





'''simple docstring'''






import argparse
import json
import re
import string
import sys

import numpy as np


A :			Optional[Any]							   =      0.5





def    lowercase_   (		lowercase__					,	lowercase__    ) ->str:
						_snake_case: List[Any]	       =		[""".""", """,""", """;""", """:"""]

						for token in remove_tokens:
												_snake_case: Dict	       =		ground_truth.replace(_a					,	''    )
												_snake_case: List[Any]	       =		prediction.replace(_a					,	''    )

						_snake_case: Any	       =		ground_truth.lower(), prediction.lower()
						_snake_case: Dict	       =		ground_truth.replace('/'					,	' '    ), prediction.replace('/'					,	' '    )
						_snake_case: Tuple	       =		set(ground_truth.split(' '    )    ), set(prediction.split(' '    )    )

						_snake_case: Tuple	       =		ground_truth.intersection(_a    )
						_snake_case: Optional[Any]	       =		ground_truth.union(_a    )
						_snake_case: Tuple	       =		len(_a    ) / len(_a    )
						return jaccard





def    lowercase_   (		lowercase__    ) ->List[str]:




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

						def white_space_fix(lowercase__    ):
												return " ".join(text.split()    )

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

						def lower(lowercase__    ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(_a    )    )    )    )





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->Union[str, Any]:
						_snake_case: List[str]	       =		0, 0, 0

						_snake_case: int	       =		"""Parties""" in qa_id

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

																		if not match_found:
																								fp += 1

						_snake_case: Tuple	       =		tp / (tp + fp) if tp + fp > 0 else np.nan
						_snake_case: Union[str, Any]	       =		tp / (tp + fn) if tp + fn > 0 else np.nan

						return precision, recall





def    lowercase_   (		lowercase__    ) ->List[Any]:
						_snake_case: str	       =		precisions[::-1]
						for i in range(1					,	len(_a    )    ):
												_snake_case: Dict	       =		max(precision_best[i - 1]					,	precision_best[i]    )
						_snake_case: str	       =		precision_best[::-1]
						return precisions





def    lowercase_   (		lowercase__					,	lowercase__    ) ->Optional[int]:
						_snake_case: Optional[int]	       =		process_precisions(_a    )
						_snake_case: Tuple	       =		np.trapz(_a					,	_a    )
						if np.isnan(_a    ):
												return 0
						return aupr





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[str]:
						_snake_case: Optional[Any]	       =		process_precisions(_a    )
						_snake_case: List[Any]	       =		0
						for prec, recall in zip(_a					,	_a    ):
												if recall >= recall_thresh:
																		_snake_case: Union[str, Any]	       =		prec
																		break
						return prec_at_recall





def    lowercase_   (		lowercase__					,	lowercase__    ) ->List[Any]:
						return normalize_answer(_a    ) == normalize_answer(_a    )





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->Union[str, Any]:
						_snake_case: Dict	       =		0
						for pred in predictions:
												for ground_truth in ground_truths:
																		_snake_case: Tuple	       =		metric_fn(_a					,	_a    )
																		if score == 1:  # break the loop when one prediction matches the ground truth
																								break
												if score == 1:
																		break
						return score





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

																								precisions.append(_a    )
																								recalls.append(_a    )

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

																								exact_match += metric_max_over_ground_truths(_a					,	_a					,	_a    )

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

						_snake_case: List[str]	       =		100.0 * fa / total
						_snake_case: Tuple	       =		100.0 * exact_match / total
						_snake_case: Union[str, Any]	       =		get_aupr(_a					,	_a    )

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










import itertools
from pathlib import Path







def       lowerCamelCase_  (    _a       :						list[str]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					[tuple(_a    ) for login in logins]

    UpperCAmelCase_     :       str  		=					{char for login in split_logins for char in login}

    for permutation in itertools.permutations(_a    ):
        UpperCAmelCase_     :       str  		=					True
        for login in logins:
            if not (
                permutation.index(login[0]    )
                < permutation.index(login[1]    )
                < permutation.index(login[2]    )
            ):
                UpperCAmelCase_     :       Optional[int]  		=					False
                break

        if satisfied:
            return int("""""".join(_a    )    )

    raise Exception("""Unable to find the secret passcode"""    )







def       lowerCamelCase_  (    _a       :						str = "keylog.txt"    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					Path(_a    ).parent.joinpath(_a    ).read_text().splitlines()

    return find_secret_passcode(_a    )


if __name__ == "__main__":
      print(F"{solution() = }")
