






"""simple docstring"""





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



def      __lowercase       (     a			:					List[str]			)       ->       Any:

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

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

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

      def lower(a			:					int			):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(a			)			)			)			)



def      __lowercase       (     a			:					Optional[int]							,		a			:					Dict			)       ->       Optional[int]:
      __snake_case   :     List[Any]	      =normalize_answer(a			).split()
      __snake_case   :     Any	      =normalize_answer(a			).split()
      __snake_case   :     Optional[int]	      =Counter(a			) & Counter(a			)
      __snake_case   :     List[str]	      =sum(common.values()			)
      if num_same == 0:
            return 0
      __snake_case   :     Any	      =1.0 * num_same / len(a			)
      __snake_case   :     Dict	      =1.0 * num_same / len(a			)
      __snake_case   :     Union[str, Any]	      =(2 * precision * recall) / (precision + recall)
      return fa



def      __lowercase       (     a			:					Tuple							,		a			:					str			)       ->       Tuple:
      return normalize_answer(a			) == normalize_answer(a			)



def      __lowercase       (     a			:					str							,		a			:					Dict							,		a			:					Optional[int]			)       ->       Optional[Any]:
      __snake_case   :     int	      =[]
      for ground_truth in ground_truths:
            __snake_case   :     Union[str, Any]	      =metric_fn(a							,		a			)
            scores_for_ground_truths.append(a			)
      return max(a			)



def      __lowercase       (     a			:					int							,		a			:					Any			)       ->       List[str]:
      __snake_case   :     List[Any]	      =0
      __snake_case   :     Optional[int]	      =[]
      for passage in dataset:
            for qa in passage["qas"]:
                  total += 1
                  if qa["id"] not in predictions:
                        __snake_case   :     Optional[int]	      =f'''Unanswered question {qa["id"]} will receive score 0.'''
                        print(a							,		file=sys.stderr			)
                        continue

                  __snake_case   :     Any	      =[x['''text'''] for x in qa['''answers''']]
                  __snake_case   :     Optional[Any]	      =predictions[qa['''id''']]

                  __snake_case   :     Tuple	      =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			)

      __snake_case   :     Any	      =exact_match / total
      __snake_case   :     Optional[Any]	      =fa / total

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


if __name__ == "__main__":
    UpperCamelCase_						:					Any      				=			"""1.0"""
    UpperCamelCase_						:					Optional[Any]      				=			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.""")
    UpperCamelCase_						:					Union[str, Any]      				=			parser.parse_args()

    with open(args.data_file) as data_file:
        UpperCamelCase_						:					List[str]      				=			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,
            )
        UpperCamelCase_						:					List[Any]      				=			dataset_json["""data"""]

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

    UpperCamelCase_				,				UpperCamelCase_						:					List[Any]      				=			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      __lowercase       (     a			:					int							,		a			:					int							,		a			:					int							,		a			:					int			)       ->       bool:
      if xa == ya == 0 or xa == ya == 0:
            return False
      __snake_case   :     Any	      =xa * xa + ya * ya
      __snake_case   :     Tuple	      =xa * xa + ya * ya
      __snake_case   :     List[Any]	      =(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      __lowercase       (     a			:					int = 50			)       ->       int:
      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() = }''')


