

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)))




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__)

