

import argparse
import json
import re
import string
import sys

import numpy as np


lowerCamelCase 	=   0.5




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





  '''simple docstring'''







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

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

  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(_A				)
  a__    =ground_truth.union(_A				)
  a__    =len(_A				) / len(_A				)
  return jaccard




def      UpperCAmelCase__							(     _A :					Optional[int]				):





  '''simple docstring'''







  def remove_articles(_A :					Union[str, Any]				):
    return re.sub(r'''\b(a|an|the)\b'''      , ''' '''      , _A				)

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

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

  def lower(_A :					Union[str, Any]				):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(_A				)				)				)				)




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





  '''simple docstring'''







  a__, a__, 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 :					List[str]				):





  '''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 :					List[Any]      , _A :					str				):





  '''simple docstring'''







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




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





  '''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 :					str      , _A :					int				):





  '''simple docstring'''







  return normalize_answer(_A				) == normalize_answer(_A				)




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





  '''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 :					List[str]      , _A :					Optional[int]				):





  '''simple docstring'''







  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(_A      , file=sys.stderr				)
          continue
        a__    =[x['''text'''] for x in qa['''answers''']]
        a__    =predictions[qa['''id''']]
        a__, 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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Tuple        =  'yoso'



   def __init__(							self,       lowercase_=50265,       lowercase_=768,       lowercase_=12,       lowercase_=12,       lowercase_=3072,       lowercase_="gelu",       lowercase_=0.1,       lowercase_=0.1,       lowercase_=4096,       lowercase_=1,       lowercase_=0.02,       lowercase_=1E-12,       lowercase_="absolute",       lowercase_=True,       lowercase_=9,       lowercase_=64,       lowercase_=None,       lowercase_=True,       lowercase_=True,       lowercase_=1,       lowercase_=0,       lowercase_=2,       **lowercase_,       )				->       List[str]:






     """simple docstring"""






     super().__init__(pad_token_id=lowercase_,       bos_token_id=lowercase_,       eos_token_id=lowercase_,       **lowercase_			)

     a__    =vocab_size
     a__    =max_position_embeddings
     a__    =hidden_size
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =intermediate_size
     a__    =hidden_act
     a__    =hidden_dropout_prob
     a__    =attention_probs_dropout_prob
     a__    =initializer_range
     a__    =type_vocab_size
     a__    =layer_norm_eps
     a__    =position_embedding_type
     a__    =use_expectation
     a__    =hash_code_len
     a__    =num_hash
     a__    =conv_window
     a__    =use_fast_hash
     a__    =lsh_backward
