import math

import torch

from lightx2v.models.networks.wan.infer.post_infer import WanPostInfer


class WanAudioPostInfer(WanPostInfer):
    def __init__(self, config):
        self.out_dim = config["out_dim"]
        self.patch_size = (1, 2, 2)

    def set_scheduler(self, scheduler):
        self.scheduler = scheduler

    def infer(self, weights, x, e, grid_sizes, valid_patch_length):
        if e.dim() == 2:
            modulation = weights.head_modulation.tensor  # 1, 2, dim
            e = (modulation + e.unsqueeze(1)).chunk(2, dim=1)
        elif e.dim() == 3:  # For Diffustion forcing
            modulation = weights.head_modulation.tensor.unsqueeze(2)  # 1, 2, seq, dim
            e = (modulation + e.unsqueeze(1)).chunk(2, dim=1)
            e = [ei.squeeze(1) for ei in e]

        norm_out = torch.nn.functional.layer_norm(x, (x.shape[1],), None, None, 1e-6).type_as(x)
        out = norm_out * (1 + e[1].squeeze(0)) + e[0].squeeze(0)
        x = weights.head.apply(out)

        x = x[:, :valid_patch_length]

        x = self.unpatchify(x, grid_sizes)
        return [u.float() for u in x]

    def unpatchify(self, x, grid_sizes):
        x = x.unsqueeze(0)
        c = self.out_dim
        out = []
        for u, v in zip(x, grid_sizes.tolist()):
            u = u[: math.prod(v)].view(*v, *self.patch_size, c)
            u = torch.einsum("fhwpqrc->cfphqwr", u)
            u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
            out.append(u)
        return out
