code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import logging
  import random
  
  import ray
  
  from transformers import RagConfig, RagRetriever, RagTokenizer
  from transformers.models.rag.retrieval_rag import CustomHFIndex
  
  
  _a      :				Any											=					logging.getLogger(__name__)
  
  
  
  
  
  class 					_UpperCAmelCase							:
  		def __init__(						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       False
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					if not self.initialized:
  								__lowerCAmelCase      		=       RagRetriever(
  								    __SCREAMING_SNAKE_CASE,question_encoder_tokenizer=__SCREAMING_SNAKE_CASE,generator_tokenizer=__SCREAMING_SNAKE_CASE,index=__SCREAMING_SNAKE_CASE,init_retrieval=__SCREAMING_SNAKE_CASE,)
  								__lowerCAmelCase      		=       True
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					self.retriever.index.init_index()
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					del self.retriever
  					__lowerCAmelCase      		=       False
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase		, __lowerCAmelCase      		=       self.retriever._main_retrieve(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       self.retriever.index.get_doc_dicts(__SCREAMING_SNAKE_CASE    )
  					return doc_ids, retrieved_doc_embeds, doc_dicts
  
  
  
  
  
  class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
  		def __init__(						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None    ):
  
  
  
  					'''simple docstring'''
  
  					if index is not None and index.is_initialized() and len(__SCREAMING_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__(
  					    __SCREAMING_SNAKE_CASE,question_encoder_tokenizer=__SCREAMING_SNAKE_CASE,generator_tokenizer=__SCREAMING_SNAKE_CASE,index=__SCREAMING_SNAKE_CASE,init_retrieval=__SCREAMING_SNAKE_CASE,)
  
  					__lowerCAmelCase      		=       retrieval_workers
  					__lowerCAmelCase      		=       question_encoder_tokenizer
  					__lowerCAmelCase      		=       generator_tokenizer
  					if len(self.retrieval_workers    ) > 0:
  								ray.get(
  								    [
  								        worker.create_rag_retriever.remote(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  								        for worker in self.retrieval_workers
  								    ]    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					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 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					if len(self.retrieval_workers    ) > 0:
  								# Select a random retrieval actor.
  								__lowerCAmelCase      		=       self.retrieval_workers[random.randint(0,len(self.retrieval_workers    ) - 1    )]
  								__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       ray.get(
  								    random_worker.retrieve.remote(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )    )
  					else:
  								__lowerCAmelCase		, __lowerCAmelCase      		=       self._main_retrieve(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  								__lowerCAmelCase      		=       self.index.get_doc_dicts(__SCREAMING_SNAKE_CASE    )
  					return retrieved_doc_embeds, doc_ids, doc_dicts
  		@classmethod
  		def 						lowerCamelCase__    (						cls,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					return super(__SCREAMING_SNAKE_CASE,cls    ).get_tokenizers(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )
  		@classmethod
  		def 						lowerCamelCase__    (						cls,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       kwargs.pop("""config""",__SCREAMING_SNAKE_CASE    ) or RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       RagTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE,config=__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       rag_tokenizer.question_encoder
  					__lowerCAmelCase      		=       rag_tokenizer.generator
  
  					if indexed_dataset is not None:
  								__lowerCAmelCase      		=       """custom"""
  								__lowerCAmelCase      		=       CustomHFIndex(config.retrieval_vector_size,__SCREAMING_SNAKE_CASE    )
  					else:
  								__lowerCAmelCase      		=       cls._build_index(__SCREAMING_SNAKE_CASE    )
  
  					return cls(
  					    __SCREAMING_SNAKE_CASE,question_encoder_tokenizer=__SCREAMING_SNAKE_CASE,generator_tokenizer=__SCREAMING_SNAKE_CASE,retrieval_workers=__SCREAMING_SNAKE_CASE,index=__SCREAMING_SNAKE_CASE,)
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					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
  					__lowerCAmelCase      		=       self._build_index(self.config    )
  
  					ray.get(
  					    [
  					        worker.create_rag_retriever.remote(
  					            self.config,self.question_encoder_tokenizer,self.generator_tokenizer,__SCREAMING_SNAKE_CASE    )
  					        for worker in self.retrieval_workers
  					    ]    )
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase = 1000			)      ->      int:
  			__lowerCAmelCase      		=       []
  			__lowerCAmelCase      		=       []
  			__lowerCAmelCase      		=       3
  			__lowerCAmelCase      		=       5
  			__lowerCAmelCase      		=       1
  			while True:
  						__lowerCAmelCase      		=       z * temp
  						if result < n:
  									zmulti.append(lowercase			)
  									temp += 1
  						else:
  									__lowerCAmelCase      		=       1
  									break
  			while True:
  						__lowerCAmelCase      		=       x * temp
  						if result < n:
  									xmulti.append(lowercase			)
  									temp += 1
  						else:
  									break
  			__lowerCAmelCase      		=       list(set(xmulti + zmulti			)			)
  			return sum(lowercase			)
  
  
  if __name__ == "__main__":
  	print(f'{solution() = }')
  
  