

"""simple docstring"""



import argparse
import json
import re
import string
import sys

import numpy as np


SCREAMING_SNAKE_CASE_       :						List[Any]					=    0.5





def 			_snake_case  (     UpperCAmelCase_	:       str       , UpperCAmelCase_	:       int      ):
     A__				      =	[""".""", """,""", """;""", """:"""]

     for token in remove_tokens:
          A__				      =	ground_truth.replace(UpperCAmelCase_       , """"""      )
          A__				      =	prediction.replace(UpperCAmelCase_       , """"""      )

     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(UpperCAmelCase_      )
     A__				      =	ground_truth.union(UpperCAmelCase_      )
     A__				      =	len(UpperCAmelCase_      ) / len(UpperCAmelCase_      )
     return jaccard





def 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]      ):

     def remove_articles(UpperCAmelCase_	:       Union[str, Any]      ):
          return re.sub(R"""\b(a|an|the)\b"""       , """ """       , UpperCAmelCase_      )

     def white_space_fix(UpperCAmelCase_	:       Optional[Any]      ):
          return " ".join(text.split()      )

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

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

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





def 			_snake_case  (     UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       Any      ):
     A__      ,					A__      ,					A__				      =	0, 0, 0

     A__				      =	"""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__				      =	False
               for pred in predictions:
                    if substr_ok:
                         A__				      =	get_jaccard(UpperCAmelCase_       , UpperCAmelCase_      ) >= IOU_THRESH or ground_truth in pred
                    else:
                         A__				      =	get_jaccard(UpperCAmelCase_       , UpperCAmelCase_      ) >= 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(UpperCAmelCase_      ) > 0
                    if substr_ok:
                         A__				      =	get_jaccard(UpperCAmelCase_       , UpperCAmelCase_      ) >= IOU_THRESH or ground_truth in pred
                    else:
                         A__				      =	get_jaccard(UpperCAmelCase_       , UpperCAmelCase_      ) >= 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 			_snake_case  (     UpperCAmelCase_	:       Tuple      ):
     A__				      =	precisions[::-1]
     for i in range(1       , len(UpperCAmelCase_      )      ):
          A__				      =	max(precision_best[i - 1]       , precision_best[i]      )
     A__				      =	precision_best[::-1]
     return precisions





def 			_snake_case  (     UpperCAmelCase_	:       str       , UpperCAmelCase_	:       Optional[int]      ):
     A__				      =	process_precisions(UpperCAmelCase_      )
     A__				      =	np.trapz(UpperCAmelCase_       , UpperCAmelCase_      )
     if np.isnan(UpperCAmelCase_      ):
          return 0
     return aupr





def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       List[str]       , UpperCAmelCase_	:       int      ):
     A__				      =	process_precisions(UpperCAmelCase_      )
     A__				      =	0
     for prec, recall in zip(UpperCAmelCase_       , UpperCAmelCase_      ):
          if recall >= recall_thresh:
               A__				      =	prec
               break
     return prec_at_recall





def 			_snake_case  (     UpperCAmelCase_	:       str       , UpperCAmelCase_	:       List[Any]      ):
     return normalize_answer(UpperCAmelCase_      ) == normalize_answer(UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       Union[str, Any]       , UpperCAmelCase_	:       List[str]       , UpperCAmelCase_	:       Any      ):
     A__				      =	0
     for pred in predictions:
          for ground_truth in ground_truths:
               A__				      =	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 			_snake_case  (     UpperCAmelCase_	:       Union[str, Any]       , UpperCAmelCase_	:       List[Any]      ):
     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(UpperCAmelCase_       , file=sys.stderr      )
                         continue
                    A__				      =	[x["""text"""] for x in qa["""answers"""]]
                    A__				      =	predictions[qa["""id"""]]
                    A__      ,					A__				      =	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__				      =	[x for _, x in sorted(zip(UpperCAmelCase_       , UpperCAmelCase_      )      )]
     recalls.sort()

     A__				      =	1_00.0 * fa / total
     A__				      =	1_00.0 * exact_match / total
     A__				      =	get_aupr(UpperCAmelCase_       , UpperCAmelCase_      )

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









"""simple docstring"""



import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
 from .tokenization_deberta_va import DebertaVaTokenizer
else:
 SCREAMING_SNAKE_CASE_       :						List[str]					=    None

SCREAMING_SNAKE_CASE_       :						int					=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_       :						Any					=    {'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_       :						Optional[int]					=    {
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

SCREAMING_SNAKE_CASE_       :						Optional[Any]					=    {
    'microsoft/deberta-v2-xlarge': 5_1_2,
    'microsoft/deberta-v2-xxlarge': 5_1_2,
    'microsoft/deberta-v2-xlarge-mnli': 5_1_2,
    'microsoft/deberta-v2-xxlarge-mnli': 5_1_2,
}

SCREAMING_SNAKE_CASE_       :						Optional[Any]					=    {
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}



class        a    (							_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       VOCAB_FILES_NAMES
    UpperCAmelCase    			=       PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase    			=       PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase    			=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase    			=       DebertaVaTokenizer

    def __init__(  self:			Optional[int]     ,    UpperCamelCase:			str=None     ,    UpperCamelCase:			str=None     ,    UpperCamelCase:			Optional[int]=False     ,    UpperCamelCase:			Optional[int]=False     ,    UpperCamelCase:			List[str]="[CLS]"     ,    UpperCamelCase:			Optional[Any]="[SEP]"     ,    UpperCamelCase:			int="[UNK]"     ,    UpperCamelCase:			List[Any]="[SEP]"     ,    UpperCamelCase:			Optional[Any]="[PAD]"     ,    UpperCamelCase:			Any="[CLS]"     ,    UpperCamelCase:			List[Any]="[MASK]"     ,    **UpperCamelCase:			List[str]     ,    ):






         """simple docstring"""


         super().__init__(
             UpperCamelCase     ,    tokenizer_file=UpperCamelCase     ,    do_lower_case=UpperCamelCase     ,    bos_token=UpperCamelCase     ,    eos_token=UpperCamelCase     ,    unk_token=UpperCamelCase     ,    sep_token=UpperCamelCase     ,    pad_token=UpperCamelCase     ,    cls_token=UpperCamelCase     ,    mask_token=UpperCamelCase     ,    split_by_punct=UpperCamelCase     ,    **UpperCamelCase     ,    )

         A__				      =	do_lower_case
         A__				      =	split_by_punct
         A__				      =	vocab_file
         A__				      =	False if not self.vocab_file else True

    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			List[Any]     ,    UpperCamelCase:			Optional[int]=None							):






         """simple docstring"""


         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         A__				      =	[self.cls_token_id]
         A__				      =	[self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep

    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			Any     ,    UpperCamelCase:			Optional[Any]=None     ,    UpperCamelCase:			Tuple=False							):






         """simple docstring"""


         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=UpperCamelCase     ,    token_ids_a=UpperCamelCase     ,    already_has_special_tokens=UpperCamelCase							)

         if token_ids_a is not None:
              return [1] + ([0] * len(UpperCamelCase							)) + [1] + ([0] * len(UpperCamelCase							)) + [1]
         return [1] + ([0] * len(UpperCamelCase							)) + [1]

    def 					UpperCamelCase						(  self:			int     ,    UpperCamelCase:			Tuple     ,    UpperCamelCase:			Dict=None							):






         """simple docstring"""


         A__				      =	[self.sep_token_id]
         A__				      =	[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							) * [0] + len(token_ids_a + sep							) * [1]





    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			str     ,    UpperCamelCase:			Optional[str] = None							):






         """simple docstring"""


         if not self.can_save_slow_tokenizer:
              raise ValueError(
                  """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                  """tokenizer."""							)

         if not os.path.isdir(UpperCamelCase							):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
              return
         A__				      =	os.path.join(
             UpperCamelCase     ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

         if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCamelCase							):
              copyfile(self.vocab_file     ,    UpperCamelCase							)

         return (out_vocab_file,)






