code: 
  
  
  
  
  
  
  import logging
  import random
  
  import ray
  
  from transformers import RagConfig, RagRetriever, RagTokenizer
  from transformers.models.rag.retrieval_rag import CustomHFIndex
  
  
  SCREAMING_SNAKE_CASE__		:       Tuple   							=							logging.getLogger(__name__)
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
  					def __init__(							self		):
  										UpperCAmelCase__			:     Union[str, Any]										= False
  
  
  
  
  					def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
  
  
  
  
  
  
  
  										if not self.initialized:
  															UpperCAmelCase__			:     Dict										= RagRetriever(
  															    _lowerCAmelCase  ,	question_encoder_tokenizer=_lowerCAmelCase  ,	generator_tokenizer=_lowerCAmelCase  ,	index=_lowerCAmelCase  ,	init_retrieval=_lowerCAmelCase  ,	)
  															UpperCAmelCase__			:     str										= True
  
  
  
  
  					def      __UpperCAmelCase     (							self		):
  										self.retriever.index.init_index()
  
  
  
  
  					def      __UpperCAmelCase     (							self		):
  										del self.retriever
  										UpperCAmelCase__			:     List[Any]										= False
  
  
  
  
  					def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
  										UpperCAmelCase__			:     str										= self.retriever._main_retrieve(_lowerCAmelCase  ,	_lowerCAmelCase		)
  										UpperCAmelCase__			:     int										= self.retriever.index.get_doc_dicts(_lowerCAmelCase		)
  										return doc_ids, retrieved_doc_embeds, doc_dicts
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		lowerCamelCase_		):
  
  
  
  
  					def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase=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  ,	)
  
  										UpperCAmelCase__			:     List[Any]										= retrieval_workers
  										UpperCAmelCase__			:     Optional[int]										= question_encoder_tokenizer
  										UpperCAmelCase__			:     List[Any]										= 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      __UpperCAmelCase     (							self		):
  										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      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
  
  
  
  
  
  
  
  										if len(self.retrieval_workers		) > 0:
  															# Select a random retrieval actor.
  															UpperCAmelCase__			:     List[str]										= self.retrieval_workers[random.randint(0  ,	len(self.retrieval_workers		) - 1		)]
  															UpperCAmelCase__			:     Optional[Any]										= ray.get(
  															    random_worker.retrieve.remote(_lowerCAmelCase  ,	_lowerCAmelCase		)		)
  										else:
  															UpperCAmelCase__			:     List[Any]										= self._main_retrieve(_lowerCAmelCase  ,	_lowerCAmelCase		)
  															UpperCAmelCase__			:     Union[str, Any]										= self.index.get_doc_dicts(_lowerCAmelCase		)
  										return retrieved_doc_embeds, doc_ids, doc_dicts
  
  
  
  
  					@classmethod
  					def      __UpperCAmelCase     (							cls  ,	_lowerCAmelCase  ,	_lowerCAmelCase=None  ,	**_lowerCAmelCase		):
  										return super(_lowerCAmelCase  ,	cls		).get_tokenizers(_lowerCAmelCase  ,	_lowerCAmelCase  ,	**_lowerCAmelCase		)
  
  
  
  
  					@classmethod
  					def      __UpperCAmelCase     (							cls  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase=None  ,	**_lowerCAmelCase		):
  										UpperCAmelCase__			:     Union[str, Any]										= kwargs.pop("""config"""  ,	_lowerCAmelCase		) or RagConfig.from_pretrained(_lowerCAmelCase  ,	**_lowerCAmelCase		)
  										UpperCAmelCase__			:     Optional[int]										= RagTokenizer.from_pretrained(_lowerCAmelCase  ,	config=_lowerCAmelCase		)
  										UpperCAmelCase__			:     Optional[int]										= rag_tokenizer.question_encoder
  										UpperCAmelCase__			:     List[str]										= rag_tokenizer.generator
  
  										if indexed_dataset is not None:
  															UpperCAmelCase__			:     int										= "custom"
  															UpperCAmelCase__			:     Dict										= CustomHFIndex(config.retrieval_vector_size  ,	_lowerCAmelCase		)
  										else:
  															UpperCAmelCase__			:     int										= cls._build_index(_lowerCAmelCase		)
  
  										return cls(
  										    _lowerCAmelCase  ,	question_encoder_tokenizer=_lowerCAmelCase  ,	generator_tokenizer=_lowerCAmelCase  ,	retrieval_workers=_lowerCAmelCase  ,	index=_lowerCAmelCase  ,	)
  
  
  
  
  					def      __UpperCAmelCase     (							self		):
  										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
  										UpperCAmelCase__			:     Any										= 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: 
  
  
  
  
  
  
  from .stack import Stack
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> bool:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Stack[str]										= Stack()
       UpperCAmelCase__			:     str										= {"""(""": """)""", """[""": """]""", """{""": """}"""}
       for bracket in parentheses:
            if bracket in bracket_pairs:
                 stack.push(__lowerCamelCase					)
            elif bracket in (")", "]", "}"):
                 if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                      return False
       return stack.is_empty()
  
  
  if __name__ == "__main__":
        from doctest import testmod
  
        testmod()
  
        SCREAMING_SNAKE_CASE__		:       Any   							=							["""((()))""", """((())""", """(()))"""]
        print("""Balanced parentheses demonstration:\n""")
        for example in examples:
              SCREAMING_SNAKE_CASE__		:       List[str]   							=							"""""" if balanced_parentheses(example) else """not """
              print(f'''{example} is {not_str}balanced''')
  
  
  
  
  
  
  