




import argparse
import json
import re
import string
import sys

import numpy as np


a_      						=   0.5




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase     						=  ['''.''', ''',''', ''';''', ''':''']

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

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

    __UpperCamelCase     						=  ground_truth.intersection(lowercase_					)
    __UpperCamelCase     						=  ground_truth.union(lowercase_					)
    __UpperCamelCase     						=  len(lowercase_					) / len(lowercase_					)
    return jaccard




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		Tuple:

    """simple docstring"""


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

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

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

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

    return white_space_fix(remove_articles(remove_punc(lower(lowercase_					)					)					)					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_					)				->		str:

    """simple docstring"""


    __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  0, 0, 0

    __UpperCamelCase     						=  '''Parties''' in qa_id

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

            if not match_found:
                fp += 1

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

    return precision, recall




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		Tuple:

    """simple docstring"""


    __UpperCamelCase     						=  precisions[::-1]
    for i in range(1    ,   len(lowercase_					)					):
        __UpperCamelCase     						=  max(precision_best[i - 1]    ,   precision_best[i]					)
    __UpperCamelCase     						=  precision_best[::-1]
    return precisions




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		Dict:

    """simple docstring"""


    __UpperCamelCase     						=  process_precisions(lowercase_					)
    __UpperCamelCase     						=  np.trapz(lowercase_    ,   lowercase_					)
    if np.isnan(lowercase_					):
        return 0
    return aupr




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_					)				->		Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase     						=  process_precisions(lowercase_					)
    __UpperCamelCase     						=  0
    for prec, recall in zip(lowercase_    ,   lowercase_					):
        if recall >= recall_thresh:
            __UpperCamelCase     						=  prec
            break
    return prec_at_recall




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		Optional[Any]:

    """simple docstring"""


    return normalize_answer(lowercase_					) == normalize_answer(lowercase_					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_					)				->		str:

    """simple docstring"""


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




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		Tuple:

    """simple docstring"""


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

                precisions.append(lowercase_					)
                recalls.append(lowercase_					)

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

                exact_match += metric_max_over_ground_truths(lowercase_    ,   lowercase_    ,   lowercase_					)

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

    __UpperCamelCase     						=  100.0 * fa / total
    __UpperCamelCase     						=  100.0 * exact_match / total
    __UpperCamelCase     						=  get_aupr(lowercase_    ,   lowercase_					)

    __UpperCamelCase     						=  get_prec_at_recall(lowercase_    ,   lowercase_    ,   recall_thresh=0.9					)
    __UpperCamelCase     						=  get_prec_at_recall(lowercase_    ,   lowercase_    ,   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_      						=   argparse.ArgumentParser(description="Evaluation for CUAD")
 parser.add_argument("dataset_file", help="Dataset file")
 parser.add_argument("prediction_file", help="Prediction File")
 a_      						=   parser.parse_args()
 with open(args.dataset_file) as dataset_file:
  a_      						=   json.load(dataset_file)
  a_      						=   dataset_json["data"]
 with open(args.prediction_file) as prediction_file:
  a_      						=   json.load(prediction_file)
 print(json.dumps(evaluate(dataset, predictions)))












def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		bool:

    """simple docstring"""


    if not isinstance(lowercase_    ,   lowercase_					):
        __UpperCamelCase     						=  F"Input value of [number={number}] must be an integer"
        raise TypeError(lowercase_					)
    if number < 0 or number % 2 == 1:
        return False
    __UpperCamelCase     						=  int(number**0.5					)
    return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






