code: 
  
  
  
  
  
  import argparse
  import json
  import re
  import string
  import sys
  
  import numpy as np
  
  
  lowerCAmelCase_   =   0.5
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						List[str]:
         _snake_case      :							List[Any]     				=						['''.''', ''',''', ''';''', ''':''']
  
         for token in remove_tokens:
                _snake_case      :							List[str]     				=						ground_truth.replace(__a      ,		''''''  )
                _snake_case      :							Optional[int]     				=						prediction.replace(__a      ,		''''''  )
  
         _snake_case      :							str     				=						ground_truth.lower(), prediction.lower()
         _snake_case      :							List[Any]     				=						ground_truth.replace('''/'''      ,		''' '''  ), prediction.replace('''/'''      ,		''' '''  )
         _snake_case      :							Union[str, Any]     				=						set(ground_truth.split(''' '''  )  ), set(prediction.split(''' '''  )  )
  
         _snake_case      :							Optional[int]     				=						ground_truth.intersection(__a  )
         _snake_case      :							List[str]     				=						ground_truth.union(__a  )
         _snake_case      :							Tuple     				=						len(__a  ) / len(__a  )
         return jaccard
  
  
  
  
  def 						A_			(  lowercase_  )			->						List[str]:
  
  
  
  
  
         def remove_articles(lowercase_  ):
                return re.sub(r'''\b(a|an|the)\b'''      ,		''' '''      ,		__a  )
  
         def white_space_fix(lowercase_  ):
                return " ".join(text.split()  )
  
         def remove_punc(lowercase_  ):
                _snake_case      :							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(__a  )  )  )  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						List[str]:
         _snake_case      :							Optional[Any]     				=						0, 0, 0
  
         _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
                       _snake_case      :							Dict     				=						False
                       for pred in predictions:
                              if substr_ok:
                                     _snake_case      :							Tuple     				=						get_jaccard(__a      ,		__a  ) >= IOU_THRESH or ground_truth in pred
                              else:
                                     _snake_case      :							Dict     				=						get_jaccard(__a      ,		__a  ) >= IOU_THRESH
                              if is_match:
                                     _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.)
                       _snake_case      :							Any     				=						False
                       for ground_truth in ground_truths:
                              assert len(__a  ) > 0
                              if substr_ok:
                                     _snake_case      :							int     				=						get_jaccard(__a      ,		__a  ) >= IOU_THRESH or ground_truth in pred
                              else:
                                     _snake_case      :							Optional[Any]     				=						get_jaccard(__a      ,		__a  ) >= IOU_THRESH
                              if is_match:
                                     _snake_case      :							Any     				=						True
  
                       if not match_found:
                              fp += 1
  
         _snake_case      :							int     				=						tp / (tp + fp) if tp + fp > 0 else np.nan
         _snake_case      :							List[Any]     				=						tp / (tp + fn) if tp + fn > 0 else np.nan
  
         return precision, recall
  
  
  
  
  def 						A_			(  lowercase_  )			->						List[str]:
         _snake_case      :							str     				=						precisions[::-1]
         for i in range(1      ,		len(__a  )  ):
                _snake_case      :							int     				=						max(precision_best[i - 1]      ,		precision_best[i]  )
         _snake_case      :							List[Any]     				=						precision_best[::-1]
         return precisions
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						Optional[Any]:
         _snake_case      :							List[Any]     				=						process_precisions(__a  )
         _snake_case      :							Optional[int]     				=						np.trapz(__a      ,		__a  )
         if np.isnan(__a  ):
                return 0
         return aupr
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Dict:
         _snake_case      :							List[str]     				=						process_precisions(__a  )
         _snake_case      :							List[Any]     				=						0
         for prec, recall in zip(__a      ,		__a  ):
                if recall >= recall_thresh:
                       _snake_case      :							Optional[Any]     				=						prec
                       break
         return prec_at_recall
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						Union[str, Any]:
         return normalize_answer(__a  ) == normalize_answer(__a  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Any:
         _snake_case      :							Tuple     				=						0
         for pred in predictions:
                for ground_truth in ground_truths:
                       _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 						A_			(  lowercase_      ,		lowercase_  )			->						str:
         _snake_case      :							Any     				=						0
         _snake_case      :							Union[str, Any]     				=						[]
         _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:
                                     _snake_case      :							List[Any]     				=						'''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
                                     print(__a      ,		file=sys.stderr  )
                                     continue
                              _snake_case      :							Any     				=						[x['''text'''] for x in qa['''answers''']]
                              _snake_case      :							Optional[int]     				=						predictions[qa['''id''']]
                              _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  )
  
         _snake_case      :							Tuple     				=						[x for _, x in sorted(zip(__a      ,		__a  )  )]
         recalls.sort()
  
         _snake_case      :							int     				=						1_00.0 * fa / total
         _snake_case      :							List[str]     				=						1_00.0 * exact_match / total
         _snake_case      :							List[Any]     				=						get_aupr(__a      ,		__a  )
  
         _snake_case      :							Optional[Any]     				=						get_prec_at_recall(__a      ,		__a      ,		recall_thresh=0.9  )
         _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__":
     lowerCAmelCase_   =   argparse.ArgumentParser(description="Evaluation for CUAD")
     parser.add_argument("dataset_file", help="Dataset file")
     parser.add_argument("prediction_file", help="Prediction File")
     lowerCAmelCase_   =   parser.parse_args()
     with open(args.dataset_file) as dataset_file:
        lowerCAmelCase_   =   json.load(dataset_file)
        lowerCAmelCase_   =   dataset_json["data"]
     with open(args.prediction_file) as prediction_file:
        lowerCAmelCase_   =   json.load(prediction_file)
     print(json.dumps(evaluate(dataset, predictions)))
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import cva
  import numpy as np
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
  
         try:
                return int(image[x_coordinate][y_coordinate] >= center  )
         except (IndexError, TypeError):
                return 0
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
         _snake_case      :							List[str]     				=						image[x_coordinate][y_coordinate]
         _snake_case      :							List[str]     				=						[1, 2, 4, 8, 16, 32, 64, 128]
  
         # skip get_neighbors_pixel if center is null
         if center is None:
                return 0
  
         # Starting from the top right, assigning value to pixels clockwise
         _snake_case      :							Any     				=						[
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		lowercase_      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		lowercase_      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate - 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate - 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate - 1      ,		lowercase_  ),
         ]
  
         # Converting the binary value to decimal.
         return sum(
             binary_value * power for binary_value, power in zip(lowercase_      ,		lowercase_  )  )
  
  
  if __name__ == "__main__":
     # Reading the image and converting it to grayscale.
     lowerCAmelCase_   =   cva.imread(
         "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
     )
  
     # Create a numpy array as the same height and width of read image
     lowerCAmelCase_   =   np.zeros((image.shape[0], image.shape[1]))
  
     # Iterating through the image and calculating the
     # local binary pattern value for each pixel.
     for i in range(0, image.shape[0]):
        for j in range(0, image.shape[1]):
           lowerCAmelCase_   =   local_binary_value(image, i, j)
  
     cva.imshow("local binary pattern", lbp_image)
     cva.waitKey(0)
     cva.destroyAllWindows()
  
  
  
  
  
  
  