# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# 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.

"""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.

Modified slightly for the ELECTRA codebase.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import collections
import json
import numpy as np
import os
import re
import string
import sys
import tensorflow.compat.v1 as tf

import configure_finetuning

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 set_opts(config: configure_finetuning.FinetuningConfig, split):
  global OPTS
  Options = collections.namedtuple("Options", [
      "data_file", "pred_file", "out_file", "na_prob_file", "na_prob_thresh",
      "out_image_dir", "verbose"])
  OPTS = Options(
      data_file=os.path.join(
          config.raw_data_dir("squad"),
          split + ("-debug" if config.debug else "") + ".json"),
      pred_file=config.qa_preds_file("squad"),
      out_file=config.qa_eval_file("squad"),
      na_prob_file=config.qa_na_file("squad"),
      na_prob_thresh=config.qa_na_threshold,
      out_image_dir=None,
      verbose=False
  )

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 tf.io.gfile.GFile(OPTS.data_file) as f:
    dataset_json = json.load(f)
    dataset = dataset_json['data']
  with tf.io.gfile.GFile(OPTS.pred_file) as f:
    preds = json.load(f)
  if OPTS.na_prob_file:
    with tf.io.gfile.GFile(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 tf.io.gfile.GFile(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()
