'''simple docstring'''




import argparse
import json
import re
import string
import sys

import numpy as np


__snake_case  : Dict		       =    0.5





def 	_UpperCAmelCase       (				_UpperCamelCase       :      str,  _UpperCamelCase       :      Optional[int]  )      -> Optional[int]:
    A_      =  ['''.''', ''',''', ''';''', ''':''']

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

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

    A_      =  ground_truth.intersection(_UpperCamelCase  )
    A_      =  ground_truth.union(_UpperCamelCase  )
    A_      =  len(_UpperCamelCase  ) / len(_UpperCamelCase  )
    return jaccard





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Any  )      -> str:

    def remove_articles(_UpperCamelCase       :      List[Any]  ):
        return re.sub(R'''\b(a|an|the)\b''',  ''' ''',  _UpperCamelCase  )

    def white_space_fix(_UpperCamelCase       :      Optional[Any]  ):
        return " ".join(text.split()  )

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

    def lower(_UpperCamelCase       :      Union[str, Any]  ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase  )  )  )  )





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Optional[int],  _UpperCamelCase       :      Dict,  _UpperCamelCase       :      int  )      -> Optional[Any]:
    A_  ,A_  ,A_      =  0, 0, 0

    A_      =  '''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_      =  False
            for pred in predictions:
                if substr_ok:
                    A_      =  get_jaccard(_UpperCamelCase,  _UpperCamelCase  ) >= IOU_THRESH or ground_truth in pred
                else:
                    A_      =  get_jaccard(_UpperCamelCase,  _UpperCamelCase  ) >= IOU_THRESH
                if is_match:
                    A_      =  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_      =  False
            for ground_truth in ground_truths:
                assert len(_UpperCamelCase  ) > 0
                if substr_ok:
                    A_      =  get_jaccard(_UpperCamelCase,  _UpperCamelCase  ) >= IOU_THRESH or ground_truth in pred
                else:
                    A_      =  get_jaccard(_UpperCamelCase,  _UpperCamelCase  ) >= IOU_THRESH
                if is_match:
                    A_      =  True

            if not match_found:
                fp += 1

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

    return precision, recall





def 	_UpperCAmelCase       (				_UpperCamelCase       :      List[str]  )      -> Optional[Any]:
    A_      =  precisions[::-1]
    for i in range(1,  len(_UpperCamelCase  )  ):
        A_      =  max(precision_best[i - 1],  precision_best[i]  )
    A_      =  precision_best[::-1]
    return precisions





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Optional[int],  _UpperCamelCase       :      Union[str, Any]  )      -> Optional[Any]:
    A_      =  process_precisions(_UpperCamelCase  )
    A_      =  np.trapz(_UpperCamelCase,  _UpperCamelCase  )
    if np.isnan(_UpperCamelCase  ):
        return 0
    return aupr





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Tuple,  _UpperCamelCase       :      Tuple,  _UpperCamelCase       :      int  )      -> Tuple:
    A_      =  process_precisions(_UpperCamelCase  )
    A_      =  0
    for prec, recall in zip(_UpperCamelCase,  _UpperCamelCase  ):
        if recall >= recall_thresh:
            A_      =  prec
            break
    return prec_at_recall





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int,  _UpperCamelCase       :      str  )      -> Tuple:
    return normalize_answer(_UpperCamelCase  ) == normalize_answer(_UpperCamelCase  )





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Union[str, Any],  _UpperCamelCase       :      Dict,  _UpperCamelCase       :      Optional[int]  )      -> Optional[int]:
    A_      =  0
    for pred in predictions:
        for ground_truth in ground_truths:
            A_      =  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 	_UpperCAmelCase       (				_UpperCamelCase       :      Optional[Any],  _UpperCamelCase       :      int  )      -> Any:
    A_      =  A_      =  A_      =  0
    A_      =  []
    A_      =  []
    for article in dataset:
        for paragraph in article["paragraphs"]:
            for qa in paragraph["qas"]:
                total += 1
                if qa["id"] not in predictions:
                    A_      =  '''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
                    print(_UpperCamelCase,  file=sys.stderr  )
                    continue
                A_      =  [x['''text'''] for x in qa['''answers''']]
                A_      =  predictions[qa['''id''']]
                A_  ,A_      =  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_      =  [x for _, x in sorted(zip(_UpperCamelCase,  _UpperCamelCase  )  )]
    recalls.sort()

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

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







'''simple docstring'''




import itertools





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int = 1_00_00_00  )      -> int:
    A_      =  [1]

    for i in itertools.count(len(_UpperCamelCase  )  ):
        A_      =  0
        for j in itertools.count(1  ):
            A_      =  -1 if j % 2 == 0 else +1
            A_      =  (j * j * 3 - j) // 2
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number
            index += j
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number

        if item == 0:
            return i
        partitions.append(_UpperCamelCase  )

    return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")






