code: 
  
  import logging
  import random
  
  import ray
  
  from transformers import RagConfig, RagRetriever, RagTokenizer
  from transformers.models.rag.retrieval_rag import CustomHFIndex
  
  
  _A							    =	logging.getLogger(__name__)
  
  
  class 							_lowerCAmelCase		:
  
  
  
  
  				def __init__( self							)    ->      Union[str, Any]:
  											SCREAMING_SNAKE_CASE			: str	     =False
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      Optional[int]:
  											if not self.initialized:
  																		SCREAMING_SNAKE_CASE			: Dict	     =RagRetriever(
  																		    snake_case_		,		question_encoder_tokenizer=snake_case_		,		generator_tokenizer=snake_case_		,		index=snake_case_		,		init_retrieval=snake_case_		,		)
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =True
  
  
  
  
  				def     __a		( self							)    ->      str:
  											self.retriever.index.init_index()
  
  
  
  
  				def     __a		( self							)    ->      str:
  											# delete the old self.retriever object before assigning the new index
  											del self.retriever
  											SCREAMING_SNAKE_CASE			: Tuple	     =False
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.retriever._main_retrieve(snake_case_		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.retriever.index.get_doc_dicts(snake_case_							)
  											return doc_ids, retrieved_doc_embeds, doc_dicts
  
  
  
  
  
  
  
  class 							_lowerCAmelCase		(     a_	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_=None							)    ->      Optional[int]:
  											if index is not None and index.is_initialized() and len(snake_case_							) > 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__(
  											    snake_case_		,		question_encoder_tokenizer=snake_case_		,		generator_tokenizer=snake_case_		,		index=snake_case_		,		init_retrieval=snake_case_		,		)
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =retrieval_workers
  											SCREAMING_SNAKE_CASE			: int	     =question_encoder_tokenizer
  											SCREAMING_SNAKE_CASE			: int	     =generator_tokenizer
  											if len(self.retrieval_workers							) > 0:
  																		ray.get(
  																		    [
  																		        worker.create_rag_retriever.remote(snake_case_		,		snake_case_		,		snake_case_		,		snake_case_							)
  																		        for worker in self.retrieval_workers
  																		    ]							)
  
  
  
  
  				def     __a		( self							)    ->      Dict:
  											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		,		snake_case_		,		snake_case_							)    ->      List[Any]:
  											if len(self.retrieval_workers							) > 0:
  																		# Select a random retrieval actor.
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.retrieval_workers[random.randint(0		,		len(self.retrieval_workers							) - 1							)]
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =ray.get(
  																		    random_worker.retrieve.remote(snake_case_		,		snake_case_							)							)
  											else:
  																		SCREAMING_SNAKE_CASE			: List[str]	     =self._main_retrieve(snake_case_		,		snake_case_							)
  																		SCREAMING_SNAKE_CASE			: Dict	     =self.index.get_doc_dicts(snake_case_							)
  											return retrieved_doc_embeds, doc_ids, doc_dicts
  
  
  
  
  				@classmethod
  				def     __a		( cls		,		snake_case_		,		snake_case_=None		,		**snake_case_							)    ->      List[Any]:
  											return super(snake_case_		,		cls							).get_tokenizers(snake_case_		,		snake_case_		,		**snake_case_							)
  
  
  
  
  				@classmethod
  				def     __a		( cls		,		snake_case_		,		snake_case_		,		snake_case_=None		,		**snake_case_							)    ->      List[str]:
  											SCREAMING_SNAKE_CASE			: Any	     =kwargs.pop('''config'''		,		snake_case_							) or RagConfig.from_pretrained(snake_case_		,		**snake_case_							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =RagTokenizer.from_pretrained(snake_case_		,		config=snake_case_							)
  											SCREAMING_SNAKE_CASE			: str	     =rag_tokenizer.question_encoder
  											SCREAMING_SNAKE_CASE			: Tuple	     =rag_tokenizer.generator
  
  											if indexed_dataset is not None:
  																		SCREAMING_SNAKE_CASE			: Any	     ='custom'
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =CustomHFIndex(config.retrieval_vector_size		,		snake_case_							)
  											else:
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =cls._build_index(snake_case_							)
  
  											return cls(
  											    snake_case_		,		question_encoder_tokenizer=snake_case_		,		generator_tokenizer=snake_case_		,		retrieval_workers=snake_case_		,		index=snake_case_		,		)
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      Tuple:
  											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
  											SCREAMING_SNAKE_CASE			: Dict	     =self._build_index(self.config							)
  
  											ray.get(
  											    [
  											        worker.create_rag_retriever.remote(
  											            self.config		,		self.question_encoder_tokenizer		,		self.generator_tokenizer		,		snake_case_							)
  											        for worker in self.retrieval_workers
  											    ]							)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from __future__ import annotations
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a )       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Any	     =m - 1, 0
  							while i >= 0 and j < n:
  														if key == mat[i][j]:
  																					print(f'Key {key} found at row- {i + 1} column- {j + 1}' )
  																					return
  														if key < mat[i][j]:
  																					i -= 1
  														else:
  																					j += 1
  							print(f'Key {key} not found' )
  def 						lowerCAmelCase_       (	)       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: int	     =[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
  							SCREAMING_SNAKE_CASE			: List[str]	     =int(input('''Enter the element to be searched:''' ) )
  							print(__a )
  							search_in_a_sorted_matrix(__a	,	len(__a )	,	len(mat[0] )	,	__a )
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  							main()
  
  
  
  
  
  
  