import math

import torch

from lightx2v.models.networks.wan.infer.post_infer import WanPostInfer
from lightx2v.utils.envs import *


class WanAudioPostInfer(WanPostInfer):
    def __init__(self, config):
        self.out_dim = config["out_dim"]
        self.patch_size = (1, 2, 2)
        self.clean_cuda_cache = config.get("clean_cuda_cache", False)
        self.infer_dtype = GET_DTYPE()
        self.sensitive_layer_dtype = GET_SENSITIVE_DTYPE()

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

    @torch.compile(disable=not CHECK_ENABLE_GRAPH_MODE())
    def infer(self, x, pre_infer_out):
        x = x[:, : pre_infer_out.valid_patch_length]
        x = self.unpatchify(x, pre_infer_out.grid_sizes)

        if self.clean_cuda_cache:
            torch.cuda.empty_cache()

        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
