mirror of
https://github.com/allenai/olmocr.git
synced 2025-10-11 00:02:41 +00:00
328 lines
13 KiB
Python
328 lines
13 KiB
Python
import argparse
|
|
import json
|
|
import logging
|
|
import os
|
|
import re
|
|
import sys
|
|
import tempfile
|
|
from concurrent.futures import ProcessPoolExecutor, as_completed
|
|
from pathlib import Path
|
|
|
|
import boto3
|
|
|
|
# Import Plotly for plotting
|
|
import plotly.express as px
|
|
import smart_open
|
|
|
|
from olmocr.data.renderpdf import render_pdf_to_base64png
|
|
from olmocr.prompts import build_finetuning_prompt
|
|
from olmocr.prompts.anchor import get_anchor_text
|
|
|
|
|
|
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 download_pdf_from_s3(s3_path: str, pdf_profile: str) -> str:
|
|
"""
|
|
Downloads a PDF file from S3 to a temporary local file and returns the local file path.
|
|
|
|
Args:
|
|
s3_path (str): S3 path in the format s3://bucket/key
|
|
pdf_profile (str): The name of the boto3 profile to use.
|
|
|
|
Returns:
|
|
str: Path to the downloaded PDF file in the local filesystem.
|
|
"""
|
|
# Parse the bucket and key from the s3_path
|
|
# s3_path format: s3://bucket_name/some/folder/file.pdf
|
|
path_without_scheme = s3_path.split("s3://", 1)[1]
|
|
bucket_name, key = path_without_scheme.split("/", 1)
|
|
|
|
# Create a session with the specified profile or default
|
|
session = boto3.Session(profile_name=pdf_profile) if pdf_profile else boto3.Session()
|
|
s3_client = session.client("s3")
|
|
|
|
# Create a temporary local file
|
|
tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
|
|
tmp_file.close() # We only want the path and not keep it locked
|
|
|
|
local_path = tmp_file.name
|
|
|
|
logging.info(f"Downloading PDF from {s3_path} to {local_path} using profile {pdf_profile}")
|
|
s3_client.download_file(bucket_name, key, local_path)
|
|
|
|
return local_path
|
|
|
|
|
|
def transform_json_object(obj):
|
|
"""
|
|
Transform a single JSON object by extracting and renaming specific fields.
|
|
|
|
Args:
|
|
obj (dict): Original JSON object.
|
|
|
|
Returns:
|
|
dict or None: Transformed JSON object, or None if there's an error.
|
|
"""
|
|
try:
|
|
transformed = {
|
|
"custom_id": obj["custom_id"],
|
|
"chat_messages": obj["body"]["messages"],
|
|
"temperature": obj["body"]["temperature"],
|
|
"max_tokens": obj["body"]["max_tokens"],
|
|
}
|
|
return transformed
|
|
except KeyError as e:
|
|
logging.error(f"Missing key {e} in object: {obj.get('custom_id', 'unknown')}")
|
|
return None
|
|
|
|
|
|
def process_file(input_file: str, output_file: str, rewrite_prompt_str: bool, pdf_profile: str):
|
|
"""
|
|
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.
|
|
rewrite_prompt_str (bool): Flag to rewrite the prompt string.
|
|
pdf_profile (str): Boto3 profile to use when fetching PDFs from S3.
|
|
"""
|
|
processed_count = 0
|
|
error_count = 0
|
|
prompt_lengths = []
|
|
|
|
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
|
|
|
|
transformed = transform_json_object(obj)
|
|
|
|
if transformed is not None and rewrite_prompt_str:
|
|
# We look for RAW_TEXT_START ... RAW_TEXT_END in the existing content
|
|
pattern = r"RAW_TEXT_START\s*\n(.*?)\nRAW_TEXT_END"
|
|
match = re.search(pattern, transformed["chat_messages"][0]["content"][0]["text"], re.DOTALL)
|
|
|
|
if match:
|
|
# We found raw page text, but we'll attempt to regenerate it
|
|
goldkey = obj["custom_id"]
|
|
# goldkey might look like: "s3://bucket/path/to/file.pdf-23"
|
|
# s3_path = everything up to the last dash
|
|
# page = everything after the dash
|
|
try:
|
|
s3_path = goldkey[: goldkey.rindex("-")]
|
|
page = int(goldkey[goldkey.rindex("-") + 1 :])
|
|
except (ValueError, IndexError) as e:
|
|
logging.error(f"Could not parse the page number from custom_id {goldkey}: {e}")
|
|
error_count += 1
|
|
continue
|
|
|
|
# If the path is an S3 path, download to a local temp file; else assume local
|
|
if is_s3_path(s3_path):
|
|
local_pdf_path = download_pdf_from_s3(s3_path, pdf_profile)
|
|
else:
|
|
local_pdf_path = s3_path
|
|
|
|
# Recalculate the anchor text
|
|
raw_page_text = get_anchor_text(local_pdf_path, page, pdf_engine="pdfreport", target_length=6000)
|
|
|
|
image_base64 = render_pdf_to_base64png(local_pdf_path, page, 1024)
|
|
|
|
transformed["chat_messages"][0]["content"][0]["text"] = build_finetuning_prompt(raw_page_text)
|
|
transformed["chat_messages"][0]["content"][1]["image_url"]["url"] = f"data:image/png;base64,{image_base64}"
|
|
|
|
# Clean up the temp PDF file if it was downloaded
|
|
if is_s3_path(s3_path):
|
|
try:
|
|
os.remove(local_pdf_path)
|
|
except OSError as remove_err:
|
|
logging.error(f"Failed to remove temporary PDF file {local_pdf_path}: {remove_err}")
|
|
|
|
if transformed is not None:
|
|
prompt_text = transformed["chat_messages"][0]["content"][0]["text"]
|
|
prompt_length = len(prompt_text)
|
|
|
|
if prompt_length > 6000:
|
|
print(transformed["custom_id"], "length ", prompt_length)
|
|
|
|
prompt_lengths.append(prompt_length)
|
|
|
|
outfile.write(json.dumps(transformed) + "\n")
|
|
processed_count += 1
|
|
else:
|
|
error_count += 1
|
|
|
|
logging.info(f"Processed '{input_file}': {processed_count} records transformed, {error_count} errors.")
|
|
return prompt_lengths
|
|
except Exception as e:
|
|
logging.exception(e)
|
|
logging.error(f"Failed to process file {input_file}: {e}")
|
|
return []
|
|
|
|
|
|
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):
|
|
import fnmatch
|
|
|
|
# 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
|
|
|
|
# Use a Boto3 session (no specific PDF profile needed here if only listing)
|
|
session = boto3.Session()
|
|
s3 = session.resource("s3")
|
|
bucket = s3.Bucket(bucket_name)
|
|
|
|
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:
|
|
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_finetuning_prompt",
|
|
action="store_true",
|
|
default=True,
|
|
help="Rewrite the input prompt from a standard OPENAI instruction format into a finetuned format.",
|
|
)
|
|
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).")
|
|
parser.add_argument("--pdf_profile", type=str, default=None, help="Boto3 profile to use for downloading PDFs from S3. Defaults to the default session.")
|
|
|
|
args = parser.parse_args()
|
|
|
|
input_dir = args.input_dir.rstrip("/")
|
|
output_dir = args.output_dir.rstrip("/")
|
|
max_jobs = args.jobs
|
|
|
|
# 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
|
|
all_prompt_lengths = []
|
|
with ProcessPoolExecutor(max_workers=max_jobs) as executor:
|
|
future_to_file = {
|
|
executor.submit(process_file, input_file, output_file, args.rewrite_finetuning_prompt, args.pdf_profile): input_file
|
|
for input_file, output_file in tasks
|
|
}
|
|
|
|
for future in as_completed(future_to_file):
|
|
input_file = future_to_file[future]
|
|
try:
|
|
prompt_lengths = future.result()
|
|
all_prompt_lengths.extend(prompt_lengths)
|
|
except Exception as exc:
|
|
logging.error(f"File {input_file} generated an exception: {exc}")
|
|
|
|
logging.info("All files have been processed.")
|
|
|
|
# Plot histogram of prompt lengths
|
|
if all_prompt_lengths:
|
|
fig = px.histogram(all_prompt_lengths, nbins=50, title="Histogram of Prompt Lengths")
|
|
fig.update_xaxes(title="Prompt Length")
|
|
fig.update_yaxes(title="Frequency")
|
|
try:
|
|
fig.write_image("prompt_lengths_histogram.png")
|
|
logging.info("Histogram of prompt lengths has been saved to 'prompt_lengths_histogram.png'.")
|
|
except Exception as e:
|
|
logging.error(f"Failed to save the histogram image: {e}")
|
|
logging.error("Please make sure that the 'kaleido' package is installed (pip install -U kaleido).")
|
|
fig.write_html("prompt_lengths_histogram.html")
|
|
logging.info("Histogram of prompt lengths has been saved to 'prompt_lengths_histogram.html'.")
|
|
else:
|
|
logging.warning("No prompt lengths were collected; histogram will not be generated.")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|