


import argparse
import json
import re
import string
import sys

import numpy as np


snake_case_  :	Optional[Any]							  =							0.5







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =['''.''', ''',''', ''';''', ''':''']

				for token in remove_tokens:
								_SCREAMING_SNAKE_CASE	     =ground_truth.replace(a__		,'''''')
								_SCREAMING_SNAKE_CASE	     =prediction.replace(a__		,'''''')

				_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(a__)
				_SCREAMING_SNAKE_CASE	     =ground_truth.union(a__)
				_SCREAMING_SNAKE_CASE	     =len(a__) / len(a__)
				return jaccard







def   lowerCamelCase(      a__):

				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__):
								_SCREAMING_SNAKE_CASE	     =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   lowerCamelCase(      a__		,a__		,a__):
				_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(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
												_SCREAMING_SNAKE_CASE	     =False
												for pred in predictions:
																if substr_ok:
																				_SCREAMING_SNAKE_CASE	     =get_jaccard(a__		,a__) >= IOU_THRESH or ground_truth in pred
																else:
																				_SCREAMING_SNAKE_CASE	     =get_jaccard(a__		,a__) >= 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(a__) > 0
																if substr_ok:
																				_SCREAMING_SNAKE_CASE	     =get_jaccard(a__		,a__) >= IOU_THRESH or ground_truth in pred
																else:
																				_SCREAMING_SNAKE_CASE	     =get_jaccard(a__		,a__) >= 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   lowerCamelCase(      a__):
				_SCREAMING_SNAKE_CASE	     =precisions[::-1]
				for i in range(1		,len(a__)):
								_SCREAMING_SNAKE_CASE	     =max(precision_best[i - 1]		,precision_best[i])
				_SCREAMING_SNAKE_CASE	     =precision_best[::-1]
				return precisions







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =process_precisions(a__)
				_SCREAMING_SNAKE_CASE	     =np.trapz(a__		,a__)
				if np.isnan(a__):
								return 0
				return aupr







def   lowerCamelCase(      a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE	     =process_precisions(a__)
				_SCREAMING_SNAKE_CASE	     =0
				for prec, recall in zip(a__		,a__):
								if recall >= recall_thresh:
												_SCREAMING_SNAKE_CASE	     =prec
												break
				return prec_at_recall







def   lowerCamelCase(      a__		,a__):
				return normalize_answer(a__) == normalize_answer(a__)







def   lowerCamelCase(      a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE	     =0
				for pred in predictions:
								for ground_truth in ground_truths:
												_SCREAMING_SNAKE_CASE	     =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   lowerCamelCase(      a__		,a__):
				_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(a__		,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(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__)

				_SCREAMING_SNAKE_CASE	     =[x for _, x in sorted(zip(a__		,a__))]
				recalls.sort()

				_SCREAMING_SNAKE_CASE	     =100.0 * fa / total
				_SCREAMING_SNAKE_CASE	     =100.0 * exact_match / total
				_SCREAMING_SNAKE_CASE	     =get_aupr(a__		,a__)

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



from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    TFDebertaForMaskedLM,
							    TFDebertaForQuestionAnswering,
							    TFDebertaForSequenceClassification,
							    TFDebertaForTokenClassification,
							    TFDebertaModel,
							)

class   A__							:




						def __init__(				self    :					Dict ,			_a    :					List[Any] ,			_a    :					int=13 ,			_a    :					List[str]=7 ,			_a    :					Optional[Any]=True ,			_a    :					Any=True ,			_a    :					int=True ,			_a    :					Dict=True ,			_a    :					Union[str, Any]=99 ,			_a    :					str=32 ,			_a    :					Dict=2 ,			_a    :					Dict=4 ,			_a    :					str=37 ,			_a    :					Dict="gelu" ,			_a    :					Any=0.1 ,			_a    :					int=0.1 ,			_a    :					List[str]=512 ,			_a    :					Optional[int]=16 ,			_a    :					Tuple=2 ,			_a    :					List[str]=0.02 ,			_a    :					Optional[int]=3 ,			_a    :					List[Any]=4 ,			_a    :					Union[str, Any]=None ,			)							->   Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =parent
										_SCREAMING_SNAKE_CASE	     =13
										_SCREAMING_SNAKE_CASE	     =7
										_SCREAMING_SNAKE_CASE	     =True
										_SCREAMING_SNAKE_CASE	     =True
										_SCREAMING_SNAKE_CASE	     =True
										_SCREAMING_SNAKE_CASE	     =True
										_SCREAMING_SNAKE_CASE	     =99
										_SCREAMING_SNAKE_CASE	     =32
										_SCREAMING_SNAKE_CASE	     =2
										_SCREAMING_SNAKE_CASE	     =4
										_SCREAMING_SNAKE_CASE	     =37
										_SCREAMING_SNAKE_CASE	     ='''gelu'''
										_SCREAMING_SNAKE_CASE	     =0.1
										_SCREAMING_SNAKE_CASE	     =0.1
										_SCREAMING_SNAKE_CASE	     =512
										_SCREAMING_SNAKE_CASE	     =16
										_SCREAMING_SNAKE_CASE	     =False
										_SCREAMING_SNAKE_CASE	     =-1
										_SCREAMING_SNAKE_CASE	     =True
										_SCREAMING_SNAKE_CASE	     =2
										_SCREAMING_SNAKE_CASE	     =0.02
										_SCREAMING_SNAKE_CASE	     =3
										_SCREAMING_SNAKE_CASE	     =4
										_SCREAMING_SNAKE_CASE	     =None




						def 							__UpperCamelCase (				self    :					Tuple     )							->   Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size     )

										_SCREAMING_SNAKE_CASE	     =None
										if self.use_input_mask:
														_SCREAMING_SNAKE_CASE	     =random_attention_mask([self.batch_size, self.seq_length]     )

										_SCREAMING_SNAKE_CASE	     =None
										if self.use_token_type_ids:
														_SCREAMING_SNAKE_CASE	     =ids_tensor([self.batch_size, self.seq_length] ,			self.type_vocab_size     )

										_SCREAMING_SNAKE_CASE	     =None
										_SCREAMING_SNAKE_CASE	     =None
										_SCREAMING_SNAKE_CASE	     =None
										if self.use_labels:
														_SCREAMING_SNAKE_CASE	     =ids_tensor([self.batch_size] ,			self.type_sequence_label_size     )
														_SCREAMING_SNAKE_CASE	     =ids_tensor([self.batch_size, self.seq_length] ,			self.num_labels     )

										_SCREAMING_SNAKE_CASE	     =DebertaConfig(
										    vocab_size=self.vocab_size ,			hidden_size=self.hidden_size ,			num_hidden_layers=self.num_hidden_layers ,			num_attention_heads=self.num_attention_heads ,			intermediate_size=self.intermediate_size ,			hidden_act=self.hidden_act ,			hidden_dropout_prob=self.hidden_dropout_prob ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob ,			max_position_embeddings=self.max_position_embeddings ,			type_vocab_size=self.type_vocab_size ,			relative_attention=self.relative_attention ,			max_relative_positions=self.max_relative_positions ,			position_biased_input=self.position_biased_input ,			initializer_range=self.initializer_range ,			return_dict=_a ,			)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




						def 							__UpperCamelCase (				self    :					Optional[int] ,			_a    :					Optional[Any] ,			_a    :					Dict ,			_a    :					List[str] ,			_a    :					Dict ,			_a    :					List[Any] ,			_a    :					Dict ,			_a    :					Tuple     )							->   Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =TFDebertaModel(config=_a     )
										_SCREAMING_SNAKE_CASE	     ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

										_SCREAMING_SNAKE_CASE	     =[input_ids, input_mask]
										_SCREAMING_SNAKE_CASE	     =model(_a     )

										_SCREAMING_SNAKE_CASE	     =model(_a     )

										self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)     )




						def 							__UpperCamelCase (				self    :					int ,			_a    :					List[Any] ,			_a    :					Tuple ,			_a    :					List[str] ,			_a    :					Optional[int] ,			_a    :					Union[str, Any] ,			_a    :					Optional[Any] ,			_a    :					Optional[int]     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =TFDebertaForMaskedLM(config=_a     )
										_SCREAMING_SNAKE_CASE	     ={
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}
										_SCREAMING_SNAKE_CASE	     =model(_a     )
										self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)     )




						def 							__UpperCamelCase (				self    :					Union[str, Any] ,			_a    :					List[str] ,			_a    :					List[Any] ,			_a    :					Optional[int] ,			_a    :					List[Any] ,			_a    :					Optional[Any] ,			_a    :					Dict ,			_a    :					Optional[int]     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.num_labels
										_SCREAMING_SNAKE_CASE	     =TFDebertaForSequenceClassification(config=_a     )
										_SCREAMING_SNAKE_CASE	     ={
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}

										_SCREAMING_SNAKE_CASE	     =model(_a     )
										self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels)     )




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					Any ,			_a    :					Optional[int] ,			_a    :					Optional[Any] ,			_a    :					Optional[Any] ,			_a    :					Optional[int] ,			_a    :					Optional[int] ,			_a    :					Any     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.num_labels
										_SCREAMING_SNAKE_CASE	     =TFDebertaForTokenClassification(config=_a     )
										_SCREAMING_SNAKE_CASE	     ={
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}
										_SCREAMING_SNAKE_CASE	     =model(_a     )
										self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.num_labels)     )




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					Dict ,			_a    :					str ,			_a    :					List[Any] ,			_a    :					Optional[Any] ,			_a    :					Optional[int] ,			_a    :					Optional[int] ,			_a    :					List[str]     )							->   Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =TFDebertaForQuestionAnswering(config=_a     )
										_SCREAMING_SNAKE_CASE	     ={
										    '''input_ids''': input_ids,
										    '''attention_mask''': input_mask,
										    '''token_type_ids''': token_type_ids,
										}

										_SCREAMING_SNAKE_CASE	     =model(_a     )
										self.parent.assertEqual(result.start_logits.shape ,			(self.batch_size, self.seq_length)     )
										self.parent.assertEqual(result.end_logits.shape ,			(self.batch_size, self.seq_length)     )




						def 							__UpperCamelCase (				self    :					str     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.prepare_config_and_inputs()
										(
										    (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , (
										    _SCREAMING_SNAKE_CASE
										)     , 
										)	     =config_and_inputs
										_SCREAMING_SNAKE_CASE	     ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
										return config, inputs_dict

@require_tf
class   A__							(						UpperCamelCase__      ,   UpperCamelCase__      ,   unittest.TestCase     ):
						UpperCAmelCase									=  (
						    (
						        TFDebertaModel,
						        TFDebertaForMaskedLM,
						        TFDebertaForQuestionAnswering,
						        TFDebertaForSequenceClassification,
						        TFDebertaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCAmelCase									=  (
						    {
						        "feature-extraction": TFDebertaModel,
						        "fill-mask": TFDebertaForMaskedLM,
						        "question-answering": TFDebertaForQuestionAnswering,
						        "text-classification": TFDebertaForSequenceClassification,
						        "token-classification": TFDebertaForTokenClassification,
						        "zero-shot": TFDebertaForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)

						UpperCAmelCase									=  False
						UpperCAmelCase									=  False




						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =TFDebertaModelTester(self     )
										_SCREAMING_SNAKE_CASE	     =ConfigTester(self ,			config_class=_a ,			hidden_size=37     )




						def 							__UpperCamelCase (				self    :					List[str]     )							->   Any:


										"""simple docstring"""





										self.config_tester.run_common_tests()




						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_a     )




						def 							__UpperCamelCase (				self    :					Any     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_a     )




						def 							__UpperCamelCase (				self    :					List[Any]     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_a     )




						def 							__UpperCamelCase (				self    :					Dict     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_a     )




						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_a     )




						@slow
						def 							__UpperCamelCase (				self    :					List[Any]     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''     )
										self.assertIsNotNone(_a     )

@require_tf
class   A__							(						unittest.TestCase     ):




						@unittest.skip(reason='''Model not available yet'''     )
						def 							__UpperCamelCase (				self    :					Tuple     )							->   List[str]:


										"""simple docstring"""





										pass




						@slow
						def 							__UpperCamelCase (				self    :					List[Any]     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''     )
										_SCREAMING_SNAKE_CASE	     =tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]     )
										_SCREAMING_SNAKE_CASE	     =tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
										_SCREAMING_SNAKE_CASE	     =model(_a ,			attention_mask=_a     )[0]

										_SCREAMING_SNAKE_CASE	     =tf.constant(
										    [
										        [
										            [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
										            [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
										            [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
										        ]
										    ]     )
										tf.debugging.assert_near(output[:, 1:4, 1:4] ,			_a ,			atol=1E-4     )