

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



def    snake_case_(_UpperCamelCase  )							->			Union[str, Any]:



  """simple docstring"""






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

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

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

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

  return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase  )  )  )  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case		=							normalize_answer(_UpperCamelCase  ).split()
  _snake_case		=							normalize_answer(_UpperCamelCase  ).split()
  _snake_case		=							Counter(_UpperCamelCase  ) & Counter(_UpperCamelCase  )
  _snake_case		=							sum(common.values()  )
  if num_same == 0:
    return 0
  _snake_case		=							1.0 * num_same / len(_UpperCamelCase  )
  _snake_case		=							1.0 * num_same / len(_UpperCamelCase  )
  _snake_case		=							(2 * precision * recall) / (precision + recall)
  return fa



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			List[str]:



  """simple docstring"""






  return normalize_answer(_UpperCamelCase  ) == normalize_answer(_UpperCamelCase  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case		=							[]
  for ground_truth in ground_truths:
    _snake_case		=							metric_fn(_UpperCamelCase				, _UpperCamelCase  )
    scores_for_ground_truths.append(_UpperCamelCase  )
  return max(_UpperCamelCase  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			int:



  """simple docstring"""






  _snake_case		=							_snake_case		=							_snake_case		=							0
  _snake_case		=							[]
  for passage in dataset:
    for qa in passage["qas"]:
      total += 1
      if qa["id"] not in predictions:
        _snake_case		=							F"""Unanswered question {qa['id']} will receive score 0."""
        print(_UpperCamelCase				, file=sys.stderr  )
        continue

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

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

      fa += metric_max_over_ground_truths(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )

  _snake_case		=							exact_match / total
  _snake_case		=							fa / total

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


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

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

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

 __A	,  __A    	=			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)




import numpy as np



def    snake_case_(_UpperCamelCase  )							->			Any:



  """simple docstring"""






  _snake_case		=							np.exp(_UpperCamelCase  )

  # Add up the all the exponentials
  _snake_case		=							np.sum(_UpperCamelCase  )

  # Divide every exponent by the sum of all exponents
  _snake_case		=							exponent_vector / sum_of_exponents

  return softmax_vector


if __name__ == "__main__":
 print(softmax((0,)))

