def __call__()

in training/flax/run_finetuning.py [0:0]

• 25 lines of code
• 8 McCabe index (conditional complexity)

    def __call__(self, features: List[Dict[str, Union[List[int], np.ndarray]]]) -> Dict[str, np.ndarray]:
        # split inputs and labels since they have to be of different lengths and need
        # different padding methods
        model_input_name = self.processor.model_input_names[0]

        # dataloader returns a list of features which we convert to a dict
        input_features = {model_input_name: [feature[model_input_name] for feature in features]}
        label_features = {"input_ids": [feature["labels"] for feature in features]}

        # reformat list to dict and set to pytorch format
        batch = self.processor.feature_extractor.pad(
            input_features,
            padding=self.input_padding,
            return_tensors="np",
        )

        labels_batch = self.processor.tokenizer.pad(
            label_features,
            max_length=self.max_target_length,
            padding=self.target_padding,
            return_tensors="np",
        )

        # if bos token is appended in previous tokenization step,
        # cut bos token here as it's append later anyways
        labels = labels_batch["input_ids"]
        if (labels[:, 0] == self.decoder_start_token_id).all().item():
            labels = labels[:, 1:]
            labels_batch.attention_mask = labels_batch.attention_mask[:, 1:]

        decoder_input_ids = shift_tokens_right(labels, self.decoder_start_token_id)

        # replace padding with -100 to ignore correctly when computing the loss
        labels = np.ma.array(labels, mask=np.not_equal(labels_batch.attention_mask, 1))
        labels = labels.filled(fill_value=-100)

        batch["labels"] = labels
        batch["decoder_input_ids"] = decoder_input_ids

        return batch