202 lines
7.5 KiB
Python
202 lines
7.5 KiB
Python
# Copyright 2024 Bloomberg Finance L.P.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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"""Main Script for M3DocVQA Dataset Creation Pipeline.
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This script orchestrates downloading PDFs or PNGs, checking for corrupted PDFs, extracting images,
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organizing them into directories, downloading/decompressing MMQA data, and creating wiki links mapping.
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Usage:
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python main.py <action> [other options]
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Actions:
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- download_pdfs: Download PDFs from URLs provided in metadata.
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- check_pdfs: Verify if the downloaded PDFs are valid.
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- extract_images: Extract images from the pages of downloaded PDFs.
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- organize_files: Organize downloaded PDFs into specified directory splits.
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- download_mmqa: Download and decompress the MMQA dataset.
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- generate_wiki_mapping: Generate a mapping of 'id' to 'url' from multiple JSONL files.
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Example:
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python main.py generate_wiki_mapping --text=MMQA_texts.jsonl --image=MMQA_images.jsonl --table=MMQA_tables.jsonl --output=id_url_mapping.jsonl
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"""
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import fire
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import json
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import jsonlines
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from pathlib import Path
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from m3docvqa.downloader import download_wiki_page
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from m3docvqa.pdf_utils import is_pdf_downloaded, is_pdf_clean, get_images_from_pdf
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from m3docvqa.split_utils import create_split_files
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from m3docvqa.mmqa_downloader import download_and_decompress_mmqa
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from m3docvqa.wiki_mapper import generate_wiki_links_mapping
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from loguru import logger
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from tqdm.auto import tqdm
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def _prepare_download(
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metadata_path: Path | str,
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output_dir: Path | str,
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first_n: int,
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doc_ids: set,
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check_downloaded: bool = False,
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) -> tuple[list[str], list[Path]]:
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"""Prepare URLs and save paths for downloading.
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Args:
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metadata_path (Path): Path to the metadata JSONL file.
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output_dir (str): Directory where files will be saved.
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first_n (int): Maximum number of entries to process.
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doc_ids (set): Set of doc ids to filter for downloading.
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Returns:
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tuple[list[str], list[Path]]: URLs and save paths for downloading.
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"""
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output_dir = Path(output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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urls, save_paths = [], []
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with jsonlines.open(metadata_path) as reader:
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for line in reader:
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if len(urls) == first_n:
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break
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doc_id = line.get("id")
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url = line.get("url")
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if doc_ids and doc_id not in doc_ids:
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continue
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save_path = output_dir / f"{doc_id}.pdf"
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if check_downloaded and is_pdf_downloaded(save_path):
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continue
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urls.append(url)
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save_paths.append(save_path)
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return urls, save_paths
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def download_pdfs(
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metadata_path: Path | str,
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pdf_dir: Path | str,
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result_log_dir: Path | str,
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per_split_doc_ids: Path | str,
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first_n: int = -1,
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proc_id: int = 0,
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n_proc: int = 1,
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check_downloaded: bool = False,
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):
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"""Download Wikipedia pages as PDFs."""
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# Load document ids for the specified split
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if per_split_doc_ids:
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with open(per_split_doc_ids, "r") as f:
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doc_ids = json.load(f)
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logger.info(f"Downloading documents with {len(doc_ids)} document IDs from {metadata_path}.")
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urls, save_paths = _prepare_download(metadata_path, pdf_dir, first_n, doc_ids, check_downloaded)
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# split urls and save_paths (both are lists) into n_proc chunks
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if n_proc > 1:
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logger.info(f"[{proc_id}/{n_proc}] Splitting {len(urls)} URLs into {n_proc} chunks")
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urls = urls[proc_id::n_proc]
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save_paths = save_paths[proc_id::n_proc]
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logger.info(f"[{proc_id}/{n_proc}] Starting download of {len(urls)} PDFs to {pdf_dir}")
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download_results = download_wiki_page(urls, save_paths, "pdf", result_log_dir, proc_id, n_proc)
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logger.info(f"[{proc_id}/{n_proc}] Download completed with {sum(download_results)} successful downloads out of {len(urls)}")
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def check_pdfs(pdf_dir: str, proc_id: int = 0, n_proc: int = 1):
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"""Verifies the integrity of downloaded PDFs."""
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corrupted_paths = []
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total_checked, corrupted_count = 0, 0
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pdf_files = list(Path(pdf_dir).glob("*.pdf"))
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for pdf_path in tqdm(pdf_files, disable=(proc_id != 0), desc="Checking PDFs"):
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total_checked += 1
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if not is_pdf_downloaded(pdf_path) or not is_pdf_clean(pdf_path):
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corrupted_paths.append(pdf_path)
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corrupted_count += 1
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logger.info(f"Checked {total_checked} PDFs: {corrupted_count} corrupted files.")
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if corrupted_paths:
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logger.warning(f"Corrupted PDFs: {corrupted_paths}")
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def extract_images(pdf_dir: str, image_dir: str, save_type='png'):
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"""Extracts images from downloaded PDFs."""
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Path(image_dir).mkdir(parents=True, exist_ok=True)
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pdf_files = list(Path(pdf_dir).glob("*.pdf"))
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if not pdf_files:
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logger.warning(f"No PDFs found in {pdf_dir} for image extraction.")
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return
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logger.info(f"Starting image extraction from {len(pdf_files)} PDFs in {pdf_dir}.")
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for pdf_path in tqdm(pdf_files, desc="Extracting images", unit="PDF"):
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get_images_from_pdf(pdf_path, save_dir=image_dir, save_type=save_type)
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logger.info(f"Images extracted from {pdf_dir} and saved to {image_dir}")
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def create_splits(split_metadata_file: str | Path, split: str):
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"""Create the per-split doc ids."""
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create_split_files(
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split_metadata_file=split_metadata_file,
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split=split,
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)
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logger.info(f"Doc Ids Files created for {split} split")
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def download_mmqa(output_dir: str):
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"""Downloads and decompresses the MMQA dataset.
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Args:
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output_dir (str): Directory where the MMQA files will be downloaded and decompressed.
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"""
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logger.info(f"Starting MMQA dataset download to {output_dir}")
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download_and_decompress_mmqa(output_dir)
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logger.info(f"MMQA dataset downloaded and decompressed successfully in {output_dir}")
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def generate_wiki_mapping(text: str, image: str, table: str, output: str = "id_url_mapping.jsonl"):
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"""Generates a mapping of 'id' to 'url' from multiple JSONL files.
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Args:
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text (str): Path to the JSONL file containing text data from multimodalqa dataset with 'id' and 'url' fields.
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image (str): Path to the JSONL file containing image data from multimodalqa dataset with 'id' and 'url' fields.
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table (str): Path to the JSONL file containing table data from multimodalqa dataset with 'id' and 'url' fields.
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output (str): Path to save the output JSONL file. Defaults to 'id_url_mapping.jsonl'.
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"""
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logger.info("Starting wiki mapping generation...")
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generate_wiki_links_mapping(text_file=text, image_file=image, table_file=table, output_file=output)
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logger.info(f"Wiki mapping successfully saved to {output}")
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def main():
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fire.Fire({
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"download_mmqa": download_mmqa,
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"generate_wiki_mapping": generate_wiki_mapping,
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"download_pdfs": download_pdfs,
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"check_pdfs": check_pdfs,
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"extract_images": extract_images,
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"create_splits": create_splits,
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})
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if __name__ == "__main__":
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main()
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