Remove extra info token pathway

train_util.py

@@ -1,13 +1,12 @@
 from __future__ import annotations
 
-import json
 import logging
 import sys
 import time
 import csv
 from datetime import datetime
 from pathlib import Path
-from typing import Any, Dict, Iterable, Tuple
+from typing import Any, Dict, Tuple
 
 import numpy as np
 import torch
@@ -61,41 +60,6 @@ def set_seed(seed: int) -> None:
         torch.cuda.manual_seed(seed)
 
 
-def load_extra_info_types_file(path: str) -> list[int]:
-    file_path = Path(path)
-    if not file_path.is_file():
-        raise FileNotFoundError(f"extra_info_types_file not found: {path}")
-
-    text = file_path.read_text(encoding="utf-8").strip()
-    if not text:
-        return []
-
-    if text.startswith("["):
-        raw_items = json.loads(text)
-        if not isinstance(raw_items, list):
-            raise ValueError("extra_info_types_file JSON must be a list")
-    else:
-        raw_items = []
-        for line in text.splitlines():
-            line = line.split("#", 1)[0].strip()
-            if line:
-                raw_items.extend(line.replace(",", " ").replace(";", " ").split())
-
-    try:
-        return [int(x) for x in raw_items]
-    except (TypeError, ValueError) as exc:
-        raise ValueError(f"Invalid extra info type id in {path}") from exc
-
-
-def format_extra_info_types(extra_info_types: Iterable[int] | None) -> str:
-    if extra_info_types is None:
-        return "all"
-    values = [int(x) for x in extra_info_types]
-    if not values:
-        return "none"
-    return str(values)
-
-
 def load_eid_file(path: str | Path) -> set[int]:
     file_path = Path(path)
     if not file_path.is_file():