






import logging
import random

import ray

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


snake_case_   :	Optional[Any]							      =logging.getLogger(__name__)





class        a__      :






		def __init__(      self						)				->				List[str]:
				__A    =							False






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__						)				->				Any:
				if not self.initialized:
						__A    =							RagRetriever(
						    lowercase__							, question_encoder_tokenizer=lowercase__							, generator_tokenizer=lowercase__							, index=lowercase__							, init_retrieval=lowercase__							, )
						__A    =							True






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				self.retriever.index.init_index()






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				# delete the old self.retriever object before assigning the new index
				del self.retriever
				__A    =							False






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__						)				->				Any:
				__A ,   __A    =							self.retriever._main_retrieve(lowercase__							, lowercase__						)
				__A    =							self.retriever.index.get_doc_dicts(lowercase__						)
				return doc_ids, retrieved_doc_embeds, doc_dicts





class        a__      (	lowerCAmelCase__		):






		def __init__(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, lowercase__=None						)				->				Optional[int]:
				if index is not None and index.is_initialized() and len(lowercase__						) > 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__(
				    lowercase__							, question_encoder_tokenizer=lowercase__							, generator_tokenizer=lowercase__							, index=lowercase__							, init_retrieval=lowercase__							, )

				__A    =							retrieval_workers
				__A    =							question_encoder_tokenizer
				__A    =							generator_tokenizer
				if len(self.retrieval_workers						) > 0:
						ray.get(
						    [
						        worker.create_rag_retriever.remote(lowercase__							, lowercase__							, lowercase__							, lowercase__						)
						        for worker in self.retrieval_workers
						    ]						)






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






		@classmethod
		def 					_lowerCamelCase					(      cls							, lowercase__							, lowercase__=None							, **lowercase__						)				->				str:
				return super(lowercase__							, cls						).get_tokenizers(lowercase__							, lowercase__							, **lowercase__						)






		@classmethod
		def 					_lowerCamelCase					(      cls							, lowercase__							, lowercase__							, lowercase__=None							, **lowercase__						)				->				Any:
				__A    =							kwargs.pop("config"							, lowercase__						) or RagConfig.from_pretrained(lowercase__							, **lowercase__						)
				__A    =							RagTokenizer.from_pretrained(lowercase__							, config=lowercase__						)
				__A    =							rag_tokenizer.question_encoder
				__A    =							rag_tokenizer.generator

				if indexed_dataset is not None:
						__A    =							"custom"
						__A    =							CustomHFIndex(config.retrieval_vector_size							, lowercase__						)
				else:
						__A    =							cls._build_index(lowercase__						)

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






		def 					_lowerCamelCase					(      self						)				->				Dict:
				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
				__A    =							self._build_index(self.config						)

				ray.get(
				    [
				        worker.create_rag_retriever.remote(
				            self.config							, self.question_encoder_tokenizer							, self.generator_tokenizer							, lowercase__						)
				        for worker in self.retrieval_workers
				    ]						)








import doctest

import projectq
from projectq.ops import H, Measure




def  A_(      A ):
	UpperCAmelCase_		    =  quantum_engine.allocate_qubit()
	H | qubit
	Measure | qubit
	return int(A )


if __name__ == "__main__":
				doctest.testmod()

				# initialises a new quantum backend
				UpperCamelCase__ :			Dict       	=			projectq.MainEngine()

				# Generate a list of 10 random numbers
				UpperCamelCase__ :			Dict       	=			[get_random_number(quantum_engine) for _ in range(10)]

				# Flushes the quantum engine from memory
				quantum_engine.flush()

				print("""Random numbers""", random_numbers_list)


