
"""simple docstring"""

import argparse
import json
import re
import string
import sys

import numpy as np


_A            = 0.5
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			Union[str, Any]		=    ['.', ',', ';', ':']

		for token in remove_tokens:
				lowerCamelCase     :			Any		=    ground_truth.replace(a_,						''			)
				lowerCamelCase     :			List[Any]		=    prediction.replace(a_,						''			)

		lowerCamelCase	,    lowerCamelCase     :			List[Any]		=    ground_truth.lower(), prediction.lower()
		lowerCamelCase	,    lowerCamelCase     :			Optional[int]		=    ground_truth.replace('/',						' '			), prediction.replace('/',						' '			)
		lowerCamelCase	,    lowerCamelCase     :			List[Any]		=    set(ground_truth.split(' '			)			), set(prediction.split(' '			)			)

		lowerCamelCase     :			Tuple		=    ground_truth.intersection(a_			)
		lowerCamelCase     :			Optional[int]		=    ground_truth.union(a_			)
		lowerCamelCase     :			Any		=    len(a_			) / len(a_			)
		return jaccard
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


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

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

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

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

		return white_space_fix(remove_articles(remove_punc(lower(a_			)			)			)			)
def 		UpperCAmelCase					(		a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			Union[str, Any]		=    0, 0, 0

		lowerCamelCase     :			Dict		=    '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
						lowerCamelCase     :			str		=    False
						for pred in predictions:
								if substr_ok:
										lowerCamelCase     :			Union[str, Any]		=    get_jaccard(a_,						a_			) >= IOU_THRESH or ground_truth in pred
								else:
										lowerCamelCase     :			Dict		=    get_jaccard(a_,						a_			) >= IOU_THRESH
								if is_match:
										lowerCamelCase     :			int		=    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     :			Union[str, Any]		=    False
						for ground_truth in ground_truths:
								assert len(a_			) > 0
								if substr_ok:
										lowerCamelCase     :			Union[str, Any]		=    get_jaccard(a_,						a_			) >= IOU_THRESH or ground_truth in pred
								else:
										lowerCamelCase     :			List[Any]		=    get_jaccard(a_,						a_			) >= IOU_THRESH
								if is_match:
										lowerCamelCase     :			List[str]		=    True

						if not match_found:
								fp += 1

		lowerCamelCase     :			Optional[Any]		=    tp / (tp + fp) if tp + fp > 0 else np.nan
		lowerCamelCase     :			int		=    tp / (tp + fn) if tp + fn > 0 else np.nan

		return precision, recall
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		lowerCamelCase     :			str		=    precisions[::-1]
		for i in range(1,						len(a_			)			):
				lowerCamelCase     :			Optional[int]		=    max(precision_best[i - 1],						precision_best[i]			)
		lowerCamelCase     :			Optional[Any]		=    precision_best[::-1]
		return precisions
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			Tuple		=    process_precisions(a_			)
		lowerCamelCase     :			Optional[Any]		=    np.trapz(a_,						a_			)
		if np.isnan(a_			):
				return 0
		return aupr
def 		UpperCAmelCase					(		a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			List[str]		=    process_precisions(a_			)
		lowerCamelCase     :			Union[str, Any]		=    0
		for prec, recall in zip(a_,						a_			):
				if recall >= recall_thresh:
						lowerCamelCase     :			str		=    prec
						break
		return prec_at_recall
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		return normalize_answer(a_			) == normalize_answer(a_			)
def 		UpperCAmelCase					(		a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			str		=    0
		for pred in predictions:
				for ground_truth in ground_truths:
						lowerCamelCase     :			List[str]		=    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 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			List[Any]		=    0
		lowerCamelCase     :			Dict		=    []
		lowerCamelCase     :			Union[str, Any]		=    []
		for article in dataset:
				for paragraph in article["paragraphs"]:
						for qa in paragraph["qas"]:
								total += 1
								if qa["id"] not in predictions:
										lowerCamelCase     :			Dict		=    'Unanswered question ' + qa['id'] + ' will receive score 0.'
										print(a_,						file=sys.stderr			)
										continue
								lowerCamelCase     :			List[Any]		=    [x['text'] for x in qa['answers']]
								lowerCamelCase     :			List[Any]		=    predictions[qa['id']]
								lowerCamelCase	,    lowerCamelCase     :			Optional[Any]		=    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_			)

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

		lowerCamelCase     :			Any		=    1_0_0.0 * fa / total
		lowerCamelCase     :			Union[str, Any]		=    1_0_0.0 * exact_match / total
		lowerCamelCase     :			Any		=    get_aupr(a_,						a_			)

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








"""simple docstring"""

from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	DPRContextEncoderTokenizer
	lowercase_        =	DPRContextEncoderTokenizerFast
	lowercase_        =	True






@require_tokenizers
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	DPRQuestionEncoderTokenizer
	lowercase_        =	DPRQuestionEncoderTokenizerFast
	lowercase_        =	True






@require_tokenizers
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	DPRReaderTokenizer
	lowercase_        =	DPRReaderTokenizerFast
	lowercase_        =	True




	@slow
	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			str		=    self.tokenizer_class.from_pretrained('bert-base-uncased'					)

			lowerCamelCase     :			int		=    tokenizer.encode('question sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    tokenizer.encode('title sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			Optional[int]		=    tokenizer.encode('text sequence ' * 4		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			Union[str, Any]		=    [[101] + text_a + [102] + text_a + [102] + text_a]
			lowerCamelCase     :			List[str]		=    BatchEncoding({'input_ids': input_ids}					)

			lowerCamelCase     :			Optional[int]		=    [[0] * len(input_ids[0]					)]
			lowerCamelCase     :			str		=    [[0] * len(input_ids[0]					)]
			lowerCamelCase     :			List[Any]		=    [0]
			lowerCamelCase     :			Tuple		=    DPRReaderOutput(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)

			lowerCamelCase	,    lowerCamelCase     :			Optional[int]		=    8, 9
			lowerCamelCase     :			Optional[int]		=    10
			lowerCamelCase     :			int		=    10
			lowerCamelCase     :			List[Any]		=    tokenizer.decode_best_spans(UpperCAmelCase_		, UpperCAmelCase_					)
			self.assertEqual(predicted_spans[0].start_index		, UpperCAmelCase_					)
			self.assertEqual(predicted_spans[0].end_index		, UpperCAmelCase_					)
			self.assertEqual(predicted_spans[0].doc_id		, 0					)




	@slow
	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase     :			Union[str, Any]		=    self.tokenizer_class.from_pretrained('bert-base-uncased'					)

			lowerCamelCase     :			List[str]		=    tokenizer.encode('question sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[str]		=    tokenizer.encode('title sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    tokenizer.encode('text sequence'		, add_special_tokens=UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    [101] + text_a + [102] + text_a + [102] + text_a
			lowerCamelCase     :			Optional[Any]		=    tokenizer(questions=['question sequence']		, titles=['title sequence']		, texts=['text sequence']					)
			self.assertIn('input_ids'		, UpperCAmelCase_					)
			self.assertIn('attention_mask'		, UpperCAmelCase_					)
			self.assertListEqual(encoded_input['input_ids'][0]		, UpperCAmelCase_					)






