text: Title: size mismatch error
  Body: When I run the "python3 app.py" for demo, it cannot load the pretrained model naver-clova-ix/donut-base-finetuned-docvqa, there is a size miss match error
  
      pretrained_model = DonutModel.from_pretrained(args.pretrained_path)
    File "/home/local/Project/chart/donut/donut/model.py", line 597, in from_pretrained
      model = super(DonutModel, cls).from_pretrained(pretrained_model_name_or_path, revision="official", *model_args, **kwargs)
    File "/home/local/.local/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3091, in from_pretrained
      ) = cls._load_pretrained_model(
    File "/home/local/.local/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3532, in _load_pretrained_model
      raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
  RuntimeError: Error(s) in loading state_dict for DonutModel:
  	size mismatch for encoder.model.layers.1.downsample.norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([512]).
  	size mismatch for encoder.model.layers.1.downsample.norm.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([512]).
  	size mismatch for encoder.model.layers.1.downsample.reduction.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
  	size mismatch for encoder.model.layers.2.downsample.norm.weight: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1024]).
  	size mismatch for encoder.model.layers.2.downsample.norm.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1024]).
  	size mismatch for encoder.model.layers.2.downsample.reduction.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([512, 1024]).
  	You may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method.
  