from diffusers import AutoencoderKL
from diffusers.configuration_utils import register_to_config
from diffusers.models.vae import Encoder, Decoder
from diffusers.utils import is_torch_version

import torch
import torch.nn as nn
from typing import Dict, Optional, Tuple, Union

class CustomizedDecoder(Decoder):
    def forward(
        self,
        sample: torch.FloatTensor,
        latent_embeds: Optional[torch.FloatTensor] = None,
        output_multi_feats = False,
    ) -> torch.FloatTensor:
        r"""The forward method of the `Decoder` class."""

        sample = self.conv_in(sample)

        upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
        multi_level_feats = []
        if self.training and self.gradient_checkpointing:

            def create_custom_forward(module):
                def custom_forward(*inputs):
                    return module(*inputs)

                return custom_forward

            if is_torch_version(">=", "1.11.0"):
                # middle
                sample = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(self.mid_block),
                    sample,
                    latent_embeds,
                    use_reentrant=False,
                )
                sample = sample.to(upscale_dtype)

                # up
                for up_block in self.up_blocks:
                    sample = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(up_block),
                        sample,
                        latent_embeds,
                        use_reentrant=False,
                    )
            else:
                # middle
                sample = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(self.mid_block), sample, latent_embeds
                )
                sample = sample.to(upscale_dtype)

                # up
                for up_block in self.up_blocks:
                    sample = torch.utils.checkpoint.checkpoint(create_custom_forward(up_block), sample, latent_embeds)
        else:
            # middle
            sample = self.mid_block(sample, latent_embeds)
            sample = sample.to(upscale_dtype)

            # up
            for up_block in self.up_blocks:
                sample = up_block(sample, latent_embeds)
                multi_level_feats.append(sample)

        # post-process
        if latent_embeds is None:
            sample = self.conv_norm_out(sample)
        else:
            sample = self.conv_norm_out(sample, latent_embeds)
        sample = self.conv_act(sample)
        sample = self.conv_out(sample)

        if output_multi_feats:
            return multi_level_feats
        else:
            return sample



class CustomizedAutoencoderKL(AutoencoderKL):
    @register_to_config
    def __init__(
        self,
        in_channels: int = 3,
        out_channels: int = 3,
        down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
        up_block_types: Tuple[str] = ("UpDecoderBlock2D",),
        block_out_channels: Tuple[int] = (64,),
        layers_per_block: int = 1,
        act_fn: str = "silu",
        latent_channels: int = 4,
        norm_num_groups: int = 32,
        sample_size: int = 32,
        scaling_factor: float = 0.18215,
        force_upcast: float = True,
    ):
        super().__init__()

        # pass init params to Encoder
        self.encoder = Encoder(
            in_channels=in_channels,
            out_channels=latent_channels,
            down_block_types=down_block_types,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            act_fn=act_fn,
            norm_num_groups=norm_num_groups,
            double_z=True,
        )

        # pass init params to Decoder
        self.decoder = CustomizedDecoder(
            in_channels=latent_channels,
            out_channels=out_channels,
            up_block_types=up_block_types,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            norm_num_groups=norm_num_groups,
            act_fn=act_fn,
        )

        self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
        self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1)

        self.use_slicing = False
        self.use_tiling = False

        # only relevant if vae tiling is enabled
        self.tile_sample_min_size = self.config.sample_size
        sample_size = (
            self.config.sample_size[0]
            if isinstance(self.config.sample_size, (list, tuple))
            else self.config.sample_size
        )
        self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))
        self.tile_overlap_factor = 0.25