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3127b61 | 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 | # Copyright 2026 Jayce-Ping
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy at http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and limitations.
"""Validate complete PolyText-Bench scores and write aggregate metrics."""
import argparse
import json
import math
from pathlib import Path
from typing import Any, Dict, List
DEFAULT_PROMPTS = Path(__file__).resolve().parent
def read_jsonl(path: Path) -> List[Dict[str, Any]]:
"""Read nonempty JSONL objects."""
rows = [json.loads(line) for line in path.read_text().splitlines() if line.strip()]
if any(not isinstance(row, dict) for row in rows):
raise TypeError(f"Expected JSON objects in {path}")
return rows
def checked_score(value: float) -> float:
"""Return a finite normalized score."""
score = float(value)
if not math.isfinite(score) or not 0 <= score <= 1:
raise ValueError(f"Expected a normalized score, got {score}")
return score
def expected_keys(prompts: Path, mode: str, repeats: int) -> List[str]:
"""Return all expected image keys for one language."""
language = mode.lower()
rows = []
for task in ("critical", "optional"):
path = prompts / f"{task}_{language}.jsonl"
if not path.is_file():
raise FileNotFoundError(f"Missing benchmark split: {path}")
rows.extend(read_jsonl(path))
keys = [
f"{row['pair_id']}_{row['task_type']}_{repeat}"
for row in rows
for repeat in range(1, repeats + 1)
]
if len(keys) != len(set(keys)):
raise ValueError(f"Benchmark contains duplicate keys for mode={mode}")
return keys
def summarize(records: List[Dict[str, Any]]) -> Dict[str, Any]:
"""Average images equally and exclude undefined optional glyph scores."""
critical = [record for record in records if record["task_type"] == "critical"]
optional = [record for record in records if record["task_type"] == "optional"]
optional_valid = [record for record in optional if record["OG_valid"]]
return dict(
CS=sum(checked_score(record["CS"]) for record in critical) / len(critical),
CG=sum(checked_score(record["CG"]) for record in critical) / len(critical),
CG_valid=f"{sum(record['CG_valid'] for record in critical)}/{len(critical)}",
OG=(
sum(checked_score(record["OG"]) for record in optional_valid) / len(optional_valid)
if optional_valid
else None
),
OG_valid=f"{len(optional_valid)}/{len(optional)}",
)
def markdown(language: str, metrics: Dict[str, Any]) -> str:
"""Render one language row as Markdown."""
values = [
(
"N/A"
if metrics[key] is None
else (
f"{metrics[key]:.3f}"
if isinstance(metrics[key], (float, int))
else str(metrics[key])
)
)
for key in ("CS", "CG", "CG_valid", "OG", "OG_valid")
]
return "\n".join(
[
"| Language | CS ↑ | CG ↑ | CG valid | OG ↑ | OG valid |",
"|---|---:|---:|---|---:|---|",
"| " + " | ".join([language] + values) + " |",
"",
]
)
def main() -> None:
"""Validate records and export JSON and Markdown summaries."""
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--prompts", type=Path, default=DEFAULT_PROMPTS)
parser.add_argument("--scores-dir", type=Path, required=True)
parser.add_argument("--mode", choices=["EN", "ZH"], required=True)
parser.add_argument("--repeats", type=int, default=4)
args = parser.parse_args()
if args.repeats < 1:
parser.error("repeats must be positive")
score_path = args.scores_dir / "eval_results.jsonl"
if not score_path.is_file():
raise FileNotFoundError(f"Missing score file: {score_path}")
records = read_jsonl(score_path)
keys = [record.get("key") for record in records]
expected = expected_keys(args.prompts, args.mode, args.repeats)
if len(keys) != len(set(keys)):
raise ValueError("Score file contains duplicate image keys")
if set(keys) != set(expected):
missing = sorted(set(expected) - set(keys))
unexpected = sorted(set(keys) - set(expected))
raise ValueError(
f"Incomplete scores: expected {len(expected)}, got {len(keys)}; "
f"missing={missing[:5]}, unexpected={unexpected[:5]}"
)
by_key = {record["key"]: record for record in records}
ordered = [by_key[key] for key in expected]
metrics = summarize(ordered)
result = dict(
complete=True,
language=args.mode.lower(),
images=len(ordered),
score_scale="0-1",
metrics=metrics,
)
args.scores_dir.mkdir(parents=True, exist_ok=True)
(args.scores_dir / "summary.json").write_text(
json.dumps(result, ensure_ascii=False, indent=2, allow_nan=False) + "\n"
)
report = markdown(args.mode.lower(), metrics)
(args.scores_dir / "scores.md").write_text(report)
print(report, end="")
if __name__ == "__main__":
main()
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