src/transformers/models/swin2sr/convert_swin2sr_original_to_pytorch.py/convert_state_dict
def convert_state_dict(orig_state_dict, config):
    for key in orig_state_dict.copy().keys():
        val = orig_state_dict.pop(key)

        if "qkv" in key:
            key_split = key.split(".")
            stage_num = int(key_split[1])
            block_num = int(key_split[4])
            dim = config.embed_dim

            if "weight" in key:
                orig_state_dict[
                    f"swin2sr.encoder.stages.{stage_num}.layers.{block_num}.attention.self.query.weight"
                ] = val[:dim, :]
                orig_state_dict[
                    f"swin2sr.encoder.stages.{stage_num}.layers.{block_num}.attention.self.key.weight"
                ] = val[dim : dim * 2, :]
                orig_state_dict[
                    f"swin2sr.encoder.stages.{stage_num}.layers.{block_num}.attention.self.value.weight"
                ] = val[-dim:, :]
            else:
                orig_state_dict[
                    f"swin2sr.encoder.stages.{stage_num}.layers.{block_num}.attention.self.query.bias"
                ] = val[:dim]
                orig_state_dict[
                    f"swin2sr.encoder.stages.{stage_num}.layers.{block_num}.attention.self.key.bias"
                ] = val[dim : dim * 2]
                orig_state_dict[
                    f"swin2sr.encoder.stages.{stage_num}.layers.{block_num}.attention.self.value.bias"
                ] = val[-dim:]
            pass
        else:
            orig_state_dict[rename_key(key, config)] = val

    return orig_state_dict