text: #!/usr/bin/env python3
  #
  # SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  # SPDX-License-Identifier: Apache-2.0
  #
  # 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.
  #
  
  # Obtained from https://rajpurkar.github.io/SQuAD-explorer/
  
  """ Official evaluation script for v1.1 of the SQuAD dataset. """
  from __future__ import print_function
  from collections import Counter
  import string
  import re
  import argparse
  import json
  import sys
  
  def normalize_answer(s):
      """Lower text and remove punctuation, articles and extra whitespace."""
      def remove_articles(text):
          return re.sub(r'\b(a|an|the)\b', ' ', text)
  
      def white_space_fix(text):
          return ' '.join(text.split())
  
      def remove_punc(text):
          exclude = set(string.punctuation)
          return ''.join(ch for ch in text if ch not in exclude)
  
      def lower(text):
          return text.lower()
  
      return white_space_fix(remove_articles(remove_punc(lower(s))))
  
  
  def f1_score(prediction, ground_truth):
      prediction_tokens = normalize_answer(prediction).split()
      ground_truth_tokens = normalize_answer(ground_truth).split()
      common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
      num_same = sum(common.values())
      if num_same == 0:
          return 0
      precision = 1.0 * num_same / len(prediction_tokens)
      recall = 1.0 * num_same / len(ground_truth_tokens)
      f1 = (2 * precision * recall) / (precision + recall)
      return f1
  
  
  def exact_match_score(prediction, ground_truth):
      return (normalize_answer(prediction) == normalize_answer(ground_truth))
  
  
  def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
      scores_for_ground_truths = []
      for ground_truth in ground_truths:
          score = metric_fn(prediction, ground_truth)
          scores_for_ground_truths.append(score)
      return max(scores_for_ground_truths)
  
  def evaluate(dataset, predictions, f1_acc):
      f1 = exact_match = total = 0
      for article in dataset:
          for paragraph in article['paragraphs']:
              for qa in paragraph['qas']:
                  total += 1
                  if qa['id'] not in predictions:
                      message = 'Unanswered question ' + qa['id'] + \
                                ' will receive score 0.'
                      print(message, file=sys.stderr)
                      continue
                  ground_truths = list(map(lambda x: x['text'], qa['answers']))
                  prediction = predictions[qa['id']]
                  exact_match += metric_max_over_ground_truths(
                      exact_match_score, prediction, ground_truths)
                  f1 += metric_max_over_ground_truths(
                      f1_score, prediction, ground_truths)
  
      exact_match = 100.0 * exact_match / total
      f1 = 100.0 * f1 / total
      if (f1 < f1_acc - 0.5):
          print("&&&& FAILED TensorRT BERT Squad Accuracy matches reference.")
      else:
          print("&&&& PASSED TensorRT BERT Squad Accuracy matches reference.")
      return {'exact_match': exact_match, 'f1': f1}
  
  if __name__ == '__main__':
      expected_version = '1.1'
      parser = argparse.ArgumentParser(
          description='Evaluation for SQuAD ' + expected_version)
      parser.add_argument('dataset_file', help='Dataset file')
      parser.add_argument('prediction_file', help='Prediction File')
      parser.add_argument('f1_acc', help='Reference Accuracy')
      args = parser.parse_args()
      with open(args.dataset_file) as dataset_file:
          dataset_json = json.load(dataset_file)
          if (dataset_json['version'] != expected_version):
              print('Evaluation expects v-' + expected_version +
                    ', but got dataset with v-' + dataset_json['version'],
                    file=sys.stderr)
          dataset = dataset_json['data']
      with open(args.prediction_file) as prediction_file:
          predictions = json.load(prediction_file)
          f1_acc = float(args.f1_acc)
      print(json.dumps(evaluate(dataset, predictions, f1_acc)))
  