code: 
  
  from sklearn.metrics import accuracy_score
  
  import datasets
  
  
  A_		:			Optional[int]										=   '\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'
  
  
  A_		:			List[Any]										=   '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    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.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> 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])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'
  
  
  A_		:			Dict										=   '\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,     _KWARGS_DESCRIPTION						)
  class 					A_   (      datasets.Metric						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[int]:
          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     ,      lowercase__     ,      lowercase__     ,      lowercase__=True     ,      lowercase__=None  )     ->      List[Any]:
          return {
              "accuracy": float(
                  accuracy_score(lowercase__     ,      lowercase__     ,      normalize=lowercase__     ,      sample_weight=lowercase__  )  )
          }
  
  
  
  
  
style_context: 
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto.configuration_auto import AutoConfig
  
  
  A_		:			int										=   logging.get_logger(__name__)
  class 					A_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       "speech-encoder-decoder"
      a__     							=       True
  
  
      def __init__(self     ,      **lowercase__  )     ->      Optional[int]:
          super().__init__(**lowercase__  )
          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}'''  )
  
          __UpperCAmelCase									=			kwargs.pop('''encoder'''  )
          __UpperCAmelCase									=			encoder_config.pop('''model_type'''  )
          __UpperCAmelCase									=			kwargs.pop('''decoder'''  )
          __UpperCAmelCase									=			decoder_config.pop('''model_type'''  )
  
          __UpperCAmelCase									=			AutoConfig.for_model(lowercase__     ,      **lowercase__  )
          __UpperCAmelCase									=			AutoConfig.for_model(lowercase__     ,      **lowercase__  )
          __UpperCAmelCase									=			True
  
  
      @classmethod
      def      lowerCAmelCase_						(cls     ,      lowercase__     ,      lowercase__     ,      **lowercase__  )     ->      PretrainedConfig:
          logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''  )
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			True
  
          return cls(encoder=encoder_config.to_dict()     ,      decoder=decoder_config.to_dict()     ,      **lowercase__  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          __UpperCAmelCase									=			copy.deepcopy(self.__dict__  )
          __UpperCAmelCase									=			self.encoder.to_dict()
          __UpperCAmelCase									=			self.decoder.to_dict()
          __UpperCAmelCase									=			self.__class__.model_type
          return output
  
  
  
  
  