

"""simple docstring"""
import argparse
import json
import re
import string
import sys
from collections import Counter







def       UpperCamelCase_					(     lowerCamelCase							:					Dict   )						->     List[Any]:



       """simple docstring"""







       def remove_articles(lowerCamelCase							:					Optional[Any]   ):
              return re.sub(r'''\b(a|an|the)\b'''     , ''' '''     , lowerCamelCase   )

       def white_space_fix(lowerCamelCase							:					str   ):
              return " ".join(text.split()   )

       def remove_punc(lowerCamelCase							:					Optional[Any]   ):
              __magic_name__			:		Optional[Any]        =     set(string.punctuation   )
              return "".join(ch for ch in text if ch not in exclude   )

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

       return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase   )   )   )   )







def       UpperCamelCase_					(     lowerCamelCase							:					Any     , lowerCamelCase							:					Any   )						->     Optional[Any]:



       """simple docstring"""







       __magic_name__			:		List[Any]        =     normalize_answer(lowerCamelCase   ).split()
       __magic_name__			:		Optional[int]        =     normalize_answer(lowerCamelCase   ).split()
       __magic_name__			:		List[str]        =     Counter(lowerCamelCase   ) & Counter(lowerCamelCase   )
       __magic_name__			:		int        =     sum(common.values()   )
       if num_same == 0:
              return 0
       __magic_name__			:		Optional[int]        =     1.0 * num_same / len(lowerCamelCase   )
       __magic_name__			:		Optional[int]        =     1.0 * num_same / len(lowerCamelCase   )
       __magic_name__			:		Optional[Any]        =     (2 * precision * recall) / (precision + recall)
       return fa







def       UpperCamelCase_					(     lowerCamelCase							:					List[Any]     , lowerCamelCase							:					Any   )						->     List[Any]:



       """simple docstring"""







       return normalize_answer(lowerCamelCase   ) == normalize_answer(lowerCamelCase   )







def       UpperCamelCase_					(     lowerCamelCase							:					Tuple     , lowerCamelCase							:					Optional[Any]     , lowerCamelCase							:					Dict   )						->     int:



       """simple docstring"""







       __magic_name__			:		Dict        =     []
       for ground_truth in ground_truths:
              __magic_name__			:		Dict        =     metric_fn(lowerCamelCase     , lowerCamelCase   )
              scores_for_ground_truths.append(lowerCamelCase   )
       return max(lowerCamelCase   )







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					Tuple   )						->     Optional[int]:



       """simple docstring"""







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

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

                     __magic_name__			:		Optional[Any]        =     metric_max_over_ground_truths(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )
                     if int(_exact_match   ) == 1:
                            correct_ids.append(qa['''id''']   )
                     exact_match += _exact_match

                     fa += metric_max_over_ground_truths(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )

       __magic_name__			:		Union[str, Any]        =     exact_match / total
       __magic_name__			:		List[str]        =     fa / total

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


if __name__ == "__main__":
 A									=						"""1.0"""
 A									=						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									=						parser.parse_args()

 with open(args.data_file) as data_file:
  A									=						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									=						dataset_json["""data"""]

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

 A			,       A									=						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_					(     lowerCamelCase							:					str   )						->     str:



       """simple docstring"""







       return " ".join([word[::-1] for word in input_str.split()]   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
