

"""simple docstring"""




import argparse
import json
import re
import string
import sys

import numpy as np


lowerCamelCase									= 0.5




def       UpperCAmelCase__    (      _A				:			int     ,				_A				:			Optional[int]							):





  '''simple docstring'''







  a__  =[".", ",", ";", ":"]

  for token in remove_tokens:
    a__  =ground_truth.replace(A_     ,				''''''							)
    a__  =prediction.replace(A_     ,				''''''							)

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

  a__  =ground_truth.intersection(A_							)
  a__  =ground_truth.union(A_							)
  a__  =len(A_							) / len(A_							)
  return jaccard




def       UpperCAmelCase__    (      _A				:			Optional[int]							):





  '''simple docstring'''







  def remove_articles(_A				:			Optional[int]							):
    return re.sub(r'''\b(a|an|the)\b'''     ,				''' '''     ,				A_							)

  def white_space_fix(_A				:			Dict							):
    return " ".join(text.split()							)

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

  def lower(_A				:			Tuple							):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(A_							)							)							)							)




def       UpperCAmelCase__    (      _A				:			List[str]     ,				_A				:			Any     ,				_A				:			List[Any]							):





  '''simple docstring'''







  a__  =0, 0, 0

  a__  ="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
      a__  =False
      for pred in predictions:
        if substr_ok:
          a__  =get_jaccard(A_     ,				A_							) >= IOU_THRESH or ground_truth in pred
        else:
          a__  =get_jaccard(A_     ,				A_							) >= 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(A_							) > 0
        if substr_ok:
          a__  =get_jaccard(A_     ,				A_							) >= IOU_THRESH or ground_truth in pred
        else:
          a__  =get_jaccard(A_     ,				A_							) >= 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       UpperCAmelCase__    (      _A				:			Optional[int]							):





  '''simple docstring'''







  a__  =precisions[::-1]
  for i in range(1     ,				len(A_							)							):
    a__  =max(precision_best[i - 1]     ,				precision_best[i]							)
  a__  =precision_best[::-1]
  return precisions




def       UpperCAmelCase__    (      _A				:			Optional[int]     ,				_A				:			List[str]							):





  '''simple docstring'''







  a__  =process_precisions(A_							)
  a__  =np.trapz(A_     ,				A_							)
  if np.isnan(A_							):
    return 0
  return aupr




def       UpperCAmelCase__    (      _A				:			Dict     ,				_A				:			Dict     ,				_A				:			Optional[int]							):





  '''simple docstring'''







  a__  =process_precisions(A_							)
  a__  =0
  for prec, recall in zip(A_     ,				A_							):
    if recall >= recall_thresh:
      a__  =prec
      break
  return prec_at_recall




def       UpperCAmelCase__    (      _A				:			List[Any]     ,				_A				:			int							):





  '''simple docstring'''







  return normalize_answer(A_							) == normalize_answer(A_							)




def       UpperCAmelCase__    (      _A				:			Optional[int]     ,				_A				:			Any     ,				_A				:			Union[str, Any]							):





  '''simple docstring'''







  a__  =0
  for pred in predictions:
    for ground_truth in ground_truths:
      a__  =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       UpperCAmelCase__    (      _A				:			str     ,				_A				:			List[Any]							):





  '''simple docstring'''







  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(A_     ,				file=sys.stderr							)
          continue
        a__  =[x["text"] for x in qa["answers"]]
        a__  =predictions[qa["id"]]
        a__  =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_							)

  a__  =[x for _, x in sorted(zip(A_     ,				A_							)							)]
  recalls.sort()

  a__  =100.0 * fa / total
  a__  =100.0 * exact_match / total
  a__  =get_aupr(A_     ,				A_							)

  a__  =get_prec_at_recall(A_     ,				A_     ,				recall_thresh=0.9							)
  a__  =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)))



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase									= {
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase									= [
           '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''FlaubertForMultipleChoice''',
           '''FlaubertForQuestionAnswering''',
           '''FlaubertForQuestionAnsweringSimple''',
           '''FlaubertForSequenceClassification''',
           '''FlaubertForTokenClassification''',
           '''FlaubertModel''',
           '''FlaubertWithLMHeadModel''',
           '''FlaubertPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase									= [
           '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFFlaubertForMultipleChoice''',
           '''TFFlaubertForQuestionAnsweringSimple''',
           '''TFFlaubertForSequenceClassification''',
           '''TFFlaubertForTokenClassification''',
           '''TFFlaubertModel''',
           '''TFFlaubertPreTrainedModel''',
           '''TFFlaubertWithLMHeadModel''',
       ]


if TYPE_CHECKING:
       from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
       from .tokenization_flaubert import FlaubertTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flaubert import (
                  FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaubertForMultipleChoice,
                  FlaubertForQuestionAnswering,
                  FlaubertForQuestionAnsweringSimple,
                  FlaubertForSequenceClassification,
                  FlaubertForTokenClassification,
                  FlaubertModel,
                  FlaubertPreTrainedModel,
                  FlaubertWithLMHeadModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_flaubert import (
                  TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFFlaubertForMultipleChoice,
                  TFFlaubertForQuestionAnsweringSimple,
                  TFFlaubertForSequenceClassification,
                  TFFlaubertForTokenClassification,
                  TFFlaubertModel,
                  TFFlaubertPreTrainedModel,
                  TFFlaubertWithLMHeadModel,
              )

else:
       import sys

       lowerCamelCase									= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
