File size: 7,686 Bytes
f08526f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | #!/usr/bin/env python3
"""Materialize Visual_Agent Parquet tables as path-based JSONL and images."""
from __future__ import annotations
import argparse
import hashlib
import json
import os
import tempfile
from pathlib import Path
from typing import Any, Iterable
import pyarrow.parquet as pq
def sha256_bytes(value: bytes) -> str:
return hashlib.sha256(value).hexdigest()
def sha256_file(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for block in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(block)
return digest.hexdigest()
def safe_relative_path(value: str) -> Path:
path = Path(value)
if path.is_absolute() or not path.parts or ".." in path.parts:
raise ValueError(f"unsafe relative path: {value!r}")
if path.parts[0] != "images":
raise ValueError(f"image path must be rooted under images/: {value!r}")
return path
def write_bytes_atomic(path: Path, value: bytes) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with tempfile.NamedTemporaryFile(
dir=path.parent,
prefix=path.name + ".",
suffix=".tmp",
delete=False,
) as handle:
temporary = Path(handle.name)
handle.write(value)
handle.flush()
os.fsync(handle.fileno())
os.replace(temporary, path)
def write_jsonl_atomic(path: Path, rows: Iterable[dict[str, Any]]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
temporary = path.with_name(path.name + ".tmp")
with temporary.open("w", encoding="utf-8") as handle:
for row in rows:
handle.write(json.dumps(row, ensure_ascii=False) + "\n")
os.replace(temporary, path)
def materialize_images(input_root: Path, training_root: Path) -> dict[str, int]:
image_shards = sorted((input_root / "images").glob("*.parquet"))
if not image_shards:
raise ValueError(f"no image Parquet shards under {input_root / 'images'}")
counts = {"rows": 0, "written": 0, "reused": 0}
seen_paths: set[str] = set()
for shard in image_shards:
parquet = pq.ParquetFile(shard)
for batch in parquet.iter_batches(batch_size=32):
for row in batch.to_pylist():
relative_text = str(row["path"])
if relative_text in seen_paths:
raise ValueError(f"duplicate image path in Parquet: {relative_text}")
seen_paths.add(relative_text)
relative = safe_relative_path(relative_text)
value = bytes(row["bytes"])
expected_size = int(row["size_bytes"])
expected_sha256 = str(row["sha256"])
if len(value) != expected_size:
raise ValueError(f"{relative_text}: byte length does not match metadata")
if sha256_bytes(value) != expected_sha256:
raise ValueError(f"{relative_text}: embedded bytes fail SHA-256 validation")
destination = training_root / relative
if destination.exists():
if destination.stat().st_size != expected_size:
raise ValueError(f"{destination}: existing file has a different size")
if sha256_file(destination) != expected_sha256:
raise ValueError(f"{destination}: existing file has different content")
counts["reused"] += 1
else:
write_bytes_atomic(destination, value)
counts["written"] += 1
counts["rows"] += 1
return counts
def materialize_samples(input_root: Path, training_root: Path) -> dict[str, int]:
sample_shards = sorted((input_root / "samples").glob("*.parquet"))
if not sample_shards:
raise ValueError(f"no sample Parquet shards under {input_root / 'samples'}")
indexed_rows: list[tuple[int, str, dict[str, Any]]] = []
seen_row_ids: set[str] = set()
for shard in sample_shards:
parquet = pq.ParquetFile(shard)
for batch in parquet.iter_batches(
batch_size=256,
columns=["row_index", "row_id", "record_json"],
):
for value in batch.to_pylist():
row_index = int(value["row_index"])
row_id = str(value["row_id"])
if row_id in seen_row_ids:
raise ValueError(f"duplicate row_id in samples table: {row_id}")
seen_row_ids.add(row_id)
record = json.loads(str(value["record_json"]))
if not isinstance(record, dict):
raise ValueError(f"{row_id}: record_json is not an object")
for image in record.get("images") or []:
relative = safe_relative_path(str(image))
if not (training_root / relative).is_file():
raise ValueError(f"{row_id}: materialized image is missing: {image}")
indexed_rows.append((row_index, row_id, record))
indexed_rows.sort(key=lambda item: item[0])
expected_indexes = list(range(len(indexed_rows)))
actual_indexes = [item[0] for item in indexed_rows]
if actual_indexes != expected_indexes:
raise ValueError("samples row_index is not a complete zero-based sequence")
combined_path = training_root / "all_training_trajectories_with_images.jsonl"
write_jsonl_atomic(combined_path, (item[2] for item in indexed_rows))
p2r_rows = [item[2] for item in indexed_rows if str(item[1]).startswith("p2r_")]
p2r_path = training_root / "p2r_v2/p2r_natural_v2_repaired_with_images.jsonl"
write_jsonl_atomic(p2r_path, p2r_rows)
return {
"rows": len(indexed_rows),
"p2r_v2_rows": len(p2r_rows),
"combined_jsonl_sha256": sha256_file(combined_path),
}
def materialize(input_root: Path, output_root: Path) -> dict[str, Any]:
input_root = input_root.resolve()
output_root = output_root.resolve()
manifest_path = input_root / "dataset_manifest.json"
if not manifest_path.is_file():
raise ValueError(f"missing manifest: {manifest_path}")
manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
training_root = output_root / "training_trajectories_natural"
training_root.mkdir(parents=True, exist_ok=True)
image_summary = materialize_images(input_root, training_root)
sample_summary = materialize_samples(input_root, training_root)
if image_summary["rows"] != int(manifest["unique_images"]):
raise ValueError("materialized image count does not match manifest")
if sample_summary["rows"] != int(manifest["samples"]):
raise ValueError("materialized sample count does not match manifest")
if sample_summary["combined_jsonl_sha256"] != str(
manifest["combined_jsonl_sha256"]
):
raise ValueError("materialized combined JSONL does not match manifest SHA-256")
return {
"output_root": str(output_root),
"samples": sample_summary,
"images": image_summary,
"combined_jsonl": str(
training_root / "all_training_trajectories_with_images.jsonl"
),
}
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--input-root", type=Path, default=Path("."))
parser.add_argument("--output-root", type=Path, required=True)
args = parser.parse_args()
print(
json.dumps(
materialize(args.input_root, args.output_root),
ensure_ascii=False,
indent=2,
sort_keys=True,
)
)
if __name__ == "__main__":
main()
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