






"""simple docstring"""

import logging
import random

import ray

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


lowercase    			=       logging.getLogger(__name__)

class 						A_							:







				def __init__(							self							:						Dict	)				->					List[Any]:
										__magic_name__            =     False







				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Any	)				->					int:
										if not self.initialized:
																__magic_name__            =     RagRetriever(
																    __lowerCamelCase						,    question_encoder_tokenizer=__lowerCamelCase						,    generator_tokenizer=__lowerCamelCase						,    index=__lowerCamelCase						,    init_retrieval=__lowerCamelCase						,    )
																__magic_name__            =     True







				def     _snake_case					(							self							:						Dict	)				->					List[str]:
										self.retriever.index.init_index()







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						str	)				->					Optional[Any]:
										__magic_name__      ,    __magic_name__            =     self.retriever._main_retrieve(__lowerCamelCase						,    __lowerCamelCase	)
										return doc_ids, retrieved_doc_embeds





class 						A_							(      snake_case_  ):







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Any=None	)				->					Union[str, Any]:
										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						,    )
										__magic_name__            =     retrieval_workers
										if len(self.retrieval_workers	) > 0:
																ray.get(
																    [
																        worker.create_rag_retriever.remote(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)
																        for worker in self.retrieval_workers
																    ]	)







				def     _snake_case					(							self							:						List[str]	)				->					List[str]:
										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     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Optional[Any]	)				->					List[str]:
										if len(self.retrieval_workers	) > 0:
																# Select a random retrieval actor.
																__magic_name__            =     self.retrieval_workers[random.randint(0						,    len(self.retrieval_workers	) - 1	)]
																__magic_name__      ,    __magic_name__            =     ray.get(random_worker.retrieve.remote(__lowerCamelCase						,    __lowerCamelCase	)	)
										else:
																__magic_name__      ,    __magic_name__            =     self._main_retrieve(__lowerCamelCase						,    __lowerCamelCase	)
										return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCamelCase	)







				@classmethod
				def     _snake_case					(							cls							:						Any						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						int=None						,    **__lowerCamelCase							:						List[str]	)				->					List[Any]:
										return super(__lowerCamelCase						,    cls	).get_tokenizers(__lowerCamelCase						,    __lowerCamelCase						,    **__lowerCamelCase	)







				@classmethod
				def     _snake_case					(							cls							:						Optional[Any]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						List[str]=None						,    **__lowerCamelCase							:						Dict	)				->					Tuple:
										__magic_name__            =     kwargs.pop("config"						,    __lowerCamelCase	) or RagConfig.from_pretrained(__lowerCamelCase						,    **__lowerCamelCase	)
										__magic_name__            =     RagTokenizer.from_pretrained(__lowerCamelCase						,    config=__lowerCamelCase	)
										__magic_name__            =     rag_tokenizer.question_encoder
										__magic_name__            =     rag_tokenizer.generator
										if indexed_dataset is not None:
																__magic_name__            =     "custom"
																__magic_name__            =     CustomHFIndex(config.retrieval_vector_size						,    __lowerCamelCase	)
										else:
																__magic_name__            =     cls._build_index(__lowerCamelCase	)
										return cls(
										    __lowerCamelCase						,    question_encoder_tokenizer=__lowerCamelCase						,    generator_tokenizer=__lowerCamelCase						,    retrieval_workers=__lowerCamelCase						,    index=__lowerCamelCase						,    )











"""simple docstring"""

from __future__ import annotations

from random import choice






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						return choice(__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:list[int] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     random_pivot(__lowerCamelCase   )

						# partition based on pivot
						# linear time
						__magic_name__            =     [e for e in lst if e < pivot]
						__magic_name__            =     [e for e in lst if e > pivot]

						# if we get lucky, pivot might be the element we want.
						# we can easily see this:
						# small (elements smaller than k)
						# + pivot (kth element)
						# + big (elements larger than k)
						if len(__lowerCamelCase   ) == k - 1:
												return pivot
						# pivot is in elements bigger than k
						elif len(__lowerCamelCase   ) < k - 1:
												return kth_number(__lowerCamelCase ,						k - len(__lowerCamelCase   ) - 1   )
						# pivot is in elements smaller than k
						else:
												return kth_number(__lowerCamelCase ,						__lowerCamelCase   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



