#code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License)

import torch


def Fourier_filter(x, threshold, scale):
    # FFT
    x_freq = torch.fft.fftn(x.float(), dim=(-2, -1))
    x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1))

    B, C, H, W = x_freq.shape
    mask = torch.ones((B, C, H, W), device=x.device)

    crow, ccol = H // 2, W //2
    mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale
    x_freq = x_freq * mask

    # IFFT
    x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1))
    x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real

    return x_filtered.to(x.dtype)


class FreeU:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
                             "b1": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 10.0, "step": 0.01}),
                             "b2": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 10.0, "step": 0.01}),
                             "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}),
                             "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}),
                              }}
    RETURN_TYPES = ("MODEL",)
    FUNCTION = "patch"

    CATEGORY = "_for_testing"

    def patch(self, model, b1, b2, s1, s2):
        def output_block_patch(h, hsp, transformer_options):
            if h.shape[1] == 1280:
                h[:,:640] = h[:,:640] * b1
                hsp = Fourier_filter(hsp, threshold=1, scale=s1)
            if h.shape[1] == 640:
                h[:,:320] = h[:,:320] * b2
                hsp = Fourier_filter(hsp, threshold=1, scale=s2)
            return h, hsp

        m = model.clone()
        m.set_model_output_block_patch(output_block_patch)
        return (m, )


NODE_CLASS_MAPPINGS = {
    "FreeU": FreeU,
}
