






"""simple docstring"""


from sklearn.metrics import mean_absolute_error

import datasets


_UpperCamelCase :    Tuple      =			"\\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"

_UpperCamelCase :    str      =			"\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


_UpperCamelCase :    Optional[int]      =			"\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 __a ( datasets.Metric					):



  def     A_					(self	:		Optional[Any] )		->				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	:		List[str] )		->				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	:		Union[str, Any]					,      lowercase_	:		List[Any]					,      lowercase_	:		Optional[int]					,      lowercase_	:		List[str]=None					,      lowercase_	:		Any="uniform_average" )		->				List[str]:
   __A						:							str      =						mean_absolute_error(__UpperCAmelCase					,      __UpperCAmelCase					,      sample_weight=__UpperCAmelCase					,      multioutput=__UpperCAmelCase )

   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


SCREAMING_SNAKE_CASE__				:  Optional[Any]						= logging.getLogger(__name__)


class 	A_       :





  """simple docstring"""

  def __init__(						self						)				->					Dict:
     a   :	List[str]						=    False
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					List[str]:
     if not self.initialized:
        a   :	Dict						=    RagRetriever(
            __UpperCAmelCase  ,					question_encoder_tokenizer=__UpperCAmelCase  ,					generator_tokenizer=__UpperCAmelCase  ,					index=__UpperCAmelCase  ,					init_retrieval=__UpperCAmelCase  ,					)
        a   :	Dict						=    True
  def 							lowercase_     (						self						)				->					List[Any]:
     self.retriever.index.init_index()
  def 							lowercase_     (						self						)				->					List[Any]:
     # delete the old self.retriever object before assigning the new index
     del self.retriever
     a   :	Dict						=    False






  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Optional[int]:
     a	, a   :	List[str]						=    self.retriever._main_retrieve(__UpperCAmelCase  ,					__UpperCAmelCase						)
     a   :	List[Any]						=    self.retriever.index.get_doc_dicts(__UpperCAmelCase						)
     return doc_ids, retrieved_doc_embeds, doc_dicts



class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None						)				->					Tuple:
     if index is not None and index.is_initialized() and len(__UpperCAmelCase						) > 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__(
         __UpperCAmelCase  ,					question_encoder_tokenizer=__UpperCAmelCase  ,					generator_tokenizer=__UpperCAmelCase  ,					index=__UpperCAmelCase  ,					init_retrieval=__UpperCAmelCase  ,					)

     a   :	Tuple						=    retrieval_workers
     a   :	List[str]						=    question_encoder_tokenizer
     a   :	Union[str, Any]						=    generator_tokenizer
     if len(self.retrieval_workers						) > 0:
        ray.get(
            [
                worker.create_rag_retriever.remote(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)
                for worker in self.retrieval_workers
            ]						)
  def 							lowercase_     (						self						)				->					Tuple:
     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 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Tuple:
     if len(self.retrieval_workers						) > 0:
        # Select a random retrieval actor.
        a   :	Dict						=    self.retrieval_workers[random.randint(0  ,					len(self.retrieval_workers						) - 1						)]
        a	, a	, a   :	Any						=    ray.get(
            random_worker.retrieve.remote(__UpperCAmelCase  ,					__UpperCAmelCase						)						)
     else:
        a	, a   :	str						=    self._main_retrieve(__UpperCAmelCase  ,					__UpperCAmelCase						)
        a   :	str						=    self.index.get_doc_dicts(__UpperCAmelCase						)
     return retrieved_doc_embeds, doc_ids, doc_dicts
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					**__UpperCAmelCase						)				->					int:
     return super(__UpperCAmelCase  ,					cls						).get_tokenizers(__UpperCAmelCase  ,					__UpperCAmelCase  ,					**__UpperCAmelCase						)
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					**__UpperCAmelCase						)				->					str:
     a   :	Union[str, Any]						=    kwargs.pop('config'  ,					__UpperCAmelCase						) or RagConfig.from_pretrained(__UpperCAmelCase  ,					**__UpperCAmelCase						)
     a   :	Optional[Any]						=    RagTokenizer.from_pretrained(__UpperCAmelCase  ,					config=__UpperCAmelCase						)
     a   :	List[str]						=    rag_tokenizer.question_encoder
     a   :	int						=    rag_tokenizer.generator

     if indexed_dataset is not None:
        a   :	Optional[int]						=    'custom'
        a   :	str						=    CustomHFIndex(config.retrieval_vector_size  ,					__UpperCAmelCase						)
     else:
        a   :	Dict						=    cls._build_index(__UpperCAmelCase						)

     return cls(
         __UpperCAmelCase  ,					question_encoder_tokenizer=__UpperCAmelCase  ,					generator_tokenizer=__UpperCAmelCase  ,					retrieval_workers=__UpperCAmelCase  ,					index=__UpperCAmelCase  ,					)






  def 							lowercase_     (						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   :	Optional[Any]						=    self._build_index(self.config						)

     ray.get(
         [
             worker.create_rag_retriever.remote(
                 self.config  ,					self.question_encoder_tokenizer  ,					self.generator_tokenizer  ,					__UpperCAmelCase						)
             for worker in self.retrieval_workers
         ]						)





