code: import argparse
  import json
  import re
  import string
  import sys
  
  import numpy as np
  
  
  lowercase__    :       int						=			0.5
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Dict:
  							lowerCAmelCase				=	['''.''', ''',''', ''';''', ''':''']
  
  							for token in remove_tokens:
  														lowerCAmelCase				=	ground_truth.replace(snake_case__     ,						''''''							)
  														lowerCAmelCase				=	prediction.replace(snake_case__     ,						''''''							)
  
  							lowerCAmelCase	,   lowerCAmelCase				=	ground_truth.lower(), prediction.lower()
  							lowerCAmelCase	,   lowerCAmelCase				=	ground_truth.replace('''/'''     ,						''' '''							), prediction.replace('''/'''     ,						''' '''							)
  							lowerCAmelCase	,   lowerCAmelCase				=	set(ground_truth.split(''' '''							)							), set(prediction.split(''' '''							)							)
  
  							lowerCAmelCase				=	ground_truth.intersection(snake_case__							)
  							lowerCAmelCase				=	ground_truth.union(snake_case__							)
  							lowerCAmelCase				=	len(snake_case__							) / len(snake_case__							)
  							return jaccard
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			Optional[Any]:
  
  							def remove_articles(snake_case__							):
  														return re.sub(R'''\b(a|an|the)\b'''     ,						''' '''     ,						snake_case__							)
  
  							def white_space_fix(snake_case__							):
  														return " ".join(text.split()							)
  
  							def remove_punc(snake_case__							):
  														lowerCAmelCase				=	set(string.punctuation							)
  														return "".join(ch for ch in text if ch not in exclude							)
  
  							def lower(snake_case__							):
  														return text.lower()
  
  							return white_space_fix(remove_articles(remove_punc(lower(snake_case__							)							)							)							)
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			List[str]:
  							lowerCAmelCase	,   lowerCAmelCase	,   lowerCAmelCase				=	0, 0, 0
  
  							lowerCAmelCase				=	'''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
  																					lowerCAmelCase				=	False
  																					for pred in predictions:
  																												if substr_ok:
  																																			lowerCAmelCase				=	get_jaccard(snake_case__     ,						snake_case__							) >= IOU_THRESH or ground_truth in pred
  																												else:
  																																			lowerCAmelCase				=	get_jaccard(snake_case__     ,						snake_case__							) >= IOU_THRESH
  																												if is_match:
  																																			lowerCAmelCase				=	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				=	False
  																					for ground_truth in ground_truths:
  																												assert len(snake_case__							) > 0
  																												if substr_ok:
  																																			lowerCAmelCase				=	get_jaccard(snake_case__     ,						snake_case__							) >= IOU_THRESH or ground_truth in pred
  																												else:
  																																			lowerCAmelCase				=	get_jaccard(snake_case__     ,						snake_case__							) >= IOU_THRESH
  																												if is_match:
  																																			lowerCAmelCase				=	True
  
  																					if not match_found:
  																												fp += 1
  
  							lowerCAmelCase				=	tp / (tp + fp) if tp + fp > 0 else np.nan
  							lowerCAmelCase				=	tp / (tp + fn) if tp + fn > 0 else np.nan
  
  							return precision, recall
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			Optional[Any]:
  							lowerCAmelCase				=	precisions[::-1]
  							for i in range(1     ,						len(snake_case__							)							):
  														lowerCAmelCase				=	max(precision_best[i - 1]     ,						precision_best[i]							)
  							lowerCAmelCase				=	precision_best[::-1]
  							return precisions
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Tuple:
  							lowerCAmelCase				=	process_precisions(snake_case__							)
  							lowerCAmelCase				=	np.trapz(snake_case__     ,						snake_case__							)
  							if np.isnan(snake_case__							):
  														return 0
  							return aupr
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			Any:
  							lowerCAmelCase				=	process_precisions(snake_case__							)
  							lowerCAmelCase				=	0
  							for prec, recall in zip(snake_case__     ,						snake_case__							):
  														if recall >= recall_thresh:
  																					lowerCAmelCase				=	prec
  																					break
  							return prec_at_recall
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Optional[int]:
  							return normalize_answer(snake_case__							) == normalize_answer(snake_case__							)
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			List[str]:
  							lowerCAmelCase				=	0
  							for pred in predictions:
  														for ground_truth in ground_truths:
  																					lowerCAmelCase				=	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      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			List[Any]:
  							lowerCAmelCase				=	lowerCAmelCase				=	lowerCAmelCase				=	0
  							lowerCAmelCase				=	[]
  							lowerCAmelCase				=	[]
  							for article in dataset:
  														for paragraph in article["paragraphs"]:
  																					for qa in paragraph["qas"]:
  																												total += 1
  																												if qa["id"] not in predictions:
  																																			lowerCAmelCase				=	'''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
  																																			print(snake_case__     ,						file=sys.stderr							)
  																																			continue
  																												lowerCAmelCase				=	[x['''text'''] for x in qa['''answers''']]
  																												lowerCAmelCase				=	predictions[qa['''id''']]
  																												lowerCAmelCase	,   lowerCAmelCase				=	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__							)
  
  							lowerCAmelCase				=	[x for _, x in sorted(zip(snake_case__     ,						snake_case__							)							)]
  							recalls.sort()
  
  							lowerCAmelCase				=	1_0_0.0 * fa / total
  							lowerCAmelCase				=	1_0_0.0 * exact_match / total
  							lowerCAmelCase				=	get_aupr(snake_case__     ,						snake_case__							)
  
  							lowerCAmelCase				=	get_prec_at_recall(snake_case__     ,						snake_case__     ,						recall_thresh=0.9							)
  							lowerCAmelCase				=	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__    :       Any						=			argparse.ArgumentParser(description='''Evaluation for CUAD''')
  	parser.add_argument('''dataset_file''', help='''Dataset file''')
  	parser.add_argument('''prediction_file''', help='''Prediction File''')
  	lowercase__    :       Optional[int]						=			parser.parse_args()
  	with open(args.dataset_file) as dataset_file:
  		lowercase__    :       Any						=			json.load(dataset_file)
  		lowercase__    :       int						=			dataset_json['''data''']
  	with open(args.prediction_file) as prediction_file:
  		lowercase__    :       List[Any]						=			json.load(prediction_file)
  	print(json.dumps(evaluate(dataset, predictions)))
  
  
  
  
style_context: import glob
  import os
  
  from get_test_info import get_tester_classes
  
  
  if __name__ == "__main__":
  	lowercase__    :       List[Any]						=			[]
  
  	lowercase__    :       Optional[Any]						=			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
  	lowercase__    :       Optional[int]						=			glob.glob(pattern)
  	# TODO: deal with TF/Flax too
  	lowercase__    :       Dict						=			[
  	    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
  	]
  
  	for test_file in test_files:
  		lowercase__    :       Tuple						=			get_tester_classes(test_file)
  		for tester_class in tester_classes:
  			# A few tester classes don't have `parent` parameter in `__init__`.
  			# TODO: deal this better
  			try:
  				lowercase__    :       Union[str, Any]						=			tester_class(parent=None)
  			except Exception:
  				continue
  			if hasattr(tester, '''get_config'''):
  				lowercase__    :       Union[str, Any]						=			tester.get_config()
  				for k, v in config.to_dict().items():
  					if isinstance(v, int):
  						lowercase__    :       int						=			None
  						if k in ["vocab_size"]:
  							lowercase__    :       Optional[int]						=			1_0_0
  						elif k in ["max_position_embeddings"]:
  							lowercase__    :       Union[str, Any]						=			1_2_8
  						elif k in ["hidden_size", "d_model"]:
  							lowercase__    :       Optional[Any]						=			4_0
  						elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
  							lowercase__    :       Dict						=			5
  						if target is not None and v > target:
  							failures.append(
  							    f'{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'
  							    f' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'
  							    f' than {target}.'
  							)
  
  	if len(failures) > 0:
  		raise Exception(f'There were {len(failures)} failures:\n' + '''\n'''.join(failures))
  
  
  
  