'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


_UpperCAmelCase						:    Dict     					=					logging.get_logger(__name__)







class lowercase_					(		_SCREAMING_SNAKE_CASE ):







			"""simple docstring"""

			def __init__(			self			: Tuple,   *UpperCamelCase__			: Tuple,   **UpperCamelCase__			: Any    )     -> Dict:
								warnings.warn(
								    'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
								    ' use ViTImageProcessor instead.',   UpperCamelCase__,   )
								super().__init__(*UpperCamelCase__,   **UpperCamelCase__    )




'''simple docstring'''






import argparse
import json
import re
import string
import sys

import numpy as np


_UpperCAmelCase						:    Dict     					=					0.5





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : List[Any]							):
     _A								=     ['.', ',', ';', ':']

     for token in remove_tokens:
          _A								=     ground_truth.replace(__snake_case      ,		''							)
          _A								=     prediction.replace(__snake_case      ,		''							)

     _A		,					_A								=     ground_truth.lower(), prediction.lower()
     _A		,					_A								=     ground_truth.replace('/'      ,		' '							), prediction.replace('/'      ,		' '							)
     _A		,					_A								=     set(ground_truth.split(' '							)							), set(prediction.split(' '							)							)

     _A								=     ground_truth.intersection(__snake_case							)
     _A								=     ground_truth.union(__snake_case							)
     _A								=     len(__snake_case							) / len(__snake_case							)
     return jaccard





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any							):

     def remove_articles(__snake_case       : Dict							):
          return re.sub(R'\b(a|an|the)\b'      ,		' '      ,		__snake_case							)

     def white_space_fix(__snake_case       : int							):
          return " ".join(text.split()							)

     def remove_punc(__snake_case       : List[str]							):
          _A								=     set(string.punctuation							)
          return "".join(ch for ch in text if ch not in exclude							)

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

     return white_space_fix(remove_articles(remove_punc(lower(__snake_case							)							)							)							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : Tuple      ,		__snake_case       : List[str]							):
     _A		,					_A		,					_A								=     0, 0, 0

     _A								=     'Parties' in qa_id

     # first check if ground truth is empty
     if len(__snake_case							) == 0:
          if len(__snake_case							) > 0:
               fp += len(__snake_case							)  # false positive for each one
     else:
          for ground_truth in ground_truths:
               assert len(__snake_case							) > 0
               # check if there is a match
               _A								=     False
               for pred in predictions:
                    if substr_ok:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= IOU_THRESH or ground_truth in pred
                    else:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= IOU_THRESH
                    if is_match:
                         _A								=     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.)
               _A								=     False
               for ground_truth in ground_truths:
                    assert len(__snake_case							) > 0
                    if substr_ok:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= IOU_THRESH or ground_truth in pred
                    else:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= IOU_THRESH
                    if is_match:
                         _A								=     True

               if not match_found:
                    fp += 1

     _A								=     tp / (tp + fp) if tp + fp > 0 else np.nan
     _A								=     tp / (tp + fn) if tp + fn > 0 else np.nan

     return precision, recall





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]							):
     _A								=     precisions[::-1]
     for i in range(1      ,		len(__snake_case							)							):
          _A								=     max(precision_best[i - 1]      ,		precision_best[i]							)
     _A								=     precision_best[::-1]
     return precisions





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any      ,		__snake_case       : Any							):
     _A								=     process_precisions(__snake_case							)
     _A								=     np.trapz(__snake_case      ,		__snake_case							)
     if np.isnan(__snake_case							):
          return 0
     return aupr





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : Union[str, Any]      ,		__snake_case       : List[str]							):
     _A								=     process_precisions(__snake_case							)
     _A								=     0
     for prec, recall in zip(__snake_case      ,		__snake_case							):
          if recall >= recall_thresh:
               _A								=     prec
               break
     return prec_at_recall





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]      ,		__snake_case       : Dict							):
     return normalize_answer(__snake_case							) == normalize_answer(__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : Tuple      ,		__snake_case       : List[Any]							):
     _A								=     0
     for pred in predictions:
          for ground_truth in ground_truths:
               _A								=     metric_fn(__snake_case      ,		__snake_case							)
               if score == 1:  # break the loop when one prediction matches the ground truth
                    break
          if score == 1:
               break
     return score





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : Tuple							):
     _A								=     _A								=     _A								=     0
     _A								=     []
     _A								=     []
     for article in dataset:
          for paragraph in article["paragraphs"]:
               for qa in paragraph["qas"]:
                    total += 1
                    if qa["id"] not in predictions:
                         _A								=     'Unanswered question ' + qa['id'] + ' will receive score 0.'
                         print(__snake_case      ,		file=sys.stderr							)
                         continue
                    _A								=     [x['text'] for x in qa['answers']]
                    _A								=     predictions[qa['id']]
                    _A		,					_A								=     compute_precision_recall(__snake_case      ,		__snake_case      ,		qa['id']							)

                    precisions.append(__snake_case							)
                    recalls.append(__snake_case							)

                    if precision == 0 and recall == 0:
                         fa += 0
                    else:
                         fa += 2 * (precision * recall) / (precision + recall)

                    exact_match += metric_max_over_ground_truths(__snake_case      ,		__snake_case      ,		__snake_case							)

     _A								=     [x for _, x in sorted(zip(__snake_case      ,		__snake_case							)							)]
     recalls.sort()

     _A								=     1_00.0 * fa / total
     _A								=     1_00.0 * exact_match / total
     _A								=     get_aupr(__snake_case      ,		__snake_case							)

     _A								=     get_prec_at_recall(__snake_case      ,		__snake_case      ,		recall_thresh=0.9							)
     _A								=     get_prec_at_recall(__snake_case      ,		__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__":
   _UpperCAmelCase						:    Union[str, Any]     					=					argparse.ArgumentParser(description='''Evaluation for CUAD''')
   parser.add_argument('''dataset_file''', help='''Dataset file''')
   parser.add_argument('''prediction_file''', help='''Prediction File''')
   _UpperCAmelCase						:    List[Any]     					=					parser.parse_args()
   with open(args.dataset_file) as dataset_file:
      _UpperCAmelCase						:    Tuple     					=					json.load(dataset_file)
      _UpperCAmelCase						:    Dict     					=					dataset_json['''data''']
   with open(args.prediction_file) as prediction_file:
      _UpperCAmelCase						:    Tuple     					=					json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))



