# from https://github.com/bebebe666/OptimalSteps


import numpy as np
import torch

def loglinear_interp(t_steps, num_steps):
    """
    Performs log-linear interpolation of a given array of decreasing numbers.
    """
    xs = np.linspace(0, 1, len(t_steps))
    ys = np.log(t_steps[::-1])

    new_xs = np.linspace(0, 1, num_steps)
    new_ys = np.interp(new_xs, xs, ys)

    interped_ys = np.exp(new_ys)[::-1].copy()
    return interped_ys


NOISE_LEVELS = {"FLUX": [0.9968, 0.9886, 0.9819, 0.975, 0.966, 0.9471, 0.9158, 0.8287, 0.5512, 0.2808, 0.001],
"Wan":[1.0, 0.997, 0.995, 0.993, 0.991, 0.989, 0.987, 0.985, 0.98, 0.975, 0.973, 0.968, 0.96, 0.946, 0.927, 0.902, 0.864, 0.776, 0.539, 0.208, 0.001],
}

class OptimalStepsScheduler:
    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"model_type": (["FLUX", "Wan"], ),
                     "steps": ("INT", {"default": 20, "min": 3, "max": 1000}),
                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                      }
               }
    RETURN_TYPES = ("SIGMAS",)
    CATEGORY = "sampling/custom_sampling/schedulers"

    FUNCTION = "get_sigmas"

    def get_sigmas(self, model_type, steps, denoise):
        total_steps = steps
        if denoise < 1.0:
            if denoise <= 0.0:
                return (torch.FloatTensor([]),)
            total_steps = round(steps * denoise)

        sigmas = NOISE_LEVELS[model_type][:]
        if (steps + 1) != len(sigmas):
            sigmas = loglinear_interp(sigmas, steps + 1)

        sigmas = sigmas[-(total_steps + 1):]
        sigmas[-1] = 0
        return (torch.FloatTensor(sigmas), )

NODE_CLASS_MAPPINGS = {
    "OptimalStepsScheduler": OptimalStepsScheduler,
}