import argparse
import json
import re
import string
import sys

import numpy as np


lowerCAmelCase__					=				0.5






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			):
       lowerCAmelCase__  			=      ['.', ',', ';', ':']

       for token in remove_tokens:
              lowerCAmelCase__  			=      ground_truth.replace(lowerCAmelCase__			,						''			)
              lowerCAmelCase__  			=      prediction.replace(lowerCAmelCase__			,						''			)

       lowerCAmelCase__			,      lowerCAmelCase__  			=      ground_truth.lower(), prediction.lower()
       lowerCAmelCase__			,      lowerCAmelCase__  			=      ground_truth.replace('/'			,						' '			), prediction.replace('/'			,						' '			)
       lowerCAmelCase__			,      lowerCAmelCase__  			=      set(ground_truth.split(' '			)			), set(prediction.split(' '			)			)

       lowerCAmelCase__  			=      ground_truth.intersection(lowerCAmelCase__			)
       lowerCAmelCase__  			=      ground_truth.union(lowerCAmelCase__			)
       lowerCAmelCase__  			=      len(lowerCAmelCase__			) / len(lowerCAmelCase__			)
       return jaccard






def    __lowerCamelCase	(		lowerCAmelCase__			):







       def remove_articles(lowerCAmelCase__			):
              return re.sub(r'\b(a|an|the)\b'			,						' '			,						lowerCAmelCase__			)

       def white_space_fix(lowerCAmelCase__			):
              return " ".join(text.split()			)

       def remove_punc(lowerCAmelCase__			):
              lowerCAmelCase__  			=      set(string.punctuation			)
              return "".join(ch for ch in text if ch not in exclude			)

       def lower(lowerCAmelCase__			):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase__			)			)			)			)






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			):
       lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__  			=      0, 0, 0

       lowerCAmelCase__  			=      'Parties' in qa_id

       # first check if ground truth is empty
       if len(lowerCAmelCase__			) == 0:
              if len(lowerCAmelCase__			) > 0:
                     fp += len(lowerCAmelCase__			)  # false positive for each one
       else:
              for ground_truth in ground_truths:
                     assert len(lowerCAmelCase__			) > 0
                     # check if there is a match
                     lowerCAmelCase__  			=      False
                     for pred in predictions:
                            if substr_ok:
                                   lowerCAmelCase__  			=      get_jaccard(lowerCAmelCase__			,						lowerCAmelCase__			) >= IOU_THRESH or ground_truth in pred
                            else:
                                   lowerCAmelCase__  			=      get_jaccard(lowerCAmelCase__			,						lowerCAmelCase__			) >= IOU_THRESH
                            if is_match:
                                   lowerCAmelCase__  			=      True

                     if match_found:
                            tp += 1
                     else:
                            fn += 1

        # now also get any fps by looping through preds
              for pred in predictions:
                     # Check if there's a match. if so, don't count (don't want to double count based on the above)
                     # but if there's no match, then this is a false positive.
                     # (Note: we get the true positives in the above loop instead of this loop so that we don't double count
                     # multiple predictions that are matched with the same answer.)
                     lowerCAmelCase__  			=      False
                     for ground_truth in ground_truths:
                            assert len(lowerCAmelCase__			) > 0
                            if substr_ok:
                                   lowerCAmelCase__  			=      get_jaccard(lowerCAmelCase__			,						lowerCAmelCase__			) >= IOU_THRESH or ground_truth in pred
                            else:
                                   lowerCAmelCase__  			=      get_jaccard(lowerCAmelCase__			,						lowerCAmelCase__			) >= IOU_THRESH
                            if is_match:
                                   lowerCAmelCase__  			=      True

                     if not match_found:
                            fp += 1

       lowerCAmelCase__  			=      tp / (tp + fp) if tp + fp > 0 else np.nan
       lowerCAmelCase__  			=      tp / (tp + fn) if tp + fn > 0 else np.nan

       return precision, recall






def    __lowerCamelCase	(		lowerCAmelCase__			):
       lowerCAmelCase__  			=      precisions[::-1]
       for i in range(1			,						len(lowerCAmelCase__			)			):
              lowerCAmelCase__  			=      max(precision_best[i - 1]			,						precision_best[i]			)
       lowerCAmelCase__  			=      precision_best[::-1]
       return precisions






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			):
       lowerCAmelCase__  			=      process_precisions(lowerCAmelCase__			)
       lowerCAmelCase__  			=      np.trapz(lowerCAmelCase__			,						lowerCAmelCase__			)
       if np.isnan(lowerCAmelCase__			):
              return 0
       return aupr






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			):
       lowerCAmelCase__  			=      process_precisions(lowerCAmelCase__			)
       lowerCAmelCase__  			=      0
       for prec, recall in zip(lowerCAmelCase__			,						lowerCAmelCase__			):
              if recall >= recall_thresh:
                     lowerCAmelCase__  			=      prec
                     break
       return prec_at_recall






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			):
       return normalize_answer(lowerCAmelCase__			) == normalize_answer(lowerCAmelCase__			)






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			):
       lowerCAmelCase__  			=      0
       for pred in predictions:
              for ground_truth in ground_truths:
                     lowerCAmelCase__  			=      metric_fn(lowerCAmelCase__			,						lowerCAmelCase__			)
                     if score == 1:  # break the loop when one prediction matches the ground truth
                            break
              if score == 1:
                     break
       return score






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			):
       lowerCAmelCase__  			=      lowerCAmelCase__  			=      lowerCAmelCase__  			=      0
       lowerCAmelCase__  			=      []
       lowerCAmelCase__  			=      []
       for article in dataset:
              for paragraph in article["paragraphs"]:
                     for qa in paragraph["qas"]:
                            total += 1
                            if qa["id"] not in predictions:
                                   lowerCAmelCase__  			=      'Unanswered question ' + qa['id'] + ' will receive score 0.'
                                   print(lowerCAmelCase__			,						file=sys.stderr			)
                                   continue
                            lowerCAmelCase__  			=      [x['text'] for x in qa['answers']]
                            lowerCAmelCase__  			=      predictions[qa['id']]
                            lowerCAmelCase__			,      lowerCAmelCase__  			=      compute_precision_recall(lowerCAmelCase__			,						lowerCAmelCase__			,						qa['id']			)

                            precisions.append(lowerCAmelCase__			)
                            recalls.append(lowerCAmelCase__			)

                            if precision == 0 and recall == 0:
                                   fa += 0
                            else:
                                   fa += 2 * (precision * recall) / (precision + recall)

                            exact_match += metric_max_over_ground_truths(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			)

       lowerCAmelCase__  			=      [x for _, x in sorted(zip(lowerCAmelCase__			,						lowerCAmelCase__			)			)]
       recalls.sort()

       lowerCAmelCase__  			=      1_0_0.0 * fa / total
       lowerCAmelCase__  			=      1_0_0.0 * exact_match / total
       lowerCAmelCase__  			=      get_aupr(lowerCAmelCase__			,						lowerCAmelCase__			)

       lowerCAmelCase__  			=      get_prec_at_recall(lowerCAmelCase__			,						lowerCAmelCase__			,						recall_thresh=0.9			)
       lowerCAmelCase__  			=      get_prec_at_recall(lowerCAmelCase__			,						lowerCAmelCase__			,						recall_thresh=0.8			)

       return {
           "exact_match": exact_match,
           "f1": fa,
           "aupr": aupr,
           "prec_at_80_recall": prec_at_aa_recall,
           "prec_at_90_recall": prec_at_aa_recall,
       }


if __name__ == "__main__":
    lowerCAmelCase__					=				argparse.ArgumentParser(description='Evaluation for CUAD')
    parser.add_argument('dataset_file', help='Dataset file')
    parser.add_argument('prediction_file', help='Prediction File')
    lowerCAmelCase__					=				parser.parse_args()
    with open(args.dataset_file) as dataset_file:
        lowerCAmelCase__					=				json.load(dataset_file)
        lowerCAmelCase__					=				dataset_json["data"]
    with open(args.prediction_file) as prediction_file:
        lowerCAmelCase__					=				json.load(prediction_file)
    print(json.dumps(evaluate(dataset, predictions)))

def    __lowerCamelCase	(		lowerCAmelCase__			):
       return " ".join([word[::-1] for word in input_str.split()]			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
