from typing import *
import torch
import torch.nn as nn
import torch.nn.functional as F
from ...modules import sparse as sp
from .base import SparseTransformerBase


class SLatEncoder(SparseTransformerBase):
    def __init__(
        self,
        resolution: int,
        in_channels: int,
        model_channels: int,
        latent_channels: int,
        num_blocks: int,
        num_heads: Optional[int] = None,
        num_head_channels: Optional[int] = 64,
        mlp_ratio: float = 4,
        attn_mode: Literal["full", "shift_window", "shift_sequence", "shift_order", "swin"] = "swin",
        window_size: int = 8,
        pe_mode: Literal["ape", "rope"] = "ape",
        use_fp16: bool = False,
        use_checkpoint: bool = False,
        qk_rms_norm: bool = False,
    ):
        super().__init__(
            in_channels=in_channels,
            model_channels=model_channels,
            num_blocks=num_blocks,
            num_heads=num_heads,
            num_head_channels=num_head_channels,
            mlp_ratio=mlp_ratio,
            attn_mode=attn_mode,
            window_size=window_size,
            pe_mode=pe_mode,
            use_fp16=use_fp16,
            use_checkpoint=use_checkpoint,
            qk_rms_norm=qk_rms_norm,
        )
        self.resolution = resolution
        self.out_layer = sp.SparseLinear(model_channels, 2 * latent_channels)

        self.initialize_weights()
        if use_fp16:
            self.convert_to_fp16()

    def initialize_weights(self) -> None:
        super().initialize_weights()
        # Zero-out output layers:
        nn.init.constant_(self.out_layer.weight, 0)
        nn.init.constant_(self.out_layer.bias, 0)

    def forward(self, x: sp.SparseTensor, sample_posterior=True, return_raw=False):
        h = super().forward(x)
        h = h.type(x.dtype)
        h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:]))
        h = self.out_layer(h)
        
        # Sample from the posterior distribution
        mean, logvar = h.feats.chunk(2, dim=-1)
        if sample_posterior:
            std = torch.exp(0.5 * logvar)
            z = mean + std * torch.randn_like(std)
        else:
            z = mean
        z = h.replace(z)
            
        if return_raw:
            return z, mean, logvar
        else:
            return z