


import argparse
import json
import re
import string
import sys

import numpy as np


__SCREAMING_SNAKE_CASE	:							Tuple           =  0.5

def 		snake_case_   (				lowercase__     :    List[str]       ,     lowercase__     :    List[str]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=[""".""", """,""", """;""", """:"""]

				for token in remove_tokens:
								_lowerCAmelCase  						=ground_truth.replace(lowercase__       ,     """"""     )
								_lowerCAmelCase  						=prediction.replace(lowercase__       ,     """"""     )

				_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(lowercase__     )
				_lowerCAmelCase  						=ground_truth.union(lowercase__     )
				_lowerCAmelCase  						=len(lowercase__     ) / len(lowercase__     )
				return jaccard

def 		snake_case_   (				lowercase__     :    List[Any]     ):






				'''simple docstring'''






				def remove_articles(lowercase__     :    Union[str, Any]     ):
								return re.sub(r"""\b(a|an|the)\b"""       ,     """ """       ,     lowercase__     )

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

				def remove_punc(lowercase__     :    Optional[Any]     ):
								_lowerCAmelCase  						=set(string.punctuation     )
								return "".join(ch for ch in text if ch not in exclude     )

				def lower(lowercase__     :    List[str]     ):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(lowercase__     )     )     )     )

def 		snake_case_   (				lowercase__     :    List[str]       ,     lowercase__     :    Union[str, Any]       ,     lowercase__     :    Tuple     ):






				'''simple docstring'''






				_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase  						=0, 0, 0

				_lowerCAmelCase  						="""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  						=False
												for pred in predictions:
																if substr_ok:
																				_lowerCAmelCase  						=get_jaccard(lowercase__       ,     lowercase__     ) >= IOU_THRESH or ground_truth in pred
																else:
																				_lowerCAmelCase  						=get_jaccard(lowercase__       ,     lowercase__     ) >= 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(lowercase__     ) > 0
																if substr_ok:
																				_lowerCAmelCase  						=get_jaccard(lowercase__       ,     lowercase__     ) >= IOU_THRESH or ground_truth in pred
																else:
																				_lowerCAmelCase  						=get_jaccard(lowercase__       ,     lowercase__     ) >= 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 		snake_case_   (				lowercase__     :    List[Any]     ):






				'''simple docstring'''






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

def 		snake_case_   (				lowercase__     :    Any       ,     lowercase__     :    int     ):






				'''simple docstring'''






				_lowerCAmelCase  						=process_precisions(lowercase__     )
				_lowerCAmelCase  						=np.trapz(lowercase__       ,     lowercase__     )
				if np.isnan(lowercase__     ):
								return 0
				return aupr

def 		snake_case_   (				lowercase__     :    Any       ,     lowercase__     :    int       ,     lowercase__     :    str     ):






				'''simple docstring'''






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

def 		snake_case_   (				lowercase__     :    Optional[int]       ,     lowercase__     :    str     ):






				'''simple docstring'''






				return normalize_answer(lowercase__     ) == normalize_answer(lowercase__     )

def 		snake_case_   (				lowercase__     :    Optional[int]       ,     lowercase__     :    List[Any]       ,     lowercase__     :    Any     ):






				'''simple docstring'''






				_lowerCAmelCase  						=0
				for pred in predictions:
								for ground_truth in ground_truths:
												_lowerCAmelCase  						=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 		snake_case_   (				lowercase__     :    Optional[int]       ,     lowercase__     :    Optional[int]     ):






				'''simple docstring'''






				_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(lowercase__       ,     file=sys.stderr     )
																				continue
																_lowerCAmelCase  						=[x["""text"""] for x in qa["""answers"""]]
																_lowerCAmelCase  						=predictions[qa["""id"""]]
																_lowerCAmelCase						,							_lowerCAmelCase  						=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  						=[x for _, x in sorted(zip(lowercase__       ,     lowercase__     )     )]
				recalls.sort()

				_lowerCAmelCase  						=1_0_0.0 * fa / total
				_lowerCAmelCase  						=1_0_0.0 * exact_match / total
				_lowerCAmelCase  						=get_aupr(lowercase__       ,     lowercase__     )

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





from __future__ import annotations

__SCREAMING_SNAKE_CASE	:							Union[str, Any]           =  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]


class        __lowerCamelCase      :


				"""simple docstring"""







				def __init__(				self     :    Dict	,							lowerCamelCase_     :    int					):
								_lowerCAmelCase  						=-1
								_lowerCAmelCase  						=-1
								_lowerCAmelCase  						=[-1] * length
								_lowerCAmelCase  						=None
								_lowerCAmelCase  						=None






				def __repr__(				self     :    Tuple					):
								return F"Node(min_value={self.minn} max_value={self.maxx})"

def 		snake_case_   (				lowercase__     :    list[int]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=Node(len(lowercase__     )     )
				_lowerCAmelCase						,							_lowerCAmelCase  						=min(lowercase__     ), max(lowercase__     )
				# Leaf node case where the node contains only one unique value
				if root.minn == root.maxx:
								return root
				_lowerCAmelCase  						=(root.minn + root.maxx) // 2

				_lowerCAmelCase  						=[]
				_lowerCAmelCase  						=[]

				for index, num in enumerate(lowercase__     ):
								if num <= pivot:
												left_arr.append(lowercase__     )
								else:
												right_arr.append(lowercase__     )
								_lowerCAmelCase  						=len(lowercase__     )
				_lowerCAmelCase  						=build_tree(lowercase__     )
				_lowerCAmelCase  						=build_tree(lowercase__     )
				return root

def 		snake_case_   (				lowercase__     :    Node | None       ,     lowercase__     :    int       ,     lowercase__     :    int     ):






				'''simple docstring'''






				if index < 0 or node is None:
								return 0
				# Leaf node cases
				if node.minn == node.maxx:
								return index + 1 if node.minn == num else 0
				_lowerCAmelCase  						=(node.minn + node.maxx) // 2
				if num <= pivot:
								# go the left subtree and map index to the left subtree
								return rank_till_index(node.left       ,     lowercase__       ,     node.map_left[index] - 1     )
				else:
								# go to the right subtree and map index to the right subtree
								return rank_till_index(node.right       ,     lowercase__       ,     index - node.map_left[index]     )

def 		snake_case_   (				lowercase__     :    Node | None       ,     lowercase__     :    int       ,     lowercase__     :    int       ,     lowercase__     :    int     ):






				'''simple docstring'''






				if start > end:
								return 0
				_lowerCAmelCase  						=rank_till_index(lowercase__       ,     lowercase__       ,     lowercase__     )
				_lowerCAmelCase  						=rank_till_index(lowercase__       ,     lowercase__       ,     start - 1     )
				return rank_till_end - rank_before_start

def 		snake_case_   (				lowercase__     :    Node | None       ,     lowercase__     :    int       ,     lowercase__     :    int       ,     lowercase__     :    int     ):






				'''simple docstring'''






				if index > (end - start) or start > end or node is None:
								return -1
				# Leaf node case
				if node.minn == node.maxx:
								return node.minn
				# Number of elements in the left subtree in interval [start, end]
				_lowerCAmelCase  						=node.map_left[end] - (
				    node.map_left[start - 1] if start else 0
				)
				if num_elements_in_left_tree > index:
								return quantile(
								    node.left       ,     lowercase__       ,     (node.map_left[start - 1] if start else 0)       ,     node.map_left[end] - 1       ,     )
				else:
								return quantile(
								    node.right       ,     index - num_elements_in_left_tree       ,     start - (node.map_left[start - 1] if start else 0)       ,     end - node.map_left[end]       ,     )

def 		snake_case_   (				lowercase__     :    Node | None       ,     lowercase__     :    int       ,     lowercase__     :    int       ,     lowercase__     :    int       ,     lowercase__     :    int     ):






				'''simple docstring'''






				if (
				    start > end
				    or node is None
				    or start_num > end_num
				    or node.minn > end_num
				    or node.maxx < start_num
				):
								return 0
				if start_num <= node.minn and node.maxx <= end_num:
								return end - start + 1
				_lowerCAmelCase  						=range_counting(
				    node.left       ,     (node.map_left[start - 1] if start else 0)       ,     node.map_left[end] - 1       ,     lowercase__       ,     lowercase__       ,     )
				_lowerCAmelCase  						=range_counting(
				    node.right       ,     start - (node.map_left[start - 1] if start else 0)       ,     end - node.map_left[end]       ,     lowercase__       ,     lowercase__       ,     )
				return left + right


if __name__ == "__main__":
		import doctest

		doctest.testmod()

