


'''simple docstring'''







import argparse
import json
import re
import string
import sys

import numpy as np


A__:   List[Any]    =	0.5


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Tuple   ,_UpperCAmelCase     :     Tuple  )  ->      Any:
       _a   :			int					 =[""".""", """,""", """;""", """:"""]

       for token in remove_tokens:
              _a   :			Optional[Any]					 =ground_truth.replace(_UpperCAmelCase   ,""""""  )
              _a   :			Union[str, Any]					 =prediction.replace(_UpperCAmelCase   ,""""""  )

       _a ,   _a   :			Tuple					 =ground_truth.lower(), prediction.lower()
       _a ,   _a   :			int					 =ground_truth.replace("""/"""   ,""" """  ), prediction.replace("""/"""   ,""" """  )
       _a ,   _a   :			Any					 =set(ground_truth.split(""" """  )  ), set(prediction.split(""" """  )  )

       _a   :			Dict					 =ground_truth.intersection(_UpperCAmelCase  )
       _a   :			List[str]					 =ground_truth.union(_UpperCAmelCase  )
       _a   :			int					 =len(_UpperCAmelCase  ) / len(_UpperCAmelCase  )
       return jaccard


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     str  )  ->      List[Any]:

       def remove_articles(_UpperCAmelCase     :     Dict  ):
              return re.sub(R"""\b(a|an|the)\b"""   ,""" """   ,_UpperCAmelCase  )

       def white_space_fix(_UpperCAmelCase     :     Dict  ):
              return " ".join(text.split()  )

       def remove_punc(_UpperCAmelCase     :     Union[str, Any]  ):
              _a   :			Optional[Any]					 =set(string.punctuation  )
              return "".join(ch for ch in text if ch not in exclude  )

       def lower(_UpperCAmelCase     :     str  ):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase  )  )  )  )


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Optional[Any]   ,_UpperCAmelCase     :     str   ,_UpperCAmelCase     :     Any  )  ->      str:
       _a ,   _a ,   _a   :			List[Any]					 =0, 0, 0

       _a   :			Optional[int]					 ="""Parties""" in qa_id

       # first check if ground truth is empty
       if len(_UpperCAmelCase  ) == 0:
              if len(_UpperCAmelCase  ) > 0:
                     fp += len(_UpperCAmelCase  )  # false positive for each one
       else:
              for ground_truth in ground_truths:
                     assert len(_UpperCAmelCase  ) > 0
                     # check if there is a match
                     _a   :			Optional[Any]					 =False
                     for pred in predictions:
                            if substr_ok:
                                   _a   :			Optional[Any]					 =get_jaccard(_UpperCAmelCase   ,_UpperCAmelCase  ) >= IOU_THRESH or ground_truth in pred
                            else:
                                   _a   :			str					 =get_jaccard(_UpperCAmelCase   ,_UpperCAmelCase  ) >= IOU_THRESH
                            if is_match:
                                   _a   :			Tuple					 =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.)
                     _a   :			Dict					 =False
                     for ground_truth in ground_truths:
                            assert len(_UpperCAmelCase  ) > 0
                            if substr_ok:
                                   _a   :			Tuple					 =get_jaccard(_UpperCAmelCase   ,_UpperCAmelCase  ) >= IOU_THRESH or ground_truth in pred
                            else:
                                   _a   :			int					 =get_jaccard(_UpperCAmelCase   ,_UpperCAmelCase  ) >= IOU_THRESH
                            if is_match:
                                   _a   :			Union[str, Any]					 =True

                     if not match_found:
                            fp += 1

       _a   :			str					 =tp / (tp + fp) if tp + fp > 0 else np.nan
       _a   :			Dict					 =tp / (tp + fn) if tp + fn > 0 else np.nan

       return precision, recall


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Union[str, Any]  )  ->      Union[str, Any]:
       _a   :			str					 =precisions[::-1]
       for i in range(1   ,len(_UpperCAmelCase  )  ):
              _a   :			Union[str, Any]					 =max(precision_best[i - 1]   ,precision_best[i]  )
       _a   :			Optional[int]					 =precision_best[::-1]
       return precisions


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     int   ,_UpperCAmelCase     :     List[Any]  )  ->      int:
       _a   :			Dict					 =process_precisions(_UpperCAmelCase  )
       _a   :			Dict					 =np.trapz(_UpperCAmelCase   ,_UpperCAmelCase  )
       if np.isnan(_UpperCAmelCase  ):
              return 0
       return aupr


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Union[str, Any]   ,_UpperCAmelCase     :     Union[str, Any]   ,_UpperCAmelCase     :     Dict  )  ->      Any:
       _a   :			Dict					 =process_precisions(_UpperCAmelCase  )
       _a   :			int					 =0
       for prec, recall in zip(_UpperCAmelCase   ,_UpperCAmelCase  ):
              if recall >= recall_thresh:
                     _a   :			Optional[int]					 =prec
                     break
       return prec_at_recall


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     int   ,_UpperCAmelCase     :     List[Any]  )  ->      Dict:
       return normalize_answer(_UpperCAmelCase  ) == normalize_answer(_UpperCAmelCase  )


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     List[Any]   ,_UpperCAmelCase     :     Tuple   ,_UpperCAmelCase     :     List[str]  )  ->      List[str]:
       _a   :			Optional[Any]					 =0
       for pred in predictions:
              for ground_truth in ground_truths:
                     _a   :			Dict					 =metric_fn(_UpperCAmelCase   ,_UpperCAmelCase  )
                     if score == 1:  # break the loop when one prediction matches the ground truth
                            break
              if score == 1:
                     break
       return score


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     str   ,_UpperCAmelCase     :     int  )  ->      List[Any]:
       _a   :			Optional[int]					 =0
       _a   :			List[str]					 =[]
       _a   :			int					 =[]
       for article in dataset:
              for paragraph in article["paragraphs"]:
                     for qa in paragraph["qas"]:
                            total += 1
                            if qa["id"] not in predictions:
                                   _a   :			List[Any]					 ="""Unanswered question """ + qa["""id"""] + """ will receive score 0."""
                                   print(_UpperCAmelCase   ,file=sys.stderr  )
                                   continue
                            _a   :			Any					 =[x["""text"""] for x in qa["""answers"""]]
                            _a   :			Dict					 =predictions[qa["""id"""]]
                            _a ,   _a   :			Optional[int]					 =compute_precision_recall(_UpperCAmelCase   ,_UpperCAmelCase   ,qa["""id"""]  )

                            precisions.append(_UpperCAmelCase  )
                            recalls.append(_UpperCAmelCase  )

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

                            exact_match += metric_max_over_ground_truths(_UpperCAmelCase   ,_UpperCAmelCase   ,_UpperCAmelCase  )

       _a   :			int					 =[x for _, x in sorted(zip(_UpperCAmelCase   ,_UpperCAmelCase  )  )]
       recalls.sort()

       _a   :			Tuple					 =1_0_0.0 * fa / total
       _a   :			str					 =1_0_0.0 * exact_match / total
       _a   :			List[str]					 =get_aupr(_UpperCAmelCase   ,_UpperCAmelCase  )

       _a   :			Any					 =get_prec_at_recall(_UpperCAmelCase   ,_UpperCAmelCase   ,recall_thresh=0.9  )
       _a   :			Tuple					 =get_prec_at_recall(_UpperCAmelCase   ,_UpperCAmelCase   ,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__":
   A__:   List[Any]    =	argparse.ArgumentParser(description='''Evaluation for CUAD''')
   parser.add_argument('''dataset_file''', help='''Dataset file''')
   parser.add_argument('''prediction_file''', help='''Prediction File''')
   A__:   Any    =	parser.parse_args()
   with open(args.dataset_file) as dataset_file:
      A__:   List[str]    =	json.load(dataset_file)
      A__:   Dict    =	dataset_json['''data''']
   with open(args.prediction_file) as prediction_file:
      A__:   str    =	json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))






'''simple docstring'''







import math


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     int  )  ->      int:
       return sum(int(_UpperCAmelCase  ) for digit in str(_UpperCAmelCase  )  )


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     int = 30  )  ->      int:
       _a   :			List[Any]					 =[]
       for digit in range(2   ,100  ):
              for power in range(2   ,100  ):
                     _a   :			Optional[Any]					 =int(math.pow(_UpperCAmelCase   ,_UpperCAmelCase  )  )
                     if digit == digit_sum(_UpperCAmelCase  ):
                            digit_to_powers.append(_UpperCAmelCase  )

       digit_to_powers.sort()
       return digit_to_powers[n - 1]


if __name__ == "__main__":
   print(solution())


