## Code adapted from [Esser, Rombach 2021]: https://compvis.github.io/taming-transformers/

import torch
import torch.nn as nn
import torch.nn.functional as F


def normalize(in_channels):
    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)

def swish(x):
    return x*torch.sigmoid(x)

class VectorQuantizer(nn.Module):
    """
    see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py
    ____________________________________________
    Discretization bottleneck part of the VQ-VAE.
    Inputs:
    - n_e : number of embeddings
    - e_dim : dimension of embedding
    - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
    _____________________________________________
    """

    def __init__(self, n_e, e_dim, beta):
        super(VectorQuantizer, self).__init__()
        self.n_e = n_e
        self.e_dim = e_dim
        self.beta = beta

        self.embedding = nn.Embedding(self.n_e, self.e_dim)
        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)

    def forward(self, z):
        z_flattened = z.view(-1, self.e_dim)

        d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
            torch.sum(self.embedding.weight**2, dim=1) - 2 * \
            torch.matmul(z_flattened, self.embedding.weight.t())
        d1 = torch.sum(z_flattened ** 2, dim=1, keepdim=True)
        d2 = torch.sum(self.embedding.weight**2, dim=1)
        d3 = torch.matmul(z_flattened, self.embedding.weight.t())

        min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
        min_encodings = torch.zeros(min_encoding_indices.shape[0], self.n_e).to(z) 
        min_encodings.scatter_(1, min_encoding_indices, 1)


        # get quantized latent vectors
        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)

        # compute loss for embedding
        loss = self.beta * torch.mean((z_q.detach()-z)**2) + \
                   torch.mean((z_q - z.detach()) ** 2)

        # preserve gradients
        z_q = z + (z_q - z).detach()

        # perplexity
        e_mean = torch.mean(min_encodings, dim=0)
        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))

        # reshape back to match original input shape
        z_q = z_q.permute(0, 2, 1).contiguous()
        return z_q, loss, (perplexity, min_encodings, min_encoding_indices)

    def get_distance(self, z):
        z = z.permute(0, 2, 1).contiguous()
        z_flattened = z.view(-1, self.e_dim)
        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z

        d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
            torch.sum(self.embedding.weight**2, dim=1) - 2 * \
            torch.matmul(z_flattened, self.embedding.weight.t())
        d = torch.reshape(d, (z.shape[0], -1, z.shape[2])).permute(0,2,1).contiguous()
        return d

    def get_codebook_entry(self, indices, shape):
        # shape specifying (batch, height, width, channel)
        min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices)
        min_encodings.scatter_(1, indices[:,None], 1)

        # get quantized latent vectors
        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)

        if shape is not None:
            z_q = z_q.view(shape)

        return z_q