
import logging
import random

import ray

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


SCREAMING_SNAKE_CASE							:int							 =				logging.getLogger(__name__)





class 							UpperCAmelCase      :




	'''simple docstring'''





	def __init__(       self					: int ):
					__A   			= False






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: Optional[Any]							,A					: str							,A					: Optional[Any]							,A					: Union[str, Any] ):
					if not self.initialized:
									__A   			= RagRetriever(
									    A							,question_encoder_tokenizer=A							,generator_tokenizer=A							,index=A							,init_retrieval=A							,)
									__A   			= True






	def 				UpperCamelCase_  (       self					: List[Any] ):
					self.retriever.index.init_index()







	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Optional[int]							,A					: Dict ):
					__A			,							__A   			= self.retriever._main_retrieve(A							,A )
					return doc_ids, retrieved_doc_embeds





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(       self					: Tuple							,A					: int							,A					: Union[str, Any]							,A					: List[Any]							,A					: Tuple							,A					: List[str]=None ):
					if index is not None and index.is_initialized() and len(A ) > 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__(
					    A							,question_encoder_tokenizer=A							,generator_tokenizer=A							,index=A							,init_retrieval=A							,)
					__A   			= retrieval_workers
					if len(self.retrieval_workers ) > 0:
									ray.get(
									    [
									        worker.create_rag_retriever.remote(A							,A							,A							,A )
									        for worker in self.retrieval_workers
									    ] )






	def 				UpperCamelCase_  (       self					: 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 				UpperCamelCase_  (       self					: Optional[Any]							,A					: List[Any]							,A					: List[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   			= ray.get(random_worker.retrieve.remote(A							,A ) )
					else:
									__A			,							__A   			= self._main_retrieve(A							,A )
					return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A )






	@classmethod
	def 				UpperCamelCase_  (       cls					: str							,A					: List[Any]							,A					: int=None							,**A					: Optional[Any] ):
					return super(A							,cls ).get_tokenizers(A							,A							,**A )







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


def 						UpperCAmelCase			(	a_						)		->	list:







				"""simple docstring"""



				if len(a_						) <= 1:
								return [tuple(a_						)]

				__A   			= []

				def generate(a_		,   a_						):
								if k == 1:
												res.append(tuple(arr[:]						)						)
												return

								generate(k - 1		,   a_						)

								for i in range(k - 1						):
												if k % 2 == 0:  # k is even
																__A			,							__A   			= arr[k - 1], arr[i]
												else:  # k is odd
																__A			,							__A   			= arr[k - 1], arr[0]
												generate(k - 1		,   a_						)

				generate(len(a_						)		,   a_						)
				return res


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:int							 =				input('Enter numbers separated by a comma:\n').strip()
					SCREAMING_SNAKE_CASE							:Dict							 =				[int(item) for item in user_input.split(',')]
					print(heaps(arr))
