'''simple docstring'''






import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def    _SCREAMING_SNAKE_CASE		(			):
     def filter_log(__snake_case       : Tuple							):
          return False if "This IS expected if you are initializing" in record.msg else True

     _A								=     datasets.utils.logging.get_logger('transformers.modeling_utils'							)
     logger.addFilter(__snake_case							)
     try:
          yield
     finally:
          logger.removeFilter(__snake_case							)


_UpperCAmelCase						:    Tuple     					=					'''\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
'''

_UpperCAmelCase						:    Union[str, Any]     					=					'''\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.
'''

_UpperCAmelCase						:    Union[str, Any]     					=					'''
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. \'en\').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bertscore = datasets.load_metric("bertscore")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")
    >>> print([round(v, 2) for v in results["f1"]])
    [1.0, 1.0]
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,						_KWARGS_DESCRIPTION )
class lowercase_					(		datasets.Metric ):







   """simple docstring"""

   def   __UpperCAmelCase      (			self			: List[str]    )     -> Union[str, Any]:
        return datasets.MetricInfo(
            description=_DESCRIPTION,   citation=_CITATION,   homepage='https://github.com/Tiiiger/bert_score',   inputs_description=_KWARGS_DESCRIPTION,   features=datasets.Features(
                {
                    'predictions': datasets.Value('string',   id='sequence'    ),
                    'references': datasets.Sequence(datasets.Value('string',   id='sequence'    ),   id='references'    ),
                }    ),   codebase_urls=['https://github.com/Tiiiger/bert_score'],   reference_urls=[
                'https://github.com/Tiiiger/bert_score',
                'https://arxiv.org/abs/1904.09675',
            ],   )
   def   __UpperCAmelCase      (			self			: List[str],   UpperCamelCase__			: Any,   UpperCamelCase__			: Optional[Any],   UpperCamelCase__			: int=None,   UpperCamelCase__			: Union[str, Any]=None,   UpperCamelCase__			: int=None,   UpperCamelCase__			: Dict=False,   UpperCamelCase__			: Any=False,   UpperCamelCase__			: Optional[Any]=None,   UpperCamelCase__			: Optional[Any]=64,   UpperCamelCase__			: List[Any]=4,   UpperCamelCase__			: Any=False,   UpperCamelCase__			: Tuple=False,   UpperCamelCase__			: Dict=None,   UpperCamelCase__			: Tuple=False,   )     -> List[Any]:
        _A								=     bert_score.utils.get_hash
        _A								=     bert_score.BERTScorer

        if version.parse(bert_score.__version__    ) >= version.parse('0.3.10'    ):
             _A								=     functools.partial(UpperCamelCase__,   use_fast_tokenizer=UpperCamelCase__    )
             _A								=     functools.partial(UpperCamelCase__,   use_fast_tokenizer=UpperCamelCase__    )
        elif use_fast_tokenizer:
             raise ImportWarning(
                 'To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'
                 'You can install it with `pip install "bert-score>=0.3.10"`.'    )

        if model_type is None:
             assert lang is not None, "either lang or model_type should be specified"
             _A								=     bert_score.utils.langamodel[lang.lower()]

        if num_layers is None:
             _A								=     bert_score.utils.modelalayers[model_type]

        _A								=     get_hash(
            model=UpperCamelCase__,   num_layers=UpperCamelCase__,   idf=UpperCamelCase__,   rescale_with_baseline=UpperCamelCase__,   use_custom_baseline=baseline_path is not None,   )

        with filter_logging_context():
             if not hasattr(self,   'cached_bertscorer'    ) or self.cached_bertscorer.hash != hashcode:
                  _A								=     scorer(
                      model_type=UpperCamelCase__,   num_layers=UpperCamelCase__,   batch_size=UpperCamelCase__,   nthreads=UpperCamelCase__,   all_layers=UpperCamelCase__,   idf=UpperCamelCase__,   device=UpperCamelCase__,   lang=UpperCamelCase__,   rescale_with_baseline=UpperCamelCase__,   baseline_path=UpperCamelCase__,   )

        ((_A)		,					(_A)		,					(_A))								=     self.cached_bertscorer.score(
            cands=UpperCamelCase__,   refs=UpperCamelCase__,   verbose=UpperCamelCase__,   batch_size=UpperCamelCase__,   )
        _A								=     {
            'precision': P.tolist(),
            'recall': R.tolist(),
            'f1': F.tolist(),
            'hashcode': hashcode,
        }
        return output_dict
   def   __UpperCAmelCase      (			self			: str,   UpperCamelCase__			: str=None,   UpperCamelCase__			: Dict=None,   **UpperCamelCase__			: Union[str, Any]    )     -> str:
        # References can be strings or lists of strings
        # Let's change strings to lists of strings with one element
        if references is not None:
             _A								=     [[ref] if isinstance(UpperCamelCase__,   UpperCamelCase__    ) else ref for ref in references]
        super().add_batch(predictions=UpperCamelCase__,   references=UpperCamelCase__,   **UpperCamelCase__    )

   def   __UpperCAmelCase      (			self			: Optional[int],   UpperCamelCase__			: int=None,   UpperCamelCase__			: Dict=None,   **UpperCamelCase__			: int    )     -> Union[str, Any]:
        # References can be strings or lists of strings
        # Let's change strings to lists of strings with one element
        if isinstance(UpperCamelCase__,   UpperCamelCase__    ):
             _A								=     [reference]
        super().add(prediction=UpperCamelCase__,   reference=UpperCamelCase__,   **UpperCamelCase__    )




'''simple docstring'''






import argparse
import json
import re
import string
import sys

import numpy as np


_UpperCAmelCase						:    Dict     					=					0.5





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : List[Any]							):
     _A								=     ['.', ',', ';', ':']

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

     _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(__snake_case							)
     _A								=     ground_truth.union(__snake_case							)
     _A								=     len(__snake_case							) / len(__snake_case							)
     return jaccard





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any							):

     def remove_articles(__snake_case       : Dict							):
          return re.sub(R'\b(a|an|the)\b'      ,		' '      ,		__snake_case							)

     def white_space_fix(__snake_case       : int							):
          return " ".join(text.split()							)

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

     def lower(__snake_case       : Optional[int]							):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(__snake_case							)							)							)							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : Tuple      ,		__snake_case       : List[str]							):
     _A		,					_A		,					_A								=     0, 0, 0

     _A								=     'Parties' in qa_id

     # first check if ground truth is empty
     if len(__snake_case							) == 0:
          if len(__snake_case							) > 0:
               fp += len(__snake_case							)  # false positive for each one
     else:
          for ground_truth in ground_truths:
               assert len(__snake_case							) > 0
               # check if there is a match
               _A								=     False
               for pred in predictions:
                    if substr_ok:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= IOU_THRESH or ground_truth in pred
                    else:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= 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(__snake_case							) > 0
                    if substr_ok:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= IOU_THRESH or ground_truth in pred
                    else:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= 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    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]							):
     _A								=     precisions[::-1]
     for i in range(1      ,		len(__snake_case							)							):
          _A								=     max(precision_best[i - 1]      ,		precision_best[i]							)
     _A								=     precision_best[::-1]
     return precisions





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any      ,		__snake_case       : Any							):
     _A								=     process_precisions(__snake_case							)
     _A								=     np.trapz(__snake_case      ,		__snake_case							)
     if np.isnan(__snake_case							):
          return 0
     return aupr





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : Union[str, Any]      ,		__snake_case       : List[str]							):
     _A								=     process_precisions(__snake_case							)
     _A								=     0
     for prec, recall in zip(__snake_case      ,		__snake_case							):
          if recall >= recall_thresh:
               _A								=     prec
               break
     return prec_at_recall





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]      ,		__snake_case       : Dict							):
     return normalize_answer(__snake_case							) == normalize_answer(__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : Tuple      ,		__snake_case       : List[Any]							):
     _A								=     0
     for pred in predictions:
          for ground_truth in ground_truths:
               _A								=     metric_fn(__snake_case      ,		__snake_case							)
               if score == 1:  # break the loop when one prediction matches the ground truth
                    break
          if score == 1:
               break
     return score





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : Tuple							):
     _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(__snake_case      ,		file=sys.stderr							)
                         continue
                    _A								=     [x['text'] for x in qa['answers']]
                    _A								=     predictions[qa['id']]
                    _A		,					_A								=     compute_precision_recall(__snake_case      ,		__snake_case      ,		qa['id']							)

                    precisions.append(__snake_case							)
                    recalls.append(__snake_case							)

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

                    exact_match += metric_max_over_ground_truths(__snake_case      ,		__snake_case      ,		__snake_case							)

     _A								=     [x for _, x in sorted(zip(__snake_case      ,		__snake_case							)							)]
     recalls.sort()

     _A								=     1_00.0 * fa / total
     _A								=     1_00.0 * exact_match / total
     _A								=     get_aupr(__snake_case      ,		__snake_case							)

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



