





from sklearn.metrics import mean_absolute_error

import datasets


_lowerCamelCase				:	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}
}
'''

_lowerCamelCase				:	int				    =       '''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


_lowerCamelCase				:	Optional[int]				    =       '''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 "raw_values" : Returns a full set of errors in case of multioutput input.

                 "uniform_average" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        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.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,					_KWARGS_DESCRIPTION						)
class 							__UpperCAmelCase  (       datasets.Metric						):







		'''simple docstring'''







		def        A     (self						:      Union[str, Any]      ):
			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						:      Tuple      ):
			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						:      Dict      ,      _lowerCAmelCase						:      Optional[int]      ,      _lowerCAmelCase						:      Optional[Any]      ,      _lowerCAmelCase						:      Optional[Any]=None      ,      _lowerCAmelCase						:      Tuple="uniform_average"      ):
			A							  =      mean_absolute_error(_A      ,      _A      ,      sample_weight=_A      ,      multioutput=_A      )

			return {"mae": mae_score}








'''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
			    ]      )

