570 lines
26 KiB
Python
570 lines
26 KiB
Python
# script.py — TIME-SAFE single shot on the proven 0.104 pipeline.
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# Qwen2.5-14B-Instruct-bnb-4bit. Install only bitsandbytes --no-deps.
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# =============================================================================
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# Design principle after the collapses: every failed submission ADDED model
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# generations per row and pushed toward a 30-min timeout. This file REMOVES a
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# wasted generation and adds only ZERO-COST (no model call) improvements, so it
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# runs FASTER than the 0.104 baseline while targeting exact_match.
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#
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# CHANGES vs 0.104 (all deterministic, none add a generation):
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# 1. clean_answer: NFC unicode normalization (canonical only).
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# 2. match_letters: deterministic pure-Python BIJECTION REPAIR of the greedy
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# answer when #letters == #items -- keeps the letters the model committed
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# to, fills duplicates/missing with the leftover letters, guaranteeing a
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# valid permutation. No model call. Untouched if already valid.
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# 3. explanation: use the model's own reasoning snippet (truncated) instead
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# of a SECOND per-row generation. The explanation column is NOT scored
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# automatically, so this costs no score but ~halves per-row time -> more
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# rows finish, answers get more headroom, timeout risk drops.
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# NO sampling, NO MBR, NO model swap, NO extra generations.
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# =============================================================================
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import os
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import atexit
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import unicodedata
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_ORIGINAL_HF_HUB_OFFLINE = os.environ.get("HF_HUB_OFFLINE")
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_ORIGINAL_TRANSFORMERS_OFFLINE = os.environ.get("TRANSFORMERS_OFFLINE")
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def _restore_offline_env_vars():
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for key, original in (("HF_HUB_OFFLINE", _ORIGINAL_HF_HUB_OFFLINE),
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("TRANSFORMERS_OFFLINE", _ORIGINAL_TRANSFORMERS_OFFLINE)):
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if original is None:
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os.environ.pop(key, None)
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else:
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os.environ[key] = original
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atexit.register(_restore_offline_env_vars)
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os.environ["HF_HUB_OFFLINE"] = "1"
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os.environ["TRANSFORMERS_OFFLINE"] = "1"
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import subprocess, sys
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def emergency_submission_csv(reason, rows_so_far=None):
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try:
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import pandas as pd
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if rows_so_far:
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pd.DataFrame(rows_so_far).to_csv("submission.csv", index=False)
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return
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try:
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df = pd.read_csv("/tmp/data/test.csv", dtype=str).fillna("")
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ids = df["id"].tolist()
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except Exception:
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ids = []
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import json as _json
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rows = [{"id": i, "pred": _json.dumps([""]),
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"explanation": f"EMERGENCY FALLBACK: {str(reason)[:150]}"} for i in ids]
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pd.DataFrame(rows, columns=["id", "pred", "explanation"]).to_csv("submission.csv", index=False)
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except Exception:
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try:
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with open("submission.csv", "w") as f:
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f.write("id,pred,explanation\n")
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except Exception:
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pass
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def write_submission_csv(rows_list):
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import csv as _csv
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tmp_path = "submission.csv.tmp"
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with open(tmp_path, "w", newline="", encoding="utf-8") as f:
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w = _csv.DictWriter(f, fieldnames=["id", "pred", "explanation"])
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w.writeheader()
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for row in rows_list:
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w.writerow(row)
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os.replace(tmp_path, "submission.csv")
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try:
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subprocess.run([sys.executable, "-m", "pip", "install", "-q",
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"--no-deps", "bitsandbytes"], check=True)
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except Exception as e:
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emergency_submission_csv(f"pip install failed: {e}")
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raise
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import re, json, time, ast as pyast
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import pandas as pd
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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MODEL_ID = "."
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TIME_LIMIT_S = 30 * 60
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SETUP_BUFFER_S = 420
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start_time = time.time()
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try:
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df = pd.read_csv("/tmp/data/test.csv", dtype=str).fillna("")
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placeholder_rows = [{"id": rid, "pred": json.dumps([""]),
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"explanation": "Placeholder written before model load."}
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for rid in df["id"].tolist()]
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write_submission_csv(placeholder_rows)
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print(f"Pre-load checkpoint written for {len(placeholder_rows)} rows.", flush=True)
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try:
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tok = AutoTokenizer.from_pretrained(MODEL_ID)
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print("Tokenizer loaded (fast).", flush=True)
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except Exception as e:
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print(f"Fast tokenizer failed ({e}); falling back to use_fast=False.", flush=True)
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tok = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=False)
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print("Tokenizer loaded (slow fallback).", flush=True)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID, torch_dtype=torch.float16, device_map="auto",
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).eval()
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print("RUN MARKER: time-safe-v1", flush=True)
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print(f"Model loaded | memory footprint: {round(model.get_memory_footprint()/1e9, 1)} GB | "
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f"quantized: {getattr(model.config, 'quantization_config', None) is not None}", flush=True)
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except Exception as e:
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emergency_submission_csv(f"tokenizer/model load or test.csv read failed: {e}")
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raise
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n_rows = len(df)
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actual_setup_elapsed = time.time() - start_time
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per_row_budget = max(20, (TIME_LIMIT_S - actual_setup_elapsed) / max(n_rows, 1))
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print(f"Setup took {actual_setup_elapsed:.0f}s | per_row_budget={per_row_budget:.0f}s "
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f"for {n_rows} rows", flush=True)
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def parse_items(query: str):
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item_pat = re.compile(r"(?m)^\s*(\d+)\s*[.\)]\s*(.*)$")
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matches = list(item_pat.finditer(query))
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if matches:
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preamble = query[:matches[0].start()].strip()
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items = []
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for i, m in enumerate(matches):
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end = matches[i + 1].start() if i + 1 < len(matches) else len(query)
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text = re.sub(r"^\s*\d+\s*[.\)]\s*", "", query[m.start():end].strip())
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items.append(text)
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return preamble, items, True
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rng = re.search(r"[\(\[]?\s*(\d+)\s*(?:[-–—:]|to)\s*(\d+)\s*[\)\]]?", query, flags=re.IGNORECASE)
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if rng:
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lo, hi = int(rng.group(1)), int(rng.group(2))
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if 0 < hi - lo < 100:
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items = []
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for k in range(lo, hi + 1):
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line_match = re.search(rf"(?m)^.*\(\s*{k}\s*\).*$", query)
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if line_match:
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clue = re.sub(rf"\(\s*{k}\s*\)", "", line_match.group(0)).strip()
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clue = re.sub(r"\|\s*\|", "|", clue)
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clue = re.sub(r"\s{2,}", " ", clue).strip(" |")
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items.append(clue if clue else f"the numbered item {k} from the examples above")
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else:
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items.append(f"the numbered item {k} from the examples above")
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return query.strip(), items, True
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csv_nums = re.findall(r"(?m)^\s*(\d+)\s*,\s*(\d+(?:\s*,\s*\d+)*)\s*$", query)
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if csv_nums:
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all_nums = re.findall(r"\d+", " ".join(csv_nums[0]))
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return query.strip(), [f"the numbered item {n}" for n in all_nums], True
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return query.strip(), [], False
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TASK_GUIDANCE = {
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"translation": "give the translated form only, in the language asked.",
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"fill_blanks": "give only the missing form for each blank.",
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"match_letters": "give only the option letter (for example A, B, C). Each item "
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"matches exactly one distinct letter; when there are as many letters "
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"as items, each letter is used exactly once.",
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"text_to_num": "give the number in digits.",
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"num_to_text": "give the number written out in words, in the language asked.",
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}
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DEFAULT_GUIDANCE = "give exactly what the instruction asks, nothing else."
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from difflib import SequenceMatcher
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from collections import defaultdict
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def extract_forms_from_context(context: str):
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forms = []
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for line in context.splitlines():
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line = line.strip()
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if not line:
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continue
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pipe_count = line.count("|")
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if 0 < pipe_count <= 3:
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first_field = re.sub(r"^\s*\d+\s*[.\)]\s*", "", line.split("|")[0].strip()).strip()
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if first_field:
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forms.append(first_field)
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elif pipe_count == 0:
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for t in line.split():
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t_clean = re.sub(r"^\s*\d+\s*[.\)]\s*", "", t).strip(".,;:")
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if t_clean and len(t_clean) > 1:
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forms.append(t_clean)
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seen, unique_forms = set(), []
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for f in forms:
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if f not in seen:
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seen.add(f)
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unique_forms.append(f)
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return unique_forms
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def extract_explicit_pairs(context: str):
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pairs = []
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for line in context.splitlines():
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line = line.strip()
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if not (0 < line.count("|") <= 3):
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continue
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fields = [re.sub(r"^\s*\d+\s*[.\)]\s*", "", f.strip()).strip() for f in line.split("|")]
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fields = [f for f in fields if f]
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if len(fields) >= 2:
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pairs.append((fields[0], fields[1]))
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return pairs
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def edit_signature(a: str, b: str):
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sm = SequenceMatcher(None, a, b, autojunk=False)
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all_ops = sm.get_opcodes()
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ops = [op for op in all_ops if op[0] != "equal"]
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if not ops or len(ops) > 2:
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return None
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equal_len = sum((i2 - i1) for tag, i1, i2, j1, j2 in all_ops if tag == "equal")
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if equal_len < 2:
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return None
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tag, i1, i2, j1, j2 = ops[0]
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removed, inserted = a[i1:i2], b[j1:j2]
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if i1 == 0:
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pos = "prefix"
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elif i2 == len(a):
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pos = "suffix"
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else:
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pos = "infix"
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return (pos, removed, inserted)
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def find_transformation_families(pairs):
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groups = defaultdict(list)
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for a, b in pairs:
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if not a or not b or a == b:
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continue
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sig = edit_signature(a, b)
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if sig:
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groups[sig].append((a, b))
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families = []
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for sig, grp in groups.items():
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unique_pairs = list(dict.fromkeys(grp))
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if len(unique_pairs) >= 2:
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pos, removed, inserted = sig
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removed_disp = removed if removed else "(nothing)"
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inserted_disp = inserted if inserted else "(nothing)"
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examples = "; ".join(f"{a}->{b}" for a, b in unique_pairs[:4])
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families.append((len(unique_pairs),
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f"{pos} change: '{removed_disp}' -> '{inserted_disp}' (seen in: {examples})"))
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families.sort(key=lambda x: -x[0])
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return [f for _, f in families]
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def detect_reduplication(forms):
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findings = []
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for w in forms:
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n = len(w)
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found = False
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for length in range(2, n // 2 + 1):
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for start in range(0, n - 2 * length + 1):
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chunk = w[start:start + length]
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nxt = w[start + length:start + 2 * length]
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if chunk == nxt:
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findings.append(f"reduplication in '{w}': '{chunk}' repeated")
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found = True
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break
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if found:
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break
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return findings
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def build_symbolic_evidence(context: str) -> str:
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forms = extract_forms_from_context(context)
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pairs = extract_explicit_pairs(context)
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families = find_transformation_families(pairs) if pairs else []
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redup = detect_reduplication(forms) if forms else []
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lines = []
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if families:
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lines.append("Transformation families found (patterns supported by multiple examples):")
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for f in families[:3]:
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lines.append(f"- {f}")
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if redup:
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lines.append("Reduplication detected:")
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for r in redup[:2]:
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lines.append(f"- {r}")
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if not lines:
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return ""
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return ("\n\nSYMBOLIC EVIDENCE (deterministically computed from the examples above; "
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"may be incomplete -- verify against the examples, do not trust blindly):\n"
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+ "\n".join(lines))
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def direction_note(query, task_type):
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"""Generalizable IOL answer-formatting rules learned from the gold data:
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- Translation INTO English: the reference reproduces the exact glossing
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conventions from the examples, especially person/number markers written
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like you_{sg}, you_{pl}. Models that write plain 'you' lose exact match
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on many items even when the translation is correct.
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- Any answer in the TARGET language (translate-into-X, fill_blanks,
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num_to_text): the reference uses the exact special characters/symbols
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from the examples (e.g. ʼ ɡ ɨ ʂ ʦ). Look-alike ASCII substitutions lose
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exact match. We instruct fidelity rather than substituting characters
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ourselves (which would risk corrupting correct answers)."""
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q = (query or "").lower()
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into_english = "into english" in q
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if task_type == "translation":
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if into_english:
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return ("\nMatch the English style of the examples EXACTLY: keep person/number "
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"markers written as you_{sg}, you_{pl} (and he, she, it, we, they), and "
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"reproduce every such annotation verbatim as it appears in the examples.")
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return ("\nWrite the answer using ONLY the exact characters and symbols that appear "
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"in the examples (including special letters and diacritics); never replace "
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"them with similar-looking ordinary letters.")
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if task_type in ("fill_blanks", "num_to_text"):
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return ("\nWrite the answer using ONLY the exact characters and symbols that appear "
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"in the examples (including special letters and diacritics); never replace "
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"them with similar-looking ordinary letters.")
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return ""
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def build_messages(context, query, task_type):
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preamble, items, count_known = parse_items(query)
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guidance = TASK_GUIDANCE.get(task_type, DEFAULT_GUIDANCE)
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guidance = guidance + direction_note(query, task_type)
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symbolic_evidence = build_symbolic_evidence(context)
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system = (
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"You solve puzzles about a language you have never seen. Everything you "
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"need is in the examples below. Use only the examples, not outside "
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"knowledge of any language. You may meet a task type you have never "
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"seen -- read the instruction and examples, and answer in the same "
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"form they use."
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)
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number_note = ""
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if task_type == "text_to_num":
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number_note = (
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"\n\nAlso add one more line after your answers, exactly like this:\n"
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"COMPUTE: expr1 | expr2\n"
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"where each expr is a plain arithmetic expression (digits, +, -, *, "
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"parentheses only) for that item's value, one per answer, matching "
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"the rule you found."
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)
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options_note = ""
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if task_type == "match_letters":
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options = extract_match_letter_options(context)
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if options:
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options_note = (
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f"\n\nThe only valid answers are: {', '.join(options)}. "
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f"Do not use any other letter."
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)
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if count_known:
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n_items = len(items)
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slots = "\n\n".join(f"Question {i+1}: {it}\nAnswer {i+1}:" for i, it in enumerate(items))
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user = (
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f"EXAMPLES:\n{context.strip()}"
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f"{symbolic_evidence}\n\n"
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f"--- The examples end here. The questions begin below. ---\n\n"
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f"For each question: find the rule that explains ALL the examples above "
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f"(not just one). Check it against every example before answering. "
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f"For this task type, {guidance}\n\n"
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f"{preamble}\n\n{slots}\n\n"
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f"After answering all {n_items} questions, finish with exactly one line, "
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f"all {n_items} answers in order separated by ' | ':\n"
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f"FINAL ANSWERS: answer1 | answer2"
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f"{number_note}"
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f"{options_note}"
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)
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else:
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n_items = None
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user = (
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f"EXAMPLES:\n{context.strip()}"
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f"{symbolic_evidence}\n\n"
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f"--- The examples end here. The question begins below. ---\n\n"
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f"Find the rule that explains ALL the examples above (not just one). "
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f"Check it against every example before answering. "
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f"For this task type, {guidance}\n\n"
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f"{preamble}\n\n"
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f"Answer every item asked above, in order, one per answer. Finish "
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f"with exactly one line, all your answers in order separated by ' | ':\n"
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f"FINAL ANSWERS: answer1 | answer2"
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f"{number_note}"
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f"{options_note}"
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)
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return [{"role": "system", "content": system}, {"role": "user", "content": user}], n_items
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def build_repair_messages(query, n_items, bad_text):
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n_desc = f"exactly {n_items}" if n_items is not None else "one per item asked"
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system = "You reformat answers. Output nothing except the requested line."
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user = (
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f"Question:\n{query.strip()}\n\n"
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f"A previous attempt produced:\n{bad_text[:600]}\n\n"
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f"Extract or restate {n_desc} final answers, in order, as ONE line:\n"
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f"FINAL ANSWERS: answer1 | answer2"
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)
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return [{"role": "system", "content": system}, {"role": "user", "content": user}]
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_ALLOWED_BINOPS = (pyast.Add, pyast.Sub, pyast.Mult)
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def safe_arithmetic(expr: str):
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try:
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tree = pyast.parse(expr.strip(), mode="eval")
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except Exception:
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return None
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def _eval(node):
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if isinstance(node, pyast.Expression):
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return _eval(node.body)
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if isinstance(node, pyast.Constant) and isinstance(node.value, (int, float)):
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return node.value
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if isinstance(node, pyast.BinOp) and isinstance(node.op, _ALLOWED_BINOPS):
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left, right = _eval(node.left), _eval(node.right)
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if left is None or right is None:
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return None
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if isinstance(node.op, pyast.Add): return left + right
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if isinstance(node.op, pyast.Sub): return left - right
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if isinstance(node.op, pyast.Mult): return left * right
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if isinstance(node, pyast.UnaryOp) and isinstance(node.op, pyast.USub):
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v = _eval(node.operand)
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return -v if v is not None else None
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return None
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return _eval(tree)
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def clean_answer(a: str) -> str:
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a = unicodedata.normalize("NFC", a)
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a = re.sub(r"(?i)^\s*(the\s+)?(final\s+)?answer\s*\d*\s*(is)?\s*:\s*", "", a).strip()
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a = re.sub(r"(?i)^\s*is\s*:\s*", "", a).strip()
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a = a.strip("* ")
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return a.strip(" .\"'“”‘’")
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def extract(text):
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m = list(re.finditer(r"final answers?\s*:?\s*\**", text, flags=re.IGNORECASE))
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if m:
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tail = text[m[-1].end():]
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stop = re.search(r"(?i)compute\s*:", tail)
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if stop:
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tail = tail[:stop.start()]
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tail = tail.replace("**", " ").strip()
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candidate = " ".join(tail.splitlines())
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parts = [clean_answer(p) for p in candidate.split("|") if p.strip()]
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if parts:
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return parts, m[-1].start()
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lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
|
||
fallback = []
|
||
for ln in lines:
|
||
ln_clean = re.sub(r"^\s*\d+\s*[.\)]\s*", "", ln)
|
||
if "|" in ln_clean:
|
||
fallback.extend(clean_answer(p) for p in ln_clean.split("|") if p.strip())
|
||
else:
|
||
fallback.append(clean_answer(ln_clean))
|
||
return fallback, None
|
||
def extract_compute_overrides(text, n_answers):
|
||
m = re.search(r"compute\s*:\s*(.+)", text, flags=re.IGNORECASE)
|
||
if not m:
|
||
return {}
|
||
exprs = [e.strip() for e in m.group(1).split("|")]
|
||
overrides = {}
|
||
for i, e in enumerate(exprs[:n_answers]):
|
||
val = safe_arithmetic(e)
|
||
if val is not None:
|
||
overrides[i] = str(int(val)) if float(val).is_integer() else str(val)
|
||
return overrides
|
||
def generate(messages, max_new_tokens, constraint_fn=None):
|
||
def _try_generate(gen_kwargs):
|
||
try:
|
||
enc = tok.apply_chat_template(
|
||
messages, add_generation_prompt=True, return_tensors="pt", return_dict=True,
|
||
).to(model.device)
|
||
input_len = enc["input_ids"].shape[-1]
|
||
with torch.no_grad():
|
||
out = model.generate(**enc, **gen_kwargs)
|
||
except Exception:
|
||
ids = tok.apply_chat_template(
|
||
messages, add_generation_prompt=True, return_tensors="pt",
|
||
).to(model.device)
|
||
input_len = ids.shape[-1]
|
||
with torch.no_grad():
|
||
out = model.generate(ids, **gen_kwargs)
|
||
return out, input_len
|
||
base_kwargs = {"max_new_tokens": max_new_tokens, "do_sample": False}
|
||
if constraint_fn is not None:
|
||
try:
|
||
out, input_len = _try_generate({**base_kwargs, "prefix_allowed_tokens_fn": constraint_fn})
|
||
except Exception:
|
||
out, input_len = _try_generate(base_kwargs)
|
||
else:
|
||
out, input_len = _try_generate(base_kwargs)
|
||
return tok.decode(out[0][input_len:], skip_special_tokens=True).strip()
|
||
EXPLANATION_FALLBACK = "Answer derived from patterns found in the examples above."
|
||
def extract_match_letter_options(context: str):
|
||
found = set()
|
||
for line in context.splitlines():
|
||
for m in re.finditer(r"(?:^|\s)([A-Z])[.\)]\s+\S", line):
|
||
found.add(m.group(1))
|
||
if not found:
|
||
return None
|
||
letters = sorted(found)
|
||
expected = [chr(ord("A") + i) for i in range(len(letters))]
|
||
if letters != expected:
|
||
return None
|
||
if not (2 <= len(letters) <= 26):
|
||
return None
|
||
return letters
|
||
def repair_bijection(answers, labels, n_items):
|
||
"""Deterministic, no model call. Keep the letters the model committed to
|
||
(first occurrence wins); fill duplicate/invalid/missing slots with the
|
||
leftover letters in order. Guarantees a valid permutation. If the answer
|
||
is already a valid permutation it is returned unchanged."""
|
||
labels_sorted = sorted(labels)
|
||
picks = []
|
||
for i in range(n_items):
|
||
a = answers[i] if i < len(answers) else ""
|
||
found = re.findall(r"[A-Za-z]", a or "")
|
||
c = found[0].upper() if found else ""
|
||
picks.append(c if c in labels else "")
|
||
result = [None] * n_items
|
||
used = set()
|
||
for i in range(n_items):
|
||
c = picks[i]
|
||
if c and c not in used:
|
||
result[i] = c
|
||
used.add(c)
|
||
missing = [l for l in labels_sorted if l not in used]
|
||
mi = 0
|
||
for i in range(n_items):
|
||
if result[i] is None:
|
||
result[i] = missing[mi] if mi < len(missing) else labels_sorted[0]
|
||
mi += 1
|
||
return result
|
||
rows = []
|
||
processed_ids = set()
|
||
try:
|
||
for _, r in df.iterrows():
|
||
try:
|
||
elapsed = time.time() - start_time
|
||
remaining = TIME_LIMIT_S - elapsed
|
||
budget_left_rows = max(n_rows - len(rows), 1)
|
||
row_budget = remaining / budget_left_rows
|
||
time_based_cap = 1280 if row_budget > per_row_budget else 640
|
||
task_type = r.get("task_type", "")
|
||
messages, n_items = build_messages(r["context"], r["query"], task_type)
|
||
if n_items:
|
||
item_based_cap = max(640, min(1536, n_items * 48 + 256))
|
||
tokens_cap = min(time_based_cap, item_based_cap)
|
||
else:
|
||
tokens_cap = time_based_cap
|
||
text = generate(messages, tokens_cap)
|
||
answers, marker_pos = extract(text)
|
||
if task_type == "text_to_num":
|
||
overrides = extract_compute_overrides(text, len(answers))
|
||
for idx, val in overrides.items():
|
||
if idx < len(answers):
|
||
answers[idx] = val
|
||
if (marker_pos is None or not answers) and remaining > SETUP_BUFFER_S:
|
||
repair_constraint = None
|
||
repair_text = generate(build_repair_messages(r["query"], n_items, text), 128,
|
||
constraint_fn=repair_constraint)
|
||
rep, rep_pos = extract(repair_text)
|
||
if rep:
|
||
answers, marker_pos = rep, rep_pos
|
||
if n_items is not None:
|
||
if len(answers) < n_items:
|
||
answers = answers + [answers[-1] if answers else ""] * (n_items - len(answers))
|
||
elif len(answers) > n_items and marker_pos is None:
|
||
answers = answers[:n_items]
|
||
if not answers:
|
||
answers = [""]
|
||
# ---- match_letters: deterministic bijection repair (no model call) ----
|
||
# For matching, the numbered items live in the CONTEXT, not the query,
|
||
# so n_items (from the query) is None here. Derive the expected count
|
||
# from the context's numbered lines, and only repair in the clean
|
||
# bijection case (context item count == number of option letters).
|
||
if task_type == "match_letters":
|
||
labels = extract_match_letter_options(r["context"])
|
||
if labels and len(labels) >= 2:
|
||
ctx_item_count = len(re.findall(r"(?m)^\s*\d+\s*[.\)]", r["context"]))
|
||
if ctx_item_count == len(labels):
|
||
answers = repair_bijection(answers, set(labels), len(labels))
|
||
# ---- explanation: cheap reasoning snippet, NO second generation ----
|
||
snippet = re.sub(r"\s{2,}", " ", text[:300]).strip()
|
||
explanation = snippet if snippet else EXPLANATION_FALLBACK
|
||
rows.append({"id": r["id"], "pred": json.dumps(answers, ensure_ascii=False),
|
||
"explanation": explanation})
|
||
processed_ids.add(r["id"])
|
||
write_submission_csv(rows)
|
||
print(f"{len(rows)}/{n_rows} answers={len(answers)} elapsed={time.time()-start_time:.0f}s", flush=True)
|
||
except Exception as e:
|
||
try:
|
||
_, fallback_items, fk = parse_items(r["query"])
|
||
n_fallback = len(fallback_items) if fk else 1
|
||
except Exception:
|
||
n_fallback = 1
|
||
rows.append({"id": r["id"], "pred": json.dumps([""] * n_fallback, ensure_ascii=False),
|
||
"explanation": EXPLANATION_FALLBACK})
|
||
processed_ids.add(r["id"])
|
||
write_submission_csv(rows)
|
||
print(f"ROW ERROR on {r['id']}: {e}", flush=True)
|
||
if time.time() - start_time > TIME_LIMIT_S - 60:
|
||
print("Time budget nearly exhausted, stopping early.", flush=True)
|
||
break
|
||
for _, r in df.iterrows():
|
||
if r["id"] in processed_ids:
|
||
continue
|
||
try:
|
||
_, fallback_items, fk = parse_items(r["query"])
|
||
n_fallback = len(fallback_items) if fk else 1
|
||
except Exception:
|
||
n_fallback = 1
|
||
rows.append({"id": r["id"], "pred": json.dumps([""] * n_fallback, ensure_ascii=False),
|
||
"explanation": EXPLANATION_FALLBACK})
|
||
write_submission_csv(rows)
|
||
print("DONE.", flush=True)
|
||
except Exception as e:
|
||
emergency_submission_csv(f"main loop failed: {e}", rows_so_far=rows if rows else None)
|
||
print(f"FATAL, but submission.csv was written with {len(rows)} rows. Error: {e}", flush=True) |