code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from sklearn.metrics import accuracy_score
  
  import datasets
  
  
  lowerCamelCase_     :     str    		=						"""
  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_     :     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}
  """
  
  
  lowerCamelCase_     :     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    __A       ( datasets.Metric    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
       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    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A=True				,					__A=None		)      ->  Dict:
       return {
           "accuracy": float(
               accuracy_score(__A				,					__A				,					normalize=__A				,					sample_weight=__A		)		)
       }
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import logging
  import random
  
  import ray
  
  from transformers import RagConfig, RagRetriever, RagTokenizer
  from transformers.models.rag.retrieval_rag import CustomHFIndex
  
  
  lowerCamelCase_     :     List[str]    		=						logging.getLogger(__name__)
  
  
  
  
  
  
  class    __A       :
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self		)      ->  Dict:
       a					=False
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A				,					__A		)      ->  Tuple:
       if not self.initialized:
           a					=RagRetriever(
               __A				,					question_encoder_tokenizer=__A				,					generator_tokenizer=__A				,					index=__A				,					init_retrieval=__A				,					)
           a					=True
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
       self.retriever.index.init_index()
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
       # delete the old self.retriever object before assigning the new index
       del self.retriever
       a					=False
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A		)      ->  str:
       a	,   a					=self.retriever._main_retrieve(__A				,					__A		)
       a					=self.retriever.index.get_doc_dicts(__A		)
       return doc_ids, retrieved_doc_embeds, doc_dicts
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A				,					__A				,					__A				,					__A				,					__A=None		)      ->  int:
       if index is not None and index.is_initialized() and len(__A		) > 0:
           raise ValueError(
               '''When using Ray for distributed fine-tuning, '''
               '''you\'ll need to provide the paths instead, '''
               '''as the dataset and the index are loaded '''
               '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''		)
  
       super().__init__(
           __A				,					question_encoder_tokenizer=__A				,					generator_tokenizer=__A				,					index=__A				,					init_retrieval=__A				,					)
  
       a					=retrieval_workers
       a					=question_encoder_tokenizer
       a					=generator_tokenizer
       if len(self.retrieval_workers		) > 0:
           ray.get(
               [
                   worker.create_rag_retriever.remote(__A				,					__A				,					__A				,					__A		)
                   for worker in self.retrieval_workers
               ]		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
       logger.info('''initializing retrieval'''		)
  
       if len(self.retrieval_workers		) > 0:
           ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]		)
       else:
           # Non-distributed training. Load index into this same process.
           self.index.init_index()
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A		)      ->  Dict:
       if len(self.retrieval_workers		) > 0:
           # Select a random retrieval actor.
           a					=self.retrieval_workers[random.randint(0				,					len(self.retrieval_workers		) - 1		)]
           a	,   a	,   a					=ray.get(
               random_worker.retrieve.remote(__A				,					__A		)		)
       else:
           a	,   a					=self._main_retrieve(__A				,					__A		)
           a					=self.index.get_doc_dicts(__A		)
       return retrieved_doc_embeds, doc_ids, doc_dicts
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A=None				,					**__A		)      ->  Tuple:
       return super(__A				,					cls		).get_tokenizers(__A				,					__A				,					**__A		)
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A				,					__A=None				,					**__A		)      ->  Optional[Any]:
       a					=kwargs.pop('''config'''				,					__A		) or RagConfig.from_pretrained(__A				,					**__A		)
       a					=RagTokenizer.from_pretrained(__A				,					config=__A		)
       a					=rag_tokenizer.question_encoder
       a					=rag_tokenizer.generator
  
       if indexed_dataset is not None:
           a					='''custom'''
           a					=CustomHFIndex(config.retrieval_vector_size				,					__A		)
       else:
           a					=cls._build_index(__A		)
  
       return cls(
           __A				,					question_encoder_tokenizer=__A				,					generator_tokenizer=__A				,					retrieval_workers=__A				,					index=__A				,					)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
       logger.info('''re-loading the new dataset with embeddings'''		)
       # access from the training loop
  
       ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]		)
  
       # build the index object again
       a					=self._build_index(self.config		)
  
       ray.get(
           [
               worker.create_rag_retriever.remote(
                   self.config				,					self.question_encoder_tokenizer				,					self.generator_tokenizer				,					__A		)
               for worker in self.retrieval_workers
           ]		)