





"""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'''



UpperCamelCase__					:						Tuple											=     [
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "be-e6", "bh-e12", "cd-e2", "ce-e4",
     "de-e1", "df-e8", "dg-e5", "dh-e10", "ef-e3", "eg-e2", "fg-e6", "gh-e6", "hi-e3"],
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "be-e6", "cd-e2", "de-e1", "df-e8",
     "ef-e3", "eg-e2", "fg-e6"],
    ["ab-e1", "ac-e3", "bc-e4", "bd-e2", "de-e1", "df-e8", "dg-e5", "ef-e3", "eg-e2",
     "eh-e12", "fg-e6", "fh-e10", "gh-e6"],
    ["ab-e1", "ac-e3", "bc-e4", "bd-e2", "bh-e12", "cd-e2", "df-e8", "dh-e10"],
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "cd-e2", "ce-e4", "de-e1", "df-e8",
     "dg-e5", "ef-e3", "eg-e2", "fg-e6"]
]
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = set()
   for row in edge_array:
      for item in row:
         distinct_edge.add(item[0]  )
   return list(SCREAMING_SNAKE_CASE_  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = ["""0"""] * len(SCREAMING_SNAKE_CASE_  )
   for i, row in enumerate(SCREAMING_SNAKE_CASE_  ):
      for item in row:
         if distinct_edge in item[0]:
            _SCREAMING_SNAKE_CASE          = """1"""
            break
   return "".join(SCREAMING_SNAKE_CASE_  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Union[str, Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = get_distinct_edge(SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE          = {}

   for item in distinct_edge:
      _SCREAMING_SNAKE_CASE          = get_bitcode(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
      # print('bit',bit)
      # bt=''.join(bit)
      _SCREAMING_SNAKE_CASE          = bit.count("""1"""  )
      _SCREAMING_SNAKE_CASE          = [s, bit]
   # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
   _SCREAMING_SNAKE_CASE          = [
       [k, v[0], v[1]]
       for k, v in sorted(frequency_table.items()			,					key=lambda SCREAMING_SNAKE_CASE_  : v[1][0]			,					reverse=SCREAMING_SNAKE_CASE_  )
   ]
   return sorted_frequency_table
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = {}
   for _, item in enumerate(SCREAMING_SNAKE_CASE_  ):
      nodes.setdefault(item[2]			,					[]  ).append(item[0]  )
   return nodes
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = {}
   for key, value in nodes.items():
      _SCREAMING_SNAKE_CASE          = value
   return cluster
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   str:




   """simple docstring"""
   return [i * 1_00 / len(SCREAMING_SNAKE_CASE_  ) for i in cluster]
def       lowerCAmelCase_		( )   ->   None:




   """simple docstring"""
   print("""\nNodes\n"""  )
   for key, value in nodes.items():
      print(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
   print("""\nSupport\n"""  )
   print(SCREAMING_SNAKE_CASE_  )
   print("""\n Cluster \n"""  )
   for key, value in sorted(cluster.items()			,					reverse=SCREAMING_SNAKE_CASE_  ):
      print(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
   print("""\n Graph\n"""  )
   for key, value in graph.items():
      print(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
   print("""\n Edge List of Frequent subgraphs \n"""  )
   for edge_list in freq_subgraph_edge_list:
      print(SCREAMING_SNAKE_CASE_  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Optional[int]:




   """simple docstring"""
   for i in cluster[ca]:
      _SCREAMING_SNAKE_CASE          = 0
      _SCREAMING_SNAKE_CASE          = ca + 1
      while ca < max(cluster.keys()  ):
         for j in cluster[ca]:
            if int(SCREAMING_SNAKE_CASE_			,					2  ) & int(SCREAMING_SNAKE_CASE_			,					2  ) == int(SCREAMING_SNAKE_CASE_			,					2  ):
               if tuple(nodes[i]  ) in graph:
                  graph[tuple(nodes[i]  )].append(nodes[j]  )
               else:
                  _SCREAMING_SNAKE_CASE          = [nodes[j]]
               count += 1
         if count == 0:
            _SCREAMING_SNAKE_CASE          = ca + 1
         else:
            break
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = cluster[max(cluster.keys()  )]
   _SCREAMING_SNAKE_CASE          = """Header"""
   _SCREAMING_SNAKE_CASE          = {}
   for i in x:
      if (["Header"],) in graph:
         graph[(["Header"],)].append(x[i]  )
      else:
         _SCREAMING_SNAKE_CASE          = [x[i]]
   for i in x:
      _SCREAMING_SNAKE_CASE          = [["""Header"""]]
   _SCREAMING_SNAKE_CASE          = 1
   while i < max(SCREAMING_SNAKE_CASE_  ) - 1:
      create_edge(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE          = i + 1
   return graph
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_=None  )   ->   List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = (path or []) + [start]
   if start == end:
      paths.append(SCREAMING_SNAKE_CASE_  )
   for node in graph[start]:
      if tuple(SCREAMING_SNAKE_CASE_  ) not in path:
         my_dfs(SCREAMING_SNAKE_CASE_			,					tuple(SCREAMING_SNAKE_CASE_  )			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = int(s / 1_00 * (len(SCREAMING_SNAKE_CASE_  ) - 1)  )
   for i in cluster[k]:
      my_dfs(SCREAMING_SNAKE_CASE_			,					tuple(cluster[k][i]  )			,					(["""Header"""],)  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = []
   for edges in paths:
      _SCREAMING_SNAKE_CASE          = []
      for j in range(len(SCREAMING_SNAKE_CASE_  ) - 1  ):
         _SCREAMING_SNAKE_CASE          = list(edges[j]  )
         for e in temp:
            _SCREAMING_SNAKE_CASE          = (e[0], e[1])
            el.append(SCREAMING_SNAKE_CASE_  )
      freq_sub_el.append(SCREAMING_SNAKE_CASE_  )
   return freq_sub_el
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   for i in range(len(SCREAMING_SNAKE_CASE_  )  ):
      for j in range(len(edge_array[i]  )  ):
         _SCREAMING_SNAKE_CASE          = edge_array[i][j].split("""-"""  )
         _SCREAMING_SNAKE_CASE          = t


if __name__ == "__main__":
     preprocess(edge_array)
     UpperCamelCase__					:						List[Any]											=     get_frequency_table(edge_array)
     UpperCamelCase__					:						List[Any]											=     get_nodes(frequency_table)
     UpperCamelCase__					:						List[str]											=     get_cluster(nodes)
     UpperCamelCase__					:						List[Any]											=     get_support(cluster)
     UpperCamelCase__					:						Union[str, Any]											=     construct_graph(cluster, nodes)
     find_freq_subgraph_given_support(60, cluster, graph)
     UpperCamelCase__					:						list											=     []
     UpperCamelCase__					:						int											=     freq_subgraphs_edge_list(paths)
     print_all()






