




'''simple docstring'''



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


UpperCAmelCase_				:     Tuple     			=						logging.get_logger(__name__)





class 			lowerCAmelCase      (       __lowerCAmelCase):
       __lowercase     :					List[Any]       				=	'''speech-encoder-decoder'''
       __lowercase     :					List[Any]       				=	True


       def __init__(      self			,     **__SCREAMING_SNAKE_CASE     )					->     List[str]:



            '''simple docstring'''




            super().__init__(**__SCREAMING_SNAKE_CASE     )
            if "encoder" not in kwargs or "decoder" not in kwargs:
                 raise ValueError(
                     F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                     F''' `decoder` sub-configurations are passed, but only {kwargs}'''     )

            __snake_case			    =     kwargs.pop('''encoder'''     )
            __snake_case			    =     encoder_config.pop('''model_type'''     )
            __snake_case			    =     kwargs.pop('''decoder'''     )
            __snake_case			    =     decoder_config.pop('''model_type'''     )

            __snake_case			    =     AutoConfig.for_model(__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     AutoConfig.for_model(__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     True


       @classmethod
       def 					lowerCAmelCase					(      cls			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE     )					->     PretrainedConfig:



            '''simple docstring'''




            logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''     )
            __snake_case			    =     True
            __snake_case			    =     True

            return cls(encoder=encoder_config.to_dict()			,     decoder=decoder_config.to_dict()			,     **__SCREAMING_SNAKE_CASE     )




       def 					lowerCAmelCase					(      self     )					->     Any:



            '''simple docstring'''




            __snake_case			    =     copy.deepcopy(self.__dict__     )
            __snake_case			    =     self.encoder.to_dict()
            __snake_case			    =     self.decoder.to_dict()
            __snake_case			    =     self.__class__.model_type
            return output












'''simple docstring'''



from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase_				:     List[Any]     			=						'''
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
'''


UpperCAmelCase_				:     List[Any]     			=						'''
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}
'''


UpperCAmelCase_				:     Union[str, 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 			lowerCAmelCase      (       datasets.Metric):


       def 					lowerCAmelCase					(      self     )					->     List[str]:



            '''simple docstring'''




            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 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=None     )					->     Any:



            '''simple docstring'''




            return {
                "accuracy": float(
                    accuracy_score(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     normalize=__SCREAMING_SNAKE_CASE			,     sample_weight=__SCREAMING_SNAKE_CASE     )     )
            }






