text: # coding=utf-8
  # Copyright 2020 The HuggingFace Team All rights reserved.
  #
  # 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.
  """
  A subclass of `Trainer` specific to Question-Answering tasks
  """
  
  from transformers import Trainer, is_datasets_available, is_torch_tpu_available
  from transformers.trainer_utils import PredictionOutput
  
  
  if is_datasets_available():
      import datasets
  
  if is_torch_tpu_available():
      import torch_xla.core.xla_model as xm
      import torch_xla.debug.metrics as met
  
  
  class QuestionAnsweringTrainer(Trainer):
      def __init__(self, *args, eval_examples=None, post_process_function=None, **kwargs):
          super().__init__(*args, **kwargs)
          self.eval_examples = eval_examples
          self.post_process_function = post_process_function
  
      def evaluate(self, eval_dataset=None, eval_examples=None, ignore_keys=None):
          eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset
          eval_dataloader = self.get_eval_dataloader(eval_dataset)
          eval_examples = self.eval_examples if eval_examples is None else eval_examples
  
          # Temporarily disable metric computation, we will do it in the loop here.
          compute_metrics = self.compute_metrics
          self.compute_metrics = None
          try:
              output = self.prediction_loop(
                  eval_dataloader,
                  description="Evaluation",
                  # No point gathering the predictions if there are no metrics, otherwise we defer to
                  # self.args.prediction_loss_only
                  prediction_loss_only=True if compute_metrics is None else None,
                  ignore_keys=ignore_keys,
              )
          finally:
              self.compute_metrics = compute_metrics
  
          # We might have removed columns from the dataset so we put them back.
          if isinstance(eval_dataset, datasets.Dataset):
              eval_dataset.set_format(type=eval_dataset.format["type"], columns=list(eval_dataset.features.keys()))
  
          if self.post_process_function is not None and self.compute_metrics is not None:
              # Reduce Bart Outputs by one dimension:
              relevant_predictions = (output.predictions[0], output.predictions[1])
              
              eval_preds = self.post_process_function(eval_examples, eval_dataset, relevant_predictions)
              metrics = self.compute_metrics(eval_preds)
  
              self.log(metrics)
          else:
              metrics = {}
  
          if self.args.tpu_metrics_debug or self.args.debug:
              # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
              xm.master_print(met.metrics_report())
  
          self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, metrics)
          return metrics
  
      def predict(self, test_dataset, test_examples, ignore_keys=None):
          test_dataloader = self.get_test_dataloader(test_dataset)
  
          # Temporarily disable metric computation, we will do it in the loop here.
          compute_metrics = self.compute_metrics
          self.compute_metrics = None
          try:
              output = self.prediction_loop(
                  test_dataloader,
                  description="Evaluation",
                  # No point gathering the predictions if there are no metrics, otherwise we defer to
                  # self.args.prediction_loss_only
                  prediction_loss_only=True if compute_metrics is None else None,
                  ignore_keys=ignore_keys,
              )
          finally:
              self.compute_metrics = compute_metrics
  
          if self.post_process_function is None or self.compute_metrics is None:
              return output
  
          # We might have removed columns from the dataset so we put them back.
          if isinstance(test_dataset, datasets.Dataset):
              test_dataset.set_format(type=test_dataset.format["type"], columns=list(test_dataset.features.keys()))
  
          eval_preds = self.post_process_function(test_examples, test_dataset, output.predictions, "test")
          metrics = self.compute_metrics(eval_preds)
  
          return PredictionOutput(predictions=eval_preds.predictions, label_ids=eval_preds.label_ids, metrics=metrics)
  