

import argparse
import json
import re
import string
import sys

import numpy as np


__A     						=      0.5



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



  """simple docstring"""






  _snake_case		=				['''.''', ''',''', ''';''', ''':''']

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

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

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



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



  """simple docstring"""






  _snake_case,	_snake_case,	_snake_case		=				0, 0, 0

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

      if not match_found:
        fp += 1

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

  return precision, recall



def  snake_case_(_UpperCamelCase	)				->      Optional[Any]:



  """simple docstring"""






  _snake_case		=				precisions[::-1]
  for i in range(1					,			len(_UpperCamelCase	)	):
    _snake_case		=				max(precision_best[i - 1]					,			precision_best[i]	)
  _snake_case		=				precision_best[::-1]
  return precisions



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      Dict:



  """simple docstring"""






  _snake_case		=				process_precisions(_UpperCamelCase	)
  _snake_case		=				np.trapz(_UpperCamelCase					,			_UpperCamelCase	)
  if np.isnan(_UpperCamelCase	):
    return 0
  return aupr



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



  """simple docstring"""






  _snake_case		=				process_precisions(_UpperCamelCase	)
  _snake_case		=				0
  for prec, recall in zip(_UpperCamelCase					,			_UpperCamelCase	):
    if recall >= recall_thresh:
      _snake_case		=				prec
      break
  return prec_at_recall



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      Tuple:



  """simple docstring"""






  return normalize_answer(_UpperCamelCase	) == normalize_answer(_UpperCamelCase	)



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



  """simple docstring"""






  _snake_case		=				0
  for pred in predictions:
    for ground_truth in ground_truths:
      _snake_case		=				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  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      Any:



  """simple docstring"""






  _snake_case		=				_snake_case		=				_snake_case		=				0
  _snake_case		=				[]
  _snake_case		=				[]
  for article in dataset:
    for paragraph in article["paragraphs"]:
      for qa in paragraph["qas"]:
        total += 1
        if qa["id"] not in predictions:
          _snake_case		=				'''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,	_snake_case		=				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	)

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

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

  _snake_case		=				get_prec_at_recall(_UpperCamelCase					,			_UpperCamelCase					,			recall_thresh=0.9	)
  _snake_case		=				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     						=      argparse.ArgumentParser(description='''Evaluation for CUAD''')
 parser.add_argument('''dataset_file''', help='''Dataset file''')
 parser.add_argument('''prediction_file''', help='''Prediction File''')
 __A     						=      parser.parse_args()
 with open(args.dataset_file) as dataset_file:
  __A     						=      json.load(dataset_file)
  __A     						=      dataset_json['''data''']
 with open(args.prediction_file) as prediction_file:
  __A     						=      json.load(prediction_file)
 print(json.dumps(evaluate(dataset, predictions)))




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



  """simple docstring"""






  _snake_case		=				index
  _snake_case		=				2 * index + 1
  _snake_case		=				2 * index + 2
  if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
    _snake_case		=				left_index

  if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
    _snake_case		=				right_index

  if largest != index:
    _snake_case,	_snake_case		=				unsorted[index], unsorted[largest]
    heapify(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)



def  snake_case_(_UpperCamelCase	)				->      Any:



  """simple docstring"""






  _snake_case		=				len(_UpperCamelCase	)
  for i in range(n // 2 - 1					,			-1					,			-1	):
    heapify(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)
  for i in range(n - 1					,			0					,			-1	):
    _snake_case,	_snake_case		=				unsorted[i], unsorted[0]
    heapify(_UpperCamelCase					,			0					,			_UpperCamelCase	)
  return unsorted


if __name__ == "__main__":
 __A     						=      input('''Enter numbers separated by a comma:\n''').strip()
 __A     						=      [int(item) for item in user_input.split(''',''')]
 print(heap_sort(unsorted))

