def decode()

in curiosity/paraphrase_models.py [0:0]

• 18 lines of code
• 7 McCabe index (conditional complexity)

    def decode(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
        """
        Finalize predictions.
        """
        predicted_indices = output_dict["predictions"]
        if not isinstance(predicted_indices, numpy.ndarray):
            predicted_indices = predicted_indices.detach().cpu().numpy()
        all_predicted_tokens = []
        for indices in predicted_indices:
            # Beam search gives us the top k results for each source sentence
            # in the batch but we just want the single best.
            if len(indices.shape) > 1:
                indices = indices[0]
            indices = list(indices)
            # Collect indices till the first end_symbol
            if self._end_index in indices:
                indices = indices[: indices.index(self._end_index)]
            predicted_tokens = [
                self.vocab.get_token_from_index(x, namespace=self._target_namespace)
                for x in indices
            ]
            all_predicted_tokens.append(predicted_tokens)
        output_dict["predicted_tokens"] = all_predicted_tokens
        return output_dict