code: 
  
  """simple docstring"""
  
  
  
  
  import logging
  import random
  
  import ray
  
  from transformers import RagConfig, RagRetriever, RagTokenizer
  from transformers.models.rag.retrieval_rag import CustomHFIndex
  
  
  lowercase__  :					Tuple												=	logging.getLogger(__name__)
  
  
  
  
  
  class     __UpperCamelCase				:
  
  
  		def __init__(self				:	Union[str, Any]):
  							A    =      False
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	str  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	str  ,  __SCREAMING_SNAKE_CASE				:	Any):
  							if not self.initialized:
  												A    =      RagRetriever(
  												    _lowerCAmelCase  ,  question_encoder_tokenizer=_lowerCAmelCase  ,  generator_tokenizer=_lowerCAmelCase  ,  index=_lowerCAmelCase  ,  init_retrieval=_lowerCAmelCase  ,  )
  												A    =      True
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
  							self.retriever.index.init_index()
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
  							# delete the old self.retriever object before assigning the new index
  							del self.retriever
  							A    =      False
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	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__		):
  
  
  		def __init__(self				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	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       SCREAMING_SNAKE_CASE__     (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       SCREAMING_SNAKE_CASE__     (self				:	str  ,  __SCREAMING_SNAKE_CASE				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	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       SCREAMING_SNAKE_CASE__     (cls				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=None  ,  **__SCREAMING_SNAKE_CASE				:	Union[str, Any]):
  							return super(_lowerCAmelCase  ,  cls).get_tokenizers(_lowerCAmelCase  ,  _lowerCAmelCase  ,  **_lowerCAmelCase)
  
  
  		@classmethod
  		def       SCREAMING_SNAKE_CASE__     (cls				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	str=None  ,  **__SCREAMING_SNAKE_CASE				:	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       SCREAMING_SNAKE_CASE__     (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
  							    ])
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __A  :					int												=	{
      'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
  }
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A  :					Any												=	[
  	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
  	    'NystromformerForMaskedLM',
  	    'NystromformerForMultipleChoice',
  	    'NystromformerForQuestionAnswering',
  	    'NystromformerForSequenceClassification',
  	    'NystromformerForTokenClassification',
  	    'NystromformerLayer',
  	    'NystromformerModel',
  	    'NystromformerPreTrainedModel',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_nystromformer import (
  		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    NystromformerForMaskedLM,
  		    NystromformerForMultipleChoice,
  		    NystromformerForQuestionAnswering,
  		    NystromformerForSequenceClassification,
  		    NystromformerForTokenClassification,
  		    NystromformerLayer,
  		    NystromformerModel,
  		    NystromformerPreTrainedModel,
  		)
  
  
  else:
  	import sys
  
  	__A  :					Optional[Any]												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  