def _save_pretrained()

in optimum/quanto/models/transformers_models.py [0:0]

• 12 lines of code
• 5 McCabe index (conditional complexity)

    def _save_pretrained(self, save_directory: Path) -> None:
        model = self._wrapped
        if getattr(model.config, "tie_word_embeddings", True):
            # The original model had tied embedding inputs and outputs
            if isinstance(model.get_input_embeddings(), QModuleMixin) or isinstance(
                model.get_output_embeddings(), QModuleMixin
            ):
                # At least one of the two is quantized, so they are not tied anymore
                model.config.tie_word_embeddings = False
        self._wrapped.save_pretrained(save_directory, safe_serialization=True)
        # Save quantization map to be able to reload the model
        qmap_name = os.path.join(save_directory, self._qmap_name())
        qmap = quantization_map(self._wrapped)
        with open(qmap_name, "w", encoding="utf8") as f:
            json.dump(qmap, f, indent=4)