#!/usr/bin/env python3
"""Download and export dslim/bert-base-NER to ONNX for the PII Redaction POC."""

from __future__ import annotations

import json
import shutil
import subprocess
import sys
from pathlib import Path

REPO_ROOT = Path(__file__).resolve().parent.parent
MODELS_DIR = REPO_ROOT / "models"
MODEL_ID = "dslim/bert-base-NER"
REQUIRED_PACKAGES = ("transformers", "optimum[onnxruntime]", "onnx", "torch")


def ensure_dependencies() -> None:
    try:
        import optimum.onnxruntime  # noqa: F401
        import transformers  # noqa: F401
    except ImportError:
        print("Installing Python dependencies (this may take a few minutes)...")
        subprocess.check_call(
            [sys.executable, "-m", "pip", "install", *REQUIRED_PACKAGES],
            stdout=sys.stdout,
            stderr=sys.stderr,
        )


def export_model() -> None:
    from optimum.onnxruntime import ORTModelForTokenClassification
    from transformers import AutoTokenizer

    MODELS_DIR.mkdir(parents=True, exist_ok=True)
    temp_dir = MODELS_DIR / "_export_temp"
    if temp_dir.exists():
        shutil.rmtree(temp_dir)
    temp_dir.mkdir()

    print(f"Exporting {MODEL_ID} to ONNX...")
    model = ORTModelForTokenClassification.from_pretrained(MODEL_ID, export=True)
    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

    model.save_pretrained(temp_dir)
    tokenizer.save_pretrained(temp_dir)

    onnx_files = sorted(temp_dir.glob("*.onnx"))
    if not onnx_files:
        raise FileNotFoundError("Export completed but no .onnx file was produced.")

    target_onnx = MODELS_DIR / "ner-model.onnx"
    shutil.copy(onnx_files[0], target_onnx)
    shutil.copy(temp_dir / "vocab.txt", MODELS_DIR / "vocab.txt")

    config_path = temp_dir / "config.json"
    with config_path.open(encoding="utf-8") as config_file:
        config = json.load(config_file)

    id2label = config.get("id2label", {})
    labels = [id2label[str(index)] for index in range(len(id2label))]
    (MODELS_DIR / "ner-labels.txt").write_text("\n".join(labels), encoding="utf-8")

    shutil.rmtree(temp_dir)

    print()
    print("NER model assets saved:")
    print(f"  {target_onnx}")
    print(f"  {MODELS_DIR / 'vocab.txt'}")
    print(f"  {MODELS_DIR / 'ner-labels.txt'}")
    print()
    print("Run from repository root:")
    print("  dotnet run --project src/PiiRedaction.ConsoleApp -- --name CustomerNameOnly")


def main() -> int:
    ensure_dependencies()
    export_model()
    return 0


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