import torch

from style_bert_vits2.models.matcha_flow import MatchaFlow


def test_matcha_flow_loss_and_sampling_with_odd_length() -> None:
    model = MatchaFlow(
        latent_channels=8,
        speaker_channels=4,
        channels=16,
        num_heads=2,
        dropout=0.0,
    )
    target = torch.randn(2, 8, 7)
    mu = torch.randn_like(target)
    mask = torch.tensor(
        [[[1, 1, 1, 1, 1, 1, 1]], [[1, 1, 1, 1, 1, 0, 0]]],
        dtype=target.dtype,
    )
    speaker = torch.randn(2, 4, 1)

    loss = model.compute_loss(target, mask, mu, speaker)
    loss.backward()

    assert loss.ndim == 0
    assert torch.isfinite(loss)
    assert any(parameter.grad is not None for parameter in model.parameters())

    sample = model(mu, mask, n_timesteps=2, speaker=speaker)
    assert sample.shape == target.shape
    assert torch.count_nonzero(sample[1, :, 5:]) == 0