import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)


def custom_convert_ldm_vae_checkpoint(checkpoint, config):
    vae_state_dict = checkpoint

    new_checkpoint = {}

    new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
    new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
    new_checkpoint["encoder.conv_out.weight"] = vae_state_dict[
        "encoder.conv_out.weight"
    ]
    new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
    new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict[
        "encoder.norm_out.weight"
    ]
    new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict[
        "encoder.norm_out.bias"
    ]

    new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
    new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
    new_checkpoint["decoder.conv_out.weight"] = vae_state_dict[
        "decoder.conv_out.weight"
    ]
    new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
    new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict[
        "decoder.norm_out.weight"
    ]
    new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict[
        "decoder.norm_out.bias"
    ]

    new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
    new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
    new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
    new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]

    # Retrieves the keys for the encoder down blocks only
    num_down_blocks = len(
        {
            ".".join(layer.split(".")[:3])
            for layer in vae_state_dict
            if "encoder.down" in layer
        }
    )
    down_blocks = {
        layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key]
        for layer_id in range(num_down_blocks)
    }

    # Retrieves the keys for the decoder up blocks only
    num_up_blocks = len(
        {
            ".".join(layer.split(".")[:3])
            for layer in vae_state_dict
            if "decoder.up" in layer
        }
    )
    up_blocks = {
        layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key]
        for layer_id in range(num_up_blocks)
    }

    for i in range(num_down_blocks):
        resnets = [
            key
            for key in down_blocks[i]
            if f"down.{i}" in key and f"down.{i}.downsample" not in key
        ]

        if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
            new_checkpoint[
                f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"
            ] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.weight")
            new_checkpoint[
                f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"
            ] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.bias")

        paths = renew_vae_resnet_paths(resnets)
        meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
        assign_to_checkpoint(
            paths,
            new_checkpoint,
            vae_state_dict,
            additional_replacements=[meta_path],
            config=config,
        )

    mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
    num_mid_res_blocks = 2
    for i in range(1, num_mid_res_blocks + 1):
        resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]

        paths = renew_vae_resnet_paths(resnets)
        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
        assign_to_checkpoint(
            paths,
            new_checkpoint,
            vae_state_dict,
            additional_replacements=[meta_path],
            config=config,
        )

    mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
    paths = renew_vae_attention_paths(mid_attentions)
    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
    assign_to_checkpoint(
        paths,
        new_checkpoint,
        vae_state_dict,
        additional_replacements=[meta_path],
        config=config,
    )
    conv_attn_to_linear(new_checkpoint)

    for i in range(num_up_blocks):
        block_id = num_up_blocks - 1 - i
        resnets = [
            key
            for key in up_blocks[block_id]
            if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
        ]

        if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
            new_checkpoint[
                f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"
            ] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.weight"]
            new_checkpoint[
                f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"
            ] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.bias"]

        paths = renew_vae_resnet_paths(resnets)
        meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
        assign_to_checkpoint(
            paths,
            new_checkpoint,
            vae_state_dict,
            additional_replacements=[meta_path],
            config=config,
        )

    mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
    num_mid_res_blocks = 2
    for i in range(1, num_mid_res_blocks + 1):
        resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]

        paths = renew_vae_resnet_paths(resnets)
        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
        assign_to_checkpoint(
            paths,
            new_checkpoint,
            vae_state_dict,
            additional_replacements=[meta_path],
            config=config,
        )

    mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
    paths = renew_vae_attention_paths(mid_attentions)
    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
    assign_to_checkpoint(
        paths,
        new_checkpoint,
        vae_state_dict,
        additional_replacements=[meta_path],
        config=config,
    )
    conv_attn_to_linear(new_checkpoint)
    return new_checkpoint


def vae_pt_to_vae_diffuser(
    checkpoint_path: str,
    output_path: str,
):
    # Only support V1
    r = requests.get(
        " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
    )
    io_obj = io.BytesIO(r.content)

    original_config = OmegaConf.load(io_obj)
    image_size = 512
    device = "cuda" if torch.cuda.is_available() else "cpu"
    checkpoint = torch.load(checkpoint_path, map_location=device)

    # Convert the VAE model.
    vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
    converted_vae_checkpoint = custom_convert_ldm_vae_checkpoint(
        checkpoint["state_dict"], vae_config
    )

    vae = AutoencoderKL(**vae_config)
    vae.load_state_dict(converted_vae_checkpoint)
    vae.save_pretrained(output_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "--vae_pt_path",
        default="/Users/cwq/code/github/lama-cleaner/scripts/anything-v4.0.vae.pt",
        type=str,
        help="Path to the VAE.pt to convert.",
    )
    parser.add_argument(
        "--dump_path",
        default="diffusion_pytorch_model.bin",
        type=str,
        help="Path to the VAE.pt to convert.",
    )

    args = parser.parse_args()

    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)