






import argparse
import json
import re
import string
import sys

import numpy as np


__snake_case							 =  0.5


def   _A       (    SCREAMING_SNAKE_CASE__  :     Optional[int]					,  SCREAMING_SNAKE_CASE__  :     Tuple	):
    UpperCamelCase	:Any     							=       [".", ",", ";", ":"]

    for token in remove_tokens:
        UpperCamelCase	:List[str]     							=       ground_truth.replace(A_					,  ''''''	)
        UpperCamelCase	:Any     							=       prediction.replace(A_					,  ''''''	)

    UpperCamelCase	:Optional[int]     							=       ground_truth.lower(), prediction.lower()
    UpperCamelCase	:List[Any]     							=       ground_truth.replace('''/'''					,  ''' '''	), prediction.replace('''/'''					,  ''' '''	)
    UpperCamelCase	:int     							=       set(ground_truth.split(''' '''	)	), set(prediction.split(''' '''	)	)

    UpperCamelCase	:Union[str, Any]     							=       ground_truth.intersection(A_	)
    UpperCamelCase	:Dict     							=       ground_truth.union(A_	)
    UpperCamelCase	:List[Any]     							=       len(A_	) / len(A_	)
    return jaccard


def   _A       (    SCREAMING_SNAKE_CASE__  :     List[Any]	):

    def remove_articles(SCREAMING_SNAKE_CASE__  :     Union[str, Any]	):
        return re.sub(R'''\b(a|an|the)\b'''					,  ''' '''					,  A_	)

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

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

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

    return white_space_fix(remove_articles(remove_punc(lower(A_	)	)	)	)


def   _A       (    SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     List[Any]	):
    UpperCamelCase	:Optional[int]     							=       0, 0, 0

    UpperCamelCase	:List[str]     							=       "Parties" in qa_id

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

            if not match_found:
                fp += 1

    UpperCamelCase	:Optional[Any]     							=       tp / (tp + fp) if tp + fp > 0 else np.nan
    UpperCamelCase	:Optional[int]     							=       tp / (tp + fn) if tp + fn > 0 else np.nan

    return precision, recall


def   _A       (    SCREAMING_SNAKE_CASE__  :     Union[str, Any]	):
    UpperCamelCase	:Tuple     							=       precisions[::-1]
    for i in range(1					,  len(A_	)	):
        UpperCamelCase	:Union[str, Any]     							=       max(precision_best[i - 1]					,  precision_best[i]	)
    UpperCamelCase	:Optional[int]     							=       precision_best[::-1]
    return precisions


def   _A       (    SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     Dict	):
    UpperCamelCase	:str     							=       process_precisions(A_	)
    UpperCamelCase	:Optional[int]     							=       np.trapz(A_					,  A_	)
    if np.isnan(A_	):
        return 0
    return aupr


def   _A       (    SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     str	):
    UpperCamelCase	:List[str]     							=       process_precisions(A_	)
    UpperCamelCase	:List[str]     							=       0
    for prec, recall in zip(A_					,  A_	):
        if recall >= recall_thresh:
            UpperCamelCase	:Union[str, Any]     							=       prec
            break
    return prec_at_recall


def   _A       (    SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     Any	):
    return normalize_answer(A_	) == normalize_answer(A_	)


def   _A       (    SCREAMING_SNAKE_CASE__  :     List[str]					,  SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     Any	):
    UpperCamelCase	:Optional[Any]     							=       0
    for pred in predictions:
        for ground_truth in ground_truths:
            UpperCamelCase	:Optional[Any]     							=       metric_fn(A_					,  A_	)
            if score == 1:  # break the loop when one prediction matches the ground truth
                break
        if score == 1:
            break
    return score


def   _A       (    SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     int	):
    UpperCamelCase	:List[Any]     							=       0
    UpperCamelCase	:Tuple     							=       []
    UpperCamelCase	:List[str]     							=       []
    for article in dataset:
        for paragraph in article["paragraphs"]:
            for qa in paragraph["qas"]:
                total += 1
                if qa["id"] not in predictions:
                    UpperCamelCase	:List[str]     							=       "Unanswered question " + qa["id"] + " will receive score 0."
                    print(A_					,  file=sys.stderr	)
                    continue
                UpperCamelCase	:Optional[int]     							=       [x["text"] for x in qa["answers"]]
                UpperCamelCase	:str     							=       predictions[qa["id"]]
                UpperCamelCase	:Optional[Any]     							=       compute_precision_recall(A_					,  A_					,  qa['''id''']	)

                precisions.append(A_	)
                recalls.append(A_	)

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

                exact_match += metric_max_over_ground_truths(A_					,  A_					,  A_	)

    UpperCamelCase	:Union[str, Any]     							=       [x for _, x in sorted(zip(A_					,  A_	)	)]
    recalls.sort()

    UpperCamelCase	:List[str]     							=       1_00.0 * fa / total
    UpperCamelCase	:Optional[Any]     							=       1_00.0 * exact_match / total
    UpperCamelCase	:str     							=       get_aupr(A_					,  A_	)

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













def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     float	):
    UpperCamelCase	:Tuple     							=       float()
    try:
        UpperCamelCase	:Union[str, Any]     							=       mass * acceleration
    except Exception:
        return -0.0
    return force


if __name__ == "__main__":
 import doctest

 # run doctest
 doctest.testmod()

 # demo
 __snake_case							 =  1_2.5
 __snake_case							 =  10
 __snake_case							 =  newtons_second_law_of_motion(mass, acceleration)
 print("""The force is """, force, """N""")





