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 numpy as np
  import os
  import re
  import string
  import sys
  
  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 f1_scores if k in qid_list) / total),
  					('f1', 100.0 * sum(f1_scores[k] for k in f1_scores if 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]
  		
  #     print('number of hasans:',len(has_ans_qids))
  #     print('number of noans:',len(no_ans_qids))
  #     print('len of preds:',len(preds))
  		
  		
  		#print golds
  #     print([dataset[list(preds.keys()).index(k)]['paragraphs'][0]['qas'][0]['question'] for k,v in preds.items() ])
  
  		exact_raw, f1_raw = get_raw_scores(dataset, preds)
  #     print('exact/f1 raw',exact_raw,f1_raw)
  		
  		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=[k for k in f1_thresh if k in has_ans_qids])
  #             print('has_ans_eval',has_ans_eval)
  						merge_eval(out_eval, has_ans_eval, 'HasAns')
  		if no_ans_qids:
  						no_ans_eval = make_eval_dict(exact_thresh, f1_thresh, qid_list=[k for k in f1_thresh if k in no_ans_qids])
  #             print('no_ans_eval',no_ans_eval)
  #             print(len(exact_thresh))
  						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()
  