





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  __A   :

			'''simple docstring'''







			def __init__(   self							):



				'''simple docstring'''







				lowerCamelCase__        =		False





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				if not self.initialized:
					lowerCamelCase__        =		RagRetriever(
					    __lowerCAmelCase	,  question_encoder_tokenizer=__lowerCAmelCase	,  generator_tokenizer=__lowerCAmelCase	,  index=__lowerCAmelCase	,  init_retrieval=__lowerCAmelCase	,  )
					lowerCamelCase__        =		True





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				self.retriever.index.init_index()





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				del self.retriever
				lowerCamelCase__        =		False





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__    ,     lowerCamelCase__        =		self.retriever._main_retrieve(__lowerCAmelCase	,  __lowerCAmelCase							)
				lowerCamelCase__        =		self.retriever.index.get_doc_dicts(__lowerCAmelCase							)
				return doc_ids, retrieved_doc_embeds, doc_dicts






class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			def __init__(   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase=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	,  )

				lowerCamelCase__        =		retrieval_workers
				lowerCamelCase__        =		question_encoder_tokenizer
				lowerCamelCase__        =		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							):



				'''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	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







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





			@classmethod
			def 		__lowerCamelCase    (   cls	,  __lowerCAmelCase	,  __lowerCAmelCase=None	,  **__lowerCAmelCase							):



				'''simple docstring'''







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





			@classmethod
			def 		__lowerCamelCase    (   cls	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase=None	,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		kwargs.pop('''config'''	,  __lowerCAmelCase							) or RagConfig.from_pretrained(__lowerCAmelCase	,  **__lowerCAmelCase							)
				lowerCamelCase__        =		RagTokenizer.from_pretrained(__lowerCAmelCase	,  config=__lowerCAmelCase							)
				lowerCamelCase__        =		rag_tokenizer.question_encoder
				lowerCamelCase__        =		rag_tokenizer.generator

				if indexed_dataset is not None:
					lowerCamelCase__        =		'''custom'''
					lowerCamelCase__        =		CustomHFIndex(config.retrieval_vector_size	,  __lowerCAmelCase							)
				else:
					lowerCamelCase__        =		cls._build_index(__lowerCAmelCase							)

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





			def 		__lowerCamelCase    (   self							):



				'''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
				lowerCamelCase__        =		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 __future__ import annotations

import math
import random





def lowerCAmelCase__(__snake_case	,__snake_case	,__snake_case							)    ->				list[int]:







	'''simple docstring'''







	lowerCamelCase__        =		d * e - 1
	lowerCamelCase__        =		0
	lowerCamelCase__        =		0
	while p == 0:
		lowerCamelCase__        =		random.randint(2	,n - 1							)
		lowerCamelCase__        =		k
		while True:
			if t % 2 == 0:
				lowerCamelCase__        =		t // 2
				lowerCamelCase__        =		(g**t) % n
				lowerCamelCase__        =		math.gcd(x - 1	,__snake_case							)
				if x > 1 and y > 1:
					lowerCamelCase__        =		y
					lowerCamelCase__        =		n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



