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29572cd 1c2734a b44418a 1c2734a 29572cd 1c2734a 29572cd 1c2734a b44418a 29572cd b44418a 29572cd b44418a 29572cd b44418a 1c2734a 29572cd 1c2734a 29572cd b44418a 29572cd b44418a 1c2734a b44418a 29572cd b44418a 29572cd 1c2734a b44418a 29572cd b44418a 29572cd b44418a 1c2734a 29572cd | 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 | """Hugging Face Hub upload utilities."""
from __future__ import annotations
import logging
import os
from datetime import datetime
from pathlib import Path
from typing import List, Optional, Tuple
from huggingface_hub import CommitOperationAdd, HfApi, create_repo
LOGGER = logging.getLogger(__name__)
DEFAULT_CHUNK_MAX_FILES = 200
DEFAULT_CHUNK_MAX_BYTES = 512 * 1024 * 1024
def _gather_files(output_dir: Path, path_in_repo: str) -> List[Tuple[Path, str, int]]:
"""Collect all files from output_dir with their repo paths and sizes."""
base = output_dir.resolve()
prefix = path_in_repo.strip("/")
entries = []
for p in sorted(base.rglob("*")):
if p.is_file():
rel = p.relative_to(base).as_posix()
entries.append((p, f"{prefix}/{rel}" if prefix else rel, p.stat().st_size))
return entries
def _make_batches(
files: List[Tuple[Path, str, int]],
max_files: int,
max_bytes: int,
) -> List[List[Tuple[Path, str, int]]]:
"""Split files into batches respecting max_files and max_bytes limits."""
if not files:
return []
batches: List[List[Tuple[Path, str, int]]] = []
current: List[Tuple[Path, str, int]] = []
current_bytes = 0
for entry in files:
current.append(entry)
current_bytes += max(entry[2], 0)
if len(current) >= max_files or current_bytes >= max_bytes:
batches.append(current)
current = []
current_bytes = 0
if current:
batches.append(current)
return batches
def maybe_upload_dataset(
*,
output_dir: Path,
repo_id: Optional[str],
path_in_repo: str,
commit_message: Optional[str],
revision: Optional[str],
) -> None:
"""Upload local files to a HuggingFace dataset repository."""
if not repo_id:
LOGGER.info("No dataset repo provided; skipping upload.")
return
commit_message = commit_message or (
"Add assembled DeepSeek OCR dataset " + datetime.utcnow().isoformat() + "Z"
)
token = os.environ.get("HF_TOKEN") or None # Treat empty string as None
api = HfApi(token=token)
max_files = int(os.environ.get("HF_UPLOAD_CHUNK_MAX_FILES", DEFAULT_CHUNK_MAX_FILES))
max_bytes = int(os.environ.get("HF_UPLOAD_CHUNK_MAX_BYTES", DEFAULT_CHUNK_MAX_BYTES))
files = _gather_files(output_dir, path_in_repo or "")
if not files:
LOGGER.info("Nothing to upload from %s", output_dir)
return
batches = _make_batches(files, max_files=max_files, max_bytes=max_bytes)
total_batches = len(batches) or 1
LOGGER.info("Uploading %d files to %s in %d commit(s)", len(files), repo_id, total_batches)
create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True, token=token)
for index, batch in enumerate(batches, start=1):
operations = [
CommitOperationAdd(path_in_repo=repo_path, path_or_fileobj=local_path)
for local_path, repo_path, _ in batch
]
message = commit_message
if total_batches > 1:
message = f"{commit_message} (batch {index}/{total_batches})"
LOGGER.info("Commit %d/%d | files=%d", index, total_batches, len(batch))
api.create_commit(
repo_id=repo_id,
repo_type="dataset",
revision=revision,
operations=operations,
commit_message=message,
)
__all__ = ["maybe_upload_dataset"]
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