"""Vector quantizer.

Copyright (2024) Bytedance Ltd. and/or its affiliates

Licensed under the Apache License, Version 2.0 (the "License"); 
you may not use this file except in compliance with the License. 
You may obtain a copy of the License at 

    http://www.apache.org/licenses/LICENSE-2.0 

Unless required by applicable law or agreed to in writing, software 
distributed under the License is distributed on an "AS IS" BASIS, 
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 
See the License for the specific language governing permissions and 
limitations under the License.

Reference: 
    https://github.com/CompVis/taming-transformers/blob/master/taming/modules/vqvae/quantize.py
    https://github.com/google-research/magvit/blob/main/videogvt/models/vqvae.py
    https://github.com/CompVis/latent-diffusion/blob/main/ldm/modules/distributions/distributions.py
"""
from typing import Mapping, Text, Tuple

import torch
from einops import rearrange
from torch.cuda.amp import autocast

class VectorQuantizer(torch.nn.Module):
    def __init__(self,
                 codebook_size: int = 1024,
                 token_size: int = 256,
                 commitment_cost: float = 0.25,
                 use_l2_norm: bool = False,
                 ):
        super().__init__()
        self.commitment_cost = commitment_cost

        self.embedding = torch.nn.Embedding(codebook_size, token_size)
        self.embedding.weight.data.uniform_(-1.0 / codebook_size, 1.0 / codebook_size)
        self.use_l2_norm = use_l2_norm

    # Ensure quantization is performed using f32
    @autocast(enabled=False)
    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, Mapping[Text, torch.Tensor]]:
        z = z.float()
        z = rearrange(z, 'b c h w -> b h w c').contiguous()
        z_flattened = rearrange(z, 'b h w c -> (b h w) c')

        if self.use_l2_norm:
            z_flattened = torch.nn.functional.normalize(z_flattened, dim=-1)
            embedding = torch.nn.functional.normalize(self.embedding.weight, dim=-1)
        else:
            embedding = self.embedding.weight
        d = torch.sum(z_flattened**2, dim=1, keepdim=True) + \
            torch.sum(embedding**2, dim=1) - 2 * \
            torch.einsum('bd,dn->bn', z_flattened, embedding.T)

        min_encoding_indices = torch.argmin(d, dim=1) # num_ele
        z_quantized = self.get_codebook_entry(min_encoding_indices).view(z.shape)

        if self.use_l2_norm:
            z = torch.nn.functional.normalize(z, dim=-1)

        # compute loss for embedding
        commitment_loss = self.commitment_cost * torch.mean((z_quantized.detach() - z) **2)
        codebook_loss = torch.mean((z_quantized - z.detach()) **2)

        loss = commitment_loss + codebook_loss

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

        # reshape back to match original input shape
        z_quantized = rearrange(z_quantized, 'b h w c -> b c h w').contiguous()

        result_dict = dict(
            quantizer_loss=loss,
            commitment_loss=commitment_loss,
            codebook_loss=codebook_loss,
            min_encoding_indices=min_encoding_indices.view(z_quantized.shape[0], z_quantized.shape[2], z_quantized.shape[3])
        )

        return z_quantized, result_dict

    def get_codebook_entry(self, indices):
        if len(indices.shape) == 1:
            z_quantized = self.embedding(indices)
        elif len(indices.shape) == 2:
            z_quantized = torch.einsum('bd,dn->bn', indices, self.embedding.weight)
        else:
            raise NotImplementedError
        if self.use_l2_norm:
            z_quantized = torch.nn.functional.normalize(z_quantized, dim=-1)
        return z_quantized
    

class DiagonalGaussianDistribution(object):
    @autocast(enabled=False)
    def __init__(self, parameters, deterministic=False):
        """Initializes a Gaussian distribution instance given the parameters.

        Args:
            parameters (torch.Tensor): The parameters for the Gaussian distribution. It is expected
                to be in shape [B, 2 * C, *], where B is batch size, and C is the embedding dimension.
                First C channels are used for mean and last C are used for logvar in the Gaussian distribution.
            deterministic (bool): Whether to use deterministic sampling. When it is true, the sampling results
                is purely based on mean (i.e., std = 0).
        """
        self.parameters = parameters
        self.mean, self.logvar = torch.chunk(parameters.float(), 2, dim=1)
        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
        self.deterministic = deterministic
        self.std = torch.exp(0.5 * self.logvar)
        self.var = torch.exp(self.logvar)
        if self.deterministic:
            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)

    @autocast(enabled=False)
    def sample(self):
        x = self.mean.float() + self.std.float() * torch.randn(self.mean.shape).to(device=self.parameters.device)
        return x

    @autocast(enabled=False)
    def mode(self):
        return self.mean

    @autocast(enabled=False)
    def kl(self):
        if self.deterministic:
            return torch.Tensor([0.])
        else:
            return 0.5 * torch.sum(torch.pow(self.mean.float(), 2)
                                    + self.var.float() - 1.0 - self.logvar.float(),
                                    dim=[1, 2])