





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




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Optional[Any]						):

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

     def white_space_fix(snake_case_		:						Optional[int]						):
          return " ".join(text.split()						)

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

     def lower(snake_case_		:						Optional[Any]						):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(snake_case_						)						)						)						)




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						List[Any] ,     snake_case_		:						Union[str, Any]						):
     __magic_name__				=       normalize_answer(snake_case_						).split()
     __magic_name__				=       normalize_answer(snake_case_						).split()
     __magic_name__				=       Counter(snake_case_						) & Counter(snake_case_						)
     __magic_name__				=       sum(common.values()						)
     if num_same == 0:
          return 0
     __magic_name__				=       1.0 * num_same / len(snake_case_						)
     __magic_name__				=       1.0 * num_same / len(snake_case_						)
     __magic_name__				=       (2 * precision * recall) / (precision + recall)
     return fa




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Union[str, Any] ,     snake_case_		:						Any						):
     return normalize_answer(snake_case_						) == normalize_answer(snake_case_						)




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						List[Any] ,     snake_case_		:						Optional[Any] ,     snake_case_		:						Optional[Any]						):
     __magic_name__				=       []
     for ground_truth in ground_truths:
          __magic_name__				=       metric_fn(snake_case_ ,     snake_case_						)
          scores_for_ground_truths.append(snake_case_						)
     return max(snake_case_						)




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

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

               __magic_name__				=       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_						)

     __magic_name__				=       exact_match / total
     __magic_name__				=       fa / total

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


if __name__ == "__main__":
    a_    :				Optional[int]					=			'1.0'
    a_    :				List[str]					=			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_    :				Dict					=			parser.parse_args()

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

    with open(args.pred_file) as pred_file:
        a_    :				List[Any]					=			json.load(pred_file)

    a_				,       a_    :				Dict					=			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'''






class 	_lowerCAmelCase     :






		'''simple docstring'''
		def __init__(						self							:       int      ,		UpperCamelCase_							:       int		)      ->			None:







									'''simple docstring'''





									_lowercase							:		Optional[Any]				=   data
									_lowercase							:		int
									_lowercase							:		Node





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node  )		->None:






							'''simple docstring'''
							_lowercase							:		str				=   0
							_lowercase							:		List[Any]				=   x





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node ,       snake_case_    :     Node  )		->None:






							'''simple docstring'''
							_lowercase			, _lowercase							:		Union[str, Any]				=   find_set(snake_case_  ), find_set(snake_case_  )
							if x == y:
														return

							elif x.rank > y.rank:
														_lowercase							:		Optional[Any]				=   x
							else:
														_lowercase							:		Optional[int]				=   y
														if x.rank == y.rank:
																					y.rank += 1





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node  )		->Node:






							'''simple docstring'''
							if x != x.parent:
														_lowercase							:		str				=   find_set(x.parent  )
							return x.parent





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node  )		->set:






							'''simple docstring'''
							_lowercase							:		str				=   ({0, 1, 2}, {3, 4, 5})
							for s in sets:
														if node.data in s:
																					return s
							_lowercase							:		Optional[Any]				=   F"{node.data} is not in {sets}"
							raise ValueError(snake_case_  )





def 				_SCREAMING_SNAKE_CASE(					)		->None:






							'''simple docstring'''
							_lowercase							:		Any				=   [Node(snake_case_  ) for i in range(6  )]
							for v in vertex:
														make_set(snake_case_  )

							union_set(vertex[0] ,       vertex[1]  )
							union_set(vertex[1] ,       vertex[2]  )
							union_set(vertex[3] ,       vertex[4]  )
							union_set(vertex[3] ,       vertex[5]  )

							for nodea in vertex:
														for nodea in vertex:
																					if find_python_set(snake_case_  ).isdisjoint(find_python_set(snake_case_  )  ):
																												assert find_set(snake_case_  ) != find_set(snake_case_  )
																					else:
																												assert find_set(snake_case_  ) == find_set(snake_case_  )


if __name__ == "__main__":
						test_disjoint_set()



