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import csv
import fnmatch
import hashlib
import io
import json
import os
import re
import shutil
import tempfile
import zipfile
from dataclasses import dataclass
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Iterable, Mapping, Sequence
TOOLKIT_VERSION = "1.0.1"
EXPECTED_ACCOUNT = "AiLLMBS"
DEFAULT_REPO_ID = "AiLLMBS/neurocell-lm-ad"
TEXT_SUFFIXES = {
".md", ".txt", ".py", ".ps1", ".json", ".csv", ".tsv", ".toml",
".yaml", ".yml", ".cff", ".ini", ".cfg", ".gitattributes", ".gitignore",
}
PROTECTED_PATTERNS = [
"**/*.h5ad", "**/*.h5", "**/*.loom", "**/*.zarr/**", "**/*.parquet",
"**/*.joblib", "**/*.pkl", "**/*.pickle", "**/*.safetensors", "**/*.bin",
"**/*.pt", "**/*.pth", "**/*.ckpt", "**/*.npz", "**/*.npy",
"**/chunks/**", "**/.cache/**", "**/hf_cache/**", "**/__pycache__/**",
"**/*.token", "**/*.pem", "**/*.key", "**/.env", "**/.env.*",
"**/*lockbox*", "**/*sealed*", "**/*predictions*", "**/*donor*manifest*",
]
SECRET_PATTERNS: dict[str, re.Pattern[str]] = {
"hf_token": re.compile(r"\bhf_[A-Za-z0-9]{20,}\b"),
"aws_access_key": re.compile(r"\bAKIA[0-9A-Z]{16}\b"),
"aws_secret_assignment": re.compile(
r"(?i)(aws_secret_access_key|secret_access_key)\s*[:=]\s*['\"]?[^\s'\"]{12,}"
),
"private_key": re.compile(r"-----BEGIN (?:RSA |EC |OPENSSH |DSA )?PRIVATE KEY-----"),
"generic_bearer": re.compile(r"(?i)authorization\s*:\s*bearer\s+[A-Za-z0-9._~+/=-]{12,}"),
"generic_token_assignment": re.compile(
r"(?i)\b(token|api[_-]?key|access[_-]?key|client[_-]?secret)\b\s*[:=]\s*['\"]([A-Za-z0-9._~+/=-]{18,})['\"]"
),
}
ABSOLUTE_PATH_PATTERNS: dict[str, re.Pattern[str]] = {
"windows_drive_path": re.compile(r"(?<![A-Za-z0-9])(?:[A-Za-z]:\\[^\r\n\"'<>|]+)"),
"windows_user_path": re.compile(r"(?i)C:\\Users\\[^\\\s]+"),
"unix_home_path": re.compile(r"(?<![A-Za-z0-9])/(?:home|Users)/[^/\s]+(?:/[^\s\"']*)?"),
}
EMAIL_PATTERN = re.compile(r"(?i)\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b")
DONOR_PATTERN = re.compile(r"\bH(?:20|21)\.33\.\d{3}\b")
CELL_BARCODE_PATTERN = re.compile(r"\b[ACGT]{16}-\d+(?:_[A-Za-z0-9]+)?\b")
ALLOWED_PUBLIC_EMAILS = {
"terms@alleninstitute.org",
"communications@alleninstitute.org",
"security@huggingface.co",
}
class ReleaseError(RuntimeError):
pass
@dataclass(frozen=True)
class InventoryRow:
source_path: str
release_path: str
classification: str
include: bool
reason: str
contains_donor_level_data: bool
contains_absolute_paths: bool
license_or_redistribution_risk: str
cleanup_action: str
source_sha256: str = ""
release_sha256: str = ""
source_size_bytes: int | None = None
release_size_bytes: int | None = None
def to_dict(self) -> dict[str, Any]:
return {
"source_path": self.source_path,
"release_path": self.release_path,
"classification": self.classification,
"include": self.include,
"reason": self.reason,
"contains_donor_level_data": self.contains_donor_level_data,
"contains_absolute_paths": self.contains_absolute_paths,
"license_or_redistribution_risk": self.license_or_redistribution_risk,
"cleanup_action": self.cleanup_action,
"source_sha256": self.source_sha256,
"release_sha256": self.release_sha256,
"source_size_bytes": self.source_size_bytes,
"release_size_bytes": self.release_size_bytes,
}
def now_utc() -> str:
return datetime.now(timezone.utc).isoformat()
def sha256_bytes(data: bytes) -> str:
return hashlib.sha256(data).hexdigest()
def sha256_file(path: Path, chunk_size: int = 8 * 1024 * 1024) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for block in iter(lambda: handle.read(chunk_size), b""):
digest.update(block)
return digest.hexdigest()
def canonical_json_bytes(value: Any) -> bytes:
return json.dumps(
value,
ensure_ascii=False,
sort_keys=True,
separators=(",", ":"),
allow_nan=False,
).encode("utf-8")
def canonical_json_sha256(value: Any) -> str:
return sha256_bytes(canonical_json_bytes(value))
def read_json(path: Path) -> Any:
with path.open("r", encoding="utf-8-sig") as handle:
return json.load(handle)
def write_json(path: Path, value: Any) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(
json.dumps(value, indent=2, ensure_ascii=False, sort_keys=True) + "\n",
encoding="utf-8",
newline="\n",
)
def read_csv(path: Path) -> list[dict[str, str]]:
with path.open("r", encoding="utf-8-sig", newline="") as handle:
return list(csv.DictReader(handle))
def write_csv(path: Path, rows: Sequence[Mapping[str, Any]], fieldnames: Sequence[str] | None = None) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
if fieldnames is None:
keys: list[str] = []
seen: set[str] = set()
for row in rows:
for key in row:
if key not in seen:
seen.add(key)
keys.append(str(key))
fieldnames = keys
with path.open("w", encoding="utf-8", newline="") as handle:
writer = csv.DictWriter(handle, fieldnames=list(fieldnames), extrasaction="ignore")
writer.writeheader()
for row in rows:
writer.writerow({key: _csv_value(row.get(key)) for key in fieldnames})
def _csv_value(value: Any) -> Any:
if value is None:
return ""
if isinstance(value, bool):
return str(value).lower()
if isinstance(value, (dict, list, tuple)):
return json.dumps(value, sort_keys=True, ensure_ascii=False)
return value
def normalize_relpath(path: str | Path) -> str:
value = str(path).replace("\\", "/")
while value.startswith("./"):
value = value[2:]
value = value.lstrip("/")
while "//" in value:
value = value.replace("//", "/")
return value
def is_text_path(path: Path) -> bool:
if path.name in {"README", "LICENSE", "NOTICE", ".gitignore", ".gitattributes"}:
return True
return path.suffix.lower() in TEXT_SUFFIXES
def read_text_safely(path: Path, max_bytes: int = 8 * 1024 * 1024) -> str | None:
if path.stat().st_size > max_bytes or not is_text_path(path):
return None
data = path.read_bytes()
if b"\x00" in data:
return None
for encoding in ("utf-8-sig", "utf-8", "cp1252"):
try:
return data.decode(encoding)
except UnicodeDecodeError:
continue
return None
def sanitize_public_text(text: str) -> tuple[str, list[dict[str, str]]]:
actions: list[dict[str, str]] = []
replacements = [
(re.compile(r"(?i)D:\\LLM_Hugging_Face\\neurocell-lm-ad(?:\\[^\r\n\"']*)?"), "${NEUROCELL_PROJECT_ROOT}"),
(re.compile(r"(?i)D:/LLM_Hugging_Face/neurocell-lm-ad(?:/[^\r\n\"']*)?"), "${NEUROCELL_PROJECT_ROOT}"),
(re.compile(r"(?i)C:\\Users\\[^\\\s]+"), "<LOCAL_USER_HOME>"),
(re.compile(r"(?<![A-Za-z0-9])/(?:home|Users)/[^/\s]+"), "<LOCAL_USER_HOME>"),
]
sanitized = text
for pattern, replacement in replacements:
sanitized, count = pattern.subn(replacement, sanitized)
if count:
actions.append({"pattern": pattern.pattern, "replacement": replacement, "count": str(count)})
return sanitized, actions
def scan_text(text: str, *, allow_donor_ids: bool = False, allow_emails: Iterable[str] = ()) -> list[dict[str, Any]]:
findings: list[dict[str, Any]] = []
allowed_emails = set(ALLOWED_PUBLIC_EMAILS) | {email.lower() for email in allow_emails}
for name, pattern in SECRET_PATTERNS.items():
for match in pattern.finditer(text):
findings.append({"severity": "ERROR", "kind": name, "offset": match.start(), "match": _redact(match.group(0))})
for name, pattern in ABSOLUTE_PATH_PATTERNS.items():
for match in pattern.finditer(text):
findings.append({"severity": "ERROR", "kind": name, "offset": match.start(), "match": _redact_path(match.group(0))})
for match in EMAIL_PATTERN.finditer(text):
email = match.group(0).lower()
if email not in allowed_emails:
findings.append({"severity": "ERROR", "kind": "email_address", "offset": match.start(), "match": _redact(email)})
if not allow_donor_ids:
for match in DONOR_PATTERN.finditer(text):
findings.append({"severity": "ERROR", "kind": "donor_identifier", "offset": match.start(), "match": match.group(0)})
for match in CELL_BARCODE_PATTERN.finditer(text):
findings.append({"severity": "ERROR", "kind": "cell_identifier", "offset": match.start(), "match": _redact(match.group(0))})
return findings
def _redact(value: str) -> str:
if len(value) <= 8:
return "<redacted>"
return value[:4] + "..." + value[-4:]
def _redact_path(value: str) -> str:
if len(value) <= 24:
return "<absolute-path>"
return value[:3] + "..." + Path(value.replace("\\", "/")).name
def matches_any(path: str | Path, patterns: Sequence[str]) -> bool:
rel = normalize_relpath(path)
candidates = {rel, rel.lower()}
for pattern in patterns:
pat = normalize_relpath(pattern)
variants = [pat]
if pat.startswith("**/"):
variants.append(pat[3:])
for candidate in candidates:
for variant in variants:
if fnmatch.fnmatchcase(candidate, variant) or fnmatch.fnmatchcase(candidate, variant.lower()):
return True
return False
def verify_file_hash(path: Path, expected: str, label: str | None = None) -> str:
if not path.exists():
raise ReleaseError(f"Missing required file: {path}")
observed = sha256_file(path)
if observed.lower() != expected.lower():
raise ReleaseError(
f"SHA-256 mismatch for {label or path.name}: expected {expected.lower()}, observed {observed.lower()}."
)
return observed
def index_zip_by_basename(path: Path) -> dict[str, list[zipfile.ZipInfo]]:
result: dict[str, list[zipfile.ZipInfo]] = {}
with zipfile.ZipFile(path) as archive:
for info in archive.infolist():
if info.is_dir():
continue
result.setdefault(Path(info.filename).name, []).append(info)
return result
def read_zip_member_by_basename(path: Path, basename: str) -> bytes:
with zipfile.ZipFile(path) as archive:
matches = [info for info in archive.infolist() if not info.is_dir() and Path(info.filename).name == basename]
if len(matches) != 1:
raise ReleaseError(f"Expected exactly one {basename!r} in {path.name}; found {len(matches)}.")
return archive.read(matches[0])
def verify_zip_against_manifest(zip_path: Path, manifest_rows: Sequence[Mapping[str, str]]) -> dict[str, Any]:
by_name = index_zip_by_basename(zip_path)
verified: list[dict[str, Any]] = []
for row in manifest_rows:
source = row.get("Path") or row.get("path") or ""
expected = row.get("Hash") or row.get("hash") or ""
basename = Path(source.replace("\\", "/")).name
matches = by_name.get(basename, [])
if len(matches) != 1:
raise ReleaseError(
f"Final archive must contain exactly one {basename!r}; found {len(matches)} in {zip_path.name}."
)
with zipfile.ZipFile(zip_path) as archive:
data = archive.read(matches[0])
observed = sha256_bytes(data)
if observed.lower() != expected.lower():
raise ReleaseError(
f"Archive member hash mismatch for {basename}: expected {expected.lower()}, observed {observed.lower()}."
)
verified.append({"file": basename, "sha256": observed, "size_bytes": len(data)})
return {"archive": zip_path.name, "verified_members": verified, "member_count": len(verified)}
def extract_zip_member_to(path: Path, member_basename: str, destination: Path) -> Path:
data = read_zip_member_by_basename(path, member_basename)
destination.parent.mkdir(parents=True, exist_ok=True)
destination.write_bytes(data)
return destination
def locate_project_file(project_root: Path, relative_path: str, script_archive: Path | None = None) -> tuple[str, bytes]:
direct = project_root / Path(relative_path)
if direct.exists() and direct.is_file():
return str(direct), direct.read_bytes()
if script_archive and script_archive.exists():
basename = Path(relative_path).name
data = read_zip_member_by_basename(script_archive, basename)
return f"{script_archive}::{basename}", data
raise ReleaseError(f"Could not locate approved source file: {relative_path}")
def ensure_separate_release_dir(project_root: Path, release_dir: Path) -> None:
project = project_root.resolve()
release = release_dir.resolve()
if project == release:
raise ReleaseError("Release directory cannot equal the frozen project directory.")
try:
release.relative_to(project)
except ValueError:
return
raise ReleaseError("Release directory must not be inside the frozen project directory.")
def parse_snapshot_inventory(path: Path) -> list[dict[str, Any]]:
rows: list[dict[str, Any]] = []
pattern = re.compile(r"^\[FILE\]\s+(.+?)\s+\|\s+Size:\s+(\d+)\s+bytes\s+\|")
with path.open("r", encoding="utf-8-sig", errors="replace") as handle:
for line in handle:
match = pattern.match(line.rstrip("\r\n"))
if not match:
continue
rows.append({"path": normalize_relpath(match.group(1)), "size_bytes": int(match.group(2))})
if not rows:
raise ReleaseError(f"No file inventory entries found in snapshot: {path}")
return rows
def classify_project_path(path: str, size_bytes: int, approved_source_names: set[str]) -> dict[str, Any]:
rel = normalize_relpath(path)
lower = rel.lower()
name = Path(rel).name
donor_level = any(token in lower for token in [
"donor", "prediction", "cell_label", "cell_metadata", "sample_manifest", "index_manifest",
"score_manifest", "common_cell", "metadata.parquet", "eligibility.csv", "per_donor",
])
absolute = False
risk = "low"
cleanup = "none"
classification = "LOCAL_ONLY"
include = False
reason = "Local research artifact not required in the public release."
release_path = ""
if lower.startswith(("data_external/", "data_interim/")):
classification = "PROHIBITED_OR_UNCERTAIN"
risk = "high: upstream SEA-AD/Allen or source data"
reason = "Raw or upstream-derived source data are not redistributed."
elif lower.startswith("data_processed/"):
classification = "LOCAL_ONLY"
risk = "high: donor/cell-level derived data"
reason = "Processed cell-, donor-, feature-, or prediction-level artifacts remain local."
elif lower.startswith("outputs/") and ("chunks/" in lower or lower.endswith((".npz", ".npy"))):
classification = "PROHIBITED_OR_UNCERTAIN"
risk = "high: embedding vectors and cell-level metadata"
reason = "C2S embedding chunks and vector outputs are excluded."
elif lower.startswith("models/") or lower.endswith((".joblib", ".pkl", ".pickle", ".safetensors", ".bin", ".pt", ".pth")):
classification = "PROHIBITED_OR_UNCERTAIN"
risk = "medium/high: serialized executable model and derivative-rights uncertainty"
reason = "Serialized model artifacts are excluded; recreate locally from the frozen code and source data."
elif "lockbox" in lower or "sealed" in lower:
classification = "PROHIBITED_OR_UNCERTAIN"
risk = "critical: lockbox-related"
reason = "Lockbox-related files and identifiers must never be uploaded."
elif lower.startswith(("__pycache__/", ".cache/", "hf_cache/", "logs/")) or lower.endswith((".pyc", ".pyo", ".tmp", ".part")):
classification = "LOCAL_ONLY"
risk = "low"
reason = "Cache, log, compiled, or temporary file."
elif name in approved_source_names:
classification = "PUBLIC_AS_IS"
include = True
reason = "Approved outcome-safe reproduction source file."
release_path = f"scripts/research_pipeline/{name}"
elif re.match(r"^(?:13_inspect_mtg_raw_matrix(?:_v2)?|16_build_ranked_gene_records|17_audit_c2s_tokenizer|27_extract_c2s_frozen_embeddings|45_build_mec_ranked_gene_records|51_fit_ad_severity_training_baselines)\.py$", name):
classification = "ARCHIVE_ONLY"
reason = "Superseded or failed implementation preserved only in the frozen research archive."
elif lower.startswith("reports/"):
if donor_level or lower.endswith(".parquet"):
classification = "LOCAL_ONLY"
risk = "high: donor/cell-level or local path content"
reason = "Donor-level report or prediction-related audit remains local."
else:
classification = "PUBLIC_AFTER_CLEANUP"
risk = "medium: absolute paths and potentially detailed identifiers"
cleanup = "regenerate aggregate-only public summary"
reason = "Only an aggregate, path-sanitized derivative is published."
elif lower.startswith("configs/"):
classification = "LOCAL_ONLY"
risk = "medium: split/lockbox identifiers"
reason = "Original configuration may expose donor assignments or lockbox policy details; publish a sanitized protocol instead."
elif lower.endswith((".zip", ".7z", ".tar", ".gz")):
classification = "ARCHIVE_ONLY"
risk = "unknown bundled content"
reason = "Research archive is retained locally and is not uploaded wholesale."
elif lower.endswith((".py", ".ps1", ".md", ".txt")):
classification = "ARCHIVE_ONLY"
risk = "low/unknown"
reason = "Not part of the approved public source subset."
elif size_bytes > 50 * 1024 * 1024:
classification = "LOCAL_ONLY"
risk = "large generated artifact"
reason = "Unexpectedly large file is excluded by default."
return {
"source_path": rel,
"release_path": release_path,
"classification": classification,
"include": include,
"reason": reason,
"contains_donor_level_data": donor_level,
"contains_absolute_paths": absolute,
"license_or_redistribution_risk": risk,
"cleanup_action": cleanup,
"source_sha256": "",
"release_sha256": "",
"source_size_bytes": size_bytes,
"release_size_bytes": None,
}
def build_release_manifest(release_dir: Path, *, exclude: Sequence[str] = ()) -> dict[str, Any]:
files: list[dict[str, Any]] = []
for path in sorted(release_dir.rglob("*")):
if not path.is_file():
continue
rel = normalize_relpath(path.relative_to(release_dir))
if matches_any(rel, list(exclude)):
continue
files.append({
"path": rel,
"size_bytes": path.stat().st_size,
"sha256": sha256_file(path),
})
return {
"schema_version": "1.0",
"generated_at_utc": now_utc(),
"file_count": len(files),
"total_size_bytes": sum(item["size_bytes"] for item in files),
"files": files,
}
def manifest_content_hash(manifest: Mapping[str, Any]) -> str:
normalized = {
"schema_version": manifest.get("schema_version"),
"files": sorted(
[
{"path": item["path"], "size_bytes": int(item["size_bytes"]), "sha256": item["sha256"]}
for item in manifest.get("files", [])
],
key=lambda item: item["path"],
),
}
return canonical_json_sha256(normalized)
def verify_release_manifest(release_dir: Path, manifest_path: Path, *, allow_extra: Iterable[str] = ()) -> dict[str, Any]:
manifest = read_json(manifest_path)
expected = {item["path"]: item for item in manifest.get("files", [])}
errors: list[str] = []
verified: list[dict[str, Any]] = []
for rel, item in expected.items():
path = release_dir / Path(rel)
if not path.exists():
errors.append(f"missing:{rel}")
continue
observed_size = path.stat().st_size
observed_hash = sha256_file(path)
if observed_size != int(item["size_bytes"]):
errors.append(f"size:{rel}")
if observed_hash.lower() != str(item["sha256"]).lower():
errors.append(f"sha256:{rel}")
verified.append({"path": rel, "sha256": observed_hash, "size_bytes": observed_size})
allowed = {normalize_relpath(item) for item in allow_extra}
observed_paths = {
normalize_relpath(path.relative_to(release_dir))
for path in release_dir.rglob("*") if path.is_file()
}
extras = sorted(observed_paths - set(expected) - allowed)
if extras:
errors.extend(f"extra:{item}" for item in extras)
return {
"status": "PASS" if not errors else "FAIL",
"errors": errors,
"verified_files": len(verified),
"manifest_file_count": len(expected),
"content_sha256": manifest_content_hash(manifest),
}
def scan_release_tree(
release_dir: Path,
*,
deny_patterns: Sequence[str],
max_file_size_bytes: int,
allow_large_paths: Sequence[str] = (),
allow_donor_paths: Sequence[str] = (),
content_scan_exclude_paths: Sequence[str] = (),
) -> dict[str, Any]:
findings: list[dict[str, Any]] = []
scanned_files = 0
for path in sorted(release_dir.rglob("*")):
if not path.is_file():
continue
scanned_files += 1
rel = normalize_relpath(path.relative_to(release_dir))
if matches_any(rel, deny_patterns):
findings.append({"severity": "ERROR", "kind": "denylisted_path", "path": rel})
if path.stat().st_size > max_file_size_bytes and not matches_any(rel, allow_large_paths):
findings.append({
"severity": "ERROR", "kind": "unexpected_large_file", "path": rel,
"size_bytes": path.stat().st_size, "limit_bytes": max_file_size_bytes,
})
if matches_any(rel, content_scan_exclude_paths):
continue
text = read_text_safely(path)
if text is None:
continue
allow_donors = matches_any(rel, allow_donor_paths)
for finding in scan_text(text, allow_donor_ids=allow_donors):
findings.append({"path": rel, **finding})
return {
"status": "PASS" if not [f for f in findings if f["severity"] == "ERROR"] else "FAIL",
"scanned_files": scanned_files,
"findings": findings,
"error_count": len([f for f in findings if f["severity"] == "ERROR"]),
}
def copy_bytes(destination: Path, data: bytes) -> None:
destination.parent.mkdir(parents=True, exist_ok=True)
destination.write_bytes(data)
def copy_text_sanitized(destination: Path, text: str) -> list[dict[str, str]]:
sanitized, actions = sanitize_public_text(text)
destination.parent.mkdir(parents=True, exist_ok=True)
destination.write_text(sanitized.replace("\r\n", "\n"), encoding="utf-8", newline="\n")
return actions
def format_float(value: Any, digits: int = 4, missing: str = "—") -> str:
if value is None or value == "":
return missing
return f"{float(value):.{digits}f}"
def markdown_table(headers: Sequence[str], rows: Sequence[Sequence[Any]]) -> str:
lines = [
"| " + " | ".join(headers) + " |",
"|" + "|".join(["---"] * len(headers)) + "|",
]
for row in rows:
lines.append("| " + " | ".join(str(value) for value in row) + " |")
return "\n".join(lines)
def assert_no_nan_inf(value: Any, path: str = "root") -> None:
import math
if isinstance(value, dict):
for key, item in value.items():
assert_no_nan_inf(item, f"{path}.{key}")
elif isinstance(value, list):
for index, item in enumerate(value):
assert_no_nan_inf(item, f"{path}[{index}]")
elif isinstance(value, float) and (math.isnan(value) or math.isinf(value)):
raise ReleaseError(f"Non-finite value at {path}")
def safe_rmtree(path: Path, *, protected_root: Path | None = None) -> None:
resolved = path.resolve()
if protected_root is not None:
protected = protected_root.resolve()
if resolved == protected:
raise ReleaseError("Refusing to delete the protected project root.")
try:
resolved.relative_to(protected)
except ValueError:
pass
else:
raise ReleaseError("Refusing to delete a directory inside the protected project root.")
if path.exists():
shutil.rmtree(path)
def temporary_directory(parent: Path, prefix: str) -> Path:
parent.mkdir(parents=True, exist_ok=True)
return Path(tempfile.mkdtemp(prefix=prefix, dir=parent))
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