






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 unittest
from timeit import timeit







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     first_num if first_num >= second_num else second_num
     _lowerCAmelCase :       List[str]	   =     max_num
     while (common_mult % first_num > 0) or (common_mult % second_num > 0):
          common_mult += max_num
     return common_mult







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     return b if a == 0 else greatest_common_divisor(b % a		,				SCREAMING_SNAKE_CASE					)







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     return first_num // greatest_common_divisor(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					) * second_num







def 						_snake_case		(     )							->   str:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     (
         "from __main__ import least_common_multiple_slow, least_common_multiple_fast"
     )
     print(
         "least_common_multiple_slow():"		,				timeit("least_common_multiple_slow(1000, 999)"		,				setup=SCREAMING_SNAKE_CASE					)		,				)
     print(
         "least_common_multiple_fast():"		,				timeit("least_common_multiple_fast(1000, 999)"		,				setup=SCREAMING_SNAKE_CASE					)		,				)





class A__     (			unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       Union[str, Any]  			=    (
     (10, 20),
     (13, 15),
     (4, 31),
     (10, 42),
     (43, 34),
     (5, 12),
     (12, 25),
     (10, 25),
     (6, 9),
 )
 _lowercase  :       Dict  			=    (20, 195, 124, 210, 1_462, 60, 300, 50, 18)

 def  __magic_name__							(		self			:						int							):






      '''simple docstring'''




      for i, (first_num, second_num) in enumerate(self.test_inputs							):
           _lowerCAmelCase :       Optional[int]	   =     least_common_multiple_slow(A_  ,    A_							)
           _lowerCAmelCase :       Optional[int]	   =     least_common_multiple_fast(A_  ,    A_							)
           with self.subTest(i=A_							):
                self.assertEqual(A_  ,    self.expected_results[i]							)
                self.assertEqual(A_  ,    self.expected_results[i]							)


if __name__ == "__main__":
  benchmark()
  unittest.main()






