






"""simple docstring"""



import argparse
import json
import re
import string
import sys
from collections import Counter
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict       ):





  '''simple docstring'''





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

  def white_space_fix(__snake_case     :    Dict       ):
    return " ".join(text.split()       )

  def remove_punc(__snake_case     :    Optional[Any]       ):
    lowercase				       =			set(string.punctuation       )
    return "".join(ch for ch in text if ch not in exclude       )

  def lower(__snake_case     :    Any       ):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(__snake_case       )       )       )       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





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





  '''simple docstring'''





  return normalize_answer(__snake_case       ) == normalize_answer(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Any						,   __snake_case     :    List[Any]						,   __snake_case     :    Dict       ):





  '''simple docstring'''





  lowercase				       =			[]
  for ground_truth in ground_truths:
    lowercase				       =			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]       ):





  '''simple docstring'''





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

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

      lowercase				       =			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       )

  lowercase				       =			exact_match / total
  lowercase				       =			fa / total

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


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

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

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

    _UpperCamelCase,						_UpperCamelCase						:    Any		=						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)




_A							=       [chr(i) for i in range(32, 126)]
_A							=       list(range(len(alphabets)))
_A							=       list(range(len(alphabets)))
_A							=       list(range(len(alphabets)))
_A							=       list(reversed(range(len(alphabets))))
_A							=       []
_A							=       _A							=       _A							=       0




def 					lowerCamelCase__					(    )		->						List[str]:
       global gear_one_pos
       global gear_two_pos
       global gear_three_pos
       UpperCamelCase_											=							gear_one[0]
       gear_one.append(a__  )
       del gear_one[0]
       gear_one_pos += 1
       if gear_one_pos % int(len(a__  )  ) == 0:
              UpperCamelCase_											=							gear_two[0]
              gear_two.append(a__  )
              del gear_two[0]
              gear_two_pos += 1
              if gear_two_pos % int(len(a__  )  ) == 0:
                     UpperCamelCase_											=							gear_three[0]
                     gear_three.append(a__  )
                     del gear_three[0]
                     gear_three_pos += 1




def 					lowerCamelCase__					(    a__	:	Optional[int]  )		->						Any:
       UpperCamelCase_											=							alphabets.index(a__  )
       UpperCamelCase_											=							gear_one[target]
       UpperCamelCase_											=							gear_two[target]
       UpperCamelCase_											=							gear_three[target]
       UpperCamelCase_											=							reflector[target]
       UpperCamelCase_											=							gear_three.index(a__  )
       UpperCamelCase_											=							gear_two.index(a__  )
       UpperCamelCase_											=							gear_one.index(a__  )
       code.append(alphabets[target]  )
       rotator()


if __name__ == "__main__":
      _A							=       list(input('''Type your message:\n'''))
      while True:
            try:
                  _A							=       int(input('''Please set token:(must be only digits)\n'''))
                  break
            except Exception as error:
                  print(error)
      for _ in range(token):
            rotator()
      for j in decode:
            engine(j)
      print('''\n''' + ''''''.join(code))
      print(
          F'''\nYour Token is {token} please write it down.\nIf you want to decode '''
          '''this message again you should input same digits as token!'''
      )



