319 lines
13 KiB
Python
319 lines
13 KiB
Python
import os
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import json
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import pandas as pd
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import torch
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import re
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import time
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import unicodedata
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# Offline Environment Setup
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os.environ["HF_HUB_OFFLINE"] = "1"
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os.environ["TRANSFORMERS_OFFLINE"] = "1"
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MODEL_ID = "."
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# Time Budget Guardrail (27.5 minutes max limit)
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T0 = time.time()
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DEADLINE = T0 + 1650
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print("Loading tokenizer and 14B AWQ model offline...", flush=True)
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tok = AutoTokenizer.from_pretrained(MODEL_ID)
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# Force GPU 0 placement ({"": 0})
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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torch_dtype=torch.float16,
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device_map={"": 0}
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).eval()
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print("Reading mounted test set...", flush=True)
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df = pd.read_csv("/tmp/data/test.csv", dtype=str).fillna("")
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# 1. Item-Count & Source Text Extraction (From Attached Script)
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_LINE_NUM = re.compile(r"^[ ]*(\d{1,3})[.)\]]", re.M)
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_LINE_LETTER = re.compile(r"^[ ]*([A-Z])[.)\]]\s", re.M)
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def detect_n_items(query, context=""):
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q = query or ""
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line_nums = [int(m) for m in _LINE_NUM.findall(q)]
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if line_nums:
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return len(set(line_nums))
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lines = [l.strip() for l in q.splitlines() if l.strip()]
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if len(lines) > 1 and lines[0].endswith((":", ".")):
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return len(lines) - 1
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if context:
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c_nums = len(set(int(m) for m in _LINE_NUM.findall(context)))
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if c_nums > 1:
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return c_nums
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return max(len(lines), 1) if lines else 1
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def extract_item_sources(query, n):
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q = query or ""
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out = []
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for ln in q.splitlines():
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s = ln.strip()
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if not s:
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continue
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m = re.match(r"^\(?(\d{1,3})\)?[.):\]]\s*(.+)$", s)
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if m:
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out.append(m.group(2).strip())
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if not out:
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lines = [l.strip() for l in q.splitlines() if l.strip()]
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if len(lines) > 1 and lines[0].endswith((":", ".")):
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out = lines[1:]
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out = [o.split("|")[0].strip() if "|" in o else o for o in out]
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out = [o for o in out if o]
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while len(out) < n:
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out.append(out[-1] if out else "?")
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return out[:n]
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# 2. Hungarian Assignment Solver for match_letters (From Attached Script)
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_OPT_LINE = re.compile(r"^[ ]*([A-Za-z])[.)]\s+(.+)$", re.M)
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_ITEM_LINE = re.compile(r"^[ ]*(\d{1,3})[.)]\s+(.+)$", re.M)
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def parse_matching_block(context):
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items = [(int(a), b.strip()) for a, b in _ITEM_LINE.findall(context or "")]
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opts = [(a, b.strip()) for a, b in _OPT_LINE.findall(context or "")]
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seen = set()
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items = [x for x in items if not (x[0] in seen or seen.add(x[0]))]
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seen = set()
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opts = [x for x in opts if not (x[0] in seen or seen.add(x[0]))]
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return items, opts
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def best_assignment(score):
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n, m = len(score), len(score[0])
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try:
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from scipy.optimize import linear_sum_assignment
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import numpy as _np
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r, c = linear_sum_assignment(-_np.array(score))
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return list(c)
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except Exception:
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pass
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used, out = set(), [0] * n
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for i in range(n):
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j = max((j for j in range(m) if j not in used), key=lambda j: score[i][j], default=0)
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used.add(j)
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out[i] = j
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return out
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def solve_matching(row, n):
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items, opts = parse_matching_block(row.get("context", ""))
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if len(items) < 2 or len(opts) < 2 or len(items) != n:
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return None
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letters = [o[0] for o in opts]
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cand_ids = []
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for L in letters:
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ids = set()
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for form in (L, " " + L):
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t = tok.encode(form, add_special_tokens=False)
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if t:
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ids.add(t[0])
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cand_ids.append(sorted(ids))
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ctx = row["context"].strip()
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prompts_m = []
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for num, itext in items:
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msgs = [
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{"role": "system", "content": "You match items to their correct counterparts in a linguistics problem. Reply with one option letter only."},
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{"role": "user", "content": f"{ctx}\n\nWhich lettered option corresponds to item {num} ({itext})? Reply with the option letter only."},
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]
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prompts_m.append(tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True))
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score = []
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for p in prompts_m:
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enc = tok(p, return_tensors="pt", max_length=6144).to(model.device)
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with torch.no_grad():
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logits = model(**enc).logits[:, -1, :].float()
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logprobs = torch.log_softmax(logits, dim=-1)
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score.append([max(logprobs[0, i].item() for i in ids) for ids in cand_ids])
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col = best_assignment(score)
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return [letters[c] for c in col]
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# 3. Task-Routed Prompting (From OUR Script)
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def get_task_prompt(task_type):
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base_prompt = (
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"You are an International Linguistics Olympiad gold medallist solving an unseen language puzzle. "
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"Strictly deduce all rules from the provided context only. Do not guess based on outside knowledge.\n\n"
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"Formatting constraints:\n"
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"<explanation>\nWrite short bullet points summarizing grammar rules and word-by-word literal translations.\n</explanation>\n"
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"<final_answers>\nAnswer 1\nAnswer 2\n</final_answers>\n"
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"CRITICAL RULE: DO NOT write question numbers (e.g. '1.', '17.') or bullets in your final answers. Output ONLY the answer text, one item per line."
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)
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t_type = str(task_type).lower()
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if "num" in t_type:
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return base_prompt + (
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"\n\nSPECIAL TASK INSTRUCTIONS (NUMBERS):\n"
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"- Identify base system and arithmetic operations.\n"
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"- Output ONLY numerical digits for numeral queries (e.g. '9' or '11'). Do NOT add periods or spaces.\n"
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"EXAMPLE OUTPUT FORMAT:\n"
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"<explanation>\n- mer=1, thef=3.\n- 1+3=4.\n</explanation>\n"
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"<final_answers>\n4\n9\n</final_answers>"
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)
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elif "fill" in t_type:
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return base_prompt + (
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"\n\nSPECIAL TASK INSTRUCTIONS (FILL BLANKS):\n"
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"- Identify prefix/suffix patterns.\n"
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"- CRITICAL: If replacing `___`, output ONLY the missing letters/suffix that fill the blank, NOT the entire word or stem.\n"
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"EXAMPLE OUTPUT FORMAT:\n"
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"<explanation>\n- Suffix '-nyy' indicates plural.\n</explanation>\n"
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"<final_answers>\nnyy\n</final_answers>"
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)
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elif "match" in t_type:
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return base_prompt + (
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"\n\nSPECIAL TASK INSTRUCTIONS (MATCHING):\n"
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"- Match each item to its corresponding option letter or symbol.\n"
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"- Output ONLY the option letter per item (e.g. 'A', 'G', 'B'). Do NOT write full sentences.\n"
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"EXAMPLE OUTPUT FORMAT:\n"
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"<explanation>\n- Item 1 matches option A.\n</explanation>\n"
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"<final_answers>\nA\nG\n</final_answers>"
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)
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else:
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return base_prompt + (
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"\n\nSPECIAL TASK INSTRUCTIONS (TRANSLATION):\n"
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"- Align morphemes and perform word-by-word literal translation in <explanation> before writing final answers.\n"
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"- CRITICAL TENSE & PUNCTUATION ACCURACY: Check tense prefixes/infixes. Ensure questions end with '?' and statements end with standard punctuation.\n"
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"EXAMPLE OUTPUT FORMAT:\n"
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"<explanation>\n- 'nɤ'=you, 'ʒip'=sleep, 'tuʔ'=past.\n- Word-by-word: you sleep past question.\n</explanation>\n"
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"<final_answers>\nDid you sleep?\nDo I go?\n</final_answers>"
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)
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# 4. Surgical Non-Destructive Answer Line Cleaner (From OUR Script)
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def clean_answer_line(line, task_type):
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raw_stripped = unicodedata.normalize('NFC', line.strip())
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raw_stripped = raw_stripped.replace(' ', ' ').replace('–', '-').replace('—', '-')
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raw_stripped = raw_stripped.replace('’', "'").replace('‘', "'").replace('“', '"').replace('”', '"')
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if re.match(r"^(?:\d+[\.\:\)]\s*)?([A-Z])[\.]?$", raw_stripped):
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return re.sub(r"^(?:\d+[\.\:\)]\s*)?([A-Z])[\.]?$", r"\1", raw_stripped)
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cleaned = re.sub(r"^\s*(?:\(?\d+[\.\:\)]|\(?\w[\.\)]|\-|\*|Item\s*\d+:?)\s*", "", raw_stripped, flags=re.IGNORECASE)
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cleaned = re.sub(r"[*`]", "", cleaned)
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cleaned = cleaned.strip('"\' ')
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cleaned = re.sub(r"\s+", " ", cleaned)
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t_type = str(task_type).lower()
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if "num" in t_type or "fill" in t_type or "match" in t_type:
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cleaned = cleaned.rstrip(".")
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if "num" in t_type and re.search(r"\d+", cleaned):
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num_match = re.search(r"\b\d+\b", cleaned)
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if num_match:
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cleaned = num_match.group(0)
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else:
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cleaned = re.sub(r"\s+([\.\?\!\,])", r"\1", cleaned)
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if any(cleaned.lower().startswith(w) for w in ["do ", "did ", "does ", "is ", "are ", "was ", "were ", "what ", "who ", "where ", "whom "]):
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if not cleaned.endswith("?"):
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cleaned = cleaned.rstrip(".!") + "?"
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return cleaned.strip()
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def repair_bijection(answers):
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if len(answers) < 2 or not all(re.fullmatch(r"[A-Z]", a or "") for a in answers):
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return answers
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universe = [chr(ord("A") + i) for i in range(len(answers))]
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if len(set(answers)) == len(answers):
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return answers
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unused = [l for l in universe if l not in set(answers)]
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seen, out = set(), []
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for a in answers:
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if a in seen and unused:
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out.append(unused.pop(0))
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else:
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out.append(a)
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seen.add(a)
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return out
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rows = []
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for idx, r in df.iterrows():
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if time.time() > DEADLINE:
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print("Deadline threshold reached. Exiting early to save valid submission.", flush=True)
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break
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task_type = r.get('task_type', '')
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query_text = r['query'].strip()
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context_text = r['context'].strip()
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num_items = detect_n_items(query_text, context_text)
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src_fallbacks = extract_item_sources(query_text, num_items)
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explanation = "Deducted rules from context."
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answers = []
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# Matching Tasks: Try Logit-Based Hungarian Assignment Solver First
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if "match" in str(task_type).lower():
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try:
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m_ans = solve_matching(r, num_items)
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if m_ans and len(m_ans) == num_items:
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answers = m_ans
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explanation = "Solved matching via logit assignment solver."
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except Exception:
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pass
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# Standard Path: Run Task-Routed CoT Generation
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if not answers:
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system_prompt = get_task_prompt(task_type)
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user_prompt = f"TASK TYPE: {task_type}\nCONTEXT:\n{context_text}\n\nQUERY:\n{query_text}"
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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]
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prompt_text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tok(prompt_text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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out = model.generate(**inputs, max_new_tokens=1536, do_sample=False, repetition_penalty=1.0)
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input_length = inputs["input_ids"].shape[1]
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text = tok.decode(out[0][input_length:], skip_special_tokens=True).strip()
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text = re.sub(r"```(?:text|json)?", "", text).strip()
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exp_match = re.search(r"<explanation>(.*?)</explanation>", text, flags=re.DOTALL | re.IGNORECASE)
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if exp_match:
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explanation = exp_match.group(1).strip()
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elif "<final_answers>" in text:
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explanation = text.split("<final_answers>")[0].replace("<explanation>", "").replace("</explanation>", "").strip()
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else:
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lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
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explanation = " ".join(lines[:2]) if lines else "Deducted language patterns from context."
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if "<final_answers>" in text:
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ans_text = text.split("<final_answers>")[-1].replace("</final_answers>", "").strip()
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raw_lines = [ln.strip() for ln in ans_text.splitlines() if ln.strip()]
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cleaned_lines = [clean_answer_line(ln, task_type) for ln in raw_lines if clean_answer_line(ln, task_type)]
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else:
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lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
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candidate_answers = [
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clean_answer_line(ln, task_type) for ln in lines
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if len(ln) < 150 and not any(k in ln.lower() for k in ["<explanation", "</explanation", "special task", "context:"])
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]
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cleaned_lines = [c for c in candidate_answers if c]
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if "match" in str(task_type).lower():
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cleaned_lines = repair_bijection(cleaned_lines)
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# Bi-directional Alignment with Source-Text Fallback
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if num_items > 0:
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if len(cleaned_lines) > num_items:
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answers = cleaned_lines[:num_items]
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elif len(cleaned_lines) < num_items:
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# Fill missing slots with foreign source text fallback to capture partial chrF
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answers = cleaned_lines + src_fallbacks[len(cleaned_lines):num_items]
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else:
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answers = cleaned_lines
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else:
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answers = cleaned_lines if cleaned_lines else src_fallbacks
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rows.append({
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"id": r["id"],
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"pred": json.dumps(answers, ensure_ascii=False),
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"explanation": explanation
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})
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# Save submission.csv incrementally after every row
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pd.DataFrame(rows).to_csv("submission.csv", index=False, encoding="utf-8-sig")
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print(f"Processed {len(rows)}/{len(df)} problems in {time.time()-T0:.1f}s", flush=True)
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print("wrote submission.csv successfully!", flush=True)
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