code: 
  
  
  
  
  
  
  import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			str:
  
  
  
  
  
  
  
       def remove_articles(__lowerCAmelCase    ):
            return re.sub(R"\b(a|an|the)\b"      ,					" "      ,					__lowerCAmelCase    )
  
       def white_space_fix(__lowerCAmelCase    ):
            return " ".join(text.split()    )
  
       def remove_punc(__lowerCAmelCase    ):
            UpperCamelCase__    :     int        =						set(string.punctuation    )
            return "".join(ch for ch in text if ch not in exclude    )
  
       def lower(__lowerCAmelCase    ):
            return text.lower()
  
       return white_space_fix(remove_articles(remove_punc(lower(__lowerCAmelCase    )    )    )    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			str:
       UpperCamelCase__    :     List[str]        =						normalize_answer(__lowerCAmelCase    ).split()
       UpperCamelCase__    :     Any        =						normalize_answer(__lowerCAmelCase    ).split()
       UpperCamelCase__    :     List[Any]        =						Counter(__lowerCAmelCase    ) & Counter(__lowerCAmelCase    )
       UpperCamelCase__    :     Optional[int]        =						sum(common.values()    )
       if num_same == 0:
            return 0
       UpperCamelCase__    :     str        =						1.0 * num_same / len(__lowerCAmelCase    )
       UpperCamelCase__    :     Union[str, Any]        =						1.0 * num_same / len(__lowerCAmelCase    )
       UpperCamelCase__    :     int        =						(2 * precision * recall) / (precision + recall)
       return fa
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			Optional[int]:
       return normalize_answer(__lowerCAmelCase    ) == normalize_answer(__lowerCAmelCase    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )				->			Dict:
       UpperCamelCase__    :     Optional[Any]        =						[]
       for ground_truth in ground_truths:
            UpperCamelCase__    :     List[Any]        =						metric_fn(__lowerCAmelCase      ,					__lowerCAmelCase    )
            scores_for_ground_truths.append(__lowerCAmelCase    )
       return max(__lowerCAmelCase    )
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			Optional[Any]:
       UpperCamelCase__    :     Optional[Any]        =						0
       UpperCamelCase__    :     Dict        =						[]
       for passage in dataset:
            for qa in passage["qas"]:
                 total += 1
                 if qa["id"] not in predictions:
                      UpperCamelCase__    :     int        =						f'Unanswered question {qa["id"]} will receive score 0.'
                      print(__lowerCAmelCase      ,					file=sys.stderr    )
                      continue
  
                 UpperCamelCase__    :     List[str]        =						[x["text"] for x in qa["answers"]]
                 UpperCamelCase__    :     Optional[Any]        =						predictions[qa["id"]]
  
                 UpperCamelCase__    :     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    )
  
       UpperCamelCase__    :     List[str]        =						exact_match / total
       UpperCamelCase__    :     List[str]        =						fa / total
  
       return {"exact_match": exact_match, "f1": fa}, correct_ids
  
  
  if __name__ == "__main__":
   lowerCamelCase			:							List[Any]       ="1.0"
   lowerCamelCase			:							List[Any]       =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.''')
   lowerCamelCase			:							List[str]       =parser.parse_args()
  
   with open(args.data_file) as data_file:
    lowerCamelCase			:							Tuple       =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,
     )
    lowerCamelCase			:							int       =dataset_json["data"]
  
   with open(args.pred_file) as pred_file:
    lowerCamelCase			:							List[str]       =json.load(pred_file)
  
   lowerCamelCase			:							str       =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)
  
style_context: 
  
  
  
  
  
  
  import os
  import unittest
  
  from transformers import MPNetTokenizerFast
  from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  
  @require_tokenizers
  class     __a						(					A__ ,     unittest.TestCase ):
   _lowerCAmelCase : str 				=				MPNetTokenizer
   _lowerCAmelCase : List[str] 				=				MPNetTokenizerFast
   _lowerCAmelCase : List[Any] 				=				True
   _lowerCAmelCase : Union[str, Any] 				=				True
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        super().setUp()
  
        UpperCamelCase__    :     List[Any]        =						[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        UpperCamelCase__    :     int        =						os.path.join(self.tmpdirname							,     VOCAB_FILES_NAMES["vocab_file"]		)
        with open(self.vocab_file							,     "w"							,     encoding="utf-8"		) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     List[str]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[str]        =						"UNwant\u00E9d,running"
        UpperCamelCase__    :     List[str]        =						"unwanted, running"
        return input_text, output_text
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						self.tokenizer_class(self.vocab_file		)
  
        UpperCamelCase__    :     Any        =						tokenizer.tokenize("UNwant\u00E9d,running"		)
        self.assertListEqual(SCREAMING_SNAKE_CASE							,     ["un", "##want", "##ed", ",", "runn", "##ing"]		)
        self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE		)							,     [9, 6, 7, 12, 10, 11]		)
  
  
  
  
  
  
  
   @slow
   def       __lowercase      (	self      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Union[str, Any]        =						self.tokenizer_class.from_pretrained("microsoft/mpnet-base"		)
  
        UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("sequence builders"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("multi-sequence build"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     Tuple        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
  
        assert encoded_sentence == [0] + text + [2]
        assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]