




import logging
import random

import ray

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


__UpperCamelCase   :			List[Any]          = logging.getLogger(__name__)

class 						__lowerCAmelCase      :
							def __init__(		self		:List[str]							):







								'''simple docstring'''







								a									=				False
							def  lowerCamelCase__				(		self		:Union[str, Any]						, __magic_name__		:List[Any]						, __magic_name__		:List[Any]						, __magic_name__		:Union[str, Any]						, __magic_name__		:Optional[Any]							):







								'''simple docstring'''







								if not self.initialized:
									a									=				RagRetriever(
									    __magic_name__						, question_encoder_tokenizer=__magic_name__						, generator_tokenizer=__magic_name__						, index=__magic_name__						, init_retrieval=__magic_name__						, )
									a									=				True
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								self.retriever.index.init_index()





							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:List[Any]						, __magic_name__		:Optional[Any]							):







								'''simple docstring'''







								a							,					a									=				self.retriever._main_retrieve(__magic_name__						, __magic_name__							)
								return doc_ids, retrieved_doc_embeds







class 						__lowerCAmelCase      (      __magic_name__					):
							def __init__(		self		:Optional[Any]						, __magic_name__		:List[str]						, __magic_name__		:List[str]						, __magic_name__		:Dict						, __magic_name__		:Union[str, Any]						, __magic_name__		:int=None							):







								'''simple docstring'''







								if index is not None and index.is_initialized() and len(__magic_name__							) > 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__(
								    __magic_name__						, question_encoder_tokenizer=__magic_name__						, generator_tokenizer=__magic_name__						, index=__magic_name__						, init_retrieval=__magic_name__						, )
								a									=				retrieval_workers
								if len(self.retrieval_workers							) > 0:
									ray.get(
									    [
									        worker.create_rag_retriever.remote(__magic_name__						, __magic_name__						, __magic_name__						, __magic_name__							)
									        for worker in self.retrieval_workers
									    ]							)
							def  lowerCamelCase__				(		self		:Tuple							):







								'''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		:Any						, __magic_name__		:Union[str, Any]						, __magic_name__		:List[Any]							):







								'''simple docstring'''







								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									=				ray.get(random_worker.retrieve.remote(__magic_name__						, __magic_name__							)							)
								else:
									a							,					a									=				self._main_retrieve(__magic_name__						, __magic_name__							)
								return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__magic_name__							)
							@classmethod
							def  lowerCamelCase__				(		cls		:List[str]						, __magic_name__		:List[Any]						, __magic_name__		:List[Any]=None						, **__magic_name__		:Optional[Any]							):







								'''simple docstring'''







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





							@classmethod
							def  lowerCamelCase__				(		cls		:Any						, __magic_name__		:Any						, __magic_name__		:List[str]						, __magic_name__		:Dict=None						, **__magic_name__		:Dict							):







								'''simple docstring'''







								a									=				kwargs.pop("""config"""						, __magic_name__							) or RagConfig.from_pretrained(__magic_name__						, **__magic_name__							)
								a									=				RagTokenizer.from_pretrained(__magic_name__						, config=__magic_name__							)
								a									=				rag_tokenizer.question_encoder
								a									=				rag_tokenizer.generator
								if indexed_dataset is not None:
									a									=				"""custom"""
									a									=				CustomHFIndex(config.retrieval_vector_size						, __magic_name__							)
								else:
									a									=				cls._build_index(__magic_name__							)
								return cls(
								    __magic_name__						, question_encoder_tokenizer=__magic_name__						, generator_tokenizer=__magic_name__						, retrieval_workers=__magic_name__						, index=__magic_name__						, )







from bisect import bisect
from itertools import accumulate





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				sorted(zip(__lowerCamelCase				,     __lowerCamelCase       )				,     key=lambda __lowerCamelCase       : x[0] / x[1]				,     reverse=__lowerCamelCase       )
	a							,					a									=				[i[0] for i in r], [i[1] for i in r]
	a									=				list(accumulate(__lowerCamelCase       )       )
	a									=				bisect(__lowerCamelCase				,     __lowerCamelCase       )
	return (
	    0
	    if k == 0
	    else sum(vl[:k]       ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
	    if k != n
	    else sum(vl[:k]       )
	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

