





'''simple docstring'''







import logging
import random

import ray

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


_a      :				Any											=					logging.getLogger(__name__)





class 					_UpperCAmelCase							:
		def __init__(						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       False
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

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



					'''simple docstring'''

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





class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
		def __init__(						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None    ):



					'''simple docstring'''

					if index is not None and index.is_initialized() and len(__SCREAMING_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__(
					    __SCREAMING_SNAKE_CASE,question_encoder_tokenizer=__SCREAMING_SNAKE_CASE,generator_tokenizer=__SCREAMING_SNAKE_CASE,index=__SCREAMING_SNAKE_CASE,init_retrieval=__SCREAMING_SNAKE_CASE,)

					__lowerCAmelCase      		=       retrieval_workers
					__lowerCAmelCase      		=       question_encoder_tokenizer
					__lowerCAmelCase      		=       generator_tokenizer
					if len(self.retrieval_workers    ) > 0:
								ray.get(
								    [
								        worker.create_rag_retriever.remote(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
								        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,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):



					'''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(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )    )
					else:
								__lowerCAmelCase		, __lowerCAmelCase      		=       self._main_retrieve(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
								__lowerCAmelCase      		=       self.index.get_doc_dicts(__SCREAMING_SNAKE_CASE    )
					return retrieved_doc_embeds, doc_ids, doc_dicts
		@classmethod
		def 						lowerCamelCase__    (						cls,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					return super(__SCREAMING_SNAKE_CASE,cls    ).get_tokenizers(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )
		@classmethod
		def 						lowerCamelCase__    (						cls,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

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

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

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







		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,__SCREAMING_SNAKE_CASE    )
					        for worker in self.retrieval_workers
					    ]    )








'''simple docstring'''







def      _lowerCAmelCase		(		lowercase = 1000			)      ->      int:
			__lowerCAmelCase      		=       []
			__lowerCAmelCase      		=       []
			__lowerCAmelCase      		=       3
			__lowerCAmelCase      		=       5
			__lowerCAmelCase      		=       1
			while True:
						__lowerCAmelCase      		=       z * temp
						if result < n:
									zmulti.append(lowercase			)
									temp += 1
						else:
									__lowerCAmelCase      		=       1
									break
			while True:
						__lowerCAmelCase      		=       x * temp
						if result < n:
									xmulti.append(lowercase			)
									temp += 1
						else:
									break
			__lowerCAmelCase      		=       list(set(xmulti + zmulti			)			)
			return sum(lowercase			)


if __name__ == "__main__":
	print(f'{solution() = }')

