olmocr/scripts/data/convertsilver_openai.py

import argparse
import json
import logging
import os
import re
import sys
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path

import smart_open
from cached_path import cached_path


def setup_logging():
    """Configure logging for the script."""
    logging.basicConfig(level=logging.INFO, format="[%(asctime)s] %(levelname)s: %(message)s", handlers=[logging.StreamHandler(sys.stdout)])


def is_s3_path(path):
    """Check if the given path is an S3 path."""
    return str(path).startswith("s3://")


def process_file(input_file: str, output_file: str, rewrite_prompt_str: bool):
    """
    Process a single JSONL file: read, transform, and write to output.

    Args:
        input_file (str): Path or URL to the input JSONL file.
        output_file (str): Path or URL to the output JSONL file.
    """
    processed_count = 0
    error_count = 0

    try:
        with smart_open.open(input_file, "r", encoding="utf-8") as infile, smart_open.open(output_file, "w", encoding="utf-8") as outfile:
            for line_number, line in enumerate(infile, 1):
                line = line.strip()
                if not line:
                    continue  # Skip empty lines
                try:
                    obj = json.loads(line)
                except json.JSONDecodeError as e:
                    logging.error(f"JSON decode error in file {input_file} at line {line_number}: {e}")
                    error_count += 1
                    continue

                if obj is not None and rewrite_prompt_str:
                    pattern = r"RAW_TEXT_START\s*\n(.*?)\nRAW_TEXT_END"

                    # Use re.DOTALL to ensure that the dot matches newline characters
                    match = re.search(pattern, obj["body"]["messages"][0]["content"][0]["text"], re.DOTALL)

                    if match:
                        # Ok, now we want to try to see if it's better if we recalculate the anchor text
                        goldkey = obj["custom_id"]
                        s3_path = goldkey[: goldkey.rindex("-")]
                        page = int(goldkey[goldkey.rindex("-") + 1 :])

                        # Save the pdf to a temporary cache folder
                        local_pdf_path = cached_path(s3_path, quiet=True)

                        from olmocr.data.buildsilver import build_page_query

                        obj = build_page_query(local_pdf_path, s3_path, page)
                        # raw_page_text = get_anchor_text(local_pdf_path, page, pdf_engine="pdfreport")

                        # from olmocr.prompts import build_openai_silver_data_prompt
                        # obj["body"]["messages"][0]["content"][0]["text"] = build_openai_silver_data_prompt(raw_page_text)

                if obj is not None:
                    outfile.write(json.dumps(obj) + "\n")
                    processed_count += 1
                else:
                    error_count += 1

        logging.info(f"Processed '{input_file}': {processed_count} records transformed, {error_count} errors.")
    except Exception as e:
        logging.exception(e)
        logging.error(f"Failed to process file {input_file}: {e}")


def construct_output_file_path(input_file_path, input_dir, output_dir):
    """
    Given an input file path, input directory, and output directory,
    construct the corresponding output file path.

    Args:
        input_file_path (str): Path to the input file.
        input_dir (str): Path to the input directory.
        output_dir (str): Path to the output directory.

    Returns:
        str: Path to the output file.
    """
    input_file = Path(input_file_path)

    if is_s3_path(input_dir):
        # For S3 paths, manually construct the relative path based on the input S3 path
        input_prefix = input_dir.split("s3://")[1]
        input_prefix = input_prefix.rstrip("*")  # Remove any glob patterns like *.jsonl

        # Remove the 's3://' part from input_file_path and extract the relative part
        input_file_key = input_file_path.split("s3://")[1]
        relative_path = input_file_key[len(input_prefix) :].lstrip("/")

        # Construct the output S3 path by appending the relative part to the output S3 directory
        output_file_path = output_dir.rstrip("/") + "/" + relative_path

    else:
        # For local paths, use the existing relative path logic
        input_dir_path = Path(input_dir)
        relative_path = input_file.relative_to(input_dir_path)
        output_file_path = str(Path(output_dir) / relative_path)

    return output_file_path


def list_input_files(input_dir):
    """
    List all JSONL files in the input directory. If input_dir is an S3 path, handle
    globbing manually by listing objects and filtering based on patterns.

    Args:
        input_dir (str): Path to the input directory or S3 URL.

    Returns:
        list: List of input file paths.
    """
    if is_s3_path(input_dir):
        # Use smart_open's s3 functionality to list files
        import fnmatch

        import boto3

        # Parse bucket and prefix
        bucket_name = input_dir.split("s3://")[1].split("/")[0]
        path_and_pattern = "/".join(input_dir.split("s3://")[1].split("/")[1:])

        # Separate the prefix and pattern
        if "/" in path_and_pattern:
            prefix = path_and_pattern.rsplit("/", 1)[0] + "/"
            pattern = path_and_pattern.rsplit("/", 1)[1]
        else:
            prefix = ""
            pattern = path_and_pattern

        # Set up S3 resource and bucket
        s3 = boto3.resource("s3")
        bucket = s3.Bucket(bucket_name)

        # Get all objects and filter them manually based on the pattern
        files = []
        for obj in bucket.objects.filter(Prefix=prefix):
            if fnmatch.fnmatch(obj.key, f"{prefix}{pattern}"):
                files.append(f"s3://{bucket_name}/{obj.key}")

        return files
    else:
        # Local path handling (with glob pattern)
        input_dir_path = Path(input_dir)
        return [str(p) for p in input_dir_path.glob("*.jsonl")]


def main():
    setup_logging()
    parser = argparse.ArgumentParser(description="Transform JSONL files by extracting and renaming specific fields.")
    parser.add_argument("--rewrite_prompt", action="store_true", default=False, help="Rewrites the input prompt by reloading the pdf from source")
    parser.add_argument("input_dir", type=str, help="Path to the input directory containing JSONL files. Can be a local path or S3 URL.")
    parser.add_argument("output_dir", type=str, help="Path to the output directory where transformed JSONL files will be saved. Can be a local path or S3 URL.")
    parser.add_argument("--jobs", "-j", type=int, default=20, help="Number of parallel jobs to run (default: 20).")
    args = parser.parse_args()

    input_dir = args.input_dir.rstrip("/")
    output_dir = args.output_dir.rstrip("/")
    max_jobs = args.jobs

    if not output_dir.startswith("s3:"):
        os.makedirs(output_dir, exist_ok=True)

    # List input files
    input_files = list_input_files(input_dir)

    if not input_files:
        logging.warning(f"No JSONL files found in '{input_dir}'. Exiting.")
        sys.exit(0)

    logging.info(f"Found {len(input_files)} JSONL files to process.")

    # Prepare tasks for parallel processing
    tasks = []
    for input_file in input_files:
        output_file = construct_output_file_path(input_file, input_dir, output_dir)
        tasks.append((input_file, output_file))

    # Process files in parallel
    with ProcessPoolExecutor(max_workers=max_jobs) as executor:
        future_to_file = {executor.submit(process_file, input_file, output_file, args.rewrite_prompt): input_file for input_file, output_file in tasks}

        for future in as_completed(future_to_file):
            input_file = future_to_file[future]
            try:
                future.result()
            except Exception as exc:
                logging.error(f"File {input_file} generated an exception: {exc}")

    logging.info("All files have been processed.")


if __name__ == "__main__":
    main()