src/transformers/models/bert/convert_bert_pytorch_checkpoint_to_original_tf.py/main
def main(raw_args=None):
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_name", type=str, required=True, help="model name e.g. bert-base-uncased")
    parser.add_argument(
        "--cache_dir", type=str, default=None, required=False, help="Directory containing pytorch model"
    )
    parser.add_argument("--pytorch_model_path", type=str, required=True, help="/path/to/<pytorch-model-name>.bin")
    parser.add_argument("--tf_cache_dir", type=str, required=True, help="Directory in which to save tensorflow model")
    args = parser.parse_args(raw_args)

    model = BertModel.from_pretrained(
        pretrained_model_name_or_path=args.model_name,
        state_dict=torch.load(args.pytorch_model_path),
        cache_dir=args.cache_dir,
    )

    convert_pytorch_checkpoint_to_tf(model=model, ckpt_dir=args.tf_cache_dir, model_name=args.model_name)