import math
from typing import Optional
from einops import rearrange

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

class Quantizer(nn.Module):
    def __init__(self, codebook_size: int, codebook_dim: int, input_dim: int, max_codebook_updates_with_revival: Optional[int] = None) -> None:
        super().__init__()
        assert math.log2(codebook_size).is_integer()
        self.revival_entropy_threshold = int(math.log2(codebook_size)) - 2
        self.max_codebook_updates_with_revival = max_codebook_updates_with_revival
        self.pre_quant_proj = nn.Linear(input_dim, codebook_dim)
        self.post_quant_proj = nn.Linear(codebook_dim, input_dim)
        codebook = torch.empty(codebook_size, codebook_dim, requires_grad=False).uniform_(-1.0 / codebook_size, 1.0 / codebook_size)
        self.register_buffer('num_codebook_updates', torch.tensor(0))
        self.register_buffer('codebook', codebook)
        self.register_buffer('codewords_freqs', torch.ones(codebook_size).div(codebook_size))

    def forward(self, z: torch.Tensor) -> dict:
        z = self.pre_quant_proj(z)
        z = F.normalize(z, dim=-1)
        b, k = z.size(0), z.size(2)
        z = rearrange(z, 'b t k e -> (b t k) e')

        cosine_similarity = torch.einsum('n e, c e -> n c', z, self.codebook)
        tokens = cosine_similarity.argmax(dim=-1)
        q = self.codebook[tokens]

        q = z + (q - z).detach()
        q = self.post_quant_proj(q)

        q = rearrange(q, '(b t k) e -> b t k e', b=b, k=k)
        tokens = rearrange(tokens, '(b t k) -> b t k', b=b, k=k)
        return {
            "q": q,
            "tokens": tokens,
        }

    def compute_codebook_entropy(self) -> float:
        probs = self.codewords_freqs[self.codewords_freqs != 0]
        return -(torch.log2(probs) * probs).sum().item()

    @torch.no_grad()
    def embed_tokens(self, tokens: torch.LongTensor) -> torch.FloatTensor:
        return self.post_quant_proj(self.codebook[tokens])