




import argparse
import json
import re
import string
import sys

import numpy as np


UpperCAmelCase__    :							int       		=     0.5


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		int:





  """simple docstring"""





  _lowercase:			Tuple      =     ['.', ',', ';', ':']

  for token in remove_tokens:
    _lowercase:			Optional[Any]      =     ground_truth.replace(UpperCamelCase       ,  ''				)
    _lowercase:			Dict      =     prediction.replace(UpperCamelCase       ,  ''				)

  _lowercase  ,       _lowercase:			Optional[Any]      =     ground_truth.lower(), prediction.lower()
  _lowercase  ,       _lowercase:			Tuple      =     ground_truth.replace('/'       ,  ' '				), prediction.replace('/'       ,  ' '				)
  _lowercase  ,       _lowercase:			str      =     set(ground_truth.split(' '				)				), set(prediction.split(' '				)				)

  _lowercase:			Tuple      =     ground_truth.intersection(UpperCamelCase				)
  _lowercase:			str      =     ground_truth.union(UpperCamelCase				)
  _lowercase:			Tuple      =     len(UpperCamelCase				) / len(UpperCamelCase				)
  return jaccard


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Dict:





  """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				):
    _lowercase:			List[Any]      =     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 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		Optional[Any]:





  """simple docstring"""





  _lowercase  ,       _lowercase  ,       _lowercase:			Optional[Any]      =     0, 0, 0

  _lowercase:			int      =     '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
      _lowercase:			List[str]      =     False
      for pred in predictions:
        if substr_ok:
          _lowercase:			str      =     get_jaccard(UpperCamelCase       ,  UpperCamelCase				) >= IOU_THRESH or ground_truth in pred
        else:
          _lowercase:			List[str]      =     get_jaccard(UpperCamelCase       ,  UpperCamelCase				) >= IOU_THRESH
        if is_match:
          _lowercase:			Optional[int]      =     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.)
      _lowercase:			List[Any]      =     False
      for ground_truth in ground_truths:
        assert len(UpperCamelCase				) > 0
        if substr_ok:
          _lowercase:			int      =     get_jaccard(UpperCamelCase       ,  UpperCamelCase				) >= IOU_THRESH or ground_truth in pred
        else:
          _lowercase:			str      =     get_jaccard(UpperCamelCase       ,  UpperCamelCase				) >= IOU_THRESH
        if is_match:
          _lowercase:			Optional[int]      =     True

      if not match_found:
        fp += 1

  _lowercase:			List[str]      =     tp / (tp + fp) if tp + fp > 0 else np.nan
  _lowercase:			Tuple      =     tp / (tp + fn) if tp + fn > 0 else np.nan

  return precision, recall


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		str:





  """simple docstring"""





  _lowercase:			Optional[Any]      =     precisions[::-1]
  for i in range(1       ,  len(UpperCamelCase				)				):
    _lowercase:			Tuple      =     max(precision_best[i - 1]       ,  precision_best[i]				)
  _lowercase:			List[Any]      =     precision_best[::-1]
  return precisions


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  _lowercase:			List[Any]      =     process_precisions(UpperCamelCase				)
  _lowercase:			List[str]      =     np.trapz(UpperCamelCase       ,  UpperCamelCase				)
  if np.isnan(UpperCamelCase				):
    return 0
  return aupr


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		Optional[Any]:





  """simple docstring"""





  _lowercase:			Dict      =     process_precisions(UpperCamelCase				)
  _lowercase:			str      =     0
  for prec, recall in zip(UpperCamelCase       ,  UpperCamelCase				):
    if recall >= recall_thresh:
      _lowercase:			Union[str, Any]      =     prec
      break
  return prec_at_recall


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		Optional[Any]:





  """simple docstring"""





  return normalize_answer(UpperCamelCase				) == normalize_answer(UpperCamelCase				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		Union[str, Any]:





  """simple docstring"""





  _lowercase:			str      =     0
  for pred in predictions:
    for ground_truth in ground_truths:
      _lowercase:			Optional[int]      =     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					(       UpperCamelCase       ,  UpperCamelCase				)      ->		Any:





  """simple docstring"""





  _lowercase:			int      =     0
  _lowercase:			str      =     []
  _lowercase:			Optional[int]      =     []
  for article in dataset:
    for paragraph in article["paragraphs"]:
      for qa in paragraph["qas"]:
        total += 1
        if qa["id"] not in predictions:
          _lowercase:			Union[str, Any]      =     'Unanswered question ' + qa['id'] + ' will receive score 0.'
          print(UpperCamelCase       ,  file=sys.stderr				)
          continue
        _lowercase:			str      =     [x['text'] for x in qa['answers']]
        _lowercase:			List[str]      =     predictions[qa['id']]
        _lowercase  ,       _lowercase:			List[Any]      =     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				)

  _lowercase:			Dict      =     [x for _, x in sorted(zip(UpperCamelCase       ,  UpperCamelCase				)				)]
  recalls.sort()

  _lowercase:			List[str]      =     100.0 * fa / total
  _lowercase:			Optional[int]      =     100.0 * exact_match / total
  _lowercase:			Tuple      =     get_aupr(UpperCamelCase       ,  UpperCamelCase				)

  _lowercase:			str      =     get_prec_at_recall(UpperCamelCase       ,  UpperCamelCase       ,  recall_thresh=0.9				)
  _lowercase:			Any      =     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__":
   UpperCAmelCase__    :							int       		=     argparse.ArgumentParser(description='Evaluation for CUAD')
   parser.add_argument('dataset_file', help='Dataset file')
   parser.add_argument('prediction_file', help='Prediction File')
   UpperCAmelCase__    :							List[str]       		=     parser.parse_args()
   with open(args.dataset_file) as dataset_file:
      UpperCAmelCase__    :							List[str]       		=     json.load(dataset_file)
      UpperCAmelCase__    :							Dict       		=     dataset_json['data']
   with open(args.prediction_file) as prediction_file:
      UpperCAmelCase__    :							Tuple       		=     json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))






import random
import sys

UpperCAmelCase__    :							str       		=     'ABCDEFGHIJKLMNOPQRSTUVWXYZ'


def 		_lowerCamelCase					(       )      ->		None:





  """simple docstring"""





  _lowercase:			Any      =     input('Enter message: '				)
  _lowercase:			List[str]      =     'LFWOAYUISVKMNXPBDCRJTQEGHZ'
  _lowercase:			Tuple      =     input('Encrypt/Decrypt [e/d]: '				)

  check_valid_key(UpperCamelCase				)

  if resp.lower().startswith('e'				):
    _lowercase:			int      =     'encrypt'
    _lowercase:			Optional[int]      =     encrypt_message(UpperCamelCase       ,  UpperCamelCase				)
  elif resp.lower().startswith('d'				):
    _lowercase:			str      =     'decrypt'
    _lowercase:			Dict      =     decrypt_message(UpperCamelCase       ,  UpperCamelCase				)

  print(F'\n{mode.title()}ion: \n{translated}'				)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		None:





  """simple docstring"""





  _lowercase:			List[Any]      =     list(UpperCamelCase				)
  _lowercase:			Union[str, Any]      =     list(UpperCamelCase				)
  key_list.sort()
  letters_list.sort()

  if key_list != letters_list:
    sys.exit('Error in the key or symbol set.'				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  return translate_message(UpperCamelCase       ,  UpperCamelCase       ,  'encrypt'				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  return translate_message(UpperCamelCase       ,  UpperCamelCase       ,  'decrypt'				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  _lowercase:			Optional[int]      =     ''
  _lowercase:			Any      =     LETTERS
  _lowercase:			Tuple      =     key

  if mode == "decrypt":
    _lowercase  ,       _lowercase:			Tuple      =     chars_b, chars_a

  for symbol in message:
    if symbol.upper() in chars_a:
      _lowercase:			int      =     chars_a.find(symbol.upper()				)
      if symbol.isupper():
        translated += chars_b[sym_index].upper()
      else:
        translated += chars_b[sym_index].lower()
    else:
      translated += symbol

  return translated


def 		_lowerCamelCase					(       )      ->		str:





  """simple docstring"""





  _lowercase:			Union[str, Any]      =     list(UpperCamelCase				)
  random.shuffle(UpperCamelCase				)
  return "".join(UpperCamelCase				)


if __name__ == "__main__":
   main()
