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import json
import random
import re
from collections import defaultdict
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import Any, Iterable
import pyarrow.parquet as pq
from albedo_eval_service.remote.dataset import EvalSample, apply_submit_protocol, format_messages
from albedo_eval_service.shared.observation_format import first_bash_block
from albedo_eval_service.shared.submit_protocol import ANY_MARKER_RE, TAILS
from .constants import (
DEFAULT_DATA_ROOT,
DEFAULT_PACK_DIR,
KEEP_ORIGINAL_RATIO,
MAX_PREFIX_CHARS,
NON_PYTHON_FRACTION,
SOURCES,
TOKENIZER_DIR,
)
from .think import wrap_completion
_EDIT_RE = re.compile(
r"sed\s+-i|tee\s+[\w./-]|cat\s*>|str_replace|git apply|patch\s+-p|applypatch|"
r"cp\s+[\w./-]|mv\s+[\w./-]|(?<![-\d&])>>?\s*(?!/dev/)[\w.][\w./-]*"
)
_DUMMY_RE = re.compile(
r"your_command_here|cat\s+<<'EOF'\s*>\s*newfile\.py|sed\s+-i\s+.*\bfilename\.py\b"
)
_PATH_RE = re.compile(r"(?:/|\./|[\w.-]+/)[\w./-]+\.[A-Za-z0-9]{1,8}")
@dataclass
class PackedExample:
sample_id: str
prompt: str
completion: str
source: str
phase: str
kind: str
family: str
language: str
repo: str
submit_command: str
submit_marker: str
rewrite_mode: str
gold_paths: list[str] = field(default_factory=list)
salt: str = ""
def as_dict(self) -> dict[str, Any]:
return asdict(self)
def is_gold_submit(text: str) -> bool:
command = first_bash_block(text) or ""
hay = command or text or ""
return bool(ANY_MARKER_RE.search(hay))
def is_edit_command(command: str) -> bool:
return bool(command and _EDIT_RE.search(command) and not _DUMMY_RE.search(command))
def gold_paths(text: str) -> list[str]:
command = first_bash_block(text) or text or ""
seen: list[str] = []
for match in _PATH_RE.finditer(command):
path = match.group(0)
if path not in seen and "filename.py" not in path and "newfile.py" not in path:
seen.append(path)
return seen
def family_of(instance_id: str, given: str = "") -> str:
if given:
return given
if "." not in (instance_id or ""):
return "pr"
tail = instance_id.rsplit(".", 1)[-1]
for prefix, family in (("pr_", "pr"), ("lm_", "lm"), ("combine", "combine")):
if tail.startswith(prefix):
return family
return "mechanical"
def phase_for(turn_idx: int, first_edit: int) -> str:
if first_edit <= 0:
return "cold" if turn_idx in (1, 2) else "explore"
if turn_idx == first_edit:
return "at_edit"
if turn_idx == max(1, first_edit - 2):
return "pre_edit"
if turn_idx in (1, 2) and turn_idx < max(1, first_edit - 2):
return "cold"
if turn_idx >= first_edit:
return "post_edit"
return "explore"
def candidate_turns(n_assistant: int, first_edit: int, golds: list[str]) -> list[tuple[int, str]]:
"""Official cold/pre_edit/at_edit cuts plus the first-edit turn and later submits."""
out: list[tuple[int, str]] = []
if n_assistant < 3:
return out
for turn_idx in (1, 2):
if turn_idx < n_assistant:
out.append((turn_idx, "cold"))
if first_edit > 0:
pre = max(1, first_edit - 2)
if pre < n_assistant:
out.append((pre, "pre_edit"))
if first_edit < n_assistant:
out.append((first_edit, "at_edit"))
edit_idx = first_edit - 1
if 0 <= edit_idx < n_assistant:
out.append((edit_idx, "at_edit"))
last_submit = next(
(turn_idx for turn_idx in range(len(golds) - 1, 2, -1) if is_gold_submit(golds[turn_idx])),
None,
)
if last_submit is not None:
out.append((last_submit, "post_edit"))
seen: set[int] = set()
unique: list[tuple[int, str]] = []
for turn_idx, phase in out:
if turn_idx in seen or turn_idx < 0 or turn_idx >= n_assistant:
continue
seen.add(turn_idx)
unique.append((turn_idx, phase_for(turn_idx, first_edit) if first_edit else phase))
return unique
def pack(
*,
dataset_root: Path = DEFAULT_DATA_ROOT,
out_dir: Path = DEFAULT_PACK_DIR,
max_examples: int = 20_000,
seed: str = "sft-pack",
n_salts: int = 2,
tokenizer_path: Path | None = None,
submit_frac: float = 0.20,
edit_frac: float = 0.35,
expand_submit_salts: bool = False,
) -> Path:
dataset_root = Path(dataset_root)
out_dir = Path(out_dir)
out_dir.mkdir(parents=True, exist_ok=True)
tokenizer = str(tokenizer_path or TOKENIZER_DIR)
salts = [f"{seed}-{i}" for i in range(max(1, n_salts))]
raw = list(_iter_raw(dataset_root, max_raw=max(max_examples * 8, 256), seed=seed))
selected = _select(
raw,
max_examples=max_examples,
seed=seed,
submit_frac=submit_frac,
edit_frac=edit_frac,
)
examples = _materialize(
selected,
salts=salts,
tokenizer_path=tokenizer,
expand_submit_salts=expand_submit_salts,
)
pack_path = out_dir / f"sft-{max_examples}-{seed}.jsonl"
with pack_path.open("w") as handle:
for example in examples:
handle.write(json.dumps(example.as_dict(), ensure_ascii=False) + "\n")
summary = _summary(examples)
(out_dir / f"sft-{max_examples}-{seed}.meta.json").write_text(
json.dumps(summary, indent=2) + "\n"
)
print(json.dumps(summary, indent=2), flush=True)
print(f"pack: {pack_path}", flush=True)
return pack_path
@dataclass
class _Raw:
source: str
shard: str
row: int
turn_idx: int
phase: str
kind: str
family: str
language: str
repo: str
instance_id: str
messages: list[dict[str, str]]
gold: str
first_edit: int
def _iter_raw(dataset_root: Path, *, max_raw: int, seed: str) -> Iterable[_Raw]:
rng = random.Random(seed)
shards_by_source = {
source: sorted((dataset_root / source / "data").glob("train-*.parquet"))
for source in SOURCES
if (dataset_root / source / "data").is_dir()
}
if not any(shards_by_source.values()):
raise FileNotFoundError(
f"no official shards under {dataset_root}/<source>/data/train-*.parquet"
)
produced = 0
cursor = {source: 0 for source in shards_by_source}
while produced < max_raw and any(
cursor[source] < len(shards) for source, shards in shards_by_source.items()
):
for source, shards in shards_by_source.items():
if produced >= max_raw or cursor[source] >= len(shards):
continue
shard = shards[cursor[source]]
cursor[source] += 1
rel = f"{source}/data/{shard.name}"
took = 0
for raw in _rows_from_shard(source, rel, shard):
yield raw
produced += 1
took += 1
if produced >= max_raw or took >= 64:
break
if produced >= max_raw:
return
def _rows_from_shard(source: str, rel: str, shard: Path) -> Iterable[_Raw]:
schema = pq.read_schema(shard)
columns = [
name
for name in (
"messages",
"turns",
"conversation",
"instance_id",
"first_edit",
"family",
"repo",
"language",
)
if name in schema.names
]
if not columns:
return
parquet = pq.ParquetFile(shard)
row_idx = 0
for batch in parquet.iter_batches(batch_size=256, columns=columns):
for row in batch.to_pylist():
current = row_idx
row_idx += 1
turns = _as_turns(
row.get("messages") or row.get("turns") or row.get("conversation")
)
assistant = [i for i, turn in enumerate(turns) if _role(turn) == "assistant"]
if len(assistant) < 3:
continue
golds = [_content(turns[i]) for i in assistant]
given_edit = row.get("first_edit")
first_edit = (
int(given_edit)
if given_edit is not None
else _first_edit(golds)
)
instance_id = str(row.get("instance_id") or "")
language = str(row.get("language") or ("rust" if source == "mini-coder-rs" else "python"))
family = family_of(instance_id, str(row.get("family") or ""))
repo = str(row.get("repo") or (instance_id.split(".")[0] if instance_id else source))
for turn_idx, phase in candidate_turns(len(assistant), first_edit, golds):
gold = golds[turn_idx]
command = first_bash_block(gold)
if not command or _DUMMY_RE.search(command):
continue
kind = (
"submit"
if is_gold_submit(gold)
else "edit"
if is_edit_command(command)
else "explore"
)
if kind == "submit" and turn_idx <= 2:
continue
prefix_turns = turns[: assistant[turn_idx]]
messages = [
{"role": _chat_role(_role(turn)), "content": _content(turn)}
for turn in prefix_turns
if _content(turn)
]
prefix_chars = sum(len(m["content"]) for m in messages)
if prefix_chars > MAX_PREFIX_CHARS or not messages:
continue
yield _Raw(
source=source,
shard=rel,
row=current,
turn_idx=turn_idx,
phase=phase,
kind=kind,
family=family,
language=language,
repo=repo,
instance_id=instance_id,
messages=messages,
gold=gold,
first_edit=first_edit,
)
def _first_edit(golds: list[str]) -> int:
for index, gold in enumerate(golds, start=1):
if is_edit_command(first_bash_block(gold)):
return index
return 0
def _select(
raw: list[_Raw],
*,
max_examples: int,
seed: str,
submit_frac: float = 0.20,
edit_frac: float = 0.35,
) -> list[_Raw]:
rng = random.Random(f"{seed}:select")
buckets: dict[tuple[str, str, str, str], list[_Raw]] = defaultdict(list)
for item in raw:
lang = "other" if item.language != "python" else "python"
buckets[(item.source, item.phase, item.kind, lang)].append(item)
for items in buckets.values():
rng.shuffle(items)
want_submit = max(1, int(max_examples * submit_frac))
want_edit = max(1, int(max_examples * edit_frac))
want_other_lang = max(1, int(max_examples * NON_PYTHON_FRACTION))
picked: list[_Raw] = []
used: set[tuple[str, int, int]] = set()
def take(predicate, limit: int) -> None:
leftover = limit
keys = sorted(buckets)
while leftover > 0:
progressed = False
for key in keys:
if leftover <= 0:
break
items = buckets[key]
kept: list[_Raw] = []
found = None
while items:
item = items.pop()
ident = (item.shard, item.row, item.turn_idx)
if ident in used or not predicate(item):
kept.append(item)
continue
found = item
break
items.extend(kept)
if found is None:
continue
used.add((found.shard, found.row, found.turn_idx))
picked.append(found)
leftover -= 1
progressed = True
if not progressed:
break
take(lambda item: item.kind == "submit", want_submit)
take(lambda item: item.kind == "edit" and item.phase == "at_edit", want_edit)
take(lambda item: item.kind == "edit", want_edit - sum(1 for i in picked if i.kind == "edit"))
take(lambda item: item.language != "python", want_other_lang)
take(lambda _item: True, max_examples - len(picked))
rng.shuffle(picked)
return picked[:max_examples]
def _materialize(
raw: list[_Raw],
*,
salts: list[str],
tokenizer_path: str,
expand_submit_salts: bool = False,
) -> list[PackedExample]:
by_salt: dict[str, list[tuple[_Raw, EvalSample]]] = defaultdict(list)
for index, item in enumerate(raw):
chosen = salts if (expand_submit_salts and item.kind == "submit") else [salts[index % len(salts)]]
for salt in chosen:
sample_id = f"{item.shard}:{item.row}:{item.turn_idx}"
sample = EvalSample(sample_id=sample_id, prompt="", messages=list(item.messages))
by_salt[salt].append((item, sample))
examples: list[PackedExample] = []
for salt, group in by_salt.items():
rewritten = apply_submit_protocol(
[sample for _, sample in group],
salt=salt,
keep_original_ratio=KEEP_ORIGINAL_RATIO,
tokenizer_path=tokenizer_path,
enable_thinking=True,
)
for (item, _), sample in zip(group, rewritten, strict=True):
bash = sample.submit_command if item.kind == "submit" else None
if item.kind == "submit" and not sample.submit_command:
continue
completion = wrap_completion(item.gold, bash)
if completion is None:
continue
if item.kind == "submit" and not ANY_MARKER_RE.search(completion):
continue
prompt = sample.prompt or format_messages(
sample.messages or item.messages,
tokenizer_path=tokenizer_path,
enable_thinking=True,
)
if item.phase == "cold" and item.kind == "submit":
continue
extra: list[PackedExample] = []
if (
item.kind == "submit"
and "cat patch.txt" in (sample.submit_command or "")
):
extra.extend(_patch_prep_example(item, sample, tokenizer_path, salt))
examples.append(
PackedExample(
sample_id=sample.sample_id,
prompt=prompt,
completion=completion,
source=item.source,
phase=item.phase,
kind=item.kind,
family=item.family,
language=item.language,
repo=item.repo,
submit_command=sample.submit_command,
submit_marker=sample.submit_marker,
rewrite_mode=sample.rewrite_mode,
gold_paths=gold_paths(item.gold),
salt=salt,
)
)
examples.extend(extra)
return examples
def _patch_prep_example(
item: _Raw, sample: EvalSample, tokenizer_path: str, salt: str
) -> list[PackedExample]:
"""If gold already built a patch, keep that as its own prior turn (protocol: separate commands)."""
prev = None
for message in reversed(item.messages):
if message.get("role") == "assistant":
prev = message.get("content") or ""
break
if not prev:
return []
command = first_bash_block(prev)
if not command or "patch.txt" not in command:
if command and command.startswith("git diff") and ">" not in command:
command = f"{command} > patch.txt"
else:
return []
completion = wrap_completion(prev, command)
if completion is None:
return []
return [
PackedExample(
sample_id=f"{sample.sample_id}:patch-prep",
prompt=sample.prompt,
completion=completion,
source=item.source,
phase="post_edit",
kind="edit",
family=item.family,
language=item.language,
repo=item.repo,
submit_command=sample.submit_command,
submit_marker=sample.submit_marker,
rewrite_mode=sample.rewrite_mode,
gold_paths=gold_paths(prev),
salt=salt,
)
]
def _summary(examples: list[PackedExample]) -> dict[str, Any]:
def count(field: str) -> dict[str, int]:
out: dict[str, int] = defaultdict(int)
for example in examples:
out[str(getattr(example, field))] += 1
return dict(sorted(out.items()))
return {
"n": len(examples),
"source": count("source"),
"phase": count("phase"),
"kind": count("kind"),
"family": count("family"),
"language": count("language"),
"rewrite_mode": count("rewrite_mode"),
"markers": count("submit_marker"),
"tails": _tail_counts(examples),
}
def _tail_counts(examples: list[PackedExample]) -> dict[str, int]:
out: dict[str, int] = defaultdict(int)
for example in examples:
command = example.submit_command or ""
if "cat patch.txt" in command:
out["patchtxt"] += 1
elif "git diff --cached" in command:
out["gitdiff"] += 1
elif command:
out["bare"] += 1
else:
out["none"] += 1
out["known_tails"] = len(TAILS)
return dict(out)
def _as_turns(value: Any) -> list[Any]:
parsed = value
if isinstance(value, str):
try:
parsed = json.loads(value)
except json.JSONDecodeError:
return []
if isinstance(parsed, dict):
for key in ("messages", "turns", "conversation"):
if isinstance(parsed.get(key), list):
return parsed[key]
return []
return parsed if isinstance(parsed, list) else []
def _role(turn: Any) -> str:
if not isinstance(turn, dict):
return ""
return str(turn.get("role") or turn.get("speaker") or turn.get("from") or "").lower()
def _content(turn: Any) -> str:
if not isinstance(turn, dict):
return str(turn or "")
for key in ("content", "text", "value", "message"):
value = turn.get(key)
if value:
return str(value)
return ""
def _chat_role(role: str) -> str:
if role in {"assistant", "system", "user"}:
return role
if role in {"human", "prompter"}:
return "user"
if role in {"gpt", "bot", "model"}:
return "assistant"
return "user"
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