"""
finetune_owlv2.py — CLI for fine-tuning OWLv2 on a COCO-format dataset.

Usage:
    uv run python scripts/finetune_owlv2.py
    uv run python scripts/finetune_owlv2.py --epochs 20 --lr 5e-5
    uv run python scripts/finetune_owlv2.py --unfreeze-vision --backbone-lr 1e-5
    uv run python scripts/finetune_owlv2.py --resume models/owlv2-finetuned/checkpoint-epoch-005

Recommended hardware:
    CUDA (Windows/Linux) — use fp16 for speed, set --device cuda
    MPS  (Apple Silicon) — fp32 only, slower but functional for small datasets
    CPU  — very slow, only for tiny sanity-check runs

Typical first run:
    1. make export              # build data/labeled/coco_export.json
    2. make finetune            # train with defaults
    3. Update app.py to load from models/owlv2-finetuned/best
"""

from __future__ import annotations

import logging
from pathlib import Path

import click
import torch
from dotenv import load_dotenv

load_dotenv()

from autolabel.config import settings
from autolabel.finetune import FinetuneConfig, run_finetune
from autolabel.utils import setup_logging

PROJECT_ROOT = Path(__file__).resolve().parent.parent
DEFAULT_OUTPUT = PROJECT_ROOT / "models" / "owlv2-finetuned"


@click.command()
@click.option(
    "--coco-json",
    default=str(settings.labeled_dir / "coco_export.json"),
    show_default=True,
    type=click.Path(exists=True, path_type=Path),
    help="COCO JSON file produced by `make export`.",
)
@click.option(
    "--image-dir",
    default=str(settings.raw_dir),
    show_default=True,
    type=click.Path(exists=True, file_okay=False, path_type=Path),
    help="Directory containing the source images (matched by file_name in COCO JSON).",
)
@click.option(
    "--output-dir",
    default=str(DEFAULT_OUTPUT),
    show_default=True,
    type=click.Path(file_okay=False, path_type=Path),
    help="Directory to save checkpoints and the best model.",
)
@click.option("--model", default=settings.model, show_default=True,
              help="Base model to fine-tune.")
@click.option("--epochs", default=10, show_default=True, type=int)
@click.option("--batch-size", default=1, show_default=True, type=int,
              help="Images per forward pass. Keep at 1 for OWLv2-large on ≤8 GB VRAM.")
@click.option("--grad-accum", default=4, show_default=True, type=int,
              help="Gradient accumulation steps. Effective batch = batch_size * grad_accum.")
@click.option("--lr", default=1e-4, show_default=True, type=float,
              help="Learning rate for detection heads.")
@click.option("--val-split", default=0.2, show_default=True, type=float,
              help="Fraction of data to use for validation.")
@click.option("--warmup-steps", default=50, show_default=True, type=int)
@click.option("--save-every", default=1, show_default=True, type=int,
              help="Save a checkpoint every N epochs.")
@click.option(
    "--unfreeze-vision", is_flag=True, default=False,
    help="Also fine-tune the ViT image encoder (needs more VRAM, slower).",
)
@click.option(
    "--backbone-lr", default=1e-5, show_default=True, type=float,
    help="LR for the vision encoder when --unfreeze-vision is set.",
)
@click.option(
    "--resume",
    default=None,
    type=click.Path(path_type=Path),
    help="Path to a saved checkpoint to resume from.",
)
@click.option(
    "--device",
    default=settings.device,
    show_default=True,
    help="Torch device: cuda | mps | cpu.",
)
@click.option("--verbose", "-v", is_flag=True, default=False)
def main(
    coco_json: Path,
    image_dir: Path,
    output_dir: Path,
    model: str,
    epochs: int,
    batch_size: int,
    grad_accum: int,
    lr: float,
    val_split: float,
    warmup_steps: int,
    save_every: int,
    unfreeze_vision: bool,
    backbone_lr: float,
    resume: Path | None,
    device: str,
    verbose: bool,
) -> None:
    """Fine-tune OWLv2 on your labeled COCO dataset."""
    setup_logging(logging.DEBUG if verbose else logging.INFO)

    dtype = torch.float16 if device == "cuda" else torch.float32

    cfg = FinetuneConfig(
        coco_json=coco_json,
        image_dir=image_dir,
        output_dir=output_dir,
        model_name=model,
        device=device,
        torch_dtype=dtype,
        epochs=epochs,
        batch_size=batch_size,
        grad_accum_steps=grad_accum,
        lr=lr,
        backbone_lr=backbone_lr if unfreeze_vision else 0.0,
        val_split=val_split,
        warmup_steps=warmup_steps,
        save_every=save_every,
        unfreeze_vision=unfreeze_vision,
        resume_from=resume,
    )

    click.echo(f"Fine-tuning OWLv2 on {coco_json}")
    click.echo(f"  device       : {device}  ({dtype})")
    click.echo(f"  epochs       : {epochs}")
    click.echo(f"  effective bs : {batch_size * grad_accum}")
    click.echo(f"  heads lr     : {lr}")
    click.echo(f"  unfreeze ViT : {unfreeze_vision}")
    click.echo(f"  output       : {output_dir}")
    click.echo()

    run_finetune(cfg)


if __name__ == "__main__":
    main()