





"""simple docstring"""
import argparse
import json
import re
import string
import sys

import numpy as np


__UpperCamelCase			:		List[Any]		    =   0.5

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  List[str]   ,			_UpperCAmelCase :  List[Any]       ):
 lowerCAmelCase  							=      ['.', ',', ';', ':']

 for token in remove_tokens:
  lowerCAmelCase  							=      ground_truth.replace(_UpperCAmelCase   ,			''       )
  lowerCAmelCase  							=      prediction.replace(_UpperCAmelCase   ,			''       )

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

 lowerCAmelCase  							=      ground_truth.intersection(_UpperCAmelCase       )
 lowerCAmelCase  							=      ground_truth.union(_UpperCAmelCase       )
 lowerCAmelCase  							=      len(_UpperCAmelCase       ) / len(_UpperCAmelCase       )
 return jaccard

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  List[str]       ):

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

 def white_space_fix(_UpperCAmelCase :  str       ):
  return " ".join(text.split()       )

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

 def lower(_UpperCAmelCase :  Optional[int]       ):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase       )       )       )       )

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Optional[int]   ,			_UpperCAmelCase :  List[Any]   ,			_UpperCAmelCase :  str       ):
 lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase  							=      0, 0, 0

 lowerCAmelCase  							=      '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
   lowerCAmelCase  							=      False
   for pred in predictions:
    if substr_ok:
     lowerCAmelCase  							=      get_jaccard(_UpperCAmelCase   ,			_UpperCAmelCase       ) >= IOU_THRESH or ground_truth in pred
    else:
     lowerCAmelCase  							=      get_jaccard(_UpperCAmelCase   ,			_UpperCAmelCase       ) >= IOU_THRESH
    if is_match:
     lowerCAmelCase  							=      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.)
   lowerCAmelCase  							=      False
   for ground_truth in ground_truths:
    assert len(_UpperCAmelCase       ) > 0
    if substr_ok:
     lowerCAmelCase  							=      get_jaccard(_UpperCAmelCase   ,			_UpperCAmelCase       ) >= IOU_THRESH or ground_truth in pred
    else:
     lowerCAmelCase  							=      get_jaccard(_UpperCAmelCase   ,			_UpperCAmelCase       ) >= IOU_THRESH
    if is_match:
     lowerCAmelCase  							=      True

   if not match_found:
    fp += 1

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

 return precision, recall

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Tuple       ):
 lowerCAmelCase  							=      precisions[::-1]
 for i in range(1   ,			len(_UpperCAmelCase       )       ):
  lowerCAmelCase  							=      max(precision_best[i - 1]   ,			precision_best[i]       )
 lowerCAmelCase  							=      precision_best[::-1]
 return precisions

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  List[Any]   ,			_UpperCAmelCase :  Optional[Any]       ):
 lowerCAmelCase  							=      process_precisions(_UpperCAmelCase       )
 lowerCAmelCase  							=      np.trapz(_UpperCAmelCase   ,			_UpperCAmelCase       )
 if np.isnan(_UpperCAmelCase       ):
  return 0
 return aupr

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Dict   ,			_UpperCAmelCase :  Dict   ,			_UpperCAmelCase :  Optional[int]       ):
 lowerCAmelCase  							=      process_precisions(_UpperCAmelCase       )
 lowerCAmelCase  							=      0
 for prec, recall in zip(_UpperCAmelCase   ,			_UpperCAmelCase       ):
  if recall >= recall_thresh:
   lowerCAmelCase  							=      prec
   break
 return prec_at_recall

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

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Tuple   ,			_UpperCAmelCase :  Dict   ,			_UpperCAmelCase :  Optional[Any]       ):
 lowerCAmelCase  							=      0
 for pred in predictions:
  for ground_truth in ground_truths:
   lowerCAmelCase  							=      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 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  int   ,			_UpperCAmelCase :  str       ):
 lowerCAmelCase  							=      lowerCAmelCase  							=      lowerCAmelCase  							=      0
 lowerCAmelCase  							=      []
 lowerCAmelCase  							=      []
 for article in dataset:
  for paragraph in article["paragraphs"]:
   for qa in paragraph["qas"]:
    total += 1
    if qa["id"] not in predictions:
     lowerCAmelCase  							=      'Unanswered question ' + qa['id'] + ' will receive score 0.'
     print(_UpperCAmelCase   ,			file=sys.stderr       )
     continue
    lowerCAmelCase  							=      [x['text'] for x in qa['answers']]
    lowerCAmelCase  							=      predictions[qa['id']]
    lowerCAmelCase							,lowerCAmelCase  							=      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       )

 lowerCAmelCase  							=      [x for _, x in sorted(zip(_UpperCAmelCase   ,			_UpperCAmelCase       )       )]
 recalls.sort()

 lowerCAmelCase  							=      100.0 * fa / total
 lowerCAmelCase  							=      100.0 * exact_match / total
 lowerCAmelCase  							=      get_aupr(_UpperCAmelCase   ,			_UpperCAmelCase       )

 lowerCAmelCase  							=      get_prec_at_recall(_UpperCAmelCase   ,			_UpperCAmelCase   ,			recall_thresh=0.9       )
 lowerCAmelCase  							=      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			:		Any		    =   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			:		Optional[Any]		    =   json.load(dataset_file)
              __UpperCamelCase			:		Union[str, Any]		    =   dataset_json['''data''']
       with open(args.prediction_file) as prediction_file:
              __UpperCamelCase			:		Dict		    =   json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))












"""simple docstring"""
import os
import sys
import time

from . import transposition_cipher as trans_cipher

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      'Prehistoric Men.txt'
 lowerCAmelCase  							=      'Output.txt'
 lowerCAmelCase  							=      int(input('Enter key: '       )       )
 lowerCAmelCase  							=      input('Encrypt/Decrypt [e/d]: '       )

 if not os.path.exists(_UpperCAmelCase       ):
  print(F'File {input_file} does not exist. Quitting...'       )
  sys.exit()
 if os.path.exists(_UpperCAmelCase       ):
  print(F'Overwrite {output_file}? [y/n]'       )
  lowerCAmelCase  							=      input('> '       )
  if not response.lower().startswith('y'       ):
   sys.exit()

 lowerCAmelCase  							=      time.time()
 if mode.lower().startswith('e'       ):
  with open(_UpperCAmelCase       ) as f:
   lowerCAmelCase  							=      f.read()
  lowerCAmelCase  							=      trans_cipher.encrypt_message(_UpperCAmelCase   ,			_UpperCAmelCase       )
 elif mode.lower().startswith('d'       ):
  with open(_UpperCAmelCase       ) as f:
   lowerCAmelCase  							=      f.read()
  lowerCAmelCase  							=      trans_cipher.decrypt_message(_UpperCAmelCase   ,			_UpperCAmelCase       )

 with open(_UpperCAmelCase   ,			'w'       ) as output_obj:
  output_obj.write(_UpperCAmelCase       )

 lowerCAmelCase  							=      round(time.time() - start_time   ,			2       )
 print(('Done (', total_time, 'seconds )')       )


if __name__ == "__main__":
       main()





