"""Validate tools.export_onnx with a tiny synthetic model."""
from __future__ import annotations

import json
from pathlib import Path

import pytest
import torch

from tools.export_onnx import (
    _check_flow_parity,
    _check_scroll_parity,
    export_flow_model,
    export_scroll_decoder,
)


@pytest.fixture
def tiny_flow_ckpt(tmp_path: Path) -> Path:
    """A flow model with seq_len=8, d_model=16, 1 layer — small but valid."""
    from tools.models import TrajectoryFlowModel

    cfg = {
        "seq_len": 8,
        "d_model": 16,
        "nhead": 2,
        "num_layers": 1,
        "dim_feedforward": 32,
        "cond_dim": 3,
    }
    model = TrajectoryFlowModel(**cfg, dropout=0.0)
    model.eval()
    out = tmp_path / "flow_ckpt"
    out.mkdir()
    torch.save(model.state_dict(), out / "flow_model.pt")
    (out / "train_config.json").write_text(json.dumps(cfg))
    return out


@pytest.fixture
def tiny_scroll_ckpt(tmp_path: Path) -> Path:
    """A scroll model with seq_len=4, latent=4, hidden=8."""
    from tools.scroll.models import ScrollCVAE

    cfg = {"seq_len": 4, "latent_dim": 4, "hidden": 8, "cond_dim": 7}
    model = ScrollCVAE(**cfg)
    model.eval()
    out = tmp_path / "scroll_ckpt"
    out.mkdir()
    torch.save(model.state_dict(), out / "scroll_model.pt")
    (out / "scroll_config.json").write_text(json.dumps(cfg))
    return out


def test_export_flow_model_parity(tiny_flow_ckpt: Path, tmp_path: Path) -> None:
    out_dir = tmp_path / "out"
    onnx_path = export_flow_model(tiny_flow_ckpt, out_dir)
    assert onnx_path.exists()
    _check_flow_parity(tiny_flow_ckpt, onnx_path)  # raises on failure


def test_export_scroll_decoder_parity(tiny_scroll_ckpt: Path, tmp_path: Path) -> None:
    out_dir = tmp_path / "out"
    onnx_path = export_scroll_decoder(tiny_scroll_ckpt, out_dir)
    assert onnx_path.exists()
    _check_scroll_parity(tiny_scroll_ckpt, onnx_path)