




'''simple docstring'''





import argparse
import json
import re
import string
import sys

import numpy as np


a		:	int       =      0.5





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    _a				:							Tuple							       =      ['.', ',', ';', ':']

    for token in remove_tokens:
        _a				:							Optional[Any]							       =      ground_truth.replace(UpperCAmelCase_   ,	""""""						)
        _a				:							Dict							       =      prediction.replace(UpperCAmelCase_   ,	""""""						)

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

    _a				:							Tuple							       =      ground_truth.intersection(UpperCAmelCase_						)
    _a				:							str							       =      ground_truth.union(UpperCAmelCase_						)
    _a				:							Tuple							       =      len(UpperCAmelCase_						) / len(UpperCAmelCase_						)
    return jaccard





def UpperCAmelCase    (       UpperCAmelCase_						):





    def remove_articles(UpperCAmelCase_						):
        return re.sub(r"""\b(a|an|the)\b"""   ,	""" """   ,	UpperCAmelCase_						)

    def white_space_fix(UpperCAmelCase_						):
        return " ".join(text.split()						)

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

    def lower(UpperCAmelCase_						):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase_						)						)						)						)





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						):
    _a				:							Optional[Any]							       =      0, 0, 0

    _a				:							int							       =      'Parties' in qa_id

    # first check if ground truth is empty
    if len(UpperCAmelCase_						) == 0:
        if len(UpperCAmelCase_						) > 0:
            fp += len(UpperCAmelCase_						)  # false positive for each one
    else:
        for ground_truth in ground_truths:
            assert len(UpperCAmelCase_						) > 0
            # check if there is a match
            _a				:							List[str]							       =      False
            for pred in predictions:
                if substr_ok:
                    _a				:							str							       =      get_jaccard(UpperCAmelCase_   ,	UpperCAmelCase_						) >= IOU_THRESH or ground_truth in pred
                else:
                    _a				:							List[str]							       =      get_jaccard(UpperCAmelCase_   ,	UpperCAmelCase_						) >= IOU_THRESH
                if is_match:
                    _a				:							Optional[int]							       =      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				:							List[Any]							       =      False
            for ground_truth in ground_truths:
                assert len(UpperCAmelCase_						) > 0
                if substr_ok:
                    _a				:							int							       =      get_jaccard(UpperCAmelCase_   ,	UpperCAmelCase_						) >= IOU_THRESH or ground_truth in pred
                else:
                    _a				:							str							       =      get_jaccard(UpperCAmelCase_   ,	UpperCAmelCase_						) >= IOU_THRESH
                if is_match:
                    _a				:							Optional[int]							       =      True

            if not match_found:
                fp += 1

    _a				:							List[str]							       =      tp / (tp + fp) if tp + fp > 0 else np.nan
    _a				:							Tuple							       =      tp / (tp + fn) if tp + fn > 0 else np.nan

    return precision, recall





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Optional[Any]							       =      precisions[::-1]
    for i in range(1   ,	len(UpperCAmelCase_						)						):
        _a				:							Tuple							       =      max(precision_best[i - 1]   ,	precision_best[i]						)
    _a				:							List[Any]							       =      precision_best[::-1]
    return precisions





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    _a				:							List[Any]							       =      process_precisions(UpperCAmelCase_						)
    _a				:							List[str]							       =      np.trapz(UpperCAmelCase_   ,	UpperCAmelCase_						)
    if np.isnan(UpperCAmelCase_						):
        return 0
    return aupr





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						):
    _a				:							Dict							       =      process_precisions(UpperCAmelCase_						)
    _a				:							str							       =      0
    for prec, recall in zip(UpperCAmelCase_   ,	UpperCAmelCase_						):
        if recall >= recall_thresh:
            _a				:							Union[str, Any]							       =      prec
            break
    return prec_at_recall





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    return normalize_answer(UpperCAmelCase_						) == normalize_answer(UpperCAmelCase_						)





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						):
    _a				:							str							       =      0
    for pred in predictions:
        for ground_truth in ground_truths:
            _a				:							Optional[int]							       =      metric_fn(UpperCAmelCase_   ,	UpperCAmelCase_						)
            if score == 1:  # break the loop when one prediction matches the ground truth
                break
        if score == 1:
            break
    return score





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

                precisions.append(UpperCAmelCase_						)
                recalls.append(UpperCAmelCase_						)

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

                exact_match += metric_max_over_ground_truths(UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						)

    _a				:							Dict							       =      [x for _, x in sorted(zip(UpperCAmelCase_   ,	UpperCAmelCase_						)						)]
    recalls.sort()

    _a				:							List[str]							       =      100.0 * fa / total
    _a				:							Optional[int]							       =      100.0 * exact_match / total
    _a				:							Tuple							       =      get_aupr(UpperCAmelCase_   ,	UpperCAmelCase_						)

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








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__    :							List[str]       		=     {
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Any       		=     ['ErnieMTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Dict       		=     [
       'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ErnieMForMultipleChoice',
       'ErnieMForQuestionAnswering',
       'ErnieMForSequenceClassification',
       'ErnieMForTokenClassification',
       'ErnieMModel',
       'ErnieMPreTrainedModel',
       'ErnieMForInformationExtraction',
   ]


if TYPE_CHECKING:
   from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_ernie_m import ErnieMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ernie_m import (
          ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
          ErnieMForInformationExtraction,
          ErnieMForMultipleChoice,
          ErnieMForQuestionAnswering,
          ErnieMForSequenceClassification,
          ErnieMForTokenClassification,
          ErnieMModel,
          ErnieMPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase__    :							List[Any]       		=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
