code: 
  
  
  
  __lowerCamelCase  				=					"""
  # Diffusers installation
  ! pip install diffusers transformers datasets accelerate
  # To install from source instead of the last release, comment the command above and uncomment the following one.
  # ! pip install git+https://github.com/huggingface/diffusers.git
  """
  
  __lowerCamelCase  				=					[{"""type""": """code""", """content""": INSTALL_CONTENT}]
style_context: 
  
  
  
  from sklearn.metrics import accuracy_score
  
  import datasets
  
  
  __lowerCamelCase  				=					"""
  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
  """
  
  
  __lowerCamelCase  				=					"""
  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}
  """
  
  
  __lowerCamelCase  				=					"""
  @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 		_snake_case      (			datasets.Metric				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """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 lowercase_		(						self	,    a	,    a	,    a=True	,    a=None					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return {
         "accuracy": float(
             accuracy_score(a	,    a	,    normalize=a	,    sample_weight=a					)					)
     }