






"""simple docstring"""



import argparse
import json
import re
import string
import sys

import numpy as np


a  :						Optional[int]					       =							0.5




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->int:
 UpperCAmelCase__ 						=				[""".""", """,""", """;""", """:"""]

 for token in remove_tokens:
  UpperCAmelCase__ 						=				ground_truth.replace(_SCREAMING_SNAKE_CASE       ,	""""""			)
  UpperCAmelCase__ 						=				prediction.replace(_SCREAMING_SNAKE_CASE       ,	""""""			)

 UpperCAmelCase__						,	UpperCAmelCase__ 						=				ground_truth.lower(), prediction.lower()
 UpperCAmelCase__						,	UpperCAmelCase__ 						=				ground_truth.replace("""/"""       ,	""" """			), prediction.replace("""/"""       ,	""" """			)
 UpperCAmelCase__						,	UpperCAmelCase__ 						=				set(ground_truth.split(""" """			)			), set(prediction.split(""" """			)			)

 UpperCAmelCase__ 						=				ground_truth.intersection(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				ground_truth.union(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				len(_SCREAMING_SNAKE_CASE			) / len(_SCREAMING_SNAKE_CASE			)
 return jaccard




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->Optional[Any]:

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

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

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

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

 return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE			)			)			)			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->List[Any]:
 UpperCAmelCase__						,	UpperCAmelCase__						,	UpperCAmelCase__ 						=				0, 0, 0

 UpperCAmelCase__ 						=				"""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
   UpperCAmelCase__ 						=				False
   for pred in predictions:
    if substr_ok:
     UpperCAmelCase__ 						=				get_jaccard(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			) >= IOU_THRESH or ground_truth in pred
    else:
     UpperCAmelCase__ 						=				get_jaccard(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			) >= IOU_THRESH
    if is_match:
     UpperCAmelCase__ 						=				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.)
   UpperCAmelCase__ 						=				False
   for ground_truth in ground_truths:
    assert len(_SCREAMING_SNAKE_CASE			) > 0
    if substr_ok:
     UpperCAmelCase__ 						=				get_jaccard(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			) >= IOU_THRESH or ground_truth in pred
    else:
     UpperCAmelCase__ 						=				get_jaccard(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			) >= IOU_THRESH
    if is_match:
     UpperCAmelCase__ 						=				True

   if not match_found:
    fp += 1

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

 return precision, recall




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




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Dict:
 UpperCAmelCase__ 						=				process_precisions(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				np.trapz(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)
 if np.isnan(_SCREAMING_SNAKE_CASE			):
  return 0
 return aupr




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->List[str]:
 UpperCAmelCase__ 						=				process_precisions(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				0
 for prec, recall in zip(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			):
  if recall >= recall_thresh:
   UpperCAmelCase__ 						=				prec
   break
 return prec_at_recall




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Optional[int]:
 return normalize_answer(_SCREAMING_SNAKE_CASE			) == normalize_answer(_SCREAMING_SNAKE_CASE			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Tuple:
 UpperCAmelCase__ 						=				0
 for pred in predictions:
  for ground_truth in ground_truths:
   UpperCAmelCase__ 						=				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       ,	_SCREAMING_SNAKE_CASE			)    ->Union[str, Any]:
 UpperCAmelCase__ 						=				UpperCAmelCase__ 						=				UpperCAmelCase__ 						=				0
 UpperCAmelCase__ 						=				[]
 UpperCAmelCase__ 						=				[]
 for article in dataset:
  for paragraph in article["paragraphs"]:
   for qa in paragraph["qas"]:
    total += 1
    if qa["id"] not in predictions:
     UpperCAmelCase__ 						=				"""Unanswered question """ + qa["""id"""] + """ will receive score 0."""
     print(_SCREAMING_SNAKE_CASE       ,	file=sys.stderr			)
     continue
    UpperCAmelCase__ 						=				[x["""text"""] for x in qa["""answers"""]]
    UpperCAmelCase__ 						=				predictions[qa["""id"""]]
    UpperCAmelCase__						,	UpperCAmelCase__ 						=				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			)

 UpperCAmelCase__ 						=				[x for _, x in sorted(zip(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)			)]
 recalls.sort()

 UpperCAmelCase__ 						=				100.0 * fa / total
 UpperCAmelCase__ 						=				100.0 * exact_match / total
 UpperCAmelCase__ 						=				get_aupr(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)

 UpperCAmelCase__ 						=				get_prec_at_recall(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	recall_thresh=0.9			)
 UpperCAmelCase__ 						=				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__":
 a  :						Tuple					       =							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  :						Tuple					       =							dataset_json['''data''']
 with open(args.prediction_file) as prediction_file:
  a  :						Optional[Any]					       =							json.load(prediction_file)
 print(json.dumps(evaluate(dataset, predictions)))











"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


a  :						List[Any]					       =							logging.get_logger(__name__)

a  :						List[Any]					       =							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a  :						Any					       =							{
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

a  :						str					       =							{
    '''microsoft/mpnet-base''': 512,
}

a  :						Union[str, Any]					       =							{
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}


class 	_UpperCamelCase ( __UpperCamelCase							):



    '''simple docstring'''


    __lowercase    :   Optional[Any]											=						VOCAB_FILES_NAMES
    __lowercase    :   Optional[int]											=						PRETRAINED_VOCAB_FILES_MAP
    __lowercase    :   Dict											=						PRETRAINED_INIT_CONFIGURATION
    __lowercase    :   Optional[Any]											=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowercase    :   Dict											=						MPNetTokenizer
    __lowercase    :   Dict											=						['input_ids', 'attention_mask']


    def __init__(      self   ,			__lowercase=None   ,			__lowercase=None   ,			__lowercase=True   ,			__lowercase="<s>"   ,			__lowercase="</s>"   ,			__lowercase="</s>"   ,			__lowercase="<s>"   ,			__lowercase="[UNK]"   ,			__lowercase="<pad>"   ,			__lowercase="<mask>"   ,			__lowercase=True   ,			__lowercase=None   ,			**__lowercase   ,			):
     super().__init__(
         __lowercase   ,			tokenizer_file=__lowercase   ,			do_lower_case=__lowercase   ,			bos_token=__lowercase   ,			eos_token=__lowercase   ,			sep_token=__lowercase   ,			cls_token=__lowercase   ,			unk_token=__lowercase   ,			pad_token=__lowercase   ,			mask_token=__lowercase   ,			tokenize_chinese_chars=__lowercase   ,			strip_accents=__lowercase   ,			**__lowercase   ,			)

     UpperCAmelCase__ 						=				json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
     if (
         pre_tok_state.get("""lowercase"""   ,			__lowercase			) != do_lower_case
         or pre_tok_state.get("""strip_accents"""   ,			__lowercase			) != strip_accents
     ):
      UpperCAmelCase__ 						=				getattr(__lowercase   ,			pre_tok_state.pop("""type"""			)			)
      UpperCAmelCase__ 						=				do_lower_case
      UpperCAmelCase__ 						=				strip_accents
      UpperCAmelCase__ 						=				pre_tok_class(**__lowercase			)

     UpperCAmelCase__ 						=				do_lower_case


    @property
    def 		A__      (      self			):
     if self._mask_token is None:
      if self.verbose:
       logger.error("""Using mask_token, but it is not set yet."""			)
      return None
     return str(self._mask_token			)


    @mask_token.setter
    def 		A__      (      self   ,			__lowercase			):
     UpperCAmelCase__ 						=				AddedToken(__lowercase   ,			lstrip=__lowercase   ,			rstrip=__lowercase			) if isinstance(__lowercase   ,			__lowercase			) else value
     UpperCAmelCase__ 						=				value


    def 		A__      (      self   ,			__lowercase   ,			__lowercase=None			):
     UpperCAmelCase__ 						=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
      return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


    def 		A__      (      self   ,			__lowercase   ,			__lowercase = None			):
     UpperCAmelCase__ 						=				[self.sep_token_id]
     UpperCAmelCase__ 						=				[self.cls_token_id]

     if token_ids_a is None:
      return len(cls + token_ids_a + sep			) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]






    def 		A__      (      self   ,			__lowercase   ,			__lowercase = None			):
     UpperCAmelCase__ 						=				self._tokenizer.model.save(__lowercase   ,			name=__lowercase			)
     return tuple(__lowercase			)



