





'''simple docstring'''




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__UpperCAmelCase         =   logging.getLogger(__name__)




class       UpperCamelCase__			:




					"""simple docstring"""


					def __init__(		self							:	str		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Any						=	False


					def lowerCamelCase_	(		self							:	Optional[int]     ,		lowerCamelCase_							:	Optional[int]     ,		lowerCamelCase_							:	Any     ,		lowerCamelCase_							:	Optional[Any]     ,		lowerCamelCase_							:	Any		):

							'''simple docstring'''



							if not self.initialized:
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	RagRetriever(
									    lowerCamelCase_     ,		question_encoder_tokenizer=lowerCamelCase_     ,		generator_tokenizer=lowerCamelCase_     ,		index=lowerCamelCase_     ,		init_retrieval=lowerCamelCase_     ,		)
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	True


					def lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



							self.retriever.index.init_index()


					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							del self.retriever
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	False



					def lowerCamelCase_	(		self							:	Any     ,		lowerCamelCase_							:	Union[str, Any]     ,		lowerCamelCase_							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Tuple						=	self.retriever._main_retrieve(lowerCamelCase_     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Tuple						=	self.retriever.index.get_doc_dicts(lowerCamelCase_		)
							return doc_ids, retrieved_doc_embeds, doc_dicts







class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""


					def __init__(		self							:	Any     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	str     ,		lowerCamelCase_							:	Tuple     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	Dict=None		):

							'''simple docstring'''



							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_     ,		)

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	retrieval_workers
							SCREAMING_SNAKE_CASE				:							Any						=	question_encoder_tokenizer
							SCREAMING_SNAKE_CASE				:							Dict						=	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 lowerCamelCase_	(		self							:	int		):

							'''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							:	int     ,		lowerCamelCase_							:	List[str]     ,		lowerCamelCase_							:	Any		):

							'''simple docstring'''



							if len(self.retrieval_workers		) > 0:
									# Select a random retrieval actor.
									SCREAMING_SNAKE_CASE				:							Dict						=	self.retrieval_workers[random.randint(0     ,		len(self.retrieval_workers		) - 1		)]
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							List[Any]						=	ray.get(
									    random_worker.retrieve.remote(lowerCamelCase_     ,		lowerCamelCase_		)		)
							else:
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Optional[int]						=	self._main_retrieve(lowerCamelCase_     ,		lowerCamelCase_		)
									SCREAMING_SNAKE_CASE				:							Tuple						=	self.index.get_doc_dicts(lowerCamelCase_		)
							return retrieved_doc_embeds, doc_ids, doc_dicts


					@classmethod
					def lowerCamelCase_	(		cls							:	Dict     ,		lowerCamelCase_							:	Tuple     ,		lowerCamelCase_							:	Union[str, Any]=None     ,		**lowerCamelCase_							:	Dict		):

							'''simple docstring'''



							return super(lowerCamelCase_     ,		cls		).get_tokenizers(lowerCamelCase_     ,		lowerCamelCase_     ,		**lowerCamelCase_		)


					@classmethod
					def lowerCamelCase_	(		cls							:	str     ,		lowerCamelCase_							:	Tuple     ,		lowerCamelCase_							:	Optional[Any]     ,		lowerCamelCase_							:	List[Any]=None     ,		**lowerCamelCase_							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	kwargs.pop("""config"""     ,		lowerCamelCase_		) or RagConfig.from_pretrained(lowerCamelCase_     ,		**lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							List[Any]						=	RagTokenizer.from_pretrained(lowerCamelCase_     ,		config=lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							int						=	rag_tokenizer.question_encoder
							SCREAMING_SNAKE_CASE				:							List[str]						=	rag_tokenizer.generator

							if indexed_dataset is not None:
									SCREAMING_SNAKE_CASE				:							List[str]						=	"""custom"""
									SCREAMING_SNAKE_CASE				:							List[str]						=	CustomHFIndex(config.retrieval_vector_size     ,		lowerCamelCase_		)
							else:
									SCREAMING_SNAKE_CASE				:							Any						=	cls._build_index(lowerCamelCase_		)

							return cls(
							    lowerCamelCase_     ,		question_encoder_tokenizer=lowerCamelCase_     ,		generator_tokenizer=lowerCamelCase_     ,		retrieval_workers=lowerCamelCase_     ,		index=lowerCamelCase_     ,		)



					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''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
							SCREAMING_SNAKE_CASE				:							Tuple						=	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
							    ]		)










'''simple docstring'''




from __future__ import annotations



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							List[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 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
		SCREAMING_SNAKE_CASE				:							Dict						=	int(input("""Enter the element to be searched:"""					)					)
		print(lowerCamelCase_					)
		search_in_a_sorted_matrix(lowerCamelCase_	,	len(lowerCamelCase_					)	,	len(mat[0]					)	,	lowerCamelCase_					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()



