



import argparse
import json
import re
import string
import sys

import numpy as np


__A				    =						0.5



def        __a     (    lowerCAmelCase_   :					List[str]			,lowerCAmelCase_   :					List[Any]				)      -> Union[str, Any]:






						'''simple docstring'''



						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        __a     (    lowerCAmelCase_   :					List[str]				)      -> List[Any]:






						'''simple docstring'''



						def remove_articles(lowerCAmelCase_   :					str				):
												return re.sub(r"""\b(a|an|the)\b"""			,""" """			,_UpperCAmelCase				)

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

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

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

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



def        __a     (    lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					List[Any]			,lowerCAmelCase_   :					str				)      -> Union[str, Any]:






						'''simple docstring'''



						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        __a     (    lowerCAmelCase_   :					Tuple				)      -> List[Any]:






						'''simple docstring'''



						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        __a     (    lowerCAmelCase_   :					List[Any]			,lowerCAmelCase_   :					Optional[Any]				)      -> List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=   process_precisions(_UpperCAmelCase				)
						UpperCAmelCase_=   np.trapz(_UpperCAmelCase			,_UpperCAmelCase				)
						if np.isnan(_UpperCAmelCase				):
												return 0
						return aupr



def        __a     (    lowerCAmelCase_   :					Dict			,lowerCAmelCase_   :					Dict			,lowerCAmelCase_   :					Optional[int]				)      -> Any:






						'''simple docstring'''



						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        __a     (    lowerCAmelCase_   :					Dict			,lowerCAmelCase_   :					List[Any]				)      -> Union[str, Any]:






						'''simple docstring'''



						return normalize_answer(_UpperCAmelCase				) == normalize_answer(_UpperCAmelCase				)



def        __a     (    lowerCAmelCase_   :					Tuple			,lowerCAmelCase_   :					Dict			,lowerCAmelCase_   :					Optional[Any]				)      -> int:






						'''simple docstring'''



						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        __a     (    lowerCAmelCase_   :					int			,lowerCAmelCase_   :					str				)      -> Union[str, Any]:






						'''simple docstring'''



						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				    =						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)))











"""simple docstring"""
__UpperCamelCase			:		Tuple		    =   10**8

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  int       ):
 if n % 10 == 0:
  return False
 lowerCAmelCase  							=      str(_UpperCAmelCase       )
 return s == s[::-1]

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      set()
 lowerCAmelCase  							=      1
 lowerCAmelCase  							=      5
 while sum_squares < LIMIT:
  lowerCAmelCase  							=      first_square + 1
  while sum_squares < LIMIT:
   if is_palindrome(_UpperCAmelCase       ):
    answer.add(_UpperCAmelCase       )
   last_square += 1
   sum_squares += last_square**2
  first_square += 1
  lowerCAmelCase  							=      first_square**2 + (first_square + 1) ** 2

 return sum(_UpperCAmelCase       )


if __name__ == "__main__":
       print(solution())





