code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import os
  
  from ...utils import logging
  from .utils import DataProcessor, InputExample
  
  
  _lowerCamelCase				:	str				    =       logging.get_logger(__name__)
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      Optional[int]      ,      _lowerCAmelCase						:      Tuple      ,      _lowerCAmelCase						:      Union[str, Any]=None      ):
  			A							  =      language
  			A							  =      train_language
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ,      _lowerCAmelCase						:      Optional[Any]      ):
  			A							  =      self.language if self.train_language is None else self.train_language
  			A							  =      self._read_tsv(os.path.join(_lowerCAmelCase      ,      F"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""      )      )
  			A							  =      []
  			for i, line in enumerate(_lowerCAmelCase      ):
  				if i == 0:
  					continue
  				A							  =      F"""train-{i}"""
  				A							  =      line[0]
  				A							  =      line[1]
  				A							  =      """contradiction""" if line[2] == """contradictory""" else line[2]
  				if not isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ):
  					raise ValueError(F"""Training input {text_a} is not a string"""      )
  				if not isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ):
  					raise ValueError(F"""Training input {text_b} is not a string"""      )
  				if not isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ):
  					raise ValueError(F"""Training label {label} is not a string"""      )
  				examples.append(InputExample(guid=_lowerCAmelCase      ,      text_a=_lowerCAmelCase      ,      text_b=_lowerCAmelCase      ,      label=_lowerCAmelCase      )      )
  			return examples
  
  
  
  
  
  
  
  		def        A     (self						:      List[str]      ,      _lowerCAmelCase						:      Union[str, Any]      ):
  			A							  =      self._read_tsv(os.path.join(_lowerCAmelCase      ,      """XNLI-1.0/xnli.test.tsv"""      )      )
  			A							  =      []
  			for i, line in enumerate(_lowerCAmelCase      ):
  				if i == 0:
  					continue
  				A							  =      line[0]
  				if language != self.language:
  					continue
  				A							  =      F"""test-{i}"""
  				A							  =      line[6]
  				A							  =      line[7]
  				A							  =      line[1]
  				if not isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ):
  					raise ValueError(F"""Training input {text_a} is not a string"""      )
  				if not isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ):
  					raise ValueError(F"""Training input {text_b} is not a string"""      )
  				if not isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ):
  					raise ValueError(F"""Training label {label} is not a string"""      )
  				examples.append(InputExample(guid=_lowerCAmelCase      ,      text_a=_lowerCAmelCase      ,      text_b=_lowerCAmelCase      ,      label=_lowerCAmelCase      )      )
  			return examples
  
  
  
  
  
  
  
  		def        A     (self						:      int      ):
  			return ["contradiction", "entailment", "neutral"]
  
  
  
  
  
  
  _lowerCamelCase				:	str				    =       {
      'xnli': XnliProcessor,
  }
  
  _lowerCamelCase				:	Optional[Any]				    =       {
      'xnli': 'classification',
  }
  
  _lowerCamelCase				:	Optional[int]				    =       {
      'xnli': 3,
  }
  
  
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
  			    ]      )
  
  