292 lines
10 KiB
Python
292 lines
10 KiB
Python
import os
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import subprocess
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import sys
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def _install_bundled_deps() -> None:
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"""Install transformers from bundled wheels (eval sandbox has no PyPI access)."""
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wheels_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "wheels")
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if not os.path.isdir(wheels_dir):
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return
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subprocess.run(
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[
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sys.executable,
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"-m",
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"pip",
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"install",
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"-q",
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"--no-index",
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f"--find-links={wheels_dir}",
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"transformers==4.56.2",
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],
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check=True,
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)
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_install_bundled_deps()
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import re
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import csv
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import json
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import shutil
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import tempfile
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# The repo is the working directory at run time, and there is no network.
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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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MAX_NEW_TOKENS = 2000
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TEMPERATURE = 0.8
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TOP_P = 0.95
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MAX_ATTEMPTS = 5
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def load_tokenizer(model_id: str = "."):
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"""Load tokenizer, converting tokenizer.json for older tokenizers if needed."""
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tokenizer_path = os.path.join(model_id, "tokenizer.json")
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with open(tokenizer_path, encoding="utf-8") as handle:
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data = json.load(handle)
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merges = data.get("model", {}).get("merges", [])
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if not merges or not isinstance(merges[0], list):
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return AutoTokenizer.from_pretrained(model_id)
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# Older tokenizers expect merge pairs as "a b" strings, not ["a", "b"] lists.
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data["model"]["merges"] = [" ".join(piece) for piece in merges]
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tmpdir = tempfile.mkdtemp()
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for name in ("tokenizer_config.json", "special_tokens_map.json"):
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src = os.path.join(model_id, name)
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if os.path.isfile(src):
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shutil.copy(src, tmpdir)
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with open(os.path.join(tmpdir, "tokenizer.json"), "w", encoding="utf-8") as handle:
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json.dump(data, handle)
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return AutoTokenizer.from_pretrained(tmpdir)
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# Short durable contract only — procedure lives in the user turn with the problem.
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SYSTEM = (
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"You solve International Linguistics Olympiad (IOL) problems using only the "
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"CONTEXT and QUERY you are given. Do not rely on prior knowledge of the language.\n"
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"Always end with a line that says exactly `FINAL ANSWERS:`, then one bare answer "
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"per line for each QUERY item, in order — no numbering, quotes, or extra text."
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)
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TASK_TYPE_HINTS = {
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"translation": (
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"return the translated form only, in the language the task asks for"
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),
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"fill_blanks": (
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"return only the missing form for each indicated blank "
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"(a word, part of a word, or phonetic transcription — match what CONTEXT uses)"
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),
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"match_letters": "return only the option letter (for example A, B, C)",
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"text_to_num": "return the number in digits",
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"num_to_text": "return the number written out in words, in the language asked",
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}
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def task_type_hint(task_type: str) -> str:
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return TASK_TYPE_HINTS.get(
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task_type,
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"return exactly what the instruction in QUERY asks for, nothing else",
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)
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def build_user_prompt(context: str, task_type: str, query: str) -> str:
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"""Detailed, instance-local instructions after the problem data (recency)."""
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return (
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f"CONTEXT:\n{context.strip()}\n\n"
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f"TASK TYPE: `{task_type}`\n\n"
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f"QUERY:\n{query.strip()}\n\n"
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"Instructions for this problem:\n"
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"1. Deduce the linguistic rules from the CONTEXT examples only.\n"
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"2. Apply those rules to every item in QUERY.\n"
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"3. Check the answer format requirements before finishing.\n"
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f"4. For this TASK TYPE (`{task_type}`): {task_type_hint(task_type)}.\n"
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"5. Draft answers, then verify they are complete (one answer for each QUERY "
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"item) and consistent with the rules and format.\n"
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"6. If needed, correct and refine.\n\n"
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"Finally, write a line that says exactly `FINAL ANSWERS:` and, below it, "
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"the answers to the QUERY items only (not CONTEXT), one answer per line "
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"in QUERY order — bare answers only, no numbering, no quotes, no extra text."
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)
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# Prefer a dedicated header line; also allow same-line answers after the colon.
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FINAL_ANSWERS_LINE_RE = re.compile(
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r"(?im)^[^\w\n]*final answers?[^\w\n]*:?[ \t]*(?=\n|$)|"
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r"(?im)^[^\w\n]*final answers?\s*:\s*"
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)
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FINAL_ANSWERS_INLINE_RE = re.compile(
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r"(?is)\bfinal answers?\s*:\s*"
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)
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def extract_raw_final(text: str) -> str:
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"""Return text after the last final-answers marker, or '' if none found."""
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line_matches = list(FINAL_ANSWERS_LINE_RE.finditer(text))
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if line_matches:
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return text[line_matches[-1].end() :]
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inline_matches = list(FINAL_ANSWERS_INLINE_RE.finditer(text))
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if inline_matches:
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return text[inline_matches[-1].end() :]
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return ""
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def expected_answer_count(query: str, task_type: str) -> int:
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if task_type == "match_letters":
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numbered = re.findall(r"^\s*\d+\.", query, re.MULTILINE)
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return len(numbered) or 1
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if "blanks" in query.lower():
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range_match = re.search(r"\((\d+)-(\d+)\)", query)
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if range_match:
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return int(range_match.group(2)) - int(range_match.group(1)) + 1
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return len(re.findall(r"\(\d+\)", query)) or 1
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numbered = re.findall(r"^\s*\d+[.)]", query, re.MULTILINE)
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return len(numbered) or 1
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def split_single_line_answer(text: str, expected: int, task_type: str) -> list[str]:
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text = text.strip()
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if expected <= 1:
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return [text]
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def try_split(pattern: str) -> list[str] | None:
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parts = [part.strip() for part in re.split(pattern, text) if part.strip()]
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return parts if len(parts) == expected else None
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if task_type == "match_letters":
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for pattern in (r"\s+", r",\s*", r";\s*"):
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if result := try_split(pattern):
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return result
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letters = re.findall(r"[A-Za-z]", text)
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if len(letters) == expected:
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return [letter.upper() for letter in letters]
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return [text]
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if task_type in ("text_to_num", "num_to_text"):
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for pattern in (r",\s*", r";\s*", r"\s+"):
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if result := try_split(pattern):
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return result
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return [text]
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for pattern in (r";\s*", r",\s*"):
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if result := try_split(pattern):
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return result
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return [text]
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def parse_answer_lines(text_after_marker: str, query: str, task_type: str) -> list[str]:
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"""Parse cleaned answer lines from the raw final-answers section."""
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answers = []
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for line in text_after_marker.splitlines():
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stripped_line = line.strip("`").strip()
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if stripped_line == "":
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continue
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match_numbered_prefix = re.match(r"^\s*\d+[.)]\s+(.*)", stripped_line)
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if match_numbered_prefix:
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cleaned_line = match_numbered_prefix.group(1).strip()
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else:
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cleaned_line = stripped_line
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cleaned_line = re.sub(r"\*\*", "", cleaned_line).strip()
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if task_type == "match_letters":
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parts = [
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part.strip("().[]")
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for part in re.split(r"[\s,;]+", cleaned_line)
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if part.strip()
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]
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if not (
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len(parts) > 1
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and all(re.fullmatch(r"[A-Za-z]", part) for part in parts)
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):
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match_letter_word = re.match(
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r"^\s*(?:\(([A-Za-z])\)|\[([A-Za-z])\]|([A-Za-z]))\.?:?\s*(.*)$",
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cleaned_line,
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)
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if match_letter_word:
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letter = (
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match_letter_word.group(1)
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or match_letter_word.group(2)
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or match_letter_word.group(3)
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)
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cleaned_line = letter.upper()
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if cleaned_line:
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answers.append(cleaned_line)
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expected = expected_answer_count(query, task_type)
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if len(answers) == 1 and expected > 1:
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answers = split_single_line_answer(answers[0], expected, task_type)
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return answers
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def postprocess_answer(text, query, task_type):
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"""Keep only the content after the last 'FINAL ANSWERS' marker."""
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text_after_marker = extract_raw_final(text)
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if not text_after_marker.strip():
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return []
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return parse_answer_lines(text_after_marker, query, task_type)
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tok = load_tokenizer(MODEL_ID)
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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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with open("/tmp/data/test.csv", encoding="utf-8", newline="") as f:
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test_rows = list(csv.DictReader(f))
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outputs_queries_types = []
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for r in test_rows:
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messages = [
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{"role": "system", "content": SYSTEM},
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{
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"role": "user",
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"content": build_user_prompt(r["context"], r["task_type"], r["query"]),
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},
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]
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ids = tok.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors="pt",
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).to(model.device)
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done = False
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attempts = 0
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text = ""
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while not done and attempts < MAX_ATTEMPTS:
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with torch.no_grad():
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out = model.generate(
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ids,
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max_new_tokens=MAX_NEW_TOKENS,
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do_sample=True,
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temperature=TEMPERATURE,
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top_p=TOP_P,
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)
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text = tok.decode(out[0][ids.shape[-1] :], skip_special_tokens=True).strip()
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attempts += 1
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if extract_raw_final(text).strip():
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done = True
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else:
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print(f"TRYING AGAIN...attempts #{attempts}/{MAX_ATTEMPTS}", flush=True)
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outputs_queries_types.append((text, r["id"], r["query"], r["task_type"]))
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print(f"{len(outputs_queries_types)}/{len(test_rows)} done", flush=True)
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rows = []
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for answer, row_id, query, task_type in outputs_queries_types:
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answers = postprocess_answer(answer, query, task_type)
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rows.append({"id": row_id, "pred": json.dumps(answers, ensure_ascii=False)})
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with open("submission.csv", "w", encoding="utf-8", newline="") as f:
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writer = csv.DictWriter(f, fieldnames=["id", "pred"])
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writer.writeheader()
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writer.writerows(rows)
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print("wrote submission.csv", flush=True)
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