code: 
  
  
  import argparse
  import json
  import re
  import string
  import sys
  
  import numpy as np
  
  
  UpperCAmelCase      :						List[str]    =		0.5
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	Dict     ,       lowerCamelCase__		:	Optional[Any]			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     [""".""", """,""", """;""", """:"""]
  
  			for token in remove_tokens:
  						lowerCamelCase								=     ground_truth.replace(lowerCamelCase__     ,       """"""			)
  						lowerCamelCase								=     prediction.replace(lowerCamelCase__     ,       """"""			)
  
  			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(lowerCamelCase__			)
  			lowerCamelCase								=     ground_truth.union(lowerCamelCase__			)
  			lowerCamelCase								=     len(lowerCamelCase__			) / len(lowerCamelCase__			)
  			return jaccard
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	str			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			def remove_articles(lowerCamelCase__		:	Optional[Any]			):
  						return re.sub(R"""\b(a|an|the)\b"""     ,       """ """     ,       lowerCamelCase__			)
  
  			def white_space_fix(lowerCamelCase__		:	Dict			):
  						return " ".join(text.split()			)
  
  			def remove_punc(lowerCamelCase__		:	Dict			):
  						lowerCamelCase								=     set(string.punctuation			)
  						return "".join(ch for ch in text if ch not in exclude			)
  
  			def lower(lowerCamelCase__		:	Union[str, Any]			):
  						return text.lower()
  
  			return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase__			)			)			)			)
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	int     ,       lowerCamelCase__		:	Optional[int]     ,       lowerCamelCase__		:	int			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     0, 0, 0
  
  			lowerCamelCase								=     """Parties""" in qa_id
  
  			# first check if ground truth is empty
  			if len(lowerCamelCase__			) == 0:
  						if len(lowerCamelCase__			) > 0:
  									fp += len(lowerCamelCase__			)  # false positive for each one
  			else:
  						for ground_truth in ground_truths:
  									assert len(lowerCamelCase__			) > 0
  									# check if there is a match
  									lowerCamelCase								=     False
  									for pred in predictions:
  												if substr_ok:
  															lowerCamelCase								=     get_jaccard(lowerCamelCase__     ,       lowerCamelCase__			) >= IOU_THRESH or ground_truth in pred
  												else:
  															lowerCamelCase								=     get_jaccard(lowerCamelCase__     ,       lowerCamelCase__			) >= 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(lowerCamelCase__			) > 0
  												if substr_ok:
  															lowerCamelCase								=     get_jaccard(lowerCamelCase__     ,       lowerCamelCase__			) >= IOU_THRESH or ground_truth in pred
  												else:
  															lowerCamelCase								=     get_jaccard(lowerCamelCase__     ,       lowerCamelCase__			) >= 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   __lowerCamelCase	(       lowerCamelCase__		:	List[Any]			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     precisions[::-1]
  			for i in range(1     ,       len(lowerCamelCase__			)			):
  						lowerCamelCase								=     max(precision_best[i - 1]     ,       precision_best[i]			)
  			lowerCamelCase								=     precision_best[::-1]
  			return precisions
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	Union[str, Any]     ,       lowerCamelCase__		:	Any			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     process_precisions(lowerCamelCase__			)
  			lowerCamelCase								=     np.trapz(lowerCamelCase__     ,       lowerCamelCase__			)
  			if np.isnan(lowerCamelCase__			):
  						return 0
  			return aupr
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	str     ,       lowerCamelCase__		:	str     ,       lowerCamelCase__		:	Union[str, Any]			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     process_precisions(lowerCamelCase__			)
  			lowerCamelCase								=     0
  			for prec, recall in zip(lowerCamelCase__     ,       lowerCamelCase__			):
  						if recall >= recall_thresh:
  									lowerCamelCase								=     prec
  									break
  			return prec_at_recall
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	str     ,       lowerCamelCase__		:	List[Any]			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			return normalize_answer(lowerCamelCase__			) == normalize_answer(lowerCamelCase__			)
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	Optional[Any]     ,       lowerCamelCase__		:	Dict     ,       lowerCamelCase__		:	Optional[Any]			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     0
  			for pred in predictions:
  						for ground_truth in ground_truths:
  									lowerCamelCase								=     metric_fn(lowerCamelCase__     ,       lowerCamelCase__			)
  									if score == 1:  # break the loop when one prediction matches the ground truth
  												break
  						if score == 1:
  									break
  			return score
  
  
  
  
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	Optional[int]     ,       lowerCamelCase__		:	Tuple			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			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(lowerCamelCase__     ,       file=sys.stderr			)
  															continue
  												lowerCamelCase								=     [x["""text"""] for x in qa["""answers"""]]
  												lowerCamelCase								=     predictions[qa["""id"""]]
  												lowerCamelCase		,		lowerCamelCase								=     compute_precision_recall(lowerCamelCase__     ,       lowerCamelCase__     ,       qa["""id"""]			)
  
  												precisions.append(lowerCamelCase__			)
  												recalls.append(lowerCamelCase__			)
  
  												if precision == 0 and recall == 0:
  															fa += 0
  												else:
  															fa += 2 * (precision * recall) / (precision + recall)
  
  												exact_match += metric_max_over_ground_truths(lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__			)
  
  			lowerCamelCase								=     [x for _, x in sorted(zip(lowerCamelCase__     ,       lowerCamelCase__			)			)]
  			recalls.sort()
  
  			lowerCamelCase								=     100.0 * fa / total
  			lowerCamelCase								=     100.0 * exact_match / total
  			lowerCamelCase								=     get_aupr(lowerCamelCase__     ,       lowerCamelCase__			)
  
  			lowerCamelCase								=     get_prec_at_recall(lowerCamelCase__     ,       lowerCamelCase__     ,       recall_thresh=0.9			)
  			lowerCamelCase								=     get_prec_at_recall(lowerCamelCase__     ,       lowerCamelCase__     ,       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      :						List[str]    =		argparse.ArgumentParser(description="Evaluation for CUAD")
  			parser.add_argument("dataset_file", help="Dataset file")
  			parser.add_argument("prediction_file", help="Prediction File")
  			UpperCAmelCase      :						List[str]    =		parser.parse_args()
  			with open(args.dataset_file) as dataset_file:
  						UpperCAmelCase      :						Any    =		json.load(dataset_file)
  						UpperCAmelCase      :						str    =		dataset_json["data"]
  			with open(args.prediction_file) as prediction_file:
  						UpperCAmelCase      :						Any    =		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_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  UpperCAmelCase      :						List[str]    =		{
      "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						str    =		["BigBirdTokenizer"]
  
  try:
  		if not is_tokenizers_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						str    =		["BigBirdTokenizerFast"]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						Dict    =		[
  		    "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "BigBirdForCausalLM",
  		    "BigBirdForMaskedLM",
  		    "BigBirdForMultipleChoice",
  		    "BigBirdForPreTraining",
  		    "BigBirdForQuestionAnswering",
  		    "BigBirdForSequenceClassification",
  		    "BigBirdForTokenClassification",
  		    "BigBirdLayer",
  		    "BigBirdModel",
  		    "BigBirdPreTrainedModel",
  		    "load_tf_weights_in_big_bird",
  		]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						Dict    =		[
  		    "FlaxBigBirdForCausalLM",
  		    "FlaxBigBirdForMaskedLM",
  		    "FlaxBigBirdForMultipleChoice",
  		    "FlaxBigBirdForPreTraining",
  		    "FlaxBigBirdForQuestionAnswering",
  		    "FlaxBigBirdForSequenceClassification",
  		    "FlaxBigBirdForTokenClassification",
  		    "FlaxBigBirdModel",
  		    "FlaxBigBirdPreTrainedModel",
  		]
  
  if TYPE_CHECKING:
  		from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig
  
  		try:
  				if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_big_bird import BigBirdTokenizer
  
  		try:
  				if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_big_bird_fast import BigBirdTokenizerFast
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_big_bird import (
  				    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    BigBirdForCausalLM,
  				    BigBirdForMaskedLM,
  				    BigBirdForMultipleChoice,
  				    BigBirdForPreTraining,
  				    BigBirdForQuestionAnswering,
  				    BigBirdForSequenceClassification,
  				    BigBirdForTokenClassification,
  				    BigBirdLayer,
  				    BigBirdModel,
  				    BigBirdPreTrainedModel,
  				    load_tf_weights_in_big_bird,
  				)
  
  		try:
  				if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_flax_big_bird import (
  				    FlaxBigBirdForCausalLM,
  				    FlaxBigBirdForMaskedLM,
  				    FlaxBigBirdForMultipleChoice,
  				    FlaxBigBirdForPreTraining,
  				    FlaxBigBirdForQuestionAnswering,
  				    FlaxBigBirdForSequenceClassification,
  				    FlaxBigBirdForTokenClassification,
  				    FlaxBigBirdModel,
  				    FlaxBigBirdPreTrainedModel,
  				)
  
  else:
  		import sys
  
  		UpperCAmelCase      :						Dict    =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  