
"""simple docstring"""




import argparse
import json
import re
import string
import sys

import numpy as np


snake_case    =		0.5

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	['.', ',', ';', ':']

					for token in remove_tokens:
										SCREAMING_SNAKE_CASE				=	ground_truth.replace(SCREAMING_SNAKE_CASE_,     ''			)
										SCREAMING_SNAKE_CASE				=	prediction.replace(SCREAMING_SNAKE_CASE_,     ''			)

					SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	ground_truth.lower(), prediction.lower()
					SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	ground_truth.replace('/',     ' '			), prediction.replace('/',     ' '			)
					SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	set(ground_truth.split(' '			)			), set(prediction.split(' '			)			)

					SCREAMING_SNAKE_CASE				=	ground_truth.intersection(SCREAMING_SNAKE_CASE_			)
					SCREAMING_SNAKE_CASE				=	ground_truth.union(SCREAMING_SNAKE_CASE_			)
					SCREAMING_SNAKE_CASE				=	len(SCREAMING_SNAKE_CASE_			) / len(SCREAMING_SNAKE_CASE_			)
					return jaccard

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):

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

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

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

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

					return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_			)			)			)			)

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	0, 0, 0

					SCREAMING_SNAKE_CASE				=	'Parties' in qa_id

					# first check if ground truth is empty
					if len(SCREAMING_SNAKE_CASE_			) == 0:
										if len(SCREAMING_SNAKE_CASE_			) > 0:
															fp += len(SCREAMING_SNAKE_CASE_			)  # false positive for each one
					else:
										for ground_truth in ground_truths:
															assert len(SCREAMING_SNAKE_CASE_			) > 0
															# check if there is a match
															SCREAMING_SNAKE_CASE				=	False
															for pred in predictions:
																				if substr_ok:
																									SCREAMING_SNAKE_CASE				=	get_jaccard(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			) >= IOU_THRESH or ground_truth in pred
																				else:
																									SCREAMING_SNAKE_CASE				=	get_jaccard(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			) >= IOU_THRESH
																				if is_match:
																									SCREAMING_SNAKE_CASE				=	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.)
															SCREAMING_SNAKE_CASE				=	False
															for ground_truth in ground_truths:
																				assert len(SCREAMING_SNAKE_CASE_			) > 0
																				if substr_ok:
																									SCREAMING_SNAKE_CASE				=	get_jaccard(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			) >= IOU_THRESH or ground_truth in pred
																				else:
																									SCREAMING_SNAKE_CASE				=	get_jaccard(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			) >= IOU_THRESH
																				if is_match:
																									SCREAMING_SNAKE_CASE				=	True

															if not match_found:
																				fp += 1

					SCREAMING_SNAKE_CASE				=	tp / (tp + fp) if tp + fp > 0 else np.nan
					SCREAMING_SNAKE_CASE				=	tp / (tp + fn) if tp + fn > 0 else np.nan

					return precision, recall

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	precisions[::-1]
					for i in range(1,     len(SCREAMING_SNAKE_CASE_			)			):
										SCREAMING_SNAKE_CASE				=	max(precision_best[i - 1],     precision_best[i]			)
					SCREAMING_SNAKE_CASE				=	precision_best[::-1]
					return precisions

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	process_precisions(SCREAMING_SNAKE_CASE_			)
					SCREAMING_SNAKE_CASE				=	np.trapz(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
					if np.isnan(SCREAMING_SNAKE_CASE_			):
										return 0
					return aupr

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	process_precisions(SCREAMING_SNAKE_CASE_			)
					SCREAMING_SNAKE_CASE				=	0
					for prec, recall in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										if recall >= recall_thresh:
															SCREAMING_SNAKE_CASE				=	prec
															break
					return prec_at_recall

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					return normalize_answer(SCREAMING_SNAKE_CASE_			) == normalize_answer(SCREAMING_SNAKE_CASE_			)

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	0
					for pred in predictions:
										for ground_truth in ground_truths:
															SCREAMING_SNAKE_CASE				=	metric_fn(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
															if score == 1:  # break the loop when one prediction matches the ground truth
																				break
										if score == 1:
															break
					return score

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	SCREAMING_SNAKE_CASE				=	SCREAMING_SNAKE_CASE				=	0
					SCREAMING_SNAKE_CASE				=	[]
					SCREAMING_SNAKE_CASE				=	[]
					for article in dataset:
										for paragraph in article["paragraphs"]:
															for qa in paragraph["qas"]:
																				total += 1
																				if qa["id"] not in predictions:
																									SCREAMING_SNAKE_CASE				=	'Unanswered question ' + qa['id'] + ' will receive score 0.'
																									print(SCREAMING_SNAKE_CASE_,     file=sys.stderr			)
																									continue
																				SCREAMING_SNAKE_CASE				=	[x['text'] for x in qa['answers']]
																				SCREAMING_SNAKE_CASE				=	predictions[qa['id']]
																				SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	compute_precision_recall(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     qa['id']			)

																				precisions.append(SCREAMING_SNAKE_CASE_			)
																				recalls.append(SCREAMING_SNAKE_CASE_			)

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

																				exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

					SCREAMING_SNAKE_CASE				=	[x for _, x in sorted(zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)			)]
					recalls.sort()

					SCREAMING_SNAKE_CASE				=	100.0 * fa / total
					SCREAMING_SNAKE_CASE				=	100.0 * exact_match / total
					SCREAMING_SNAKE_CASE				=	get_aupr(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

					SCREAMING_SNAKE_CASE				=	get_prec_at_recall(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     recall_thresh=0.9			)
					SCREAMING_SNAKE_CASE				=	get_prec_at_recall(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     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__":
	snake_case    =		argparse.ArgumentParser(description='Evaluation for CUAD')
	parser.add_argument('dataset_file', help='Dataset file')
	parser.add_argument('prediction_file', help='Prediction File')
	snake_case    =		parser.parse_args()
	with open(args.dataset_file) as dataset_file:
		snake_case    =		json.load(dataset_file)
		snake_case    =		dataset_json['data']
	with open(args.prediction_file) as prediction_file:
		snake_case    =		json.load(prediction_file)
	print(json.dumps(evaluate(dataset, predictions)))


"""simple docstring"""




# Lint as: python3

import hashlib
from typing import Union

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										# Data already in bytes, returns as it as
										return hash_data
					elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										# We keep the data as it as for it ot be later encoded to UTF-8
										# However replace `\\` with `/` for Windows compatibility
										SCREAMING_SNAKE_CASE				=	hash_data.replace('\\',     '/'			)
					elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										SCREAMING_SNAKE_CASE				=	str(SCREAMING_SNAKE_CASE_			)
					else:
										# If data is not of the required type, raise error
										raise InvalidKeyError(SCREAMING_SNAKE_CASE_			)

					return hash_data.encode('utf-8'			)




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__			)    ->    Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	'\nFAILURE TO GENERATE DATASET: Invalid key type detected'
									SCREAMING_SNAKE_CASE				=	f'''\nFound Key {hash_data} of type {type(lowercase__			)}'''
									SCREAMING_SNAKE_CASE				=	'\nKeys should be either str, int or bytes type'
									super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''			)






class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__		,	lowercase__		,	lowercase__=""			)    ->    Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	key
									SCREAMING_SNAKE_CASE				=	duplicate_key_indices
									SCREAMING_SNAKE_CASE				=	fix_msg
									SCREAMING_SNAKE_CASE				=	'Found multiple examples generated with the same key'
									if len(lowercase__			) <= 20:
														SCREAMING_SNAKE_CASE				=	f'''\nThe examples at index {', '.join(lowercase__			)} have the key {key}'''
									else:
														SCREAMING_SNAKE_CASE				=	f'''\nThe examples at index {', '.join(duplicate_key_indices[:20]			)}... ({len(lowercase__			) - 20} more) have the key {key}'''
									SCREAMING_SNAKE_CASE				=	'\n' + fix_msg if fix_msg else ''
									super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''			)






class      UpperCamelCase	:

				"""simple docstring"""



				def __init__(				self		,	lowercase__			)    ->    Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	hashlib.mda(_as_bytes(lowercase__			)			)




				def 							A					(				self		,	lowercase__			)    ->    int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self._split_mda.copy()
									SCREAMING_SNAKE_CASE				=	_as_bytes(lowercase__			)
									mda.update(lowercase__			)
									# Convert to integer with hexadecimal conversion
									return int(mda.hexdigest()		,	16			)
