





"""simple docstring"""




import argparse
import json
import re
import string
import sys

import numpy as np


UpperCamelCase_           =0.5





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					int     =    ['''.''', ''',''', ''';''', ''':''']

						for token in remove_tokens:
												_UpperCamelCase			:					List[str]     =    ground_truth.replace(_lowercase       ,						''''''   )
												_UpperCamelCase			:					List[Any]     =    prediction.replace(_lowercase       ,						''''''   )

						_UpperCamelCase   ,							_UpperCamelCase			:					int     =    ground_truth.lower(), prediction.lower()
						_UpperCamelCase   ,							_UpperCamelCase			:					int     =    ground_truth.replace('''/'''       ,						''' '''   ), prediction.replace('''/'''       ,						''' '''   )
						_UpperCamelCase   ,							_UpperCamelCase			:					Dict     =    set(ground_truth.split(''' '''   )   ), set(prediction.split(''' '''   )   )

						_UpperCamelCase			:					Union[str, Any]     =    ground_truth.intersection(_lowercase   )
						_UpperCamelCase			:					Union[str, Any]     =    ground_truth.union(_lowercase   )
						_UpperCamelCase			:					List[str]     =    len(_lowercase   ) / len(_lowercase   )
						return jaccard





def        a_				(							_lowercase   ):

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

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

						def remove_punc(_lowercase   ):
												_UpperCamelCase			:					Optional[int]     =    set(string.punctuation   )
												return "".join(ch for ch in text if ch not in exclude   )

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

						return white_space_fix(remove_articles(remove_punc(lower(_lowercase   )   )   )   )





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase			:					List[Any]     =    0, 0, 0

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

																		if not match_found:
																								fp += 1

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

						return precision, recall





def        a_				(							_lowercase   ):
						_UpperCamelCase			:					Union[str, Any]     =    precisions[::-1]
						for i in range(1       ,						len(_lowercase   )   ):
												_UpperCamelCase			:					Dict     =    max(precision_best[i - 1]       ,						precision_best[i]   )
						_UpperCamelCase			:					List[str]     =    precision_best[::-1]
						return precisions





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Optional[Any]     =    process_precisions(_lowercase   )
						_UpperCamelCase			:					List[Any]     =    np.trapz(_lowercase       ,						_lowercase   )
						if np.isnan(_lowercase   ):
												return 0
						return aupr





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Dict     =    process_precisions(_lowercase   )
						_UpperCamelCase			:					Optional[int]     =    0
						for prec, recall in zip(_lowercase       ,						_lowercase   ):
												if recall >= recall_thresh:
																		_UpperCamelCase			:					Union[str, Any]     =    prec
																		break
						return prec_at_recall





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





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Optional[int]     =    0
						for pred in predictions:
												for ground_truth in ground_truths:
																		_UpperCamelCase			:					int     =    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        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Dict     =    0
						_UpperCamelCase			:					int     =    []
						_UpperCamelCase			:					int     =    []
						for article in dataset:
												for paragraph in article["paragraphs"]:
																		for qa in paragraph["qas"]:
																								total += 1
																								if qa["id"] not in predictions:
																														_UpperCamelCase			:					int     =    '''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
																														print(_lowercase       ,						file=sys.stderr   )
																														continue
																								_UpperCamelCase			:					int     =    [x['''text'''] for x in qa['''answers''']]
																								_UpperCamelCase			:					Optional[int]     =    predictions[qa['''id''']]
																								_UpperCamelCase   ,							_UpperCamelCase			:					Any     =    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   )

						_UpperCamelCase			:					Optional[int]     =    [x for _, x in sorted(zip(_lowercase       ,						_lowercase   )   )]
						recalls.sort()

						_UpperCamelCase			:					Optional[int]     =    100.0 * fa / total
						_UpperCamelCase			:					Any     =    100.0 * exact_match / total
						_UpperCamelCase			:					Optional[Any]     =    get_aupr(_lowercase       ,						_lowercase   )

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










"""simple docstring"""




import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.electra.modeling_flax_electra import (
						    FlaxElectraForCausalLM,
						    FlaxElectraForMaskedLM,
						    FlaxElectraForMultipleChoice,
						    FlaxElectraForPreTraining,
						    FlaxElectraForQuestionAnswering,
						    FlaxElectraForSequenceClassification,
						    FlaxElectraForTokenClassification,
						    FlaxElectraModel,
						)




class  _a						(      unittest.TestCase				):






				def __init__( self		:    str, lowerCAmelCase__		:    Tuple, lowerCAmelCase__		:    List[str]=1_3, lowerCAmelCase__		:    Optional[int]=7, lowerCAmelCase__		:    Optional[Any]=True, lowerCAmelCase__		:    Optional[int]=True, lowerCAmelCase__		:    Any=True, lowerCAmelCase__		:    str=True, lowerCAmelCase__		:    Any=9_9, lowerCAmelCase__		:    Union[str, Any]=2_4, lowerCAmelCase__		:    Optional[Any]=3_2, lowerCAmelCase__		:    Tuple=5, lowerCAmelCase__		:    int=4, lowerCAmelCase__		:    List[Any]=3_7, lowerCAmelCase__		:    Dict="gelu", lowerCAmelCase__		:    Any=0.1, lowerCAmelCase__		:    Any=0.1, lowerCAmelCase__		:    str=5_1_2, lowerCAmelCase__		:    Tuple=1_6, lowerCAmelCase__		:    int=2, lowerCAmelCase__		:    int=0.02, lowerCAmelCase__		:    Optional[int]=4, )	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    parent
										_UpperCamelCase			:					Tuple     =    batch_size
										_UpperCamelCase			:					str     =    seq_length
										_UpperCamelCase			:					Any     =    is_training
										_UpperCamelCase			:					Union[str, Any]     =    use_attention_mask
										_UpperCamelCase			:					List[Any]     =    use_token_type_ids
										_UpperCamelCase			:					str     =    use_labels
										_UpperCamelCase			:					Union[str, Any]     =    vocab_size
										_UpperCamelCase			:					int     =    hidden_size
										_UpperCamelCase			:					Optional[int]     =    embedding_size
										_UpperCamelCase			:					int     =    num_hidden_layers
										_UpperCamelCase			:					int     =    num_attention_heads
										_UpperCamelCase			:					Tuple     =    intermediate_size
										_UpperCamelCase			:					Optional[int]     =    hidden_act
										_UpperCamelCase			:					Any     =    hidden_dropout_prob
										_UpperCamelCase			:					Union[str, Any]     =    attention_probs_dropout_prob
										_UpperCamelCase			:					Any     =    max_position_embeddings
										_UpperCamelCase			:					List[str]     =    type_vocab_size
										_UpperCamelCase			:					Union[str, Any]     =    type_sequence_label_size
										_UpperCamelCase			:					str     =    initializer_range
										_UpperCamelCase			:					Union[str, Any]     =    num_choices






				def 				snake_case	( self		:    Optional[Any]							)	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    ids_tensor([self.batch_size, self.seq_length], self.vocab_size							)

										_UpperCamelCase			:					Dict     =    None
										if self.use_attention_mask:
																_UpperCamelCase			:					Union[str, Any]     =    random_attention_mask([self.batch_size, self.seq_length]							)

										_UpperCamelCase			:					Optional[Any]     =    None
										if self.use_token_type_ids:
																_UpperCamelCase			:					Union[str, Any]     =    ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size							)

										_UpperCamelCase			:					Optional[Any]     =    ElectraConfig(
										    vocab_size=self.vocab_size, hidden_size=self.hidden_size, embedding_size=self.embedding_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, initializer_range=self.initializer_range, )

										return config, input_ids, token_type_ids, attention_mask






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




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    self.prepare_config_and_inputs()
										_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase			:					Tuple     =    config_and_inputs
										_UpperCamelCase			:					List[Any]     =    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
										return config, inputs_dict




@require_flax
class  _a						(      _lowerCAmelCase	,				unittest.TestCase				):
				UpperCamelCase								=    True

				UpperCamelCase								=    (
				    (
				        FlaxElectraModel,
				        FlaxElectraForCausalLM,
				        FlaxElectraForMaskedLM,
				        FlaxElectraForPreTraining,
				        FlaxElectraForTokenClassification,
				        FlaxElectraForQuestionAnswering,
				        FlaxElectraForMultipleChoice,
				        FlaxElectraForSequenceClassification,
				    )
				    if is_flax_available()
				    else ()
				)






				def 				snake_case	( self		:    Union[str, Any]							)	->      Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    FlaxElectraModelTester(self							)






				@slow
				def 				snake_case	( self		:    Optional[Any]							)	->      str:




										'''simple docstring'''



										for model_class_name in self.all_model_classes:
																if model_class_name == FlaxElectraForMaskedLM:
																						_UpperCamelCase			:					int     =    model_class_name.from_pretrained('''google/electra-small-generator'''							)
																else:
																						_UpperCamelCase			:					Any     =    model_class_name.from_pretrained('''google/electra-small-discriminator'''							)
																_UpperCamelCase			:					List[str]     =    model(np.ones((1, 1)							)							)
																self.assertIsNotNone(lowerCAmelCase__							)



