text: """Official evaluation script for SQuAD version 2.0.
  
  In addition to basic functionality, we also compute additional statistics and
  plot precision-recall curves if an additional na_prob.json file is provided.
  This file is expected to map question ID's to the model's predicted probability
  that a question is unanswerable.
  """
  import argparse
  import collections
  import json
  import string
  import sys
  
  import numpy as np
  import os
  import re
  
  OPTS = None
  
  
  def parse_args():
      parser = argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.')
      parser.add_argument('data_file', metavar='data.json', help='Input data JSON file.')
      parser.add_argument('pred_file', metavar='pred.json', help='Model predictions.')
      parser.add_argument('--out-file', '-o', metavar='eval.json',
                          help='Write accuracy metrics to file (default is stdout).')
      parser.add_argument('--na-prob-file', '-n', metavar='na_prob.json',
                          help='Model estimates of probability of no answer.')
      parser.add_argument('--na-prob-thresh', '-t', type=float, default=1.0,
                          help='Predict "" if no-answer probability exceeds this (default = 1.0).')
      parser.add_argument('--out-image-dir', '-p', metavar='out_images', default=None,
                          help='Save precision-recall curves to directory.')
      parser.add_argument('--verbose', '-v', action='store_true')
      if len(sys.argv) == 1:
          parser.print_help()
          sys.exit(1)
      return parser.parse_args()
  
  
  def make_qid_to_has_ans(dataset):
      qid_to_has_ans = {}
      for article in dataset:
          for p in article['paragraphs']:
              for qa in p['qas']:
                  qid_to_has_ans[qa['id']] = bool(qa['answers'])
      return qid_to_has_ans
  
  
  def normalize_answer(s):
      """Lower text and remove punctuation, articles and extra whitespace."""
  
      def remove_articles(text):
          regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
          return re.sub(regex, ' ', 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 get_tokens(s):
      if not s: return []
      return normalize_answer(s).split()
  
  
  def compute_exact(a_gold, a_pred):
      return int(normalize_answer(a_gold) == normalize_answer(a_pred))
  
  
  def compute_f1(a_gold, a_pred):
      gold_toks = get_tokens(a_gold)
      pred_toks = get_tokens(a_pred)
      common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
      num_same = sum(common.values())
      if len(gold_toks) == 0 or len(pred_toks) == 0:
          # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
          return int(gold_toks == pred_toks)
      if num_same == 0:
          return 0
      precision = 1.0 * num_same / len(pred_toks)
      recall = 1.0 * num_same / len(gold_toks)
      f1 = (2 * precision * recall) / (precision + recall)
      return f1
  
  
  def get_raw_scores(dataset, preds):
      exact_scores = {}
      f1_scores = {}
      for article in dataset:
          for p in article['paragraphs']:
              for qa in p['qas']:
                  qid = qa['id']
                  gold_answers = [a['text'] for a in qa['answers']
                                  if normalize_answer(a['text'])]
                  if not gold_answers:
                      # For unanswerable questions, only correct answer is empty string
                      gold_answers = ['']
                  if qid not in preds:
                      print('Missing prediction for %s' % qid)
                      continue
                  a_pred = preds[qid]
                  # Take max over all gold answers
                  exact_scores[qid] = max(compute_exact(a, a_pred) for a in gold_answers)
                  f1_scores[qid] = max(compute_f1(a, a_pred) for a in gold_answers)
      return exact_scores, f1_scores
  
  
  def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh):
      new_scores = {}
      for qid, s in scores.items():
          pred_na = na_probs[qid] > na_prob_thresh
          if pred_na:
              new_scores[qid] = float(not qid_to_has_ans[qid])
          else:
              new_scores[qid] = s
      return new_scores
  
  
  def make_eval_dict(exact_scores, f1_scores, qid_list=None):
      if not qid_list:
          total = len(exact_scores)
          return collections.OrderedDict([
              ('exact', 100.0 * sum(exact_scores.values()) / total),
              ('f1', 100.0 * sum(f1_scores.values()) / total),
              ('total', total),
          ])
      else:
          total = len(qid_list)
          return collections.OrderedDict([
              ('exact', 100.0 * sum(exact_scores[k] for k in qid_list) / total),
              ('f1', 100.0 * sum(f1_scores[k] for k in qid_list) / total),
              ('total', total),
          ])
  
  
  def merge_eval(main_eval, new_eval, prefix):
      for k in new_eval:
          main_eval['%s_%s' % (prefix, k)] = new_eval[k]
  
  
  def plot_pr_curve(precisions, recalls, out_image, title):
      plt.step(recalls, precisions, color='b', alpha=0.2, where='post')
      plt.fill_between(recalls, precisions, step='post', alpha=0.2, color='b')
      plt.xlabel('Recall')
      plt.ylabel('Precision')
      plt.xlim([0.0, 1.05])
      plt.ylim([0.0, 1.05])
      plt.title(title)
      plt.savefig(out_image)
      plt.clf()
  
  
  def make_precision_recall_eval(scores, na_probs, num_true_pos, qid_to_has_ans,
                                 out_image=None, title=None):
      qid_list = sorted(na_probs, key=lambda k: na_probs[k])
      true_pos = 0.0
      cur_p = 1.0
      cur_r = 0.0
      precisions = [1.0]
      recalls = [0.0]
      avg_prec = 0.0
      for i, qid in enumerate(qid_list):
          if qid_to_has_ans[qid]:
              true_pos += scores[qid]
          cur_p = true_pos / float(i + 1)
          cur_r = true_pos / float(num_true_pos)
          if i == len(qid_list) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
              # i.e., if we can put a threshold after this point
              avg_prec += cur_p * (cur_r - recalls[-1])
              precisions.append(cur_p)
              recalls.append(cur_r)
      if out_image:
          plot_pr_curve(precisions, recalls, out_image, title)
      return {'ap': 100.0 * avg_prec}
  
  
  def run_precision_recall_analysis(main_eval, exact_raw, f1_raw, na_probs,
                                    qid_to_has_ans, out_image_dir):
      if out_image_dir and not os.path.exists(out_image_dir):
          os.makedirs(out_image_dir)
      num_true_pos = sum(1 for v in qid_to_has_ans.values() if v)
      if num_true_pos == 0:
          return
      pr_exact = make_precision_recall_eval(
          exact_raw, na_probs, num_true_pos, qid_to_has_ans,
          out_image=os.path.join(out_image_dir, 'pr_exact.png'),
          title='Precision-Recall curve for Exact Match score')
      pr_f1 = make_precision_recall_eval(
          f1_raw, na_probs, num_true_pos, qid_to_has_ans,
          out_image=os.path.join(out_image_dir, 'pr_f1.png'),
          title='Precision-Recall curve for F1 score')
      oracle_scores = {k: float(v) for k, v in qid_to_has_ans.items()}
      pr_oracle = make_precision_recall_eval(
          oracle_scores, na_probs, num_true_pos, qid_to_has_ans,
          out_image=os.path.join(out_image_dir, 'pr_oracle.png'),
          title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)')
      merge_eval(main_eval, pr_exact, 'pr_exact')
      merge_eval(main_eval, pr_f1, 'pr_f1')
      merge_eval(main_eval, pr_oracle, 'pr_oracle')
  
  
  def histogram_na_prob(na_probs, qid_list, image_dir, name):
      if not qid_list:
          return
      x = [na_probs[k] for k in qid_list]
      weights = np.ones_like(x) / float(len(x))
      plt.hist(x, weights=weights, bins=20, range=(0.0, 1.0))
      plt.xlabel('Model probability of no-answer')
      plt.ylabel('Proportion of dataset')
      plt.title('Histogram of no-answer probability: %s' % name)
      plt.savefig(os.path.join(image_dir, 'na_prob_hist_%s.png' % name))
      plt.clf()
  
  
  def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
      num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
      cur_score = num_no_ans
      best_score = cur_score
      best_thresh = 0.0
      qid_list = sorted(na_probs, key=lambda k: na_probs[k])
      for i, qid in enumerate(qid_list):
          if qid not in scores: continue
          if qid_to_has_ans[qid]:
              diff = scores[qid]
          else:
              if preds[qid]:
                  diff = -1
              else:
                  diff = 0
          cur_score += diff
          if cur_score > best_score:
              best_score = cur_score
              best_thresh = na_probs[qid]
      return 100.0 * best_score / len(scores), best_thresh
  
  
  def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
      best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans)
      best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans)
      main_eval['best_exact'] = best_exact
      main_eval['best_exact_thresh'] = exact_thresh
      main_eval['best_f1'] = best_f1
      main_eval['best_f1_thresh'] = f1_thresh
  
  
  def main():
      with open(OPTS.data_file) as f:
          dataset_json = json.load(f)
          dataset = dataset_json['data']
      with open(OPTS.pred_file) as f:
          preds = json.load(f)
      if OPTS.na_prob_file:
          with open(OPTS.na_prob_file) as f:
              na_probs = json.load(f)
      else:
          na_probs = {k: 0.0 for k in preds}
      qid_to_has_ans = make_qid_to_has_ans(dataset)  # maps qid to True/False
      has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
      no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
      exact_raw, f1_raw = get_raw_scores(dataset, preds)
      exact_thresh = apply_no_ans_threshold(exact_raw, na_probs, qid_to_has_ans,
                                            OPTS.na_prob_thresh)
      f1_thresh = apply_no_ans_threshold(f1_raw, na_probs, qid_to_has_ans,
                                         OPTS.na_prob_thresh)
      out_eval = make_eval_dict(exact_thresh, f1_thresh)
      if has_ans_qids:
          has_ans_eval = make_eval_dict(exact_thresh, f1_thresh, qid_list=has_ans_qids)
          merge_eval(out_eval, has_ans_eval, 'HasAns')
      if no_ans_qids:
          no_ans_eval = make_eval_dict(exact_thresh, f1_thresh, qid_list=no_ans_qids)
          merge_eval(out_eval, no_ans_eval, 'NoAns')
      if OPTS.na_prob_file:
          find_all_best_thresh(out_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans)
      if OPTS.na_prob_file and OPTS.out_image_dir:
          run_precision_recall_analysis(out_eval, exact_raw, f1_raw, na_probs,
                                        qid_to_has_ans, OPTS.out_image_dir)
          histogram_na_prob(na_probs, has_ans_qids, OPTS.out_image_dir, 'hasAns')
          histogram_na_prob(na_probs, no_ans_qids, OPTS.out_image_dir, 'noAns')
      if OPTS.out_file:
          with open(OPTS.out_file, 'w') as f:
              json.dump(out_eval, f)
      else:
          print(json.dumps(out_eval, indent=2))
  
  
  def my_evaluation(dataset, preds, na_probs=None, na_prob_thresh=1.0):
      has_na_prob_score = False if na_probs is None else True
      if na_probs is None:
          na_probs = {k: 0.0 for k in preds}
      qid_to_has_ans = make_qid_to_has_ans(dataset)  # maps qid to True/False
      has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
      no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
      exact_raw, f1_raw = get_raw_scores(dataset, preds)
      exact_thresh = apply_no_ans_threshold(exact_raw, na_probs, qid_to_has_ans, na_prob_thresh)
      f1_thresh = apply_no_ans_threshold(f1_raw, na_probs, qid_to_has_ans, na_prob_thresh)
      out_eval = make_eval_dict(exact_thresh, f1_thresh)
      if has_ans_qids:
          has_ans_eval = make_eval_dict(exact_thresh, f1_thresh, qid_list=has_ans_qids)
          merge_eval(out_eval, has_ans_eval, 'HasAns')
      if no_ans_qids:
          no_ans_eval = make_eval_dict(exact_thresh, f1_thresh, qid_list=no_ans_qids)
          merge_eval(out_eval, no_ans_eval, 'NoAns')
      if has_na_prob_score:
          find_all_best_thresh(out_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans)
      return out_eval
  
  
  if __name__ == '__main__':
      OPTS = parse_args()
      if OPTS.out_image_dir:
          import matplotlib
          matplotlib.use('Agg')
          import matplotlib.pyplot as plt
      main()
  