code: 
  
  import argparse
  import json
  import re
  import string
  import sys
  
  import numpy as np
  
  
  snake_case_    						=				0.5
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Optional[Any]			,  SCREAMING_SNAKE_CASE_	:					str    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     int   = ['.', ',', ';', ':']
  
       for token in remove_tokens:
            lowercase__					:     List[Any]   = ground_truth.replace(SCREAMING_SNAKE_CASE_			,  ''    )
            lowercase__					:     Optional[int]   = prediction.replace(SCREAMING_SNAKE_CASE_			,  ''    )
  
       lowercase__   ,				lowercase__					:     Any   = ground_truth.lower(), prediction.lower()
       lowercase__   ,				lowercase__					:     int   = ground_truth.replace('/'			,  ' '    ), prediction.replace('/'			,  ' '    )
       lowercase__   ,				lowercase__					:     Union[str, Any]   = set(ground_truth.split(' '    )    ), set(prediction.split(' '    )    )
  
       lowercase__					:     Tuple   = ground_truth.intersection(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Optional[Any]   = ground_truth.union(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     List[Any]   = len(SCREAMING_SNAKE_CASE_    ) / len(SCREAMING_SNAKE_CASE_    )
       return jaccard
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Optional[Any]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       def remove_articles(SCREAMING_SNAKE_CASE_	:					Tuple    ):
            return re.sub(R'\b(a|an|the)\b'			,  ' '			,  SCREAMING_SNAKE_CASE_    )
  
       def white_space_fix(SCREAMING_SNAKE_CASE_	:					str    ):
            return " ".join(text.split()    )
  
       def remove_punc(SCREAMING_SNAKE_CASE_	:					str    ):
            lowercase__					:     Union[str, Any]   = set(string.punctuation    )
            return "".join(ch for ch in text if ch not in exclude    )
  
       def lower(SCREAMING_SNAKE_CASE_	:					int    ):
            return text.lower()
  
       return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_    )    )    )    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Optional[Any]			,  SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					List[Any]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__   ,				lowercase__   ,				lowercase__					:     Optional[int]   = 0, 0, 0
  
       lowercase__					:     Union[str, Any]   = 'Parties' in qa_id
  
       # first check if ground truth is empty
       if len(SCREAMING_SNAKE_CASE_    ) == 0:
            if len(SCREAMING_SNAKE_CASE_    ) > 0:
                 fp += len(SCREAMING_SNAKE_CASE_    )  # false positive for each one
       else:
            for ground_truth in ground_truths:
                 assert len(SCREAMING_SNAKE_CASE_    ) > 0
                 # check if there is a match
                 lowercase__					:     List[Any]   = False
                 for pred in predictions:
                      if substr_ok:
                           lowercase__					:     List[Any]   = get_jaccard(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ) >= IOU_THRESH or ground_truth in pred
                      else:
                           lowercase__					:     int   = get_jaccard(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ) >= IOU_THRESH
                      if is_match:
                           lowercase__					:     List[Any]   = 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.)
                 lowercase__					:     str   = False
                 for ground_truth in ground_truths:
                      assert len(SCREAMING_SNAKE_CASE_    ) > 0
                      if substr_ok:
                           lowercase__					:     Optional[Any]   = get_jaccard(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ) >= IOU_THRESH or ground_truth in pred
                      else:
                           lowercase__					:     str   = get_jaccard(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ) >= IOU_THRESH
                      if is_match:
                           lowercase__					:     Dict   = True
  
                 if not match_found:
                      fp += 1
  
       lowercase__					:     Optional[int]   = tp / (tp + fp) if tp + fp > 0 else np.nan
       lowercase__					:     Dict   = tp / (tp + fn) if tp + fn > 0 else np.nan
  
       return precision, recall
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[Any]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     str   = precisions[::-1]
       for i in range(1			,  len(SCREAMING_SNAKE_CASE_    )    ):
            lowercase__					:     Any   = max(precision_best[i - 1]			,  precision_best[i]    )
       lowercase__					:     Optional[int]   = precision_best[::-1]
       return precisions
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Dict			,  SCREAMING_SNAKE_CASE_	:					Optional[int]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Dict   = process_precisions(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     int   = np.trapz(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
       if np.isnan(SCREAMING_SNAKE_CASE_    ):
            return 0
       return aupr
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Union[str, Any]			,  SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					Tuple    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Dict   = process_precisions(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Tuple   = 0
       for prec, recall in zip(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ):
            if recall >= recall_thresh:
                 lowercase__					:     Optional[int]   = prec
                 break
       return prec_at_recall
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					int    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       return normalize_answer(SCREAMING_SNAKE_CASE_    ) == normalize_answer(SCREAMING_SNAKE_CASE_    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Dict			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					List[str]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     int   = 0
       for pred in predictions:
            for ground_truth in ground_truths:
                 lowercase__					:     Union[str, Any]   = metric_fn(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
                 if score == 1:  # break the loop when one prediction matches the ground truth
                      break
            if score == 1:
                 break
       return score
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					Tuple    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Tuple   = 0
       lowercase__					:     Optional[int]   = []
       lowercase__					:     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:
                           lowercase__					:     Dict   = 'Unanswered question ' + qa['id'] + ' will receive score 0.'
                           print(SCREAMING_SNAKE_CASE_			,  file=sys.stderr    )
                           continue
                      lowercase__					:     Optional[int]   = [x['text'] for x in qa['answers']]
                      lowercase__					:     Any   = predictions[qa['id']]
                      lowercase__   ,				lowercase__					:     Optional[Any]   = compute_precision_recall(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  qa['id']    )
  
                      precisions.append(SCREAMING_SNAKE_CASE_    )
                      recalls.append(SCREAMING_SNAKE_CASE_    )
  
                      if precision == 0 and recall == 0:
                           fa += 0
                      else:
                           fa += 2 * (precision * recall) / (precision + recall)
  
                      exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
       lowercase__					:     int   = [x for _, x in sorted(zip(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )    )]
       recalls.sort()
  
       lowercase__					:     Union[str, Any]   = 100.0 * fa / total
       lowercase__					:     Dict   = 100.0 * exact_match / total
       lowercase__					:     Any   = get_aupr(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
       lowercase__					:     Dict   = get_prec_at_recall(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  recall_thresh=0.9    )
       lowercase__					:     Union[str, Any]   = get_prec_at_recall(SCREAMING_SNAKE_CASE_			,  SCREAMING_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__":
         snake_case_    						=				argparse.ArgumentParser(description='''Evaluation for CUAD''')
         parser.add_argument('''dataset_file''', help='''Dataset file''')
         parser.add_argument('''prediction_file''', help='''Prediction File''')
         snake_case_    						=				parser.parse_args()
         with open(args.dataset_file) as dataset_file:
                snake_case_    						=				json.load(dataset_file)
                snake_case_    						=				dataset_json['''data''']
         with open(args.prediction_file) as prediction_file:
                snake_case_    						=				json.load(prediction_file)
         print(json.dumps(evaluate(dataset, predictions)))
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  snake_case_    						=				{
      '''configuration_donut_swin''': ['''DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DonutSwinConfig'''],
      '''processing_donut''': ['''DonutProcessor'''],
  }
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''DonutSwinModel''',
             '''DonutSwinPreTrainedModel''',
         ]
  
  try:
         if not is_vision_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				['''DonutFeatureExtractor''']
         snake_case_    						=				['''DonutImageProcessor''']
  
  
  if TYPE_CHECKING:
         from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
         from .processing_donut import DonutProcessor
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_donut_swin import (
                    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DonutSwinModel,
                    DonutSwinPreTrainedModel,
                )
  
         try:
                if not is_vision_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .feature_extraction_donut import DonutFeatureExtractor
                from .image_processing_donut import DonutImageProcessor
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  