


import argparse
import json
import re
import string
import sys
from collections import Counter
def   snake_case_    (			__lowercase							):

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

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

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

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

   return white_space_fix(remove_articles(remove_punc(lower(__lowercase							)							)							)							)
def   snake_case_    (			__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   List[Any]							= normalize_answer(__lowercase							).split()
   UpperCAmelCase_				:   Dict							= normalize_answer(__lowercase							).split()
   UpperCAmelCase_				:   Tuple							= Counter(__lowercase							) & Counter(__lowercase							)
   UpperCAmelCase_				:   Union[str, Any]							= sum(common.values()							)
   if num_same == 0:
      return 0
   UpperCAmelCase_				:   Union[str, Any]							= 1.0 * num_same / len(__lowercase							)
   UpperCAmelCase_				:   Optional[int]							= 1.0 * num_same / len(__lowercase							)
   UpperCAmelCase_				:   Any							= (2 * precision * recall) / (precision + recall)
   return fa
def   snake_case_    (			__lowercase      ,							__lowercase							):
   return normalize_answer(__lowercase							) == normalize_answer(__lowercase							)
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   Optional[Any]							= []
   for ground_truth in ground_truths:
      UpperCAmelCase_				:   int							= metric_fn(__lowercase      ,							__lowercase							)
      scores_for_ground_truths.append(__lowercase							)
   return max(__lowercase							)
def   snake_case_    (			__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   int							= 0
   UpperCAmelCase_				:   Optional[Any]							= []
   for passage in dataset:
      for qa in passage["qas"]:
         total += 1
         if qa["id"] not in predictions:
            UpperCAmelCase_				:   Any							= F'''Unanswered question {qa['id']} will receive score 0.'''
            print(__lowercase      ,							file=sys.stderr							)
            continue

         UpperCAmelCase_				:   Union[str, Any]							= [x['''text'''] for x in qa['''answers''']]
         UpperCAmelCase_				:   Optional[int]							= predictions[qa['''id''']]

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

         fa += metric_max_over_ground_truths(__lowercase      ,							__lowercase      ,							__lowercase							)

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

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


if __name__ == "__main__":
       __UpperCamelCase					:	Dict          =				'1.0'
       __UpperCamelCase					:	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.')
       __UpperCamelCase					:	int          =				parser.parse_args()

       with open(args.data_file) as data_file:
              __UpperCamelCase					:	Union[str, 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,
                     )
              __UpperCamelCase					:	Optional[int]          =				dataset_json['data']

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

       __UpperCamelCase       ,       __UpperCamelCase					:	Optional[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)



def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   Optional[int]							= int((lower_bound + upper_bound) // 2							)
   if array[r] == value:
      return r
   if lower_bound >= upper_bound:
      return -1
   if array[r] < value:
      return binary_search(__lowercase      ,							r + 1      ,							__lowercase      ,							__lowercase							)
   else:
      return binary_search(__lowercase      ,							__lowercase      ,							r - 1      ,							__lowercase							)
def   snake_case_    (			__lowercase      ,							__lowercase							):
   UpperCAmelCase_				:   Union[str, Any]							= 0
   if matrix[index][0] == value:
      return [index, 0]
   while index < len(__lowercase							) and matrix[index][0] < value:
      UpperCAmelCase_				:   str							= binary_search(matrix[index]      ,							0      ,							len(matrix[index]							) - 1      ,							__lowercase							)
      if r != -1:
         return [index, r]
      index += 1
   return [-1, -1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()