import torch

from TTS.vocoder.models.melgan_generator import MelganGenerator
from TTS.vocoder.layers.pqmf import PQMF


class MultibandMelganGenerator(MelganGenerator):
    def __init__(self,
                 in_channels=80,
                 out_channels=4,
                 proj_kernel=7,
                 base_channels=384,
                 upsample_factors=(2, 8, 2, 2),
                 res_kernel=3,
                 num_res_blocks=3):
        super(MultibandMelganGenerator,
              self).__init__(in_channels=in_channels,
                             out_channels=out_channels,
                             proj_kernel=proj_kernel,
                             base_channels=base_channels,
                             upsample_factors=upsample_factors,
                             res_kernel=res_kernel,
                             num_res_blocks=num_res_blocks)
        self.pqmf_layer = PQMF(N=4, taps=62, cutoff=0.15, beta=9.0)

    def pqmf_analysis(self, x):
        return self.pqmf_layer.analysis(x)

    def pqmf_synthesis(self, x):
        return self.pqmf_layer.synthesis(x)

    @torch.no_grad()
    def inference(self, cond_features):
        cond_features = cond_features.to(self.layers[1].weight.device)
        cond_features = torch.nn.functional.pad(
            cond_features,
            (self.inference_padding, self.inference_padding),
            'replicate')
        return self.pqmf_synthesis(self.layers(cond_features))
