
"""simple docstring"""


import argparse
import json
import re
import string
import sys

import numpy as np


_lowercase											=			0.5






def  _snake_case			(						snake_case__				:       Optional[Any]		, snake_case__				:       Optional[int]       ):
	A  				=				['.', ',', ';', ':']

	for token in remove_tokens:
		A  				=				ground_truth.replace(snake_case__		, ''       )
		A  				=				prediction.replace(snake_case__		, ''       )

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

	A  				=				ground_truth.intersection(snake_case__       )
	A  				=				ground_truth.union(snake_case__       )
	A  				=				len(snake_case__       ) / len(snake_case__       )
	return jaccard






def  _snake_case			(						snake_case__				:       str       ):

	def remove_articles(snake_case__				:       Tuple       ):
		return re.sub(r'\b(a|an|the)\b'		, ' '		, snake_case__       )

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

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

	def lower(snake_case__				:       Union[str, Any]       ):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(snake_case__       )       )       )       )






def  _snake_case			(						snake_case__				:       str		, snake_case__				:       int		, snake_case__				:       Tuple       ):
	A				, A				, A  				=				0, 0, 0

	A  				=				'Parties' in qa_id

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

			if not match_found:
				fp += 1

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

	return precision, recall






def  _snake_case			(						snake_case__				:       Dict       ):
	A  				=				precisions[::-1]
	for i in range(1		, len(snake_case__       )       ):
		A  				=				max(precision_best[i - 1]		, precision_best[i]       )
	A  				=				precision_best[::-1]
	return precisions






def  _snake_case			(						snake_case__				:       str		, snake_case__				:       Any       ):
	A  				=				process_precisions(snake_case__       )
	A  				=				np.trapz(snake_case__		, snake_case__       )
	if np.isnan(snake_case__       ):
		return 0
	return aupr






def  _snake_case			(						snake_case__				:       Any		, snake_case__				:       Optional[int]		, snake_case__				:       Optional[int]       ):
	A  				=				process_precisions(snake_case__       )
	A  				=				0
	for prec, recall in zip(snake_case__		, snake_case__       ):
		if recall >= recall_thresh:
			A  				=				prec
			break
	return prec_at_recall






def  _snake_case			(						snake_case__				:       Dict		, snake_case__				:       Optional[int]       ):
	return normalize_answer(snake_case__       ) == normalize_answer(snake_case__       )






def  _snake_case			(						snake_case__				:       Union[str, Any]		, snake_case__				:       int		, snake_case__				:       Tuple       ):
	A  				=				0
	for pred in predictions:
		for ground_truth in ground_truths:
			A  				=				metric_fn(snake_case__		, snake_case__       )
			if score == 1:  # break the loop when one prediction matches the ground truth
				break
		if score == 1:
			break
	return score






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

				precisions.append(snake_case__       )
				recalls.append(snake_case__       )

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

				exact_match += metric_max_over_ground_truths(snake_case__		, snake_case__		, snake_case__       )

	A  				=				[x for _, x in sorted(zip(snake_case__		, snake_case__       )       )]
	recalls.sort()

	A  				=				100.0 * fa / total
	A  				=				100.0 * exact_match / total
	A  				=				get_aupr(snake_case__		, snake_case__       )

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

"""simple docstring"""


import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 			lowerCAmelCase_			( _lowercase						,   unittest.TestCase      ):



						'''simple docstring'''


						_lowerCamelCase:      List[str] =      HerbertTokenizer
						_lowerCamelCase:      Any =      HerbertTokenizerFast
						_lowerCamelCase:      Optional[int] =      True


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				Tuple:
							super().setUp()

							# Use a simpler test file without japanese/chinese characters
							with open(F'{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'					,encoding='utf-8' ) as f_data:
								A  				=				f_data.read().replace('\n\n'					,'\n' ).strip()

							A  				=				[
							    '<s>',
							    '</s>',
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    'w</w>',
							    'r</w>',
							    't</w>',
							    'lo',
							    'low',
							    'er</w>',
							    'low</w>',
							    'lowest</w>',
							    'newer</w>',
							    'wider</w>',
							    ',</w>',
							    '<unk>',
							]
							A  				=				dict(zip(A_					,range(len(A_ ) ) ) )
							A  				=				['l o 123', 'lo w 1456', 'e r</w> 1789', '']

							A  				=				os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['vocab_file'] )
							A  				=				os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['merges_file'] )
							with open(self.vocab_file					,'w' ) as fp:
								fp.write(json.dumps(A_ ) )
							with open(self.merges_file					,'w' ) as fp:
								fp.write('\n'.join(A_ ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : List[str] )       ->				int:
							A  				=				'lower newer'
							A  				=				'lower newer'
							return input_text, output_text


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				int:
							A  				=				self.tokenizer_class(vocab_file=self.vocab_file					,merges_file=self.merges_file )

							A  				=				'lower'
							A  				=				['low', 'er</w>']
							A  				=				tokenizer.tokenize(A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				tokens + ['<unk>']
							A  				=				[16, 17, 23]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ )					,A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				Optional[int]:
							if not self.test_rust_tokenizer:
								return

							A  				=				self.get_tokenizer()
							A  				=				self.get_rust_tokenizer()

							A  				=				'lower,newer'

							A  				=				tokenizer.tokenize(A_ )
							A  				=				rust_tokenizer.tokenize(A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				tokenizer.encode(A_					,add_special_tokens=A_ )
							A  				=				rust_tokenizer.encode(A_					,add_special_tokens=A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				self.get_rust_tokenizer()
							A  				=				tokenizer.encode(A_ )
							A  				=				rust_tokenizer.encode(A_ )
							self.assertListEqual(A_					,A_ )




						@slow
						def   _SCREAMING_SNAKE_CASE (	self       : str )       ->				List[Any]:
							A  				=				self.tokenizer_class.from_pretrained('allegro/herbert-base-cased' )

							A  				=				tokenizer.encode('konstruowanie sekwencji'					,add_special_tokens=A_ )
							A  				=				tokenizer.encode('konstruowanie wielu sekwencji'					,add_special_tokens=A_ )

							A  				=				tokenizer.build_inputs_with_special_tokens(A_ )
							A  				=				tokenizer.build_inputs_with_special_tokens(A_					,A_ )

							assert encoded_sentence == [0] + text + [2]
							assert encoded_pair == [0] + text + [2] + text_a + [2]