import torch
import gradio as gr

from modules import scripts


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:
    def patch(self, model, b1, b2, s1, s2):
        model_channels = model.model.model_config.unet_config["model_channels"]
        scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
        on_cpu_devices = {}

        def output_block_patch(h, hsp, transformer_options):
            scale = scale_dict.get(h.shape[1], None)
            if scale is not None:
                h[:, :h.shape[1] // 2] = h[:, :h.shape[1] // 2] * scale[0]
                if hsp.device not in on_cpu_devices:
                    try:
                        hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
                    except:
                        print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.")
                        on_cpu_devices[hsp.device] = True
                        hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
                else:
                    hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)

            return h, hsp

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


class FreeU_V2:
    def patch(self, model, b1, b2, s1, s2):
        model_channels = model.model.diffusion_model.config["model_channels"]
        scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
        on_cpu_devices = {}

        def output_block_patch(h, hsp, transformer_options):
            scale = scale_dict.get(h.shape[1], None)
            if scale is not None:
                hidden_mean = h.mean(1).unsqueeze(1)
                B = hidden_mean.shape[0]
                hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True)
                hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True)
                hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3)

                h[:, :h.shape[1] // 2] = h[:, :h.shape[1] // 2] * ((scale[0] - 1) * hidden_mean + 1)

                if hsp.device not in on_cpu_devices:
                    try:
                        hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
                    except:
                        print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.")
                        on_cpu_devices[hsp.device] = True
                        hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
                else:
                    hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)

            return h, hsp

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


opFreeU_V2 = FreeU_V2()


class FreeUForForge(scripts.Script):
    sorting_priority = 12

    def title(self):
        return "FreeU Integrated"

    def show(self, is_img2img):
        # make this extension visible in both txt2img and img2img tab.
        return scripts.AlwaysVisible

    def ui(self, *args, **kwargs):
        with gr.Accordion(open=False, label=self.title(), elem_id="extensions-freeu",
                          elem_classes=["extensions-freeu"]):
            freeu_enabled = gr.Checkbox(label='Enabled', value=False)
            freeu_b1 = gr.Slider(label='B1', minimum=0, maximum=2, step=0.01, value=1.01)
            freeu_b2 = gr.Slider(label='B2', minimum=0, maximum=2, step=0.01, value=1.02)
            freeu_s1 = gr.Slider(label='S1', minimum=0, maximum=4, step=0.01, value=0.99)
            freeu_s2 = gr.Slider(label='S2', minimum=0, maximum=4, step=0.01, value=0.95)

        return freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2

    def process_before_every_sampling(self, p, *script_args, **kwargs):
        # This will be called before every sampling.
        # If you use highres fix, this will be called twice.

        freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2 = script_args

        if not freeu_enabled:
            return

        unet = p.sd_model.forge_objects.unet

        # unet = set_freeu_v2_patch(unet, freeu_b1, freeu_b2, freeu_s1, freeu_s2)
        unet = opFreeU_V2.patch(unet, freeu_b1, freeu_b2, freeu_s1, freeu_s2)[0]

        p.sd_model.forge_objects.unet = unet

        # Below codes will add some logs to the texts below the image outputs on UI.
        # The extra_generation_params does not influence results.
        p.extra_generation_params.update(dict(
            freeu_enabled=freeu_enabled,
            freeu_b1=freeu_b1,
            freeu_b2=freeu_b2,
            freeu_s1=freeu_s1,
            freeu_s2=freeu_s2,
        ))

        return