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import argparse
import json
import logging
import random
import time
import numpy as np
from functools import partial
from pprint import pformat
from datasets import load_dataset
from datasets.utils.logging import set_verbosity_info
from manual_sharding import save_manual_shards
from utils import get_replacements, redact_pii_batch
REPONAME_TOKEN = "<reponame>"
FILENAME_TOKEN = "<filename>"
STARS_TOKEN = "<gh_stars>"
def get_num_stars_bucket(num_stars: int) -> str:
if num_stars is None or num_stars == 0:
return "0"
elif num_stars <= 10:
return "1-10"
elif num_stars <= 100:
return "10-100"
elif num_stars <= 1000:
return "100-1000"
else:
return "1000+"
def content_with_meta(example):
res = ""
if np.random.binomial(n=1, p=0.2):
res += f"{REPONAME_TOKEN}{example['max_stars_repo_name']}"
if np.random.binomial(n=1, p=0.2):
res += f"{FILENAME_TOKEN}{example['max_stars_repo_path']}"
if np.random.binomial(n=1, p=0.2):
num_stars = get_num_stars_bucket(example["max_stars_count"])
res += f"{STARS_TOKEN}{num_stars}"
if len(res) > 0:
res += "\n"
res += example["content"]
return {"content_with_meta": res}
def parseArgs():
parser = argparse.ArgumentParser(description="PII detection and redaction")
parser.add_argument(
"--dataset_name",
default="bigcode/pii-for-code",
type=str,
help="HF repo name/path of the dataset.",
)
# add arg true add metadata
parser.add_argument(
"--add_metadata",
action="store_true",
help="If set, we add metadata to the text",
)
parser.add_argument(
"--num_load_proc",
default=64,
type=int,
help="Number of processes to use for loading the dataset",
)
parser.add_argument(
"--text_column",
default="content",
type=str,
help="Text column to use, if will be renamed to content",
)
parser.add_argument(
"--split",
default="train",
type=str,
help="Dataset split to process",
)
parser.add_argument(
"--batch_size",
default=100,
type=int,
help="Batch size for the PII detection/redaction",
)
parser.add_argument(
"--seed",
default=0,
type=int,
help="Seed for random",
)
parser.add_argument(
"--num_proc",
default=96,
type=int,
help="Number of processes to use for the PII detection/redaction",
)
parser.add_argument(
"--no_redaction",
action="store_true",
help="If set, we don't perform redaction",
)
parser.add_argument(
"--load_replacements",
default=True,
help="If set, we load the replacements from file replacements.json",
)
parser.add_argument(
"--add_reference_text",
default=True,
type=bool,
help="If True we add the reference text with PII between delimiters \
in the redacted text -used for visualization-",
)
parser.add_argument(
"--check_all_files",
action="store_true",
help="If set, we check all files, not only the ones that contain PII",
)
parser.add_argument(
"--check_sampling_size",
default=0,
type=int,
help="Number of samples to check for PII",
)
# for saving the dataset: either push to HF or save locally with datasets or save manual shards
parser.add_argument(
"--save_mode",
default="manual_shards",
type=str,
choices=["hub", "local", "manual_shards"],
help="How to save the dataset",
)
parser.add_argument(
"--save_mode_checks",
default="hub",
type=str,
choices=["hub", "local", "manual_shards"],
help="How to save the checks dataset",
)
# add argument for name of dataset on the hub
parser.add_argument(
"--target_dataset",
default="bigcode-pii2",
type=str,
help="HF repo name of the target dataset in save_mode=hub.",
)
parser.add_argument(
"--hub_username",
default="loubnabnl",
type=str,
help="Username for the hub",
)
parser.add_argument(
"--save_path_disk",
default="/fsx/loubna/data/the-stack-march-no-pii",
type=str,
help="Path to save the dataset on disk in save_mode=local.",
)
return parser.parse_args()
def get_check_ds(ds, args):
if not args.check_all_files:
ds_checks = ds.filter(
lambda exs: exs["modified"],
batched=True,
batch_size=args.batch_size,
num_proc=args.num_proc,
)
else:
ds_checks = ds
if not args.check_sampling_size:
sampling_size = len(ds_checks)
idx_samples = random.sample(
range(len(ds_checks)), min(len(ds_checks), sampling_size)
)
ds_checks = ds_checks.select(idx_samples)
return ds_checks
def main():
set_verbosity_info()
args = parseArgs()
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
handlers=[
logging.FileHandler(f"logs/pii-{args.dataset_name.split('/')[-1]}.log"),
logging.StreamHandler(),
],
)
logger.info(
f"** The job is running with the following arguments: **\n{args}\n **** "
)
logger.info(f" ===== Loading {args.dataset_name} =====")
ds = load_dataset(
args.dataset_name,
split=args.split,
use_auth_token=True,
num_proc=args.num_load_proc,
)
if args.text_column != "content":
ds = ds.rename_column(args.text_column, "content")
# redact PII in the dataset
logger.info(f" ===== Applying PII redaction =====")
random.seed(args.seed)
replacements = get_replacements()
with open("replacements.json", "w") as f:
json.dump(replacements, f)
logging.info(f"Using the following replacements:\n{pformat(replacements)}")
ds = ds.map(
partial(
redact_pii_batch,
replacements=replacements,
add_references=args.add_reference_text,
),
batched=True,
batch_size=args.batch_size,
num_proc=args.num_proc,
)
logging.info(f"Dataset info after PII redaction:\n{ds}")
# check the dataset
logger.info(
f" ===== Checking {args.check_sampling_size} samples from those modified in the dataset ====="
)
ds_checks = get_check_ds(ds, args)
# save checks dataset
if len(ds_checks) == 0:
logger.info("Dataset was empty. Not saving anything.")
else:
logger.info(f"Checks dataset info {ds_checks}")
if args.save_mode_checks == "hub":
logger.info(
f"Pushing the checks dataset to the Hub as {args.target_dataset}_checks"
)
ds_checks.push_to_hub(args.target_dataset + "_checks", private=True)
elif args.save_mode_checks == "local":
logger.info(f"Saving the checks dataset to disk")
ds_checks.save_to_disk(args.save_path_disk + "_checks")
elif args.save_mode_checks == "manual_shards":
logger.info(f"Saving the checks dataset in manual shards")
save_manual_shards(
ds_checks,
user=args.hub_username,
remote_dataset_repo=args.target_dataset + "_checks",
local_dir="/fsx/loubna/data/the-stack-march-no-pii_checks",
)
logger.info("Removing columns that are not needed for the final dataset")
columns = ["content", "modified", "entities"]
if args.add_reference_text:
columns.append("references")
ds = ds.remove_columns(columns)
ds = ds.rename_column("new_content", "content")
logger.info(f"Dataset info after removing columns:\n{ds}")
if args.add_metadata:
logger.info(f" ===== Adding metadata =====")
ds = ds.map(
content_with_meta, remove_columns=["content"], num_proc=args.num_proc
)
ds = ds.rename_column("content_with_meta", "content")
# save the final dataset
if args.save_mode == "hub":
logger.info(
f" ===== Pushing the dataset to the Hub as: {args.target_dataset} ====="
)
ds.push_to_hub(args.target_dataset, private=True)
elif args.save_mode == "local":
logger.info(f" ===== Saving the dataset to disk =====")
ds.save_to_disk(args.save_path_disk)
elif args.save_mode == "manual_shards":
logger.info(
f" ===== Saving the dataset in manual shards to {args.save_path_disk} ====="
)
save_manual_shards(
ds,
user=args.hub_username,
remote_dataset_repo="the-stack-no-pii-march",
local_dir=args.save_path_disk,
)
logger.info(f" ===== Dataset saved successfully =====")
if __name__ == "__main__":
main()
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