



'''simple docstring'''

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



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   Optional[int]:





  def remove_articles(lowerCAmelCase_						):
    return re.sub(r'\b(a|an|the)\b'		,    ' '		,    __a						)

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

  def remove_punc(lowerCAmelCase_						):
    _a  :   Dict				 =							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(__a						)						)						)						)



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   str:
  _a  :   Optional[int]				 =							normalize_answer(__a						).split()
  _a  :   List[Any]				 =							normalize_answer(__a						).split()
  _a  :   Dict				 =							Counter(__a						) & Counter(__a						)
  _a  :   Any				 =							sum(common.values()						)
  if num_same == 0:
    return 0
  _a  :   Optional[int]				 =							1.0 * num_same / len(__a						)
  _a  :   Any				 =							1.0 * num_same / len(__a						)
  _a  :   int				 =							(2 * precision * recall) / (precision + recall)
  return fa



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Tuple:
  return normalize_answer(__a						) == normalize_answer(__a						)



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   str:
  _a  :   Optional[Any]				 =							[]
  for ground_truth in ground_truths:
    _a  :   Optional[Any]				 =							metric_fn(__a		,    __a						)
    scores_for_ground_truths.append(__a						)
  return max(__a						)



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   Dict:
  _a  :   Optional[int]				 =							0
  _a  :   Tuple				 =							[]
  for passage in dataset:
    for qa in passage["qas"]:
      total += 1
      if qa["id"] not in predictions:
        _a  :   List[Any]				 =							f"""Unanswered question {qa['id']} will receive score 0."""
        print(__a		,    file=sys.stderr						)
        continue

      _a  :   Dict				 =							[x['''text'''] for x in qa['''answers''']]
      _a  :   Optional[int]				 =							predictions[qa['''id''']]

      _a  :   Union[str, Any]				 =							metric_max_over_ground_truths(__a		,    __a		,    __a						)
      if int(_exact_match						) == 1:
        correct_ids.append(qa['id']						)
      exact_match += _exact_match

      fa += metric_max_over_ground_truths(__a		,    __a		,    __a						)

  _a  :   Union[str, Any]				 =							exact_match / total
  _a  :   Optional[Any]				 =							fa / total

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


if __name__ == "__main__":
       __lowerCAmelCase		    =     '''1.0'''
       __lowerCAmelCase		    =     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		    =     parser.parse_args()

       with open(args.data_file) as data_file:
              __lowerCAmelCase		    =     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		    =     dataset_json['''data''']

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

       __lowerCAmelCase    ,      __lowerCAmelCase		    =     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'''

from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					int            =      SqueezeBertTokenizer
							lowerCAmelCase :					int            =      SqueezeBertTokenizerFast
							lowerCAmelCase :					List[Any]            =      True







							def 							__lowercase    (							self      : List[str]			,**_UpperCAmelCase      : Tuple	):
									return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							@slow
							def 							__lowercase    (							self      : Tuple	):
									_a  :   Optional[int]				 =							SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'	)

									_a  :   int				 =							tokenizer.encode('sequence builders'			,add_special_tokens=_UpperCAmelCase	)
									_a  :   List[Any]				 =							tokenizer.encode('multi-sequence build'			,add_special_tokens=_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	)
									_a  :   Dict				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,_UpperCAmelCase	)

									assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
									assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
									    tokenizer.sep_token_id
									]




