import argparse
import json
import re
import string
import sys
from collections import Counter






def  __lowerCamelCase						(		lowerCAmelCase__      ):





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

       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(_A      )      )      )      )






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   normalize_answer(_A      ).split()
       lowerCAmelCase__	      =   normalize_answer(_A      ).split()
       lowerCAmelCase__	      =   Counter(_A      ) & Counter(_A      )
       lowerCAmelCase__	      =   sum(common.values()      )
       if num_same == 0:
              return 0
       lowerCAmelCase__	      =   1.0 * num_same / len(_A      )
       lowerCAmelCase__	      =   1.0 * num_same / len(_A      )
       lowerCAmelCase__	      =   (2 * precision * recall) / (precision + recall)
       return fa






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






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   []
       for ground_truth in ground_truths:
              lowerCAmelCase__	      =   metric_fn(_A      ,    _A      )
              scores_for_ground_truths.append(_A      )
       return max(_A      )






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   0
       lowerCAmelCase__	      =   []
       for passage in dataset:
              for qa in passage["qas"]:
                     total += 1
                     if qa["id"] not in predictions:
                            lowerCAmelCase__	      =   F"""Unanswered question {qa['id']} will receive score 0."""
                            print(_A      ,    file=sys.stderr      )
                            continue

                     lowerCAmelCase__	      =   [x['text'] for x in qa['answers']]
                     lowerCAmelCase__	      =   predictions[qa['id']]

                     lowerCAmelCase__	      =   metric_max_over_ground_truths(_A      ,    _A      ,    _A      )
                     if int(_exact_match      ) == 1:
                            correct_ids.append(qa['id']      )
                     exact_match += _exact_match

                     fa += metric_max_over_ground_truths(_A      ,    _A      ,    _A      )

       lowerCAmelCase__	      =   exact_match / total
       lowerCAmelCase__	      =   fa / total

       return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
    lowerCAmelCase__      		=					'1.0'
    lowerCAmelCase__      		=					argparse.ArgumentParser('Official evaluation script for ReCoRD v1.0.')
    parser.add_argument('data_file', help='The dataset file in JSON format.')
    parser.add_argument('pred_file', help='The model prediction file in JSON format.')
    parser.add_argument('--output_correct_ids', action='store_true', help='Output the correctly answered query IDs.')
    lowerCAmelCase__      		=					parser.parse_args()

    with open(args.data_file) as data_file:
        lowerCAmelCase__      		=					json.load(data_file)
        if dataset_json["version"] != expected_version:
            print(
                F'Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json["version"]}',
                file=sys.stderr,
            )
        lowerCAmelCase__      		=					dataset_json['data']

    with open(args.pred_file) as pred_file:
        lowerCAmelCase__      		=					json.load(pred_file)

    lowerCAmelCase__      		=					evaluate(dataset, predictions)

    if args.output_correct_ids:
        print(F'Output {len(correct_ids)} correctly answered question IDs.')
        with open('correct_ids.json', 'w') as f:
            json.dump(correct_ids, f)




'''simple docstring'''




from itertools import combinations, product

def       __UpperCamelCase     (     _A     :					int       , _A     :					int       , _A     :					int       , _A     :					int					)     ->   bool:




	"""simple docstring"""





	if xa == ya == 0 or xa == ya == 0:
		return False
	lowerCAmelCase       :     Optional[Any]   				=			xa * xa + ya * ya
	lowerCAmelCase       :     Tuple   				=			xa * xa + ya * ya
	lowerCAmelCase       :     List[str]   				=			(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
	return (
	    a_square + b_square == c_square
	    or a_square + c_square == b_square
	    or b_square + c_square == a_square
	)

def       __UpperCamelCase     (     _A     :					int = 50					)     ->   int:




	"""simple docstring"""





	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1					)       , repeat=2					)       , 2					)
	    if is_right(*_A       , *_A					)					)


if __name__ == "__main__":
				print(f"""{solution() = }""")
