""" StepProbe: Shared utilities. """ import json import os import re import random import hashlib from typing import List, Dict, Any, Optional import numpy as np def set_seed(seed: int = 42): """Set random seed for reproducibility.""" random.seed(seed) np.random.seed(seed) try: import torch torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) except ImportError: pass def load_jsonl(path: str) -> List[dict]: """Load a JSONL file.""" records = [] with open(path, "r", encoding="utf-8") as f: for line in f: line = line.strip() if line: records.append(json.loads(line)) return records def save_jsonl(records: List[dict], path: str): """Save records to a JSONL file.""" os.makedirs(os.path.dirname(path) or ".", exist_ok=True) with open(path, "w", encoding="utf-8") as f: for r in records: f.write(json.dumps(r, ensure_ascii=False) + "\n") def load_json(path: str) -> dict: with open(path, "r", encoding="utf-8") as f: return json.load(f) def save_json(data: dict, path: str): os.makedirs(os.path.dirname(path) or ".", exist_ok=True) with open(path, "w", encoding="utf-8") as f: json.dump(data, f, indent=2, ensure_ascii=False) def extract_number(text: str) -> Optional[str]: """Extract the final numeric answer from text.""" # Try boxed format m = re.search(r"\\boxed\{([^}]+)\}", text) if m: return m.group(1).strip() # Try "answer is X" m = re.search(r"(?:the\s+)?(?:final\s+)?answer\s+is[:\s]+([^\n.]+)", text, re.IGNORECASE) if m: return m.group(1).strip() # Last number nums = re.findall(r"-?\d+(?:,\d{3})*(?:\.\d+)?", text) if nums: return nums[-1].replace(",", "") return None def normalize_answer(ans: str) -> str: """Normalize an answer string for comparison.""" if ans is None: return "" ans = str(ans).strip() # Remove LaTeX wrappers ans = re.sub(r"\\text\{([^}]*)\}", r"\1", ans) ans = re.sub(r"\$", "", ans) ans = re.sub(r"\\%", "%", ans) # Remove commas in numbers ans = re.sub(r"(\d),(\d)", r"\1\2", ans) # Trim trailing zeros after decimal if "." in ans: ans = ans.rstrip("0").rstrip(".") return ans.lower().strip() def check_answer(predicted: str, gold: str) -> bool: """Check if a predicted answer matches the gold answer.""" pred_norm = normalize_answer(predicted) gold_norm = normalize_answer(gold) if not pred_norm or not gold_norm: return False # Direct match if pred_norm == gold_norm: return True # Numeric match try: return abs(float(pred_norm) - float(gold_norm)) < 1e-6 except (ValueError, TypeError): pass # Check if gold is contained if gold_norm in pred_norm: return True return False def extract_gsm8k_answer(answer_text: str) -> str: """Extract numeric answer from GSM8K format '#### 42'.""" m = re.search(r"####\s*(.*)", answer_text) if m: return m.group(1).strip() return extract_number(answer_text) or "" def get_gpu_memory_gb() -> float: """Get current GPU memory usage in GB.""" try: import torch if torch.cuda.is_available(): return torch.cuda.max_memory_allocated() / 1e9 except ImportError: pass return 0.0 def hash_text(text: str) -> str: """Create a short hash of text for dedup.""" return hashlib.md5(text.encode()).hexdigest()[:12] def truncate_text(text: str, max_chars: int = 500) -> str: """Truncate text for display.""" if len(text) <= max_chars: return text return text[:max_chars] + "..." def print_table(headers: List[str], rows: List[List[str]], col_widths: Optional[List[int]] = None): """Print a formatted text table.""" if col_widths is None: col_widths = [] for i, h in enumerate(headers): w = len(h) for row in rows: if i < len(row): w = max(w, len(str(row[i]))) col_widths.append(w + 2) fmt = "".join(f"{{:<{w}}}" for w in col_widths) print(fmt.format(*headers)) print("-" * sum(col_widths)) for row in rows: padded = [str(row[i]) if i < len(row) else "" for i in range(len(headers))] print(fmt.format(*padded))