






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
										    ]		)














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''')






