src/transformers/models/imagegpt/convert_imagegpt_original_tf2_to_pytorch.py/convert_imagegpt_checkpoint_to_pytorch
def convert_imagegpt_checkpoint_to_pytorch(imagegpt_checkpoint_path, model_size, pytorch_dump_folder_path):
    # Construct configuration depending on size
    MODELS = {"small": (512, 8, 24), "medium": (1024, 8, 36), "large": (1536, 16, 48)}
    n_embd, n_head, n_layer = MODELS[model_size]  # set model hyperparameters
    config = ImageGPTConfig(n_embd=n_embd, n_layer=n_layer, n_head=n_head)
    model = ImageGPTForCausalLM(config)

    # Load weights from numpy
    load_tf_weights_in_imagegpt(model, config, imagegpt_checkpoint_path)

    # Save pytorch-model
    pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
    pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
    print(f"Save PyTorch model to {pytorch_weights_dump_path}")
    torch.save(model.state_dict(), pytorch_weights_dump_path)
    print(f"Save configuration file to {pytorch_config_dump_path}")
    with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
        f.write(config.to_json_string())