






'''simple docstring'''







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


def     _UpperCamelCase				(  snake_case__				):

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

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

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

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

      return white_space_fix(remove_articles(remove_punc(lower(snake_case__				)				)				)				)


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


def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      return normalize_answer(snake_case__				) == normalize_answer(snake_case__				)


def     _UpperCamelCase				(  snake_case__				,      snake_case__				,      snake_case__				):
      __A		 =   []
      for ground_truth in ground_truths:
            __A		 =   metric_fn(snake_case__				,      snake_case__				)
            scores_for_ground_truths.append(snake_case__				)
      return max(snake_case__				)


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

                  __A		 =   [x["text"] for x in qa["answers"]]
                  __A		 =   predictions[qa["id"]]

                  __A		 =   metric_max_over_ground_truths(snake_case__				,      snake_case__				,      snake_case__				)
                  if int(_exact_match				) == 1:
                        correct_ids.append(qa["id"]				)
                  exact_match += _exact_match

                  fa += metric_max_over_ground_truths(snake_case__				,      snake_case__				,      snake_case__				)

      __A		 =   exact_match / total
      __A		 =   fa / total

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


if __name__ == "__main__":
       __A   :							Any     	=						'1.0'
       __A   :							Tuple     	=						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.')
       __A   :							Tuple     	=						parser.parse_args()

       with open(args.data_file) as data_file:
              __A   :							Optional[int]     	=						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,
                     )
              __A   :							Dict     	=						dataset_json['data']

       with open(args.pred_file) as pred_file:
              __A   :							Optional[int]     	=						json.load(pred_file)

       __A	,		__A   :							Tuple     	=						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'''







def     _UpperCamelCase				(  snake_case__ = 1_000				):
      __A		 =   []
      __A		 =   []
      __A		 =   3
      __A		 =   5
      __A		 =   1
      while True:
            __A		 =   z * temp
            if result < n:
                  zmulti.append(snake_case__				)
                  temp += 1
            else:
                  __A		 =   1
                  break
      while True:
            __A		 =   x * temp
            if result < n:
                  xmulti.append(snake_case__				)
                  temp += 1
            else:
                  break
      __A		 =   list(set(xmulti + zmulti				)				)
      return sum(snake_case__				)


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


