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import json
import os
import logging
import argparse
from datasets import Dataset
import io
# Configure logging for detailed output
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def load_questions_from_meta_qa(meta_qa_file):
with open(meta_qa_file, "r") as f:
questions = [line.strip() for line in f if line.strip()]
return questions
def process_parquet_files(data_dir, output_jsonl, meta_qa_file=None):
"""
Process Parquet files to generate a JSONL file with QA list creation.
Args:
data_dir (str): Directory containing Parquet files.
output_jsonl (str): Output JSONL file path.
meta_qa_file (str, optional): Path to the meta_qa_en.txt file for QA list creation.
Returns:
None
"""
# Load questions if meta_qa_file is provided
questions = None
if meta_qa_file:
questions = load_questions_from_meta_qa(meta_qa_file)
jsonl_data = []
parquet_files = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.endswith(".parquet")]
for parquet_file in parquet_files:
dataset = Dataset.from_parquet(parquet_file)
for row in dataset:
json_item = {
"internal_id": row["internal_id"],
"url": row["url"],
"video_path": row["video_path"],
"prompt": row["prompt"],
"annotation": row["annotation"],
"meta_result": row["meta_result"],
"meta_mask": row["meta_mask"],
}
# Process QA pairs if questions are provided
if questions:
qa_list = []
meta_result = row["meta_result"]
meta_mask = row["meta_mask"]
for idx, mask in enumerate(meta_mask):
if mask == 1: # Add questions only if the mask is 1
question = questions[idx]
if "[[prompt]]" in question:
question = question.replace("[[prompt]]", row["prompt"])
answer = 'yes' if meta_result[idx] == 1 else 'no'
qa_list.append({"question": question, "answer": answer})
json_item["qa_list"] = qa_list
jsonl_data.append(json_item)
with open(output_jsonl, "w") as outfile:
for json_item in jsonl_data:
outfile.write(json.dumps(json_item) + "\n")
logger.info(f"Finished writing JSONL file with {len(jsonl_data)} items.")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Convert Video dataset Parquet files to JSONL format with QA list generation.")
parser.add_argument("--data_dir", type=str, default='train', help="Directory containing Parquet files.")
parser.add_argument("--output_jsonl", type=str, default='annotation.jsonl', help="Path to the output JSONL file.")
parser.add_argument("--meta_qa_file", type=str, default="meta_qa_en.txt", help="Optional: Path to the meta_qa_en.txt file for QA list generation.")
args = parser.parse_args()
process_parquet_files(
data_dir=args.data_dir,
output_jsonl=args.output_jsonl,
meta_qa_file=args.meta_qa_file
) |