text: # Copyright The PyTorch Lightning team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import torch
  from torchmetrics import Accuracy, Precision, Recall
  
  from lightning_transformers.core.nlp import HFTransformer
  
  
  class MultipleChoiceTransformer(HFTransformer):
      """Defines ``LightningModule`` for the Multiple Choice Task.
  
      Args:
          *args: :class:`lightning_transformers.core.nlp.HFTransformer` arguments.
          downstream_model_type: Downstream HuggingFace AutoModel to load.
              (default ``transformers.AutoModelForMultipleChoice``)
          **kwargs: :class:`lightning_transformers.core.nlp.HFTransformer` arguments.
      """
  
      def __init__(self, *args, downstream_model_type: str = "transformers.AutoModelForMultipleChoice", **kwargs) -> None:
          super().__init__(downstream_model_type, *args, **kwargs)
  
      def training_step(self, batch, batch_idx):
          loss = self._step(batch, batch_idx, "train")
          return loss
  
      def validation_step(self, batch, batch_idx, dataloader_idx=0):
          loss = self._step(batch, batch_idx, "val")
          return loss
  
      def test_step(self, batch, batch_idx, dataloader_idx=0):
          loss = self._step(batch, batch_idx, "test")
          return loss
  
      def _step(self, batch, batch_idx, mode):
          outputs = self.model(**batch)
          loss, logits = outputs[:2]
          preds = torch.argmax(logits, axis=1)
          metric_dict = self.compute_metrics(preds, batch["labels"], mode=mode)
          self.log_dict(metric_dict, prog_bar=True, on_step=False, on_epoch=True)
          self.log(f"{mode}_loss", loss, prog_bar=True, sync_dist=True)
          return loss
  
      def configure_metrics(self, stage: str):
          self.prec = Precision(num_classes=self.num_classes)
          self.recall = Recall(num_classes=self.num_classes)
          self.acc = Accuracy()
          self.metrics = {"precision": self.prec, "recall": self.recall, "accuracy": self.acc}
  
      @property
      def num_classes(self):
          return self.trainer.datamodule.num_classes
  
      def compute_metrics(self, preds, labels, mode="val"):
          # Remove ignored index (special tokens)
          # Not required by all models. Only required for classification
          return {f"{mode}_{k}": metric(preds, labels) for k, metric in self.metrics.items()}
  