Datasets:
(fix): read published shards from the hub when the checkout has lfs pointers
Browse files
src/ai_tech_articles/__init__.py
CHANGED
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@@ -57,6 +57,44 @@ def load_dotenv(path: Path | None = None) -> list[str]:
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return loaded
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def local_parquet_files() -> list[Path]:
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"""The parquet shard(s) in ``data/``, if the repo is checked out."""
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return sorted(DATA_DIR.glob("train-*.parquet"))
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@@ -73,7 +111,7 @@ def load_table(local_first: bool = True) -> pa.Table:
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if files:
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import pyarrow.parquet as pq
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return pa.concat_tables([pq.read_table(f) for f in files])
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from datasets import load_dataset
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return load_dataset(HF_REPO_ID, split="train").data.table
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return loaded
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LFS_POINTER_PREFIX = b"version https://git-lfs.github.com/spec/v1"
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def lfs_pointer(path: Path) -> dict[str, str] | None:
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"""The parsed pointer if `path` is a Git LFS pointer file, else None.
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A checkout without LFS objects (GitHub Actions by default) leaves pointer
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text where the parquet should be.
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"""
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try:
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with open(path, "rb") as fh:
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head = fh.read(512)
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except OSError:
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return None
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if not head.startswith(LFS_POINTER_PREFIX):
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return None
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fields = dict(line.split(" ", 1) for line in head.decode("utf-8", "replace").splitlines() if " " in line)
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return fields
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def shard_file(path: Path) -> Path:
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"""A readable parquet file for `path`: itself, or the Hub's copy when the
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checkout only has the LFS pointer (verified against the pointer's sha256)."""
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pointer = lfs_pointer(path)
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if pointer is None:
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return path
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import hashlib
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from huggingface_hub import hf_hub_download
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local = Path(hf_hub_download(HF_REPO_ID, f"data/{path.name}", repo_type="dataset"))
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want = pointer.get("oid", "").removeprefix("sha256:")
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got = hashlib.sha256(local.read_bytes()).hexdigest()
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if want and got != want:
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raise RuntimeError(f"{path.name}: Hub copy sha256 {got} does not match the LFS pointer {want}")
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return local
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def local_parquet_files() -> list[Path]:
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"""The parquet shard(s) in ``data/``, if the repo is checked out."""
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return sorted(DATA_DIR.glob("train-*.parquet"))
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if files:
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import pyarrow.parquet as pq
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return pa.concat_tables([pq.read_table(shard_file(f)) for f in files])
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from datasets import load_dataset
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return load_dataset(HF_REPO_ID, split="train").data.table
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