code: """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 os
  import re
  import string
  import sys
  
  import numpy as np
  
  
  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"]["text"])
      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 = [t for t in qa["answers"]["text"] if normalize_answer(t)]
                  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))
  
  
  if __name__ == "__main__":
      OPTS = parse_args()
      if OPTS.out_image_dir:
          import matplotlib
  
          matplotlib.use("Agg")
          import matplotlib.pyplot as plt
      main()
  