

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
											    ]							)












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()






