text: import argparse
  import os
  import torch
  import torch.nn.parallel
  import torch.optim
  import torch.utils.data
  import torch.utils.data.distributed
  
  from repvgg import get_RepVGG_func_by_name, repvgg_model_convert
  
  parser = argparse.ArgumentParser(description='RepVGG Conversion')
  parser.add_argument('load', metavar='LOAD', help='path to the weights file')
  parser.add_argument('save', metavar='SAVE', help='path to the weights file')
  parser.add_argument('-a', '--arch', metavar='ARCH', default='RepVGG-A0')
  
  def convert():
      args = parser.parse_args()
  
      if 'plus' in args.arch:
          from repvggplus import get_RepVGGplus_func_by_name
          train_model = get_RepVGGplus_func_by_name(args.arch)(deploy=False, use_checkpoint=False)
      else:
          repvgg_build_func = get_RepVGG_func_by_name(args.arch)
          train_model = repvgg_build_func(deploy=False)
  
      if os.path.isfile(args.load):
          print("=> loading checkpoint '{}'".format(args.load))
          checkpoint = torch.load(args.load)
          if 'state_dict' in checkpoint:
              checkpoint = checkpoint['state_dict']
          elif 'model' in checkpoint:
              checkpoint = checkpoint['model']
          ckpt = {k.replace('module.', ''): v for k, v in checkpoint.items()}  # strip the names
          print(ckpt.keys())
          train_model.load_state_dict(ckpt)
      else:
          print("=> no checkpoint found at '{}'".format(args.load))
  
      if 'plus' in args.arch:
          train_model.switch_repvggplus_to_deploy()
          torch.save(train_model.state_dict(), args.save)
      else:
          repvgg_model_convert(train_model, save_path=args.save)
  
  
  if __name__ == '__main__':
      convert()