'''simple docstring'''




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





def      _UpperCAmelCase    (							_UpperCamelCase    :	str   )	->      List[str]:

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

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

    def remove_punc(_UpperCamelCase    :	List[str]   ):
        A_     =   set(string.punctuation   )
        return "".join(ch for ch in text if ch not in exclude   )

    def lower(_UpperCamelCase    :	str   ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase   )   )   )   )





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





def      _UpperCAmelCase    (							_UpperCamelCase    :	Dict,   _UpperCamelCase    :	Optional[int]   )	->      int:
    return normalize_answer(_UpperCamelCase   ) == normalize_answer(_UpperCamelCase   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	Dict,   _UpperCamelCase    :	List[str],   _UpperCamelCase    :	str   )	->      Optional[Any]:
    A_     =   []
    for ground_truth in ground_truths:
        A_     =   metric_fn(_UpperCamelCase,   _UpperCamelCase   )
        scores_for_ground_truths.append(_UpperCamelCase   )
    return max(_UpperCamelCase   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	int,   _UpperCamelCase    :	str   )	->      List[str]:
    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(_UpperCamelCase,   file=sys.stderr   )
                continue

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

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

            fa += metric_max_over_ground_truths(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )

    A_     =   exact_match / total
    A_     =   fa / total

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


if __name__ == "__main__":
  __snake_case   :      Any						    =  '1.0'
  __snake_case   :      Optional[int]						    =  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.')
  __snake_case   :      Optional[int]						    =  parser.parse_args()

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

  with open(args.pred_file) as pred_file:
    __snake_case   :      Optional[Any]						    =  json.load(pred_file)

  __snake_case    ,    __snake_case   :      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'''




import math





def      _UpperCAmelCase    (							_UpperCamelCase    :	int   )	->      float:
    A_     =   math.log(2,   10   ) * number
    A_     =   round((log_number - int(_UpperCamelCase   )),   15   )

    return difference





def      _UpperCAmelCase    (							_UpperCamelCase    :	int = 67_89_10   )	->      int:
    A_     =   90
    A_     =   0

    A_     =   math.log(1.2_3,   10   )
    A_     =   math.log(1.2_4,   10   )
    A_     =   0

    while position < number:
        A_     =   log_difference(_UpperCamelCase   )

        if difference >= upper_limit:
            power_iterator += 93

        elif difference < lower_limit:
            power_iterator += 1_96

        else:
            A_     =   power_iterator
            power_iterator += 1_96
            position += 1

    return previous_power


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")






