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import os
import json
import tempfile
import uuid
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import pyarrow as pa
import pyarrow.parquet as pq
from huggingface_hub import CommitScheduler
from huggingface_hub.hf_api import HfApi
###################################
# Parquet scheduler #
# Uploads data in parquet format #
###################################
class ParquetScheduler(CommitScheduler):
"""
Usage: configure the scheduler with a repo id. Once started, you can add data to be uploaded to the Hub. 1 `.append`
call will result in 1 row in your final dataset.
```py
# Start scheduler
>>> scheduler = ParquetScheduler(repo_id="my-parquet-dataset")
# Append some data to be uploaded
>>> scheduler.append({...})
>>> scheduler.append({...})
>>> scheduler.append({...})
```
The scheduler will automatically infer the schema from the data it pushes.
Optionally, you can manually set the schema yourself:
```py
>>> scheduler = ParquetScheduler(
... repo_id="my-parquet-dataset",
... schema={
... "prompt": {"_type": "Value", "dtype": "string"},
... "negative_prompt": {"_type": "Value", "dtype": "string"},
... "guidance_scale": {"_type": "Value", "dtype": "int64"},
... "image": {"_type": "Image"},
... },
... )
See https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Value for the list of
possible values.
"""
def __init__(
self,
*,
repo_id: str,
schema: Optional[Dict[str, Dict[str, str]]] = None,
every: Union[int, float] = 5,
path_in_repo: Optional[str] = "data",
repo_type: Optional[str] = "dataset",
revision: Optional[str] = None,
private: bool = False,
token: Optional[str] = None,
allow_patterns: Union[List[str], str, None] = None,
ignore_patterns: Union[List[str], str, None] = None,
hf_api: Optional[HfApi] = None,
) -> None:
super().__init__(
repo_id=repo_id,
folder_path="dummy", # not used by the scheduler
every=every,
path_in_repo=path_in_repo,
repo_type=repo_type,
revision=revision,
private=private,
token=token,
allow_patterns=allow_patterns,
ignore_patterns=ignore_patterns,
hf_api=hf_api,
)
self._rows: List[Dict[str, Any]] = []
self._schema = schema
def append(self, row: Dict[str, Any]) -> None:
"""Add a new item to be uploaded."""
with self.lock:
self._rows.append(row)
def push_to_hub(self):
# Check for new rows to push
with self.lock:
rows = self._rows
self._rows = []
if not rows:
return
print(f"Got {len(rows)} item(s) to commit.")
# Load images + create 'features' config for datasets library
schema: Dict[str, Dict] = self._schema or {}
path_to_cleanup: List[Path] = []
for row in rows:
for key, value in row.items():
# Infer schema (for `datasets` library)
if key not in schema:
schema[key] = _infer_schema(key, value)
# Load binary files if necessary
if schema[key]["_type"] in ("Image", "Audio"):
# It's an image or audio: we load the bytes and remember to cleanup the file
file_path = Path(value)
if file_path.is_file():
row[key] = {
"path": file_path.name,
"bytes": file_path.read_bytes(),
}
path_to_cleanup.append(file_path)
# Complete rows if needed
for row in rows:
for feature in schema:
if feature not in row:
row[feature] = None
# Export items to Arrow format
table = pa.Table.from_pylist(rows)
# Add metadata (used by datasets library)
table = table.replace_schema_metadata(
{"huggingface": json.dumps({"info": {"features": schema}})}
)
# Write to parquet file
archive_file = tempfile.NamedTemporaryFile(delete=False)
pq.write_table(table, archive_file.name)
archive_file.close()
# Upload
self.api.upload_file(
repo_id=self.repo_id,
repo_type=self.repo_type,
revision=self.revision,
path_in_repo=f"{uuid.uuid4()}.parquet",
path_or_fileobj=archive_file.name,
)
print("Commit completed.")
# Cleanup
os.unlink(archive_file.name)
for path in path_to_cleanup:
path.unlink(missing_ok=True)
def _infer_schema(key: str, value: Any) -> Dict[str, str]:
"""
Infer schema for the `datasets` library.
See https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Value.
"""
# In short any column_name in the dataset has any of these keywords
# the column will be inferred into the correct column type accordingly
if "image" in key:
return {"_type": "Image"}
if "audio" in key:
return {"_type": "Audio"}
if isinstance(value, int):
return {"_type": "Value", "dtype": "int64"}
if isinstance(value, float):
return {"_type": "Value", "dtype": "float64"}
if isinstance(value, bool):
return {"_type": "Value", "dtype": "bool"}
if isinstance(value, bytes):
return {"_type": "Value", "dtype": "binary"}
# Otherwise in last resort => convert it to a string
return {"_type": "Value", "dtype": "string"} |