
import math
import argparse
import os
import torch
from safetensors.torch import load_file, save_file
import library.model_util as model_util
import lora


def load_state_dict(file_name, dtype):
  if os.path.splitext(file_name)[1] == '.safetensors':
    sd = load_file(file_name)
  else:
    sd = torch.load(file_name, map_location='cpu')
  for key in list(sd.keys()):
    if type(sd[key]) == torch.Tensor:
      sd[key] = sd[key].to(dtype)
  return sd


def save_to_file(file_name, model, state_dict, dtype):
  if dtype is not None:
    for key in list(state_dict.keys()):
      if type(state_dict[key]) == torch.Tensor:
        state_dict[key] = state_dict[key].to(dtype)

  if os.path.splitext(file_name)[1] == '.safetensors':
    save_file(model, file_name)
  else:
    torch.save(model, file_name)


def merge_to_sd_model(text_encoder, unet, models, ratios, merge_dtype):
  text_encoder.to(merge_dtype)
  unet.to(merge_dtype)

  # create module map
  name_to_module = {}
  for i, root_module in enumerate([text_encoder, unet]):
    if i == 0:
      prefix = lora.LoRANetwork.LORA_PREFIX_TEXT_ENCODER
      target_replace_modules = lora.LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE
    else:
      prefix = lora.LoRANetwork.LORA_PREFIX_UNET
      target_replace_modules = lora.LoRANetwork.UNET_TARGET_REPLACE_MODULE

    for name, module in root_module.named_modules():
      if module.__class__.__name__ in target_replace_modules:
        for child_name, child_module in module.named_modules():
          if child_module.__class__.__name__ == "Linear" or child_module.__class__.__name__ == "Conv2d":
            lora_name = prefix + '.' + name + '.' + child_name
            lora_name = lora_name.replace('.', '_')
            name_to_module[lora_name] = child_module

  for model, ratio in zip(models, ratios):
    print(f"loading: {model}")
    lora_sd = load_state_dict(model, merge_dtype)

    print(f"merging...")
    for key in lora_sd.keys():
      if "lora_down" in key:
        up_key = key.replace("lora_down", "lora_up")
        alpha_key = key[:key.index("lora_down")] + 'alpha'

        # find original module for this lora
        module_name = '.'.join(key.split('.')[:-2])               # remove trailing ".lora_down.weight"
        if module_name not in name_to_module:
          print(f"no module found for LoRA weight: {key}")
          continue
        module = name_to_module[module_name]
        # print(f"apply {key} to {module}")

        down_weight = lora_sd[key]
        up_weight = lora_sd[up_key]

        dim = down_weight.size()[0]
        alpha = lora_sd.get(alpha_key, dim)
        scale = alpha / dim

        # W <- W + U * D
        weight = module.weight
        # print(module_name, down_weight.size(), up_weight.size())
        if len(weight.size()) == 2:
          # linear
          weight = weight + ratio * (up_weight @ down_weight) * scale
        elif down_weight.size()[2:4] == (1, 1):
          # conv2d 1x1
          weight = weight + ratio * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)
                                     ).unsqueeze(2).unsqueeze(3) * scale
        else:
          # conv2d 3x3
          conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
          # print(conved.size(), weight.size(), module.stride, module.padding)
          weight = weight + ratio * conved * scale

        module.weight = torch.nn.Parameter(weight)


def merge_lora_models(models, ratios, merge_dtype):
  base_alphas = {}                          # alpha for merged model
  base_dims = {}

  merged_sd = {}
  for model, ratio in zip(models, ratios):
    print(f"loading: {model}")
    lora_sd = load_state_dict(model, merge_dtype)

    # get alpha and dim
    alphas = {}                             # alpha for current model
    dims = {}                               # dims for current model
    for key in lora_sd.keys():
      if 'alpha' in key:
        lora_module_name = key[:key.rfind(".alpha")]
        alpha = float(lora_sd[key].detach().numpy())
        alphas[lora_module_name] = alpha
        if lora_module_name not in base_alphas:
          base_alphas[lora_module_name] = alpha
      elif "lora_down" in key:
        lora_module_name = key[:key.rfind(".lora_down")]
        dim = lora_sd[key].size()[0]
        dims[lora_module_name] = dim
        if lora_module_name not in base_dims:
          base_dims[lora_module_name] = dim

    for lora_module_name in dims.keys():
      if lora_module_name not in alphas:
        alpha = dims[lora_module_name]
        alphas[lora_module_name] = alpha
        if lora_module_name not in base_alphas:
          base_alphas[lora_module_name] = alpha

    print(f"dim: {list(set(dims.values()))}, alpha: {list(set(alphas.values()))}")

    # merge
    print(f"merging...")
    for key in lora_sd.keys():
      if 'alpha' in key:
        continue

      lora_module_name = key[:key.rfind(".lora_")]

      base_alpha = base_alphas[lora_module_name]
      alpha = alphas[lora_module_name]

      scale = math.sqrt(alpha / base_alpha) * ratio

      if key in merged_sd:
        assert merged_sd[key].size() == lora_sd[key].size(
        ), f"weights shape mismatch merging v1 and v2, different dims? / 重みのサイズが合いません。v1とv2、または次元数の異なるモデルはマージできません"
        merged_sd[key] = merged_sd[key] + lora_sd[key] * scale
      else:
        merged_sd[key] = lora_sd[key] * scale

  # set alpha to sd
  for lora_module_name, alpha in base_alphas.items():
    key = lora_module_name + ".alpha"
    merged_sd[key] = torch.tensor(alpha)

  print("merged model")
  print(f"dim: {list(set(base_dims.values()))}, alpha: {list(set(base_alphas.values()))}")

  return merged_sd


def merge(args):
  assert len(args.models) == len(args.ratios), f"number of models must be equal to number of ratios / モデルの数と重みの数は合わせてください"

  def str_to_dtype(p):
    if p == 'float':
      return torch.float
    if p == 'fp16':
      return torch.float16
    if p == 'bf16':
      return torch.bfloat16
    return None

  merge_dtype = str_to_dtype(args.precision)
  save_dtype = str_to_dtype(args.save_precision)
  if save_dtype is None:
    save_dtype = merge_dtype

  if args.sd_model is not None:
    print(f"loading SD model: {args.sd_model}")

    text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.sd_model)

    merge_to_sd_model(text_encoder, unet, args.models, args.ratios, merge_dtype)

    print(f"saving SD model to: {args.save_to}")
    model_util.save_stable_diffusion_checkpoint(args.v2, args.save_to, text_encoder, unet,
                                                args.sd_model, 0, 0, save_dtype, vae)
  else:
    state_dict = merge_lora_models(args.models, args.ratios, merge_dtype)

    print(f"saving model to: {args.save_to}")
    save_to_file(args.save_to, state_dict, state_dict, save_dtype)


def setup_parser() -> argparse.ArgumentParser:
  parser = argparse.ArgumentParser()
  parser.add_argument("--v2", action='store_true',
                      help='load Stable Diffusion v2.x model / Stable Diffusion 2.xのモデルを読み込む')
  parser.add_argument("--save_precision", type=str, default=None,
                      choices=[None, "float", "fp16", "bf16"], help="precision in saving, same to merging if omitted / 保存時に精度を変更して保存する、省略時はマージ時の精度と同じ")
  parser.add_argument("--precision", type=str, default="float",
                      choices=["float", "fp16", "bf16"], help="precision in merging (float is recommended) / マージの計算時の精度（floatを推奨）")
  parser.add_argument("--sd_model", type=str, default=None,
                      help="Stable Diffusion model to load: ckpt or safetensors file, merge LoRA models if omitted / 読み込むモデル、ckptまたはsafetensors。省略時はLoRAモデル同士をマージする")
  parser.add_argument("--save_to", type=str, default=None,
                      help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors")
  parser.add_argument("--models", type=str, nargs='*',
                      help="LoRA models to merge: ckpt or safetensors file / マージするLoRAモデル、ckptまたはsafetensors")
  parser.add_argument("--ratios", type=float, nargs='*',
                      help="ratios for each model / それぞれのLoRAモデルの比率")

  return parser


if __name__ == '__main__':
  parser = setup_parser()

  args = parser.parse_args()
  merge(args)
