import torch

def project(v0, v1):
    v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3])
    v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1
    v0_orthogonal = v0 - v0_parallel
    return v0_parallel, v0_orthogonal

class APG:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "model": ("MODEL",),
                "eta": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "tooltip": "Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1."}),
                "norm_threshold": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 50.0, "step": 0.1, "tooltip": "Normalize guidance vector to this value, normalization disable at a setting of 0."}),
                "momentum": ("FLOAT", {"default": 0.0, "min": -5.0, "max": 1.0, "step": 0.01, "tooltip":"Controls a running average of guidance during diffusion, disabled at a setting of 0."}),
            }
        }
    RETURN_TYPES = ("MODEL",)
    FUNCTION = "patch"
    CATEGORY = "sampling/custom_sampling"

    def patch(self, model, eta, norm_threshold, momentum):
        running_avg = 0
        prev_sigma = None

        def pre_cfg_function(args):
            nonlocal running_avg, prev_sigma

            if len(args["conds_out"]) == 1: return args["conds_out"]

            cond = args["conds_out"][0]
            uncond = args["conds_out"][1]
            sigma = args["sigma"][0]
            cond_scale = args["cond_scale"]

            if prev_sigma is not None and sigma > prev_sigma:
                running_avg = 0
            prev_sigma = sigma

            guidance = cond - uncond

            if momentum != 0:
                if not torch.is_tensor(running_avg):
                    running_avg = guidance
                else:
                    running_avg = momentum * running_avg + guidance
                guidance = running_avg

            if norm_threshold > 0:
                guidance_norm = guidance.norm(p=2, dim=[-1, -2, -3], keepdim=True)
                scale = torch.minimum(
                    torch.ones_like(guidance_norm),
                    norm_threshold / guidance_norm
                )
                guidance = guidance * scale

            guidance_parallel, guidance_orthogonal = project(guidance, cond)
            modified_guidance = guidance_orthogonal + eta * guidance_parallel

            modified_cond = (uncond + modified_guidance) + (cond - uncond) / cond_scale

            return [modified_cond, uncond] + args["conds_out"][2:]

        m = model.clone()
        m.set_model_sampler_pre_cfg_function(pre_cfg_function)
        return (m,)

NODE_CLASS_MAPPINGS = {
    "APG": APG,
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "APG": "Adaptive Projected Guidance",
}