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()