
import argparse
import json
import re
import string
import sys

import numpy as np


A__       :   Optional[Any]   						=					0.5



def      _a     (   __UpperCamelCase       :  List[Any]	,__UpperCamelCase       :  Optional[int]    ):
			lowerCAmelCase__ :    int				      =			['''.''', ''',''', ''';''', ''':''']

			for token in remove_tokens:
						lowerCAmelCase__ :    int				      =			ground_truth.replace(lowercase_	,''''''    )
						lowerCAmelCase__ :    Tuple				      =			prediction.replace(lowercase_	,''''''    )

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

			lowerCAmelCase__ :    Union[str, Any]				      =			ground_truth.intersection(lowercase_    )
			lowerCAmelCase__ :    int				      =			ground_truth.union(lowercase_    )
			lowerCAmelCase__ :    int				      =			len(lowercase_    ) / len(lowercase_    )
			return jaccard



def      _a     (   __UpperCamelCase       :  int    ):

			def remove_articles(__UpperCamelCase       :  Union[str, Any]    ):
						return re.sub(R'''\b(a|an|the)\b'''	,''' '''	,lowercase_    )

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

			def remove_punc(__UpperCamelCase       :  str    ):
						lowerCAmelCase__ :    List[str]				      =			set(string.punctuation    )
						return "".join(ch for ch in text if ch not in exclude    )

			def lower(__UpperCamelCase       :  str    ):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(lowercase_    )    )    )    )



def      _a     (   __UpperCamelCase       :  Tuple	,__UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  str    ):
			lowerCAmelCase__ :    Tuple				      =			0, 0, 0

			lowerCAmelCase__ :    Optional[int]				      =			'''Parties''' in qa_id

			# first check if ground truth is empty
			if len(lowercase_    ) == 0:
						if len(lowercase_    ) > 0:
									fp += len(lowercase_    )  # false positive for each one
			else:
						for ground_truth in ground_truths:
									assert len(lowercase_    ) > 0
									# check if there is a match
									lowerCAmelCase__ :    Optional[Any]				      =			False
									for pred in predictions:
												if substr_ok:
															lowerCAmelCase__ :    List[Any]				      =			get_jaccard(lowercase_	,lowercase_    ) >= IOU_THRESH or ground_truth in pred
												else:
															lowerCAmelCase__ :    Any				      =			get_jaccard(lowercase_	,lowercase_    ) >= IOU_THRESH
												if is_match:
															lowerCAmelCase__ :    Union[str, Any]				      =			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__ :    Tuple				      =			False
									for ground_truth in ground_truths:
												assert len(lowercase_    ) > 0
												if substr_ok:
															lowerCAmelCase__ :    str				      =			get_jaccard(lowercase_	,lowercase_    ) >= IOU_THRESH or ground_truth in pred
												else:
															lowerCAmelCase__ :    Dict				      =			get_jaccard(lowercase_	,lowercase_    ) >= IOU_THRESH
												if is_match:
															lowerCAmelCase__ :    Optional[Any]				      =			True

									if not match_found:
												fp += 1

			lowerCAmelCase__ :    Dict				      =			tp / (tp + fp) if tp + fp > 0 else np.nan
			lowerCAmelCase__ :    str				      =			tp / (tp + fn) if tp + fn > 0 else np.nan

			return precision, recall



def      _a     (   __UpperCamelCase       :  str    ):
			lowerCAmelCase__ :    Optional[Any]				      =			precisions[::-1]
			for i in range(1	,len(lowercase_    )    ):
						lowerCAmelCase__ :    Any				      =			max(precision_best[i - 1]	,precision_best[i]    )
			lowerCAmelCase__ :    Tuple				      =			precision_best[::-1]
			return precisions



def      _a     (   __UpperCamelCase       :  int	,__UpperCamelCase       :  Union[str, Any]    ):
			lowerCAmelCase__ :    List[str]				      =			process_precisions(lowercase_    )
			lowerCAmelCase__ :    Tuple				      =			np.trapz(lowercase_	,lowercase_    )
			if np.isnan(lowercase_    ):
						return 0
			return aupr



def      _a     (   __UpperCamelCase       :  int	,__UpperCamelCase       :  Any	,__UpperCamelCase       :  Optional[Any]    ):
			lowerCAmelCase__ :    Optional[int]				      =			process_precisions(lowercase_    )
			lowerCAmelCase__ :    Dict				      =			0
			for prec, recall in zip(lowercase_	,lowercase_    ):
						if recall >= recall_thresh:
									lowerCAmelCase__ :    Optional[Any]				      =			prec
									break
			return prec_at_recall



def      _a     (   __UpperCamelCase       :  Any	,__UpperCamelCase       :  List[str]    ):
			return normalize_answer(lowercase_    ) == normalize_answer(lowercase_    )



def      _a     (   __UpperCamelCase       :  str	,__UpperCamelCase       :  List[str]	,__UpperCamelCase       :  Dict    ):
			lowerCAmelCase__ :    Any				      =			0
			for pred in predictions:
						for ground_truth in ground_truths:
									lowerCAmelCase__ :    Optional[int]				      =			metric_fn(lowercase_	,lowercase_    )
									if score == 1:  # break the loop when one prediction matches the ground truth
												break
						if score == 1:
									break
			return score



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

												precisions.append(lowercase_    )
												recalls.append(lowercase_    )

												if precision == 0 and recall == 0:
															fa += 0
												else:
															fa += 2 * (precision * recall) / (precision + recall)

												exact_match += metric_max_over_ground_truths(lowercase_	,lowercase_	,lowercase_    )

			lowerCAmelCase__ :    int				      =			[x for _, x in sorted(zip(lowercase_	,lowercase_    )    )]
			recalls.sort()

			lowerCAmelCase__ :    Dict				      =			100.0 * fa / total
			lowerCAmelCase__ :    str				      =			100.0 * exact_match / total
			lowerCAmelCase__ :    Tuple				      =			get_aupr(lowercase_	,lowercase_    )

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





import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def      _a     (   __UpperCamelCase       :  NDArray[floataa]	,__UpperCamelCase       :  NDArray[floataa]    ):
			lowerCAmelCase__      ,				lowerCAmelCase__ :    Any				      =			np.shape(__UpperCamelCase    )

			lowerCAmelCase__ :    NDArray[floataa]				      =			np.zeros((rows, 1)	,dtype=__UpperCamelCase    )
			for row in reversed(range(__UpperCamelCase    )    ):
						lowerCAmelCase__ :    List[str]				      =			0
						for col in range(row + 1	,__UpperCamelCase    ):
									total += coefficients[row, col] * x[col]

						lowerCAmelCase__ :    int				      =			(vector[row] - total) / coefficients[row, row]

			return x



def      _a     (   __UpperCamelCase       :  NDArray[floataa]	,__UpperCamelCase       :  NDArray[floataa]    ):
			lowerCAmelCase__      ,				lowerCAmelCase__ :    str				      =			np.shape(__UpperCamelCase    )
			if rows != columns:
						return np.array(()	,dtype=__UpperCamelCase    )

			# augmented matrix
			lowerCAmelCase__ :    NDArray[floataa]				      =			np.concatenate((coefficients, vector)	,axis=1    )
			lowerCAmelCase__ :    List[str]				      =			augmented_mat.astype('''float64'''    )

			# scale the matrix leaving it triangular
			for row in range(rows - 1    ):
						lowerCAmelCase__ :    str				      =			augmented_mat[row, row]
						for col in range(row + 1	,__UpperCamelCase    ):
									lowerCAmelCase__ :    Optional[int]				      =			augmented_mat[col, row] / pivot
									augmented_mat[col, :] -= factor * augmented_mat[row, :]

			lowerCAmelCase__ :    int				      =			retroactive_resolution(
			    augmented_mat[:, 0:columns]	,augmented_mat[:, columns : columns + 1]    )

			return x


if __name__ == "__main__":
							import doctest

							doctest.testmod()



