Datasets:

jed-lee
/

mirm_style_datasets

"""prep raid for contrastive style training (which model family wrote it, or human).
src   : https://huggingface.co/datasets/liamdugan/raid  (train.csv, 11.8gb, mit)
paper : dugan et al. (2024), "raid: a shared benchmark for robust evaluation of
        machine-generated text detectors", acl 2024.
text is only edge-stripped (see edge_strip), nothing else changes.
train split only. official test has no labels, and extra is other domains
(code, czech, german).
    primary_id_int          model family: 0 human, 1 cohere, 2 llama, 3 mistral,
                            4 mpt, 5 openai (gpt3, chatgpt, gpt4), 6 gpt2
                            (base + chat versions merged)
    secondary_category_int  human src doc (raid source_id)
source_id links a human text to every gen made from its prompt + every attacked
copy of those. so 1 group = same prompt, diff families, i.e. hard negs. split by
group so attacked copies stay w/ their original. docs that share an identical text
get split together too. model, domain, decoding, rep penalty + attack are kept as
metadata cols, not labels.
file is too big for memory (11.8gb vs 24gb ram), so it's streamed twice:
  pass 1  everything except the text, plus a hash per text. decides kept rows,
          the split per group and every id
  pass 2  streams again, writes kept rows only, in parquet shards
run:     python3 code/preprocessing/jed/raid/preprocess_raid.py
writes:  code/preprocessing/jed/raid/out/   (git-ignored)
"""
import hashlib
import json
import os
import random
import re

import pandas as pd
import pyarrow as pa
import pyarrow.csv as pacsv
import pyarrow.parquet as pq
from dotenv import load_dotenv
from huggingface_hub import hf_hub_download

HERE = os.path.dirname(os.path.abspath(__file__))
OUT = os.path.join(HERE, "out")
load_dotenv(os.path.join(HERE, "..", ".env"))

SOURCE_REPO = "liamdugan/raid"
SOURCE_FILE = "train.csv"
SEED = 42
VAL_FRACTION = 0.10
ROWS_PER_SHARD = 500_000  # ~200-400mb per parquet file

# model family is the label (peter, 5 oct). human 0, rest alphabetical. binary
# human vs ai is still just primary_id_int == 0.
# gpt2 got its own id (peter ok'd it, 7 oct): it's a 2019 base model, a diff
# generation from gpt3 / chatgpt / gpt4, and writes very diff (40% cut off mid
# sentence, 7.5% looping). base + chat of cohere / mistral / mpt stay together
# since they're the same release. new id is appended so the old ones keep meaning
PRIMARY = {"human": 0, "cohere": 1, "llama": 2, "mistral": 3, "mpt": 4, "openai": 5, "gpt2": 6}
FAMILY = {
    "human": "human",
    "cohere": "cohere", "cohere-chat": "cohere",
    "llama-chat": "llama",
    "mistral": "mistral", "mistral-chat": "mistral",
    "mpt": "mpt", "mpt-chat": "mpt",
    "gpt2": "gpt2", "gpt3": "openai", "chatgpt": "openai", "gpt4": "openai",
}
META = ["model", "domain", "decoding", "repetition_penalty", "attack"]
COLUMNS = ["text", "primary_id_int", "secondary_category_int", "source_record_id"] + META


def stream(path, columns):
    """yield batches of the chosen cols. texts have newlines in them, and every col
    is read as str so ids + empty cells never get type-guessed"""
    reader = pacsv.open_csv(
        path,
        read_options=pacsv.ReadOptions(block_size=64 << 20),
        parse_options=pacsv.ParseOptions(newlines_in_values=True),
        convert_options=pacsv.ConvertOptions(
            include_columns=columns,
            column_types={c: pa.string() for c in columns},
            strings_can_be_null=False,
        ),
    )
    for batch in reader:
        yield batch


# whitespace + zero-width + other invisible chars. python's strip() keeps u+200b,
# and raid has rows that are nothing but zero-width spaces
INVISIBLE = re.compile(r"[\s\u200b-\u200f\u2028-\u202f\u2060-\u206f\ufeff]+")


def visible_len(s):
    return len(INVISIBLE.sub("", s))


def edge_strip(s):
    # only the whitespace at the very start + end goes. it's api / file artifacts, not
    # style: 99.7% of gpt3 texts start w/ "\n\n" and 99.7% of cohere base texts start
    # w/ a space, which no other model does. so it's a free label shortcut. the words,
    # and any attack inside the text (zero-width chars, extra spaces), stay as they are
    return s.strip()


def text_hash(s):
    # 16-byte digest, collisions over ~6m texts are basically impossible
    return hashlib.blake2b(s.encode("utf-8"), digest_size=16).digest()


def pass_one(path):
    """everything needed to decide the output, w/o holding the texts in memory"""
    cols = ["id", "adv_source_id", "source_id", "generation"] + META
    parts = []
    for batch in stream(path, cols):
        df = batch.to_pandas()
        df["generation"] = [edge_strip(t) for t in df["generation"]]
        df["h"] = [text_hash(t) for t in df["generation"]]
        df["visible"] = [visible_len(t) for t in df["generation"]]
        parts.append(df.drop(columns="generation"))
    df = pd.concat(parts, ignore_index=True)
    df["row"] = range(len(df))
    unknown = set(df["model"]) - set(FAMILY)
    assert not unknown, f"models w/ no family: {unknown}"
    df["label"] = df["model"].map(FAMILY)
    return df


def check_source_structure(df):
    """raid's own promise: an attacked row points (adv_source_id) at its unattacked
    version, and both share 1 source_id. if this breaks, the grouping is wrong"""
    base = df[df["attack"] == "none"].set_index("id")["source_id"]
    assert base.index.is_unique, "unattacked ids are not unique"
    attacked = df[df["attack"] != "none"]
    parent = attacked["adv_source_id"].map(base)
    assert parent.notna().all(), "an attacked row points at a missing original"
    assert (parent == attacked["source_id"]).all(), "attacked row and original differ in source_id"
    human = df[df["model"] == "human"]
    assert (human.loc[human["attack"] == "none", "id"] == human.loc[human["attack"] == "none", "source_id"]).all(), \
        "a human original is not its own source"


def clean(df):
    stats = {"rows_raw": len(df)}
    keep = df["visible"] > 0
    stats["removed_empty_or_invisible"] = int((~keep).sum())

    # same text from 2 families (or human + a model) is ambiguous, so every copy goes
    n_labels = df[keep].groupby("h")["label"].nunique()
    conflicting = set(n_labels[n_labels > 1].index)
    is_conflict = df["h"].isin(conflicting) & keep
    stats["removed_conflicting"] = int(is_conflict.sum())
    keep &= ~is_conflict

    # exact dups keep 1 copy. an attack that changed nothing leaves a copy of its
    # original (e.g. number attack on a text w/ no digits), so prefer the unattacked
    # row, then file order
    kept = df[keep].assign(attacked=lambda d: d["attack"] != "none")
    first = kept.sort_values(["h", "attacked", "row"]).drop_duplicates("h", keep="first")["row"]
    is_dup = keep & ~df["row"].isin(set(first))
    stats["removed_duplicates"] = int(is_dup.sum())
    keep &= ~is_dup

    # kept on purpose (real model output, spec doesn't remove them), just counted so
    # short refusals like "sorry, but i can't assist with that." show up in stats
    stats["kept_under_50_visible_chars"] = int((df[keep]["visible"] < 50).sum())
    return df[keep].copy(), stats


def bundles(links):
    """src docs that share any identical text (even a removed dup) are linked into 1
    bundle. w/o this, an attacked copy could land in val while the identical text it
    came from sits in train under another doc"""
    parent = {}

    def find(x):
        while parent.get(x, x) != x:
            parent[x] = parent.get(parent[x], parent[x])
            x = parent[x]
        return x

    shared = links.groupby("h")["source_id"].unique()
    for docs in shared[shared.map(len) > 1]:
        for d in docs[1:]:
            parent[find(d)] = find(docs[0])
    return {d: find(d) for d in links["source_id"].unique()}


def split_and_ids(df, links):
    """no labelled held-out split, so hold out 10% of src docs (seed 42). whole
    bundles move together: the human text, its gens, their attacks + any other doc
    sharing a text w/ them"""
    bundle_of = bundles(links)
    members = {}
    for d, b in bundle_of.items():
        members.setdefault(b, []).append(d)
    order = sorted(members)
    random.Random(SEED).shuffle(order)
    target = round(len(bundle_of) * VAL_FRACTION)
    val_groups = set()
    for b in order:
        if len(val_groups) >= target:
            break
        val_groups.update(members[b])
    df["split"] = ["validation" if g in val_groups else "train" for g in df["source_id"]]

    # train groups get 0..n-1 w/ no gaps for random access in the trainer,
    # val groups continue after them
    train_groups = sorted(set(df.loc[df["split"] == "train", "source_id"]))
    secondary = {g: i for i, g in enumerate(train_groups + sorted(val_groups & set(df["source_id"])))}
    df["primary_id_int"] = df["label"].map(PRIMARY).astype("int64")
    df["secondary_category_int"] = df["source_id"].map(secondary).astype("int64")
    return df, secondary, len(bundle_of) - len(members)


def check(df, links):
    tr, va = df[df["split"] == "train"], df[df["split"] == "validation"]
    split_of = dict(zip(df["source_id"], df["split"]))
    assert (links["source_id"].map(split_of).groupby(links["h"]).nunique() == 1).all(), \
        "an identical text (kept or removed copy) touches both splits"
    assert not set(tr["h"]) & set(va["h"]), "a text appears in both splits"
    assert not set(tr["source_id"]) & set(va["source_id"]), "a source document is in both splits"
    for col in ("primary_id_int", "secondary_category_int"):
        ids = sorted(tr[col].unique())
        assert ids == list(range(len(ids))), f"train {col} is not contiguous from 0"
    assert (tr["primary_id_int"].value_counts() >= 2).all(), "a class has <2 train texts"
    for part, name in ((tr, "train"), (va, "validation")):
        assert set(part["primary_id_int"]) == set(PRIMARY.values()), f"a family is missing from {name}"
    assert df["id"].is_unique, "RAID id is not unique, so it cannot be source_record_id"
    assert (df["primary_id_int"] == df["model"].map(FAMILY).map(PRIMARY)).all(), "label does not match model"


def pass_two(path, plan, n_raw):
    """stream again in the same order + write kept rows, shard by shard. rows are
    matched by position in the file, never by raid id, so a removed row can't sneak
    back in"""
    writers, counts, shard = {}, {"train": 0, "validation": 0}, {"train": 0, "validation": 0}
    schema = pa.schema([(c, pa.int64() if c.endswith("_int") else pa.string()) for c in COLUMNS])

    def writer(split):
        if split not in writers or counts[split] >= ROWS_PER_SHARD * (shard[split] + 1):
            if split in writers:
                writers[split].close()
                shard[split] += 1
            name = os.path.join(OUT, f"{split}-{shard[split]:05d}.parquet")
            writers[split] = pq.ParquetWriter(name, schema, compression="zstd")
        return writers[split]

    plan = plan.set_index("row")
    offset = 0
    for batch in stream(path, ["id", "generation"]):
        df = batch.to_pandas()
        df["generation"] = [edge_strip(t) for t in df["generation"]]  # same as pass 1
        df.index = range(offset, offset + len(df))
        offset += len(df)
        df = df.join(plan, how="inner")  # rows not in the plan were dropped in pass 1
        assert (df["id"] == df["plan_id"]).all(), "pass 2 rows are out of step with pass 1"
        for split, part in df.groupby("split"):
            part = part.rename(columns={"generation": "text", "id": "source_record_id"})
            for c in META:
                part[c] = part[c].where(part[c] != "", None)  # human rows have no decoding / rep penalty
            writer(split).write_table(pa.Table.from_pandas(part[COLUMNS], schema=schema, preserve_index=False))
            counts[split] += len(part)
    assert offset == n_raw, "pass 2 read a different number of rows than pass 1"
    for w in writers.values():
        w.close()
    return counts


def main():
    os.makedirs(OUT, exist_ok=True)
    for f in os.listdir(OUT):  # clear stale shards from an earlier run, else they get uploaded too
        os.remove(os.path.join(OUT, f))
    path = hf_hub_download(SOURCE_REPO, SOURCE_FILE, repo_type="dataset",
                           token=os.environ.get("HF_TOKEN") or None)

    df = pass_one(path)
    check_source_structure(df)
    links = df.loc[df["visible"] > 0, ["h", "source_id"]]
    df, stats = clean(df)
    df, secondary, stats["source_documents_joined_by_shared_text"] = split_and_ids(df, links)
    check(df, links)

    plan = df[["row", "id", "split", "primary_id_int", "secondary_category_int"] + META].rename(columns={"id": "plan_id"})
    written = pass_two(path, plan, stats["rows_raw"])
    assert written == df["split"].value_counts().to_dict(), "pass 2 wrote a different number of rows"

    counts = {}
    for name in ("train", "validation"):
        part = df[df["split"] == name]
        counts[name] = {
            "rows": int(len(part)),
            "source_documents": int(part["source_id"].nunique()),
            "per_class": {k: int((part["primary_id_int"] == v).sum()) for k, v in PRIMARY.items()},
            "per_model": {k: int(v) for k, v in part["model"].value_counts().sort_index().items()},
            "per_attack": {k: int(v) for k, v in part["attack"].value_counts().sort_index().items()},
        }
    with open(os.path.join(OUT, "mappings.json"), "w") as f:
        json.dump({
            "primary_id_int": PRIMARY,
            "secondary_category_int": {"meaning": "RAID source_id (human source document) -> id",
                                       "map": secondary},
        }, f)
    with open(os.path.join(OUT, "stats.json"), "w") as f:
        json.dump({"cleaning": stats, "splits": counts}, f, indent=1)
    print(json.dumps({"cleaning": stats, "splits": counts}, indent=1))


if __name__ == "__main__":
    main()

	"""prep raid for contrastive style training (which model family wrote it, or human).

	src : https://huggingface.co/datasets/liamdugan/raid (train.csv, 11.8gb, mit)
	paper : dugan et al. (2024), "raid: a shared benchmark for robust evaluation of
	machine-generated text detectors", acl 2024.

	text is only edge-stripped (see edge_strip), nothing else changes.

	train split only. official test has no labels, and extra is other domains
	(code, czech, german).

	primary_id_int model family: 0 human, 1 cohere, 2 llama, 3 mistral,
	4 mpt, 5 openai (gpt3, chatgpt, gpt4), 6 gpt2
	(base + chat versions merged)
	secondary_category_int human src doc (raid source_id)

	source_id links a human text to every gen made from its prompt + every attacked
	copy of those. so 1 group = same prompt, diff families, i.e. hard negs. split by
	group so attacked copies stay w/ their original. docs that share an identical text
	get split together too. model, domain, decoding, rep penalty + attack are kept as
	metadata cols, not labels.

	file is too big for memory (11.8gb vs 24gb ram), so it's streamed twice:
	pass 1 everything except the text, plus a hash per text. decides kept rows,
	the split per group and every id
	pass 2 streams again, writes kept rows only, in parquet shards

	run: python3 code/preprocessing/jed/raid/preprocess_raid.py
	writes: code/preprocessing/jed/raid/out/ (git-ignored)
	"""
	import hashlib
	import json
	import os
	import random
	import re

	import pandas as pd
	import pyarrow as pa
	import pyarrow.csv as pacsv
	import pyarrow.parquet as pq
	from dotenv import load_dotenv
	from huggingface_hub import hf_hub_download

	HERE = os.path.dirname(os.path.abspath(__file__))
	OUT = os.path.join(HERE, "out")
	load_dotenv(os.path.join(HERE, "..", ".env"))

	SOURCE_REPO = "liamdugan/raid"
	SOURCE_FILE = "train.csv"
	SEED = 42
	VAL_FRACTION = 0.10
	ROWS_PER_SHARD = 500_000 # ~200-400mb per parquet file

	# model family is the label (peter, 5 oct). human 0, rest alphabetical. binary
	# human vs ai is still just primary_id_int == 0.
	# gpt2 got its own id (peter ok'd it, 7 oct): it's a 2019 base model, a diff
	# generation from gpt3 / chatgpt / gpt4, and writes very diff (40% cut off mid
	# sentence, 7.5% looping). base + chat of cohere / mistral / mpt stay together
	# since they're the same release. new id is appended so the old ones keep meaning
	PRIMARY = {"human": 0, "cohere": 1, "llama": 2, "mistral": 3, "mpt": 4, "openai": 5, "gpt2": 6}
	FAMILY = {
	"human": "human",
	"cohere": "cohere", "cohere-chat": "cohere",
	"llama-chat": "llama",
	"mistral": "mistral", "mistral-chat": "mistral",
	"mpt": "mpt", "mpt-chat": "mpt",
	"gpt2": "gpt2", "gpt3": "openai", "chatgpt": "openai", "gpt4": "openai",
	}
	META = ["model", "domain", "decoding", "repetition_penalty", "attack"]
	COLUMNS = ["text", "primary_id_int", "secondary_category_int", "source_record_id"] + META


	def stream(path, columns):
	"""yield batches of the chosen cols. texts have newlines in them, and every col
	is read as str so ids + empty cells never get type-guessed"""
	reader = pacsv.open_csv(
	path,
	read_options=pacsv.ReadOptions(block_size=64 << 20),
	parse_options=pacsv.ParseOptions(newlines_in_values=True),
	convert_options=pacsv.ConvertOptions(
	include_columns=columns,
	column_types={c: pa.string() for c in columns},
	strings_can_be_null=False,
	),
	)
	for batch in reader:
	yield batch


	# whitespace + zero-width + other invisible chars. python's strip() keeps u+200b,
	# and raid has rows that are nothing but zero-width spaces
	INVISIBLE = re.compile(r"[\s\u200b-\u200f\u2028-\u202f\u2060-\u206f\ufeff]+")


	def visible_len(s):
	return len(INVISIBLE.sub("", s))


	def edge_strip(s):
	# only the whitespace at the very start + end goes. it's api / file artifacts, not
	# style: 99.7% of gpt3 texts start w/ "\n\n" and 99.7% of cohere base texts start
	# w/ a space, which no other model does. so it's a free label shortcut. the words,
	# and any attack inside the text (zero-width chars, extra spaces), stay as they are
	return s.strip()


	def text_hash(s):
	# 16-byte digest, collisions over ~6m texts are basically impossible
	return hashlib.blake2b(s.encode("utf-8"), digest_size=16).digest()


	def pass_one(path):
	"""everything needed to decide the output, w/o holding the texts in memory"""
	cols = ["id", "adv_source_id", "source_id", "generation"] + META
	parts = []
	for batch in stream(path, cols):
	df = batch.to_pandas()
	df["generation"] = [edge_strip(t) for t in df["generation"]]
	df["h"] = [text_hash(t) for t in df["generation"]]
	df["visible"] = [visible_len(t) for t in df["generation"]]
	parts.append(df.drop(columns="generation"))
	df = pd.concat(parts, ignore_index=True)
	df["row"] = range(len(df))
	unknown = set(df["model"]) - set(FAMILY)
	assert not unknown, f"models w/ no family: {unknown}"
	df["label"] = df["model"].map(FAMILY)
	return df


	def check_source_structure(df):
	"""raid's own promise: an attacked row points (adv_source_id) at its unattacked
	version, and both share 1 source_id. if this breaks, the grouping is wrong"""
	base = df[df["attack"] == "none"].set_index("id")["source_id"]
	assert base.index.is_unique, "unattacked ids are not unique"
	attacked = df[df["attack"] != "none"]
	parent = attacked["adv_source_id"].map(base)
	assert parent.notna().all(), "an attacked row points at a missing original"
	assert (parent == attacked["source_id"]).all(), "attacked row and original differ in source_id"
	human = df[df["model"] == "human"]
	assert (human.loc[human["attack"] == "none", "id"] == human.loc[human["attack"] == "none", "source_id"]).all(), \
	"a human original is not its own source"


	def clean(df):
	stats = {"rows_raw": len(df)}
	keep = df["visible"] > 0
	stats["removed_empty_or_invisible"] = int((~keep).sum())

	# same text from 2 families (or human + a model) is ambiguous, so every copy goes
	n_labels = df[keep].groupby("h")["label"].nunique()
	conflicting = set(n_labels[n_labels > 1].index)
	is_conflict = df["h"].isin(conflicting) & keep
	stats["removed_conflicting"] = int(is_conflict.sum())
	keep &= ~is_conflict

	# exact dups keep 1 copy. an attack that changed nothing leaves a copy of its
	# original (e.g. number attack on a text w/ no digits), so prefer the unattacked
	# row, then file order
	kept = df[keep].assign(attacked=lambda d: d["attack"] != "none")
	first = kept.sort_values(["h", "attacked", "row"]).drop_duplicates("h", keep="first")["row"]
	is_dup = keep & ~df["row"].isin(set(first))
	stats["removed_duplicates"] = int(is_dup.sum())
	keep &= ~is_dup

	# kept on purpose (real model output, spec doesn't remove them), just counted so
	# short refusals like "sorry, but i can't assist with that." show up in stats
	stats["kept_under_50_visible_chars"] = int((df[keep]["visible"] < 50).sum())
	return df[keep].copy(), stats


	def bundles(links):
	"""src docs that share any identical text (even a removed dup) are linked into 1
	bundle. w/o this, an attacked copy could land in val while the identical text it
	came from sits in train under another doc"""
	parent = {}

	def find(x):
	while parent.get(x, x) != x:
	parent[x] = parent.get(parent[x], parent[x])
	x = parent[x]
	return x

	shared = links.groupby("h")["source_id"].unique()
	for docs in shared[shared.map(len) > 1]:
	for d in docs[1:]:
	parent[find(d)] = find(docs[0])
	return {d: find(d) for d in links["source_id"].unique()}


	def split_and_ids(df, links):
	"""no labelled held-out split, so hold out 10% of src docs (seed 42). whole
	bundles move together: the human text, its gens, their attacks + any other doc
	sharing a text w/ them"""
	bundle_of = bundles(links)
	members = {}
	for d, b in bundle_of.items():
	members.setdefault(b, []).append(d)
	order = sorted(members)
	random.Random(SEED).shuffle(order)
	target = round(len(bundle_of) * VAL_FRACTION)
	val_groups = set()
	for b in order:
	if len(val_groups) >= target:
	break
	val_groups.update(members[b])
	df["split"] = ["validation" if g in val_groups else "train" for g in df["source_id"]]

	# train groups get 0..n-1 w/ no gaps for random access in the trainer,
	# val groups continue after them
	train_groups = sorted(set(df.loc[df["split"] == "train", "source_id"]))
	secondary = {g: i for i, g in enumerate(train_groups + sorted(val_groups & set(df["source_id"])))}
	df["primary_id_int"] = df["label"].map(PRIMARY).astype("int64")
	df["secondary_category_int"] = df["source_id"].map(secondary).astype("int64")
	return df, secondary, len(bundle_of) - len(members)


	def check(df, links):
	tr, va = df[df["split"] == "train"], df[df["split"] == "validation"]
	split_of = dict(zip(df["source_id"], df["split"]))
	assert (links["source_id"].map(split_of).groupby(links["h"]).nunique() == 1).all(), \
	"an identical text (kept or removed copy) touches both splits"
	assert not set(tr["h"]) & set(va["h"]), "a text appears in both splits"
	assert not set(tr["source_id"]) & set(va["source_id"]), "a source document is in both splits"
	for col in ("primary_id_int", "secondary_category_int"):
	ids = sorted(tr[col].unique())
	assert ids == list(range(len(ids))), f"train {col} is not contiguous from 0"
	assert (tr["primary_id_int"].value_counts() >= 2).all(), "a class has <2 train texts"
	for part, name in ((tr, "train"), (va, "validation")):
	assert set(part["primary_id_int"]) == set(PRIMARY.values()), f"a family is missing from {name}"
	assert df["id"].is_unique, "RAID id is not unique, so it cannot be source_record_id"
	assert (df["primary_id_int"] == df["model"].map(FAMILY).map(PRIMARY)).all(), "label does not match model"


	def pass_two(path, plan, n_raw):
	"""stream again in the same order + write kept rows, shard by shard. rows are
	matched by position in the file, never by raid id, so a removed row can't sneak
	back in"""
	writers, counts, shard = {}, {"train": 0, "validation": 0}, {"train": 0, "validation": 0}
	schema = pa.schema([(c, pa.int64() if c.endswith("_int") else pa.string()) for c in COLUMNS])

	def writer(split):
	if split not in writers or counts[split] >= ROWS_PER_SHARD * (shard[split] + 1):
	if split in writers:
	writers[split].close()
	shard[split] += 1
	name = os.path.join(OUT, f"{split}-{shard[split]:05d}.parquet")
	writers[split] = pq.ParquetWriter(name, schema, compression="zstd")
	return writers[split]

	plan = plan.set_index("row")
	offset = 0
	for batch in stream(path, ["id", "generation"]):
	df = batch.to_pandas()
	df["generation"] = [edge_strip(t) for t in df["generation"]] # same as pass 1
	df.index = range(offset, offset + len(df))
	offset += len(df)
	df = df.join(plan, how="inner") # rows not in the plan were dropped in pass 1
	assert (df["id"] == df["plan_id"]).all(), "pass 2 rows are out of step with pass 1"
	for split, part in df.groupby("split"):
	part = part.rename(columns={"generation": "text", "id": "source_record_id"})
	for c in META:
	part[c] = part[c].where(part[c] != "", None) # human rows have no decoding / rep penalty
	writer(split).write_table(pa.Table.from_pandas(part[COLUMNS], schema=schema, preserve_index=False))
	counts[split] += len(part)
	assert offset == n_raw, "pass 2 read a different number of rows than pass 1"
	for w in writers.values():
	w.close()
	return counts


	def main():
	os.makedirs(OUT, exist_ok=True)
	for f in os.listdir(OUT): # clear stale shards from an earlier run, else they get uploaded too
	os.remove(os.path.join(OUT, f))
	path = hf_hub_download(SOURCE_REPO, SOURCE_FILE, repo_type="dataset",
	token=os.environ.get("HF_TOKEN") or None)

	df = pass_one(path)
	check_source_structure(df)
	links = df.loc[df["visible"] > 0, ["h", "source_id"]]
	df, stats = clean(df)
	df, secondary, stats["source_documents_joined_by_shared_text"] = split_and_ids(df, links)
	check(df, links)

	plan = df[["row", "id", "split", "primary_id_int", "secondary_category_int"] + META].rename(columns={"id": "plan_id"})
	written = pass_two(path, plan, stats["rows_raw"])
	assert written == df["split"].value_counts().to_dict(), "pass 2 wrote a different number of rows"

	counts = {}
	for name in ("train", "validation"):
	part = df[df["split"] == name]
	counts[name] = {
	"rows": int(len(part)),
	"source_documents": int(part["source_id"].nunique()),
	"per_class": {k: int((part["primary_id_int"] == v).sum()) for k, v in PRIMARY.items()},
	"per_model": {k: int(v) for k, v in part["model"].value_counts().sort_index().items()},
	"per_attack": {k: int(v) for k, v in part["attack"].value_counts().sort_index().items()},
	}
	with open(os.path.join(OUT, "mappings.json"), "w") as f:
	json.dump({
	"primary_id_int": PRIMARY,
	"secondary_category_int": {"meaning": "RAID source_id (human source document) -> id",
	"map": secondary},
	}, f)
	with open(os.path.join(OUT, "stats.json"), "w") as f:
	json.dump({"cleaning": stats, "splits": counts}, f, indent=1)
	print(json.dumps({"cleaning": stats, "splits": counts}, indent=1))


	if __name__ == "__main__":
	main()