code: 
  
  
  
  
  import argparse
  import json
  import re
  import string
  import sys
  
  import numpy as np
  
  
  _A							    =	0.5
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			List[str]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: List[Any]	     =['''.''', ''',''', ''';''', ''':''']
  
  							for token in remove_tokens:
  														SCREAMING_SNAKE_CASE			: List[str]	     =ground_truth.replace(__a	,	'''''' )
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =prediction.replace(__a	,	'''''' )
  
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: str	     =ground_truth.lower(), prediction.lower()
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: List[Any]	     =ground_truth.replace('''/'''	,	''' ''' ), prediction.replace('''/'''	,	''' ''' )
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =set(ground_truth.split(''' ''' ) ), set(prediction.split(''' ''' ) )
  
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =ground_truth.intersection(__a )
  							SCREAMING_SNAKE_CASE			: List[str]	     =ground_truth.union(__a )
  							SCREAMING_SNAKE_CASE			: Tuple	     =len(__a ) / len(__a )
  							return jaccard
  def 						lowerCAmelCase_       (	__a )       ->			List[str]:
  
  
  
  
  
  							"""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 ):
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =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 )       ->			List[str]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Optional[Any]	     =0, 0, 0
  
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     ='''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			: Dict	     =False
  																					for pred in predictions:
  																												if substr_ok:
  																																			SCREAMING_SNAKE_CASE			: Tuple	     =get_jaccard(__a	,	__a ) >= IOU_THRESH or ground_truth in pred
  																												else:
  																																			SCREAMING_SNAKE_CASE			: Dict	     =get_jaccard(__a	,	__a ) >= IOU_THRESH
  																												if is_match:
  																																			SCREAMING_SNAKE_CASE			: 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.)
  																					SCREAMING_SNAKE_CASE			: Any	     =False
  																					for ground_truth in ground_truths:
  																												assert len(__a ) > 0
  																												if substr_ok:
  																																			SCREAMING_SNAKE_CASE			: int	     =get_jaccard(__a	,	__a ) >= IOU_THRESH or ground_truth in pred
  																												else:
  																																			SCREAMING_SNAKE_CASE			: Optional[Any]	     =get_jaccard(__a	,	__a ) >= IOU_THRESH
  																												if is_match:
  																																			SCREAMING_SNAKE_CASE			: Any	     =True
  
  																					if not match_found:
  																												fp += 1
  
  							SCREAMING_SNAKE_CASE			: int	     =tp / (tp + fp) if tp + fp > 0 else np.nan
  							SCREAMING_SNAKE_CASE			: List[Any]	     =tp / (tp + fn) if tp + fn > 0 else np.nan
  
  							return precision, recall
  def 						lowerCAmelCase_       (	__a )       ->			List[str]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: str	     =precisions[::-1]
  							for i in range(1	,	len(__a ) ):
  														SCREAMING_SNAKE_CASE			: int	     =max(precision_best[i - 1]	,	precision_best[i] )
  							SCREAMING_SNAKE_CASE			: List[Any]	     =precision_best[::-1]
  							return precisions
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			Optional[Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: List[Any]	     =process_precisions(__a )
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =np.trapz(__a	,	__a )
  							if np.isnan(__a ):
  														return 0
  							return aupr
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Dict:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: List[str]	     =process_precisions(__a )
  							SCREAMING_SNAKE_CASE			: List[Any]	     =0
  							for prec, recall in zip(__a	,	__a ):
  														if recall >= recall_thresh:
  																					SCREAMING_SNAKE_CASE			: Optional[Any]	     =prec
  																					break
  							return prec_at_recall
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			Union[str, Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							return normalize_answer(__a ) == normalize_answer(__a )
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Any:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Tuple	     =0
  							for pred in predictions:
  														for ground_truth in ground_truths:
  																					SCREAMING_SNAKE_CASE			: int	     =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 )       ->			str:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Any	     =0
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[]
  							SCREAMING_SNAKE_CASE			: 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:
  																																			SCREAMING_SNAKE_CASE			: List[Any]	     ='''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
  																																			print(__a	,	file=sys.stderr )
  																																			continue
  																												SCREAMING_SNAKE_CASE			: Any	     =[x['''text'''] for x in qa['''answers''']]
  																												SCREAMING_SNAKE_CASE			: Optional[int]	     =predictions[qa['''id''']]
  																												SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Optional[int]	     =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			: Tuple	     =[x for _, x in sorted(zip(__a	,	__a ) )]
  							recalls.sort()
  
  							SCREAMING_SNAKE_CASE			: int	     =100.0 * fa / total
  							SCREAMING_SNAKE_CASE			: List[str]	     =100.0 * exact_match / total
  							SCREAMING_SNAKE_CASE			: List[Any]	     =get_aupr(__a	,	__a )
  
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =get_prec_at_recall(__a	,	__a	,	recall_thresh=0.9 )
  							SCREAMING_SNAKE_CASE			: int	     =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)))
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _A							    =	{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}
  
  try:
  							if not is_torch_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """RegNetForImageClassification""",
  							    """RegNetModel""",
  							    """RegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_tf_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """TFRegNetForImageClassification""",
  							    """TFRegNetModel""",
  							    """TFRegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_flax_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """FlaxRegNetForImageClassification""",
  							    """FlaxRegNetModel""",
  							    """FlaxRegNetPreTrainedModel""",
  							]
  
  
  if TYPE_CHECKING:
  							from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
  
  							try:
  														if not is_torch_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_regnet import (
  														    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    RegNetForImageClassification,
  														    RegNetModel,
  														    RegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_tf_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_tf_regnet import (
  														    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    TFRegNetForImageClassification,
  														    TFRegNetModel,
  														    TFRegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_flax_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_flax_regnet import (
  														    FlaxRegNetForImageClassification,
  														    FlaxRegNetModel,
  														    FlaxRegNetPreTrainedModel,
  														)
  
  
  else:
  							import sys
  
  							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)
  
  
  
  
  
  
  