

'''simple docstring'''




import argparse
import json
import re
import string
import sys

import numpy as np


_a :       str       							=  0.5


def 					lowerCamelCase__						(  SCREAMING_SNAKE_CASE					:      Union[str, Any]				,      SCREAMING_SNAKE_CASE					:      Union[str, Any]  ):
  UpperCAmelCase  							=		['.', ',', ';', ':']

  for token in remove_tokens:
    UpperCAmelCase  							=		ground_truth.replace(_UpperCamelCase				,      ''  )
    UpperCAmelCase  							=		prediction.replace(_UpperCamelCase				,      ''  )

  UpperCAmelCase							,					UpperCAmelCase  							=		ground_truth.lower(), prediction.lower()
  UpperCAmelCase							,					UpperCAmelCase  							=		ground_truth.replace('/'				,      ' '  ), prediction.replace('/'				,      ' '  )
  UpperCAmelCase							,					UpperCAmelCase  							=		set(ground_truth.split(' '  )  ), set(prediction.split(' '  )  )

  UpperCAmelCase  							=		ground_truth.intersection(_UpperCamelCase  )
  UpperCAmelCase  							=		ground_truth.union(_UpperCamelCase  )
  UpperCAmelCase  							=		len(_UpperCamelCase  ) / len(_UpperCamelCase  )
  return jaccard


def 					lowerCamelCase__						(  SCREAMING_SNAKE_CASE					:      Tuple  ):

  def remove_articles(SCREAMING_SNAKE_CASE					:      Union[str, Any]  ):
    return re.sub(R'\b(a|an|the)\b'				,      ' '				,      _UpperCamelCase  )

  def white_space_fix(SCREAMING_SNAKE_CASE					:      List[Any]  ):
    return " ".join(text.split()  )

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

  def lower(SCREAMING_SNAKE_CASE					:      Tuple  ):
    return text.lower()

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


def 					lowerCamelCase__						(  SCREAMING_SNAKE_CASE					:      Tuple				,      SCREAMING_SNAKE_CASE					:      Optional[Any]				,      SCREAMING_SNAKE_CASE					:      Any  ):
  UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase  							=		0, 0, 0

  UpperCAmelCase  							=		'Parties' in qa_id

  # first check if ground truth is empty
  if len(_UpperCamelCase  ) == 0:
    if len(_UpperCamelCase  ) > 0:
      fp += len(_UpperCamelCase  )  # false positive for each one
  else:
    for ground_truth in ground_truths:
      assert len(_UpperCamelCase  ) > 0
      # check if there is a match
      UpperCAmelCase  							=		False
      for pred in predictions:
        if substr_ok:
          UpperCAmelCase  							=		get_jaccard(_UpperCamelCase				,      _UpperCamelCase  ) >= IOU_THRESH or ground_truth in pred
        else:
          UpperCAmelCase  							=		get_jaccard(_UpperCamelCase				,      _UpperCamelCase  ) >= IOU_THRESH
        if is_match:
          UpperCAmelCase  							=		True

      if match_found:
        tp += 1
      else:
        fn += 1

        # now also get any fps by looping through preds
    for pred in predictions:
      # Check if there's a match. if so, don't count (don't want to double count based on the above)
      # but if there's no match, then this is a false positive.
      # (Note: we get the true positives in the above loop instead of this loop so that we don't double count
      # multiple predictions that are matched with the same answer.)
      UpperCAmelCase  							=		False
      for ground_truth in ground_truths:
        assert len(_UpperCamelCase  ) > 0
        if substr_ok:
          UpperCAmelCase  							=		get_jaccard(_UpperCamelCase				,      _UpperCamelCase  ) >= IOU_THRESH or ground_truth in pred
        else:
          UpperCAmelCase  							=		get_jaccard(_UpperCamelCase				,      _UpperCamelCase  ) >= IOU_THRESH
        if is_match:
          UpperCAmelCase  							=		True

      if not match_found:
        fp += 1

  UpperCAmelCase  							=		tp / (tp + fp) if tp + fp > 0 else np.nan
  UpperCAmelCase  							=		tp / (tp + fn) if tp + fn > 0 else np.nan

  return precision, recall


def 					lowerCamelCase__						(  SCREAMING_SNAKE_CASE					:      List[str]  ):
  UpperCAmelCase  							=		precisions[::-1]
  for i in range(1				,      len(_UpperCamelCase  )  ):
    UpperCAmelCase  							=		max(precision_best[i - 1]				,      precision_best[i]  )
  UpperCAmelCase  							=		precision_best[::-1]
  return precisions


def 					lowerCamelCase__						(  SCREAMING_SNAKE_CASE					:      Tuple				,      SCREAMING_SNAKE_CASE					:      Optional[int]  ):
  UpperCAmelCase  							=		process_precisions(_UpperCamelCase  )
  UpperCAmelCase  							=		np.trapz(_UpperCamelCase				,      _UpperCamelCase  )
  if np.isnan(_UpperCamelCase  ):
    return 0
  return aupr


def 					lowerCamelCase__						(  SCREAMING_SNAKE_CASE					:      List[str]				,      SCREAMING_SNAKE_CASE					:      Dict				,      SCREAMING_SNAKE_CASE					:      Tuple  ):
  UpperCAmelCase  							=		process_precisions(_UpperCamelCase  )
  UpperCAmelCase  							=		0
  for prec, recall in zip(_UpperCamelCase				,      _UpperCamelCase  ):
    if recall >= recall_thresh:
      UpperCAmelCase  							=		prec
      break
  return prec_at_recall


def 					lowerCamelCase__						(  SCREAMING_SNAKE_CASE					:      Union[str, Any]				,      SCREAMING_SNAKE_CASE					:      Union[str, Any]  ):
  return normalize_answer(_UpperCamelCase  ) == normalize_answer(_UpperCamelCase  )


def 					lowerCamelCase__						(  SCREAMING_SNAKE_CASE					:      Tuple				,      SCREAMING_SNAKE_CASE					:      int				,      SCREAMING_SNAKE_CASE					:      Tuple  ):
  UpperCAmelCase  							=		0
  for pred in predictions:
    for ground_truth in ground_truths:
      UpperCAmelCase  							=		metric_fn(_UpperCamelCase				,      _UpperCamelCase  )
      if score == 1:  # break the loop when one prediction matches the ground truth
        break
    if score == 1:
      break
  return score


def 					lowerCamelCase__						(  SCREAMING_SNAKE_CASE					:      List[str]				,      SCREAMING_SNAKE_CASE					:      str  ):
  UpperCAmelCase  							=		UpperCAmelCase  							=		UpperCAmelCase  							=		0
  UpperCAmelCase  							=		[]
  UpperCAmelCase  							=		[]
  for article in dataset:
    for paragraph in article["paragraphs"]:
      for qa in paragraph["qas"]:
        total += 1
        if qa["id"] not in predictions:
          UpperCAmelCase  							=		'Unanswered question ' + qa['id'] + ' will receive score 0.'
          print(_UpperCamelCase				,      file=sys.stderr  )
          continue
        UpperCAmelCase  							=		[x['text'] for x in qa['answers']]
        UpperCAmelCase  							=		predictions[qa['id']]
        UpperCAmelCase							,					UpperCAmelCase  							=		compute_precision_recall(_UpperCamelCase				,      _UpperCamelCase				,      qa['id']  )

        precisions.append(_UpperCamelCase  )
        recalls.append(_UpperCamelCase  )

        if precision == 0 and recall == 0:
          fa += 0
        else:
          fa += 2 * (precision * recall) / (precision + recall)

        exact_match += metric_max_over_ground_truths(_UpperCamelCase				,      _UpperCamelCase				,      _UpperCamelCase  )

  UpperCAmelCase  							=		[x for _, x in sorted(zip(_UpperCamelCase				,      _UpperCamelCase  )  )]
  recalls.sort()

  UpperCAmelCase  							=		100.0 * fa / total
  UpperCAmelCase  							=		100.0 * exact_match / total
  UpperCAmelCase  							=		get_aupr(_UpperCamelCase				,      _UpperCamelCase  )

  UpperCAmelCase  							=		get_prec_at_recall(_UpperCamelCase				,      _UpperCamelCase				,      recall_thresh=0.9  )
  UpperCAmelCase  							=		get_prec_at_recall(_UpperCamelCase				,      _UpperCamelCase				,      recall_thresh=0.8  )

  return {
      "exact_match": exact_match,
      "f1": fa,
      "aupr": aupr,
      "prec_at_80_recall": prec_at_aa_recall,
      "prec_at_90_recall": prec_at_aa_recall,
  }


if __name__ == "__main__":
  _a :       List[str]       							=  argparse.ArgumentParser(description='Evaluation for CUAD')
  parser.add_argument('dataset_file', help='Dataset file')
  parser.add_argument('prediction_file', help='Prediction File')
  _a :       Dict       							=  parser.parse_args()
  with open(args.dataset_file) as dataset_file:
    _a :       List[Any]       							=  json.load(dataset_file)
    _a :       str       							=  dataset_json['data']
  with open(args.prediction_file) as prediction_file:
    _a :       Union[str, Any]       							=  json.load(prediction_file)
  print(json.dumps(evaluate(dataset, predictions)))



'''simple docstring'''




__snake_case   :      List[Any]						    =  [chr(i) for i in range(32, 126)]
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      List[Any]						    =  list(reversed(range(len(alphabets))))
__snake_case   :      Optional[int]						    =  []
__snake_case   :      List[Any]						    =  0





def      _UpperCAmelCase    (							)	->      Any:
    global gear_one_pos
    global gear_two_pos
    global gear_three_pos
    A_     =   gear_one[0]
    gear_one.append(_UpperCamelCase   )
    del gear_one[0]
    gear_one_pos += 1
    if gear_one_pos % int(len(_UpperCamelCase   )   ) == 0:
        A_     =   gear_two[0]
        gear_two.append(_UpperCamelCase   )
        del gear_two[0]
        gear_two_pos += 1
        if gear_two_pos % int(len(_UpperCamelCase   )   ) == 0:
            A_     =   gear_three[0]
            gear_three.append(_UpperCamelCase   )
            del gear_three[0]
            gear_three_pos += 1





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple   )	->      Any:
    A_     =   alphabets.index(_UpperCamelCase   )
    A_     =   gear_one[target]
    A_     =   gear_two[target]
    A_     =   gear_three[target]
    A_     =   reflector[target]
    A_     =   gear_three.index(_UpperCamelCase   )
    A_     =   gear_two.index(_UpperCamelCase   )
    A_     =   gear_one.index(_UpperCamelCase   )
    code.append(alphabets[target]   )
    rotator()


if __name__ == "__main__":
  __snake_case   :      List[str]						    =  list(input('Type your message:\n'))
  while True:
    try:
      __snake_case   :      Any						    =  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!'
  )






