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34c53b5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | from __future__ import annotations
import argparse
import gzip
import hashlib
import json
import random
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional, Tuple
REPO_ROOT = Path(__file__).resolve().parents[1]
DEFAULT_INPUT = REPO_ROOT / "data" / "external" / "caption_emporium" / "furry-e621-safe-llama3.2-11b" / "train.jsonl.gz"
DEFAULT_OUTPUT_DIR = REPO_ROOT / "data" / "external" / "caption_emporium" / "t5_rewrite_splits"
CAPTION_FIELDS = ("caption_short", "caption_medium", "caption_long")
def _iter_jsonl(path: Path) -> Iterable[Dict[str, Any]]:
if path.suffix == ".gz":
with gzip.open(path, "rt", encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
yield json.loads(line)
else:
with path.open("r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
yield json.loads(line)
def _canonicalize_tag(tag: str) -> str:
t = " ".join(str(tag or "").strip().split()).lower()
return t.replace(" ", "_").replace("\\(", "(").replace("\\)", ")")
def _flatten_tags(raw: Any) -> List[str]:
cats = raw
if isinstance(raw, str):
try:
cats = json.loads(raw)
except json.JSONDecodeError:
return []
if not isinstance(cats, dict):
return []
out = set()
for vals in cats.values():
if not isinstance(vals, list):
continue
for tag in vals:
ct = _canonicalize_tag(str(tag))
if ct:
out.add(ct)
return sorted(out)
def _split_name(sample_id: Any, val_frac: float, test_frac: float) -> str:
key = str(sample_id).encode("utf-8")
digest = hashlib.blake2b(key, digest_size=8).hexdigest()
bucket = int(digest, 16) % 10000
test_cut = int(round(test_frac * 10000))
val_cut = test_cut + int(round(val_frac * 10000))
if bucket < test_cut:
return "test"
if bucket < val_cut:
return "val"
return "train"
def _reservoir_add(
arr: List[Dict[str, Any]],
item: Dict[str, Any],
cap: Optional[int],
seen_count: int,
rng: random.Random,
) -> None:
if cap is None:
arr.append(item)
return
if cap <= 0:
return
if len(arr) < cap:
arr.append(item)
return
j = rng.randint(0, seen_count - 1)
if j < cap:
arr[j] = item
def _write_jsonl(path: Path, rows: List[Dict[str, Any]]) -> None:
with path.open("w", encoding="utf-8") as f:
for row in rows:
f.write(json.dumps(row, ensure_ascii=False) + "\n")
def main() -> int:
ap = argparse.ArgumentParser(description="Build T5 rewrite fine-tuning splits from CaptionEmporium JSONL(.gz)")
ap.add_argument("--input", type=Path, default=DEFAULT_INPUT)
ap.add_argument("--output-dir", type=Path, default=DEFAULT_OUTPUT_DIR)
ap.add_argument("--val-frac", type=float, default=0.01)
ap.add_argument("--test-frac", type=float, default=0.01)
ap.add_argument("--max-train", type=int, default=60000, help="Reservoir cap for train split (0 disables)")
ap.add_argument("--max-val", type=int, default=3000, help="Reservoir cap for val split (0 disables)")
ap.add_argument("--max-test", type=int, default=3000, help="Reservoir cap for test split (0 disables)")
ap.add_argument("--seed", type=int, default=42)
ap.add_argument("--task-prefix", type=str, default="caption_to_tags:")
args = ap.parse_args()
input_path = args.input if args.input.is_absolute() else (REPO_ROOT / args.input).resolve()
if not input_path.is_file():
raise FileNotFoundError(f"Input dataset not found: {input_path}")
output_dir = args.output_dir if args.output_dir.is_absolute() else (REPO_ROOT / args.output_dir).resolve()
output_dir.mkdir(parents=True, exist_ok=True)
rng = random.Random(args.seed)
split_rows: Dict[str, List[Dict[str, Any]]] = {"train": [], "val": [], "test": []}
split_seen = {"train": 0, "val": 0, "test": 0}
split_caps: Dict[str, Optional[int]] = {
"train": None if args.max_train == 0 else args.max_train,
"val": None if args.max_val == 0 else args.max_val,
"test": None if args.max_test == 0 else args.max_test,
}
rows_total = 0
rows_with_tags = 0
examples_total = 0
prefix = (args.task_prefix or "").strip()
for obj in _iter_jsonl(input_path):
rows_total += 1
sid = obj.get("id", rows_total)
tags = _flatten_tags(obj.get("tags_ground_truth_categorized"))
if not tags:
continue
rows_with_tags += 1
target_text = ", ".join(tags)
split = _split_name(sid, args.val_frac, args.test_frac)
for field in CAPTION_FIELDS:
caption = str(obj.get(field, "") or "").strip()
if not caption:
continue
source_text = f"{prefix} {caption}".strip() if prefix else caption
rec = {
"id": sid,
"caption_field": field,
"source_text": source_text,
"target_text": target_text,
}
split_seen[split] += 1
_reservoir_add(
split_rows[split],
rec,
split_caps[split],
split_seen[split],
rng,
)
examples_total += 1
for name in ("train", "val", "test"):
rng.shuffle(split_rows[name])
_write_jsonl(output_dir / f"{name}.jsonl", split_rows[name])
meta = {
"input_path": str(input_path),
"output_dir": str(output_dir),
"seed": args.seed,
"val_frac": args.val_frac,
"test_frac": args.test_frac,
"max_train": args.max_train,
"max_val": args.max_val,
"max_test": args.max_test,
"task_prefix": prefix,
"rows_total": rows_total,
"rows_with_tags": rows_with_tags,
"examples_total_pre_cap": examples_total,
"examples_written": {k: len(v) for k, v in split_rows.items()},
"examples_seen_by_split_pre_cap": split_seen,
"caption_fields": list(CAPTION_FIELDS),
}
with (output_dir / "meta.json").open("w", encoding="utf-8") as f:
json.dump(meta, f, ensure_ascii=False, indent=2)
print(json.dumps(meta, ensure_ascii=False, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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