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import gzip
import multiprocessing
import os
import shutil
import time
from argparse import Namespace
from collections import Counter
import numpy as np
from datasets import load_dataset, utils
import re
from huggingface_hub import Repository
from multiprocessing import Pool
from tqdm import tqdm
# Settings
config = {
"dataset_name": "./data/github",
"num_workers": 96,
"line_max": 1000,
"out_path": "./data/github-code",
"repo_name": "github-code",
"org": "lvwerra",
"shard_size": 1000 << 20}
args = Namespace(**config)
PATTERN = re.compile(r'\s+')
def hash_func(text):
return hashlib.md5(re.sub(PATTERN, '', text).encode("utf-8")).hexdigest()
def get_hash(example):
"""Get hash of content field."""
return {"hash": hash_func(example["content"])}
def line_stats(example):
"""Calculates mean and max line length of file."""
line_lengths = [len(line) for line in example["content"].splitlines()]
return {"line_mean": np.mean(line_lengths), "line_max": max(line_lengths)}
def alpha_stats(example):
"""Calculates mean and max line length of file."""
alpha_frac = np.mean([c.isalnum() for c in example["content"]])
return {"alpha_frac": alpha_frac}
def check_uniques(example, uniques):
"""Check if current hash is still in set of unique hashes and remove if true."""
if example["hash"] in uniques:
return True
return False
def is_autogenerated(example, scan_width=5):
"""Check if file is autogenerated by looking for keywords in the first few lines of the file."""
keywords = ["auto-generated", "autogenerated", "automatically generated"]
lines = example["content"].splitlines()
for _, line in zip(range(scan_width), lines):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
return {"autogenerated": False}
def preprocess(example):
"""Chain all preprocessing steps into one function to not fill cache."""
results = dict()
return results
def filter(example, uniques, args):
"""Filter dataset with heuristics."""
if not check_uniques(example, uniques):
return False
elif example["line_max"] > args.line_max:
return False
return True
def save_shard(shard_tuple):
"""Save shard"""
filename, shard = shard_tuple
# Load dataset
t_start = time.time()
ds = load_dataset(args.dataset_name, split="train", chunksize=40<<20)
print(f"Time to load dataset: {time.time()-t_start:.2f}")
# Run preprocessing
t_start = time.time()
ds =, num_proc=args.num_workers)
print(f"Time to preprocess dataset: {time.time()-t_start:.2f}")
# Deduplicate hashes
uniques = set(ds.unique("hash"))
frac = len(uniques) / len(ds)
print(f"Fraction of duplicates: {1-frac:.2%}")
# Deduplicate data and apply heuristics
t_start = time.time()
ds = ds.filter(filter, fn_kwargs={"uniques": uniques, "args": args})
ds = ds.remove_columns(["line_mean", "line_max", "copies", "hash"])
print(f"Time to filter dataset: {time.time()-t_start:.2f}")
print(f"Size of filtered dataset: {len(ds)}")
# Save dataset in repo
repo = Repository(
local_dir=args.out_path, + "/" + args.repo_name,
os.mkdir(args.out_path + "/data")
if ds._indices is not None:
dataset_nbytes = * len(ds._indices) / len(
dataset_nbytes =
num_shards = int(dataset_nbytes / args.shard_size) + 1
print(f"Number of shards: {num_shards}")
t_start = time.time()
shards = (ds.shard(num_shards=num_shards, index=i, contiguous=True) for i in range(num_shards))
filenames = (f"{args.out_path}/data/train-{index:05d}-of-{num_shards:05d}.parquet" for index in range(num_shards))
with Pool(16) as p:
list(tqdm(p.imap_unordered(save_shard, zip(filenames, shards), chunksize=4), total=num_shards))
print(f"Time to save dataset: {time.time()-t_start:.2f}")
# To push to hub run `git add/commit/push` inside dataset repo folder