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Download examples/_common.py from docling-project/DeskForge-1M: direct link, hf CLI and curl.
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https://huggingface.co/datasets/docling-project/DeskForge-1M/resolve/main/examples/_common.py
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3.13 kB
| """Shared bits: iterate a WebDataset tar with or without the library. | |
| The canonical shards are ordinary uncompressed tars, so `webdataset` is a | |
| convenience and not a requirement. These examples use it when it is installed | |
| and fall back to eighty lines of `tarfile` when it is not, which is also the | |
| clearest statement of what the format actually is. | |
| """ | |
| from __future__ import annotations | |
| import io | |
| import json | |
| import tarfile | |
| from pathlib import Path | |
| from typing import Any, Dict, Iterator, List | |
| #: The four members every observation carries, keyed by the part of the member | |
| #: name after the first dot. | |
| EXTENSIONS = ("png", "leaf.json", "screentag.txt", "record.json") | |
| def iter_tar(path) -> Iterator[Dict[str, Any]]: | |
| """Yield one dict per observation from one shard, in stored order. | |
| Members of a sample are written consecutively, so grouping is a matter of | |
| watching the key change - no buffering of the whole shard. | |
| """ | |
| current_key = None | |
| sample: Dict[str, Any] = {} | |
| with tarfile.open(str(path), "r:") as tar: | |
| for member in tar: | |
| if not member.isfile(): | |
| continue | |
| key, _, extension = member.name.partition(".") | |
| if key != current_key: | |
| if sample: | |
| yield sample | |
| current_key, sample = key, {"__key__": key} | |
| handle = tar.extractfile(member) | |
| if handle is None: | |
| continue | |
| sample[extension] = handle.read() | |
| if sample: | |
| yield sample | |
| def decode(sample: Dict[str, Any], with_image: bool = True) -> Dict[str, Any]: | |
| """Bytes to usable objects. The PNG is decoded only if asked for.""" | |
| out: Dict[str, Any] = {"key": sample["__key__"]} | |
| if "record.json" in sample: | |
| out["record"] = json.loads(sample["record.json"]) | |
| if "leaf.json" in sample: | |
| out["elements"] = json.loads(sample["leaf.json"]) | |
| if "screentag.txt" in sample: | |
| out["screentag"] = sample["screentag.txt"].decode("utf-8") | |
| if with_image and "png" in sample: | |
| from PIL import Image | |
| out["image"] = Image.open(io.BytesIO(sample["png"])) | |
| return out | |
| def shards_for(root, split: str) -> List[Path]: | |
| return sorted(Path(root).joinpath("data", split).glob("*.tar")) | |
| def shards_for_rank(root, split: str, rank: int = 0, world_size: int = 1, | |
| worker: int = 0, num_workers: int = 1) -> List[Path]: | |
| """Partition shards so no two readers touch the same file. | |
| Sharding by file, not by sample, is what keeps a distributed run from | |
| reading everything `world_size` times. Every rank must get the same number | |
| of shards or the epoch never ends on the short ranks; the tail is dropped. | |
| """ | |
| shards = shards_for(root, split) | |
| per_reader = len(shards) // max(1, world_size * num_workers) | |
| if per_reader == 0: | |
| raise ValueError( | |
| "%d shards cannot feed %d readers; lower world_size or num_workers" | |
| % (len(shards), world_size * num_workers)) | |
| index = rank * num_workers + worker | |
| return shards[index * per_reader:(index + 1) * per_reader] | |