from vector_quantize_pytorch import FSQ
import torch
import torch.nn as nn
class FSQWrapper(nn.Module):
    def __init__(self, dim, levels):
        super().__init__()
        level_dim = len(levels)
        self.quantizer = FSQ(
            levels = levels
        )
        self.down = nn.Linear(dim, level_dim)
        self.up = nn.Linear(level_dim, dim)
    def forward(self, x):
        x = x.transpose(1,2)
        x = self.down(x)
        z_q, indices = self.quantizer(x)
        x_q = self.up(z_q)
        x_q = x_q.transpose(1,2)
        loss = torch.tensor(0.0).to(x.device)
        return x_q, indices, loss
    
    def decode(self, indices):
        z_q = self.quantizer.indices_to_codes(indices)
        x_q = self.up(z_q)
        return x_q.transpose(1,2)
    
if __name__ == '__main__':
    model = FSQWrapper(512, [8,4,4,4])
    x = torch.randn(1, 512, 10000)
    x_q, indices, loss = model(x)
    x_q = model.decode(indices)
    print(indices.min(), indices.max())
    print(loss)