| |
| """Convert APM prompt CSV files into JSONL examples for model inference.""" |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import csv |
| import hashlib |
| import json |
| import re |
| from pathlib import Path |
| from typing import Any, Dict, Iterable, List |
|
|
|
|
| FILENAME_RE = re.compile(r"prompts_(?P<language>.+)_(?P<noise>N\d+)\.csv$") |
| ALPHA_RE = re.compile(r"alpha_(?P<value>\d+(?:\.\d+)?)$") |
| DEFAULT_INSTRUCTION = ( |
| "Rewrite the noisy user prompt into a clear prompt that preserves the user's " |
| "intent. Return only the rewritten prompt; do not ask clarification questions " |
| "or add explanations." |
| ) |
|
|
|
|
| def parse_args() -> argparse.Namespace: |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument( |
| "--dataset-dir", |
| type=Path, |
| default=Path("."), |
| help="Directory containing prompts_<language>_<noise>.csv files.", |
| ) |
| parser.add_argument( |
| "--output-dir", |
| type=Path, |
| default=Path("benchmark_inputs"), |
| help="Directory where JSONL inference inputs will be written.", |
| ) |
| parser.add_argument( |
| "--no-instruction", |
| action="store_true", |
| help="Do not include the default mediation instruction in each row.", |
| ) |
| parser.add_argument( |
| "--overwrite", |
| action="store_true", |
| help="Replace existing output files.", |
| ) |
| return parser.parse_args() |
|
|
|
|
| def alpha_columns(fieldnames: Iterable[str]) -> List[str]: |
| columns = [name for name in fieldnames if ALPHA_RE.fullmatch(name)] |
| return sorted(columns, key=lambda name: float(ALPHA_RE.fullmatch(name).group("value"))) |
|
|
|
|
| def example_id(payload: Dict[str, Any]) -> str: |
| stable = json.dumps(payload, ensure_ascii=False, sort_keys=True, separators=(",", ":")) |
| return hashlib.sha1(stable.encode("utf-8")).hexdigest() |
|
|
|
|
| def iter_examples(csv_path: Path, include_instruction: bool) -> Iterable[Dict[str, Any]]: |
| match = FILENAME_RE.match(csv_path.name) |
| if not match: |
| return |
|
|
| language = match.group("language") |
| noise = match.group("noise") |
|
|
| with csv_path.open("r", encoding="utf-8-sig", newline="") as f: |
| reader = csv.DictReader(f) |
| if reader.fieldnames is None: |
| return |
| alphas = alpha_columns(reader.fieldnames) |
|
|
| for row_index, row in enumerate(reader): |
| clean_text = row.get("Clean_text", "") |
| category = row.get("Category", "") |
| for alpha_col in alphas: |
| alpha_value = float(ALPHA_RE.fullmatch(alpha_col).group("value")) |
| noisy_prompt = row.get(alpha_col, "") |
| identity = { |
| "language": language, |
| "noise": noise, |
| "row_index": row_index, |
| "alpha": alpha_value, |
| "clean_text": clean_text, |
| "noisy_prompt": noisy_prompt, |
| } |
| example = { |
| "example_id": example_id(identity), |
| "language": language, |
| "noise": noise, |
| "category": category, |
| "alpha": alpha_value, |
| "clean_text": clean_text, |
| "noisy_prompt": noisy_prompt, |
| "source_file": csv_path.name, |
| "row_index": row_index, |
| } |
| if include_instruction: |
| example["instruction"] = DEFAULT_INSTRUCTION |
| yield example |
|
|
|
|
| def main() -> None: |
| args = parse_args() |
| csv_paths = sorted(args.dataset_dir.glob("prompts_*_N*.csv")) |
| if not csv_paths: |
| raise SystemExit(f"No prompt CSV files found in {args.dataset_dir}") |
|
|
| total = 0 |
| args.output_dir.mkdir(parents=True, exist_ok=True) |
|
|
| for csv_path in csv_paths: |
| match = FILENAME_RE.match(csv_path.name) |
| if not match: |
| continue |
|
|
| language = match.group("language") |
| noise = match.group("noise") |
| out_dir = args.output_dir / noise |
| out_dir.mkdir(parents=True, exist_ok=True) |
| out_path = out_dir / f"{language}.jsonl" |
|
|
| if out_path.exists() and not args.overwrite: |
| raise SystemExit(f"{out_path} already exists; pass --overwrite to replace it") |
|
|
| count = 0 |
| with out_path.open("w", encoding="utf-8") as f: |
| for example in iter_examples(csv_path, include_instruction=not args.no_instruction): |
| f.write(json.dumps(example, ensure_ascii=False) + "\n") |
| count += 1 |
|
|
| total += count |
| print(f"Wrote {count:5d} examples -> {out_path}") |
|
|
| print(f"Prepared {total} inference examples") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
