


'''simple docstring'''





import argparse
import json
import re
import string
import sys

import numpy as np


lowerCamelCase		      = 0.5




def 			a_   (					SCREAMING_SNAKE_CASE__      :   int	,    SCREAMING_SNAKE_CASE__      :   Dict  ):
       '''simple docstring'''





       _lowerCamelCase :	int						=[".", ",", ";", ":"]

       for token in remove_tokens:
              _lowerCamelCase :	Dict						=ground_truth.replace(lowercase__	,    ''  )
              _lowerCamelCase :	Dict						=prediction.replace(lowercase__	,    ''  )

       _lowerCamelCase :	Any						=ground_truth.lower(), prediction.lower()
       _lowerCamelCase :	Dict						=ground_truth.replace('/'	,    ' '  ), prediction.replace('/'	,    ' '  )
       _lowerCamelCase :	Tuple						=set(ground_truth.split(' '  )  ), set(prediction.split(' '  )  )

       _lowerCamelCase :	Union[str, Any]						=ground_truth.intersection(lowercase__  )
       _lowerCamelCase :	Optional[Any]						=ground_truth.union(lowercase__  )
       _lowerCamelCase :	Dict						=len(lowercase__  ) / len(lowercase__  )
       return jaccard




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Union[str, Any]  ):
       '''simple docstring'''





       def remove_articles(SCREAMING_SNAKE_CASE__      :   List[str]  ):
              return re.sub(r'\b(a|an|the)\b'	,    ' '	,    lowercase__  )

       def white_space_fix(SCREAMING_SNAKE_CASE__      :   List[str]  ):
              return " ".join(text.split()  )

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

       def lower(SCREAMING_SNAKE_CASE__      :   Dict  ):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(lowercase__  )  )  )  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Any	,    SCREAMING_SNAKE_CASE__      :   Tuple	,    SCREAMING_SNAKE_CASE__      :   Union[str, Any]  ):
       '''simple docstring'''





       _lowerCamelCase :	List[str]						=0, 0, 0

       _lowerCamelCase :	Union[str, Any]						="Parties" in qa_id

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

                     if not match_found:
                            fp += 1

       _lowerCamelCase :	Tuple						=tp / (tp + fp) if tp + fp > 0 else np.nan
       _lowerCamelCase :	Union[str, Any]						=tp / (tp + fn) if tp + fn > 0 else np.nan

       return precision, recall




def 			a_   (					SCREAMING_SNAKE_CASE__      :   List[Any]  ):
       '''simple docstring'''





       _lowerCamelCase :	Optional[Any]						=precisions[::-1]
       for i in range(1	,    len(lowercase__  )  ):
              _lowerCamelCase :	List[str]						=max(precision_best[i - 1]	,    precision_best[i]  )
       _lowerCamelCase :	Optional[Any]						=precision_best[::-1]
       return precisions




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Tuple	,    SCREAMING_SNAKE_CASE__      :   Any  ):
       '''simple docstring'''





       _lowerCamelCase :	int						=process_precisions(lowercase__  )
       _lowerCamelCase :	Optional[int]						=np.trapz(lowercase__	,    lowercase__  )
       if np.isnan(lowercase__  ):
              return 0
       return aupr




def 			a_   (					SCREAMING_SNAKE_CASE__      :   List[str]	,    SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   Union[str, Any]  ):
       '''simple docstring'''





       _lowerCamelCase :	List[str]						=process_precisions(lowercase__  )
       _lowerCamelCase :	List[Any]						=0
       for prec, recall in zip(lowercase__	,    lowercase__  ):
              if recall >= recall_thresh:
                     _lowerCamelCase :	Dict						=prec
                     break
       return prec_at_recall




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Union[str, Any]	,    SCREAMING_SNAKE_CASE__      :   Optional[Any]  ):
       '''simple docstring'''





       return normalize_answer(lowercase__  ) == normalize_answer(lowercase__  )




def 			a_   (					SCREAMING_SNAKE_CASE__      :   List[Any]	,    SCREAMING_SNAKE_CASE__      :   int	,    SCREAMING_SNAKE_CASE__      :   Optional[Any]  ):
       '''simple docstring'''





       _lowerCamelCase :	Optional[Any]						=0
       for pred in predictions:
              for ground_truth in ground_truths:
                     _lowerCamelCase :	Optional[Any]						=metric_fn(lowercase__	,    lowercase__  )
                     if score == 1:  # break the loop when one prediction matches the ground truth
                            break
              if score == 1:
                     break
       return score




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Tuple	,    SCREAMING_SNAKE_CASE__      :   List[str]  ):
       '''simple docstring'''





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

                            precisions.append(lowercase__  )
                            recalls.append(lowercase__  )

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

                            exact_match += metric_max_over_ground_truths(lowercase__	,    lowercase__	,    lowercase__  )

       _lowerCamelCase :	List[str]						=[x for _, x in sorted(zip(lowercase__	,    lowercase__  )  )]
       recalls.sort()

       _lowerCamelCase :	List[str]						=100.0 * fa / total
       _lowerCamelCase :	List[Any]						=100.0 * exact_match / total
       _lowerCamelCase :	Dict						=get_aupr(lowercase__	,    lowercase__  )

       _lowerCamelCase :	List[Any]						=get_prec_at_recall(lowercase__	,    lowercase__	,    recall_thresh=0.9  )
       _lowerCamelCase :	Optional[int]						=get_prec_at_recall(lowercase__	,    lowercase__	,    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 List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


lowerCamelCase		      = logging.get_logger(__name__)

lowerCamelCase		      = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase		      = {
    'vocab_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json',
    },
    'merges_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'openai/clip-vit-base-patch32': (
            'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'
        ),
    },
}

lowerCamelCase		      = {
    'openai/clip-vit-base-patch32': 77,
}




class A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						Dict   =VOCAB_FILES_NAMES
      UpperCamelCase__							:						Optional[int]   =PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__							:						Optional[int]   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__							:						Optional[Any]   =['input_ids', 'attention_mask']
      UpperCamelCase__							:						str   =CLIPTokenizer






      def __init__(      self		:  str    ,     lowercase_		:  Optional[Any]=None    ,     lowercase_		:  Optional[int]=None    ,     lowercase_		:  int=None    ,     lowercase_		:  Optional[int]="<|endoftext|>"    ,     lowercase_		:  Union[str, Any]="<|startoftext|>"    ,     lowercase_		:  Tuple="<|endoftext|>"    ,     lowercase_		:  int="<|endoftext|>"    ,     **lowercase_		:  Optional[Any]    ,     )      ->		Any:
             """simple docstring"""




             super().__init__(
                 lowercase_    ,     lowercase_    ,     tokenizer_file=lowercase_    ,     unk_token=lowercase_    ,     bos_token=lowercase_    ,     eos_token=lowercase_    ,     pad_token=lowercase_    ,     **lowercase_    ,     )

             if not isinstance(self.backend_tokenizer.pre_tokenizer    ,     pre_tokenizers.Sequence				):
                    raise ValueError(
                        'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
                        ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
                        ' to be compatible with this version.The easiest way to do so is'
                        ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
                        ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
                        ' transformers.'				)

             self._wrap_decode_method_backend_tokenizer()






      def  lowerCamelCase (      self		:  Any				)      ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase :	int						=self.backend_tokenizer.decode

             def new_decode_method(*lowercase_		:  List[str]    ,     **lowercase_		:  int				):
                    _lowerCamelCase :	Tuple						=orig_decode_method(*lowercase_    ,     **lowercase_				)
                    _lowerCamelCase :	Dict						=text.replace(self.backend_tokenizer.model.end_of_word_suffix    ,     ' '				).strip()
                    return text

             _lowerCamelCase :	List[Any]						=new_decode_method






      def  lowerCamelCase (      self		:  Dict    ,     lowercase_		:  List[int]    ,     lowercase_		:  Optional[List[int]] = None				)      ->		List[int]:
             """simple docstring"""




             _lowerCamelCase :	Union[str, Any]						=[self.bos_token_id]
             _lowerCamelCase :	str						=[self.eos_token_id]

             if token_ids_a is None:
                    return bos_token + token_ids_a + eos_token
             return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token






      def  lowerCamelCase (      self		:  Any    ,     lowercase_		:  List[int]    ,     lowercase_		:  Optional[List[int]] = None				)      ->		List[int]:
             """simple docstring"""




             _lowerCamelCase :	int						=[self.bos_token_id]
             _lowerCamelCase :	Any						=[self.eos_token_id]

             if token_ids_a is None:
                    return len(bos_token + token_ids_a + eos_token				) * [0]
             return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token				) * [0]






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  str    ,     lowercase_		:  Optional[str] = None				)      ->		Tuple[str]:
             """simple docstring"""




             _lowerCamelCase :	str						=self._tokenizer.model.save(lowercase_    ,     name=lowercase_				)
             return tuple(lowercase_				)


