import torch
from musubi_tuner.ltx_2.components.protocols import DiffusionStepProtocol
from musubi_tuner.ltx_2.utils import to_velocity


class EulerDiffusionStep(DiffusionStepProtocol):
    """
    First-order Euler method for diffusion sampling.
    Takes a single step from the current noise level (sigma) to the next by
    computing velocity from the denoised prediction and applying: sample + velocity * dt.
    """

    def step(
        self, sample: torch.Tensor, denoised_sample: torch.Tensor, sigmas: torch.Tensor, step_index: int
    ) -> torch.Tensor:
        sigma = sigmas[step_index]
        sigma_next = sigmas[step_index + 1]
        dt = sigma_next - sigma
        velocity = to_velocity(sample, sigma, denoised_sample)

        return (sample.to(torch.float32) + velocity.to(torch.float32) * dt).to(sample.dtype)