code: 
  
  
  
  
  
  from sklearn.metrics import mean_absolute_error
  
  import datasets
  
  
  _lowerCamelCase				:	str				    =       '\\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'
  
  _lowerCamelCase				:	List[Any]				    =       '\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'
  
  
  _lowerCamelCase				:	List[Any]				    =       '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric(\"mae\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,					_KWARGS_DESCRIPTION						)
  class 							__UpperCAmelCase  (       datasets.Metric						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  			return datasets.MetricInfo(
  			    description=_DESCRIPTION      ,      citation=_CITATION      ,      inputs_description=_KWARGS_DESCRIPTION      ,      features=datasets.Features(self._get_feature_types()      )      ,      reference_urls=[
  			        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"""
  			    ]      ,      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ):
  			if self.config_name == "multilist":
  				return {
  				    "predictions": datasets.Sequence(datasets.Value("""float"""      )      ),
  				    "references": datasets.Sequence(datasets.Value("""float"""      )      ),
  				}
  			else:
  				return {
  				    "predictions": datasets.Value("""float"""      ),
  				    "references": datasets.Value("""float"""      ),
  				}
  
  
  
  
  
  
  
  		def        A     (self						:      str      ,      _lowerCAmelCase						:      Optional[int]      ,      _lowerCAmelCase						:      Union[str, Any]      ,      _lowerCAmelCase						:      int=None      ,      _lowerCAmelCase						:      List[str]="uniform_average"      ):
  			A							  =      mean_absolute_error(_lowerCAmelCase      ,      _lowerCAmelCase      ,      sample_weight=_lowerCAmelCase      ,      multioutput=_lowerCAmelCase      )
  
  			return {"mae": mae_score}
  
  
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				:	Tuple				    =       logging.getLogger(__name__)
  
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      Union[str, Any]      ):
  			A							  =      False
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Any      ):
  			if not self.initialized:
  				A							  =      RagRetriever(
  				    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      init_retrieval=_lowerCAmelCase      ,      )
  				A							  =      True
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ):
  			self.retriever.index.init_index()
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ):
  			# delete the old self.retriever object before assigning the new index
  			del self.retriever
  			A							  =      False
  
  
  
  
  
  
  
  		def        A     (self						:      List[str]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ):
  			A  ,    A							  =      self.retriever._main_retrieve(_lowerCAmelCase      ,      _lowerCAmelCase      )
  			A							  =      self.retriever.index.get_doc_dicts(_lowerCAmelCase      )
  			return doc_ids, retrieved_doc_embeds, doc_dicts
  
  
  
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      Tuple      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      Tuple      ,      _lowerCAmelCase						:      str=None      ):
  			if index is not None and index.is_initialized() and len(_lowerCAmelCase      ) > 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__(
  			    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      init_retrieval=_lowerCAmelCase      ,      )
  
  			A							  =      retrieval_workers
  			A							  =      question_encoder_tokenizer
  			A							  =      generator_tokenizer
  			if len(self.retrieval_workers      ) > 0:
  				ray.get(
  				    [
  				        worker.create_rag_retriever.remote(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      )
  				        for worker in self.retrieval_workers
  				    ]      )
  
  
  
  
  
  
  
  		def        A     (self						:      List[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        A     (self						:      str      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Tuple      ):
  			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(_lowerCAmelCase      ,      _lowerCAmelCase      )      )
  			else:
  				A  ,    A							  =      self._main_retrieve(_lowerCAmelCase      ,      _lowerCAmelCase      )
  				A							  =      self.index.get_doc_dicts(_lowerCAmelCase      )
  			return retrieved_doc_embeds, doc_ids, doc_dicts
  
  
  
  
  
  
  
  		@classmethod
  		def        A     (cls						:      Union[str, Any]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      Optional[Any]=None      ,      **_lowerCAmelCase						:      Union[str, Any]      ):
  			return super(_lowerCAmelCase      ,      cls      ).get_tokenizers(_lowerCAmelCase      ,      _lowerCAmelCase      ,      **_lowerCAmelCase      )
  
  
  
  
  
  
  
  		@classmethod
  		def        A     (cls						:      List[Any]      ,      _lowerCAmelCase						:      Optional[int]      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      str=None      ,      **_lowerCAmelCase						:      List[str]      ):
  			A							  =      kwargs.pop("""config"""      ,      _lowerCAmelCase      ) or RagConfig.from_pretrained(_lowerCAmelCase      ,      **_lowerCAmelCase      )
  			A							  =      RagTokenizer.from_pretrained(_lowerCAmelCase      ,      config=_lowerCAmelCase      )
  			A							  =      rag_tokenizer.question_encoder
  			A							  =      rag_tokenizer.generator
  
  			if indexed_dataset is not None:
  				A							  =      """custom"""
  				A							  =      CustomHFIndex(config.retrieval_vector_size      ,      _lowerCAmelCase      )
  			else:
  				A							  =      cls._build_index(_lowerCAmelCase      )
  
  			return cls(
  			    _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      retrieval_workers=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      )
  
  
  
  
  
  
  
  		def        A     (self						:      List[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      ,      _lowerCAmelCase      )
  			        for worker in self.retrieval_workers
  			    ]      )
  
  