



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





def        __magic_name__			(							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	):
      SCREAMING_SNAKE_CASE_:	List[Any]            =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        __magic_name__			(							lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Any            =normalize_answer(lowercase	).split()
   SCREAMING_SNAKE_CASE_:	Any            =normalize_answer(lowercase	).split()
   SCREAMING_SNAKE_CASE_:	Dict            =Counter(lowercase	) & Counter(lowercase	)
   SCREAMING_SNAKE_CASE_:	Tuple            =sum(common.values()	)
   if num_same == 0:
      return 0
   SCREAMING_SNAKE_CASE_:	Dict            =1.0 * num_same / len(lowercase	)
   SCREAMING_SNAKE_CASE_:	Dict            =1.0 * num_same / len(lowercase	)
   SCREAMING_SNAKE_CASE_:	int            =(2 * precision * recall) / (precision + recall)
   return fa





def        __magic_name__			(							lowercase							,     lowercase	):
   return normalize_answer(lowercase	) == normalize_answer(lowercase	)





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	int            =[]
   for ground_truth in ground_truths:
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =metric_fn(lowercase							,     lowercase	)
      scores_for_ground_truths.append(lowercase	)
   return max(lowercase	)





def        __magic_name__			(							lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[int]            =0
   for article in dataset:
      for paragraph in article["paragraphs"]:
         for qa in paragraph["qas"]:
            total += 1
            if qa["id"] not in predictions:
               SCREAMING_SNAKE_CASE_:	Tuple            ="""Unanswered question """ + qa["""id"""] + """ will receive score 0."""
               print(lowercase							,     file=sys.stderr	)
               continue
            SCREAMING_SNAKE_CASE_:	List[str]            =[x["""text"""] for x in qa["""answers"""]]
            SCREAMING_SNAKE_CASE_:	Union[str, Any]            =predictions[qa["""id"""]]
            exact_match += metric_max_over_ground_truths(lowercase							,     lowercase							,     lowercase	)
            fa += metric_max_over_ground_truths(lowercase							,     lowercase							,     lowercase	)

   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =100.0 * exact_match / total
   SCREAMING_SNAKE_CASE_:	int            =100.0 * fa / total

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


if __name__ == "__main__":
   _UpperCAmelCase       =     """1.1"""
   _UpperCAmelCase       =     argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
   parser.add_argument("""dataset_file""", help="""Dataset file""")
   parser.add_argument("""prediction_file""", help="""Prediction File""")
   _UpperCAmelCase       =     parser.parse_args()
   with open(args.dataset_file) as dataset_file:
      _UpperCAmelCase       =     json.load(dataset_file)
      if dataset_json["version"] != expected_version:
         print(
             """Evaluation expects v-""" + expected_version + """, but got dataset with v-""" + dataset_json["""version"""],
             file=sys.stderr,
         )
      _UpperCAmelCase       =     dataset_json["""data"""]
   with open(args.prediction_file) as prediction_file:
      _UpperCAmelCase       =     json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))








"""simple docstring"""
from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   List[str]						)       ->   None:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =vocab
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =normalizers.BertNormalizer(
         clean_text=lowerCAmelCase    ,						handle_chinese_chars=lowerCAmelCase    ,						strip_accents=lowerCAmelCase    ,						lowercase=lowerCAmelCase    ,						)
     try:
        import rjieba
     except ImportError:
        raise ImportError(
            """You need to install rjieba to use RoFormerTokenizer. """
            """See https://pypi.org/project/rjieba/ for installation."""						)
     SCREAMING_SNAKE_CASE_:	Dict            =rjieba



  def 		lowerCamelCase__    (  self	:   Tuple    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   NormalizedString						)       ->   List[NormalizedString]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =[]

     # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
     for token, start, end in self.jieba.tokenize(str(lowerCAmelCase						)    ,						hmm=lowerCAmelCase						):
        if token in self.vocab:
           splits.append(normalized_string[start:end]						)
        else:
           SCREAMING_SNAKE_CASE_:	List[Any]            =self.normalizers.normalize_str(lowerCAmelCase						).split()
           for token in token_list:
              if token:
                 SCREAMING_SNAKE_CASE_:	Dict            =start + len(lowerCAmelCase						)
                 splits.append(normalized_string[start:end]						)
                 SCREAMING_SNAKE_CASE_:	Optional[Any]            =end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

     return splits





  def 		lowerCamelCase__    (  self	:   Union[str, Any]    ,						lowerCAmelCase	:   PreTokenizedString						)       ->   Tuple:


     '''simple docstring'''




     pretok.split(self.jieba_split						)



