
"""simple docstring"""




import argparse
import json
import re
import string
import sys

import numpy as np


A__   :						str 							=     0.5



def 				a__			(					lowerCAmelCase						:							List[str]							,     lowerCAmelCase						:							Tuple		):






     '''simple docstring'''
     UpperCAmelCase__				:			List[str]							=    [".", ",", ";", ":"]

     for token in remove_tokens:
          UpperCAmelCase__				:			Optional[Any]							=    ground_truth.replace(lowerCAmelCase							,     ""		)
          UpperCAmelCase__				:			str							=    prediction.replace(lowerCAmelCase							,     ""		)

     UpperCAmelCase__				,						UpperCAmelCase__				:			List[Any]							=    ground_truth.lower(), prediction.lower()
     UpperCAmelCase__				,						UpperCAmelCase__				:			Optional[Any]							=    ground_truth.replace("/"							,     " "		), prediction.replace("/"							,     " "		)
     UpperCAmelCase__				,						UpperCAmelCase__				:			str							=    set(ground_truth.split(" "		)		), set(prediction.split(" "		)		)

     UpperCAmelCase__				:			Optional[int]							=    ground_truth.intersection(lowerCAmelCase		)
     UpperCAmelCase__				:			Optional[Any]							=    ground_truth.union(lowerCAmelCase		)
     UpperCAmelCase__				:			Optional[int]							=    len(lowerCAmelCase		) / len(lowerCAmelCase		)
     return jaccard



def 				a__			(					lowerCAmelCase						:							List[str]		):






     '''simple docstring'''

     def remove_articles(lowerCAmelCase						:							str		):
          return re.sub(R"\b(a|an|the)\b"							,     " "							,     lowerCAmelCase		)

     def white_space_fix(lowerCAmelCase						:							str		):
          return " ".join(text.split()		)

     def remove_punc(lowerCAmelCase						:							List[Any]		):
          UpperCAmelCase__				:			Optional[int]							=    set(string.punctuation		)
          return "".join(ch for ch in text if ch not in exclude		)

     def lower(lowerCAmelCase						:							Optional[int]		):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase		)		)		)		)



def 				a__			(					lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							List[str]							,     lowerCAmelCase						:							Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			Optional[int]							=    0, 0, 0

     UpperCAmelCase__				:			Dict							=    "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
               UpperCAmelCase__				:			Tuple							=    False
               for pred in predictions:
                    if substr_ok:
                         UpperCAmelCase__				:			List[str]							=    get_jaccard(lowerCAmelCase							,     lowerCAmelCase		) >= IOU_THRESH or ground_truth in pred
                    else:
                         UpperCAmelCase__				:			List[Any]							=    get_jaccard(lowerCAmelCase							,     lowerCAmelCase		) >= IOU_THRESH
                    if is_match:
                         UpperCAmelCase__				:			List[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.)
               UpperCAmelCase__				:			Tuple							=    False
               for ground_truth in ground_truths:
                    assert len(lowerCAmelCase		) > 0
                    if substr_ok:
                         UpperCAmelCase__				:			Any							=    get_jaccard(lowerCAmelCase							,     lowerCAmelCase		) >= IOU_THRESH or ground_truth in pred
                    else:
                         UpperCAmelCase__				:			Optional[Any]							=    get_jaccard(lowerCAmelCase							,     lowerCAmelCase		) >= IOU_THRESH
                    if is_match:
                         UpperCAmelCase__				:			Dict							=    True

               if not match_found:
                    fp += 1

     UpperCAmelCase__				:			Union[str, Any]							=    tp / (tp + fp) if tp + fp > 0 else np.nan
     UpperCAmelCase__				:			List[str]							=    tp / (tp + fn) if tp + fn > 0 else np.nan

     return precision, recall



def 				a__			(					lowerCAmelCase						:							Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[Any]							=    precisions[::-1]
     for i in range(1							,     len(lowerCAmelCase		)		):
          UpperCAmelCase__				:			List[Any]							=    max(precision_best[i - 1]							,     precision_best[i]		)
     UpperCAmelCase__				:			Any							=    precision_best[::-1]
     return precisions



def 				a__			(					lowerCAmelCase						:							List[Any]							,     lowerCAmelCase						:							str		):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[Any]							=    process_precisions(lowerCAmelCase		)
     UpperCAmelCase__				:			int							=    np.trapz(lowerCAmelCase							,     lowerCAmelCase		)
     if np.isnan(lowerCAmelCase		):
          return 0
     return aupr



def 				a__			(					lowerCAmelCase						:							Optional[int]							,     lowerCAmelCase						:							Optional[int]							,     lowerCAmelCase						:							Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__				:			Dict							=    process_precisions(lowerCAmelCase		)
     UpperCAmelCase__				:			Optional[int]							=    0
     for prec, recall in zip(lowerCAmelCase							,     lowerCAmelCase		):
          if recall >= recall_thresh:
               UpperCAmelCase__				:			Any							=    prec
               break
     return prec_at_recall



def 				a__			(					lowerCAmelCase						:							Any							,     lowerCAmelCase						:							int		):






     '''simple docstring'''
     return normalize_answer(lowerCAmelCase		) == normalize_answer(lowerCAmelCase		)



def 				a__			(					lowerCAmelCase						:							Optional[Any]							,     lowerCAmelCase						:							Any							,     lowerCAmelCase						:							int		):






     '''simple docstring'''
     UpperCAmelCase__				:			List[Any]							=    0
     for pred in predictions:
          for ground_truth in ground_truths:
               UpperCAmelCase__				:			Optional[Any]							=    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 				a__			(					lowerCAmelCase						:							List[Any]							,     lowerCAmelCase						:							List[Any]		):






     '''simple docstring'''
     UpperCAmelCase__				:			List[Any]							=    0
     UpperCAmelCase__				:			List[str]							=    []
     UpperCAmelCase__				:			Optional[Any]							=    []
     for article in dataset:
          for paragraph in article["paragraphs"]:
               for qa in paragraph["qas"]:
                    total += 1
                    if qa["id"] not in predictions:
                         UpperCAmelCase__				:			List[str]							=    "Unanswered question " + qa["id"] + " will receive score 0."
                         print(lowerCAmelCase							,     file=sys.stderr		)
                         continue
                    UpperCAmelCase__				:			Any							=    [x["text"] for x in qa["answers"]]
                    UpperCAmelCase__				:			List[str]							=    predictions[qa["id"]]
                    UpperCAmelCase__				,						UpperCAmelCase__				:			Union[str, Any]							=    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		)

     UpperCAmelCase__				:			int							=    [x for _, x in sorted(zip(lowerCAmelCase							,     lowerCAmelCase		)		)]
     recalls.sort()

     UpperCAmelCase__				:			Union[str, Any]							=    100.0 * fa / total
     UpperCAmelCase__				:			Tuple							=    100.0 * exact_match / total
     UpperCAmelCase__				:			Optional[int]							=    get_aupr(lowerCAmelCase							,     lowerCAmelCase		)

     UpperCAmelCase__				:			List[Any]							=    get_prec_at_recall(lowerCAmelCase							,     lowerCAmelCase							,     recall_thresh=0.9		)
     UpperCAmelCase__				:			Optional[int]							=    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__":
    A__   :						Any 							=     argparse.ArgumentParser(description="""Evaluation for CUAD""")
    parser.add_argument("""dataset_file""", help="""Dataset file""")
    parser.add_argument("""prediction_file""", help="""Prediction File""")
    A__   :						Optional[Any] 							=     parser.parse_args()
    with open(args.dataset_file) as dataset_file:
        A__   :						int 							=     json.load(dataset_file)
        A__   :						int 							=     dataset_json["""data"""]
    with open(args.prediction_file) as prediction_file:
        A__   :						List[str] 							=     json.load(prediction_file)
    print(json.dumps(evaluate(dataset, predictions)))





"""simple docstring"""




def 				a__			(					lowerCAmelCase						:							str		):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[Any]							=    str(lowerCAmelCase		).strip()
     if not oct_string:
          raise ValueError("Empty string was passed to the function"		)
     UpperCAmelCase__				:			Tuple							=    oct_string[0] == "-"
     if is_negative:
          UpperCAmelCase__				:			List[Any]							=    oct_string[1:]
     if not oct_string.isdigit() or not all(0 <= int(lowerCAmelCase		) <= 7 for char in oct_string		):
          raise ValueError("Non-octal value was passed to the function"		)
     UpperCAmelCase__				:			Optional[int]							=    0
     for char in oct_string:
          UpperCAmelCase__				:			Tuple							=    8 * decimal_number + int(lowerCAmelCase		)
     if is_negative:
          UpperCAmelCase__				:			int							=    -decimal_number
     return decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()



