except Exception:
|
|
pass
|
|
|
|
try:
|
|
ats = getattr(tokenizer, "additional_special_tokens", None)
|
|
ats_ids = getattr(tokenizer, "additional_special_tokens_ids", None)
|
|
if isinstance(ats, list) and isinstance(ats_ids, list) and tgt_code in ats:
|
|
idx = ats.index(tgt_code)
|
|
if 0 <= idx < len(ats_ids) and isinstance(ats_ids[idx], int):
|
|
return ats_ids[idx]
|
|
except Exception:
|
|
pass
|
|
|
|
LOG.warning("No pude resolver lang code id para '%s'. Uso fallback (eos/bos).", tgt_code)
|
|
return getattr(tokenizer, "eos_token_id", None) or getattr(tokenizer, "bos_token_id", None) or 0
|
|
|
|
|
|
@torch.inference_mode()
|
|
def translate_text(src_lang: str, tgt_lang: str, text: str, num_beams: int = 1, _tries: int = 0) -> str:
|
|
if not text or not text.strip():
|
|
return ""
|
|
|
|
tok, mdl, device = get_universal_components()
|
|
src_code = map_to_nllb(src_lang) or "eng_Latn"
|
|
tgt_code = map_to_nllb(tgt_lang) or "spa_Latn"
|
|
|
|
try:
|
|
tok.src_lang = src_code
|
|
except Exception:
|
|
pass
|
|
|
|
forced_bos = _forced_bos_id(tok, mdl, tgt_code)
|
|
|
|
safe_len = _safe_src_len(tok)
|
|
parts = _token_chunks(tok, text, safe_len)
|
|
outs: List[str] = []
|
|
|
|
try:
|
|
autocast_ctx = torch.amp.autocast("cuda", dtype=torch.float16) if device.type == "cuda" else contextlib.nullcontext()
|
|
for p in parts:
|
|
enc = tok(p, return_tensors="pt", truncation=True, max_length=safe_len)
|
|
enc = {k: v.to(device) for k, v in enc.items()}
|
|
|
|
gen_kwargs = dict(
|
|
forced_bos_token_id=forced_bos,
|
|
max_new_tokens=MAX_NEW_TOKENS,
|
|
num_beams=max(1, int(num_beams)),
|
|
do_sample=False,
|
|
use_cache=False,
|
|
)
|
|
if int(num_beams) > 1:
|
|
gen_kwargs["early_stopping"] = True
|
|
|
|
with autocast_ctx:
|
|
generated = mdl.generate(**enc, **gen_kwargs)
|
|
|
|
out = tok.batch_decode(generated, skip_special_tokens=True)[0].strip()
|
|
outs.append(out)
|
|
|
|
del enc, generated
|
|
if device.type == "cuda":
|
|
_free_cuda()
|
|
|
|
return "\n".join([o for o in outs if o]).strip()
|
|
|
|
except Exception as e:
|
|
if device.type == "cuda" and _is_cuda_mem_error(e) and _tries < 2:
|
|
LOG.warning("CUDA OOM/allocator: intento de recuperación %d. Detalle: %s", _tries + 1, e)
|
|
global _MODEL, _DEVICE, _CUDA_DISABLED
|
|
_CUDA_DISABLED = True
|
|
try:
|
|
if _MODEL is not None:
|
|
del _MODEL
|
|
except Exception:
|
|
pass
|
|
_free_cuda()
|
|
_MODEL = None
|
|
_DEVICE = None
|
|
time.sleep(1.0)
|
|
return translate_text(src_lang, tgt_lang, text, num_beams=num_beams, _tries=_tries + 1)
|
|
raise
|
|
|
|
|
|
def _sent_token_len(tokenizer, sent: str) -> int:
|
|
return len(tokenizer(sent, add_special_tokens=False).input_ids)
|
|
|
|
|
|
def _pack_sentences_to_token_chunks(
|
|
tokenizer, sentences: List[str], max_tokens: int, overlap_sents: int = 0
|
|
) -> List[List[str]]:
|
|
chunks: List[List[str]] = []
|
|
cur: List[str] = []
|
|
cur_tokens = 0
|
|
for s in sentences:
|
|
slen = _sent_token_len(tokenizer, s)
|
|
if slen > max_tokens:
|
|
ids = tokenizer(s, add_special_tokens=False).input_ids
|
|
step = max_tokens
|
|
for i in range(0, len(ids), step):
|
|
sub = tokenizer.decode(ids[i : i + step], skip_special_tokens=True)
|
|
if cur:
|
|
chunks.append(cur)
|
|
cur = []
|
|
cur_tokens = 0
|
|
chunks.append([sub])
|
|
continue
|
|
|
|
if cur_tokens + slen <= max_tokens:
|
|
cur.append(s)
|
|
cur_tokens += slen
|
|
else:
|
|
if cur:
|
|
chunks.append(cur)
|
|
if overlap_sents > 0 and len(cur) > 0:
|
|
overlap = cur[-overlap_sents:]
|
|
cur = overlap + [s]
|
|
cur_tokens = sum(_sent_token_len(tokenizer, x) for x in cur)
|
|
else:
|
|
cur = [s]
|
|
cur_tokens = slen
|
|
if cur:
|
|
chunks.append(cur)
|
|
return chunks
|
|
|
|
|
|
def _smart_concatenate(parts: List[str], tail_window: int = 120) -> str:
|
|
if not parts:
|
|
return ""
|
|
out = parts[0]
|
|
for nxt in parts[1:]:
|
|
tail = out[-tail_window:]
|
|
cut = 0
|
|
for k in range(min(len(tail), len(nxt)), 20, -1):
|
|
if nxt.startswith(tail[-k:]):
|
|
cut = k
|
|
break
|
|
out += ("" if cut == 0 else nxt[cut:]) if nxt else ""
|
|
return out
|
|
|
|
|
|
def translate_article_full(
|
|
src_lang: str,
|
|
tgt_lang: str,
|
|
text: str,
|
|
num_beams: int,
|
|
) -> str:
|
|
if not text or not text.strip():
|
|
return ""
|
|
|
|
if not CHUNK_BY_SENTENCES:
|
|
return translate_text(src_lang, tgt_lang, text, num_beams=num_beams)
|
|
|
|
tok, _, _ = get_universal_components()
|
|
safe_len = _safe_src_len(tok)
|
|
max_chunk_tokens = min(CHUNK_MAX_TOKENS, safe_len)
|
|
|
|
sents = split_into_sentences(text)
|
|
if not sents:
|
|
return ""
|
|
|
|
chunks_sents = _pack_sentences_to_token_chunks(
|
|
tok, sents, max_tokens=max_chunk_tokens, overlap_sents=CHUNK_OVERLAP_SENTS
|
|
)
|
|
|
|
translated_parts: List[str] = []
|
|
for group in chunks_sents:
|
|
chunk_text = " ".join(group)
|
|
translated = translate_text(src_lang, tgt_lang, chunk_text, num_beams=num_beams)
|
|
translated_parts.append(translated)
|
|
|
|
return _smart_concatenate([p for p in translated_parts if p])
|
|
|
|
|
|
def process_batch(conn, rows):
|
|
done_rows = []
|
|
error_rows = []
|
|
|
|
for r in rows:
|
|
tr_id = r["tr_id"]
|
|
lang_to = normalize_lang(r["lang_to"], "es") or "es"
|
|
lang_from = normalize_lang(r["lang_from"]) or detect_lang(r["titulo"] or "", r["resumen"] or "") or "en"
|
|
|
|
title = (r["titulo"] or "").strip()
|
|
body = (r["resumen"] or "").strip()
|
|
|
|
if (map_to_nllb(lang_from) or "eng_Latn") == (map_to_nllb(lang_to) or "spa_Latn"):
|
|
done_rows.append((title, body, lang_from, tr_id))
|
|
continue
|
|
|
|
try:
|
|
title_tr = translate_text(lang_from, lang_to, title, num_beams=NUM_BEAMS_TITLE) if title else ""
|
|
body_tr = translate_article_full(lang_from, lang_to, body, num_beams=NUM_BEAMS_BODY) if body else ""
|
|
|
|
if _norm(title_tr) == _norm(title):
|
|
title_tr = ""
|
|
if _norm(body_tr) == _norm(body):
|
|
body_tr = ""
|
|
|
|
done_rows.append((title_tr, body_tr, lang_from, tr_id))
|
|
except Exception as e:
|
|
LOG.exception("Error traduciendo fila")
|
|
error_rows.append((str(e)[:1500], tr_id))
|
|
|
|
with conn.cursor() as cur:
|
|
if done_rows:
|
|
execute_values(
|
|
cur,
|
|
"""
|
|
UPDATE traducciones AS t
|
|
SET titulo_trad = v.titulo_trad,
|
|
resumen_trad = v.resumen_trad,
|
|
lang_from = COALESCE(t.lang_from, v.lang_from),
|
|
status = 'done',
|
|
error = NULL
|
|
FROM (VALUES %s) AS v(titulo_trad, resumen_trad, lang_from, id)
|
|
WHERE t.id = v.id;
|
|
""",
|
|
done_rows,
|
|
)
|
|
|
|
if error_rows:
|
|
execute_values(
|
|
cur,
|
|
"""
|
|
UPDATE traducciones AS t
|
|
SET status = 'error',
|
|
error = v.error
|
|
FROM (VALUES %s) AS v(error, id)
|
|
WHERE t.id = v.id;
|
|
""",
|
|
error_rows,
|
|
)
|
|
conn.commit()
|
|
|
|
|
|
def main():
|
|
LOG.info(
|
|
"Arrancando worker de traducción (NLLB). TARGET_LANGS=%s, BATCH=%s, ENQUEUE=%s, DEVICE=%s, "
|
|
"BEAMS(title/body)=%s/%s, CHUNK_BY_SENTENCES=%s, CHUNK_MAX_TOKENS=%s, OVERLAP_SENTS=%s",
|
|
TARGET_LANGS,
|
|
BATCH_SIZE,
|
|
ENQUEUE_MAX,
|
|
DEVICE_CFG,
|
|
NUM_BEAMS_TITLE,
|
|
NUM_BEAMS_BODY,
|
|
CHUNK_BY_SENTENCES,
|
|
CHUNK_MAX_TOKENS,
|
|
CHUNK_OVERLAP_SENTS,
|
|
)
|
|
get_universal_components()
|
|
|
|
while True:
|
|
any_work = False
|
|
with get_conn() as conn:
|
|
ensure_indexes(conn)
|
|
for lt in TARGET_LANGS:
|
|
lt = normalize_lang(lt, "es") or "es"
|
|
ensure_pending(conn, lt, ENQUEUE_MAX)
|
|
while True:
|
|
rows = fetch_pending_batch(conn, lt, BATCH_SIZE)
|
|
if not rows:
|
|
break
|
|
any_work = True
|
|
LOG.info("[%s] Procesando %d elementos…", lt, len(rows))
|
|
process_batch(conn, rows)
|
|
if not any_work:
|
|
time.sleep(SLEEP_IDLE)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
|
|
main()
|
|
|