


'''simple docstring'''



from sklearn.metrics import accuracy_score

import datasets


A__		: Dict												=  """
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


A__		: int												=  """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


A__		: Any												=  """
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""





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





    """simple docstring"""






    def 				lowercase_    (							self     )    ->					List[Any]:
         return datasets.MetricInfo(
             description=_DESCRIPTION      ,					citation=_CITATION      ,					inputs_description=_KWARGS_DESCRIPTION      ,					features=datasets.Features(
                 {
                     'predictions': datasets.Sequence(datasets.Value('int32'     )     ),
                     'references': datasets.Sequence(datasets.Value('int32'     )     ),
                 }
                 if self.config_name == 'multilabel'
                 else {
                     'predictions': datasets.Value('int32'     ),
                     'references': datasets.Value('int32'     ),
                 }     )      ,					reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']      ,					)







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_=None     )    ->					Union[str, Any]:
         return {
             "accuracy": float(
                 accuracy_score(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					normalize=SCREAMING_SNAKE_CASE_      ,					sample_weight=SCREAMING_SNAKE_CASE_     )     )
         }










'''simple docstring'''



import torch

from transformers import AutoTokenizer





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    def __init__(							self      ,					SCREAMING_SNAKE_CASE_     )    ->					int:
         __lowerCamelCase       :     Dict        =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					int:

         if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) and all(isinstance(_x      ,					SCREAMING_SNAKE_CASE_     ) for _x in x     ):
              __lowerCamelCase       :     Union[str, Any]        =		None
              for l in x:
                   __lowerCamelCase       :     Tuple        =		self.tokenizer(
                       SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=3_84      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					)
                   __lowerCamelCase       :     str        =		torch.tensor([len(SCREAMING_SNAKE_CASE_     )]     )
                   if d is not None:
                        for k in d.keys():
                             __lowerCamelCase       :     List[Any]        =		torch.cat((d[k], t[k])      ,					0     )
                   else:
                        __lowerCamelCase       :     str        =		t

              __lowerCamelCase       :     List[Any]        =		torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'     )     )
              __lowerCamelCase       :     Tuple        =		torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'     )     )

         elif isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) and all(isinstance(_x      ,					SCREAMING_SNAKE_CASE_     ) for _x in x     ):
              __lowerCamelCase       :     Optional[Any]        =		self.tokenizer(
                  SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=3_84      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					)

         else:
              raise Exception(
                  'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
                  ' strings` for supports are supported.'     )

         return d







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=0.7_0     )    ->					str:
         __lowerCamelCase       :     str        =		[]
         for idx in range(len(W_query['input_ids']     )     ):
              __lowerCamelCase       :     Optional[Any]        =		range(p_start.shape[1]     )

              __lowerCamelCase       :     List[Any]        =		[]
              for start_id in start_indexes:
                   for end_id in end_indexes:
                        if start_id < end_id:
                             output.append(
                                 (
                                     start_id,
                                     end_id,
                                     p_start[idx][start_id].item(),
                                     p_end[idx][end_id].item(),
                                 )     )

              output.sort(key=lambda SCREAMING_SNAKE_CASE_     : (tup[2] * tup[3])      ,					reverse=SCREAMING_SNAKE_CASE_     )
              __lowerCamelCase       :     str        =		[]
              for k in range(len(SCREAMING_SNAKE_CASE_     )     ):
                   if output[k][2] * output[k][3] >= thresh:
                        __lowerCamelCase    ,	__lowerCamelCase       :     Union[str, Any]        =		output[k][0], output[k][1]
                        __lowerCamelCase       :     List[str]        =		self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]     )
                        temp.append((decoded, output[k][2] * output[k][3])     )

              final_outputs.append(SCREAMING_SNAKE_CASE_     )

         return final_outputs






