



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							:    Optional[Any])->							Union[str, Any]:
						__lowerCAmelCase	   =False



	def 		UpperCamelCase							(						self							:    Dict				,	snake_case_							:    Tuple				,	snake_case_							:    Dict				,	snake_case_							:    List[Any]				,	snake_case_							:    Any)->							List[Any]:
						if not self.initialized:
											__lowerCAmelCase	   =RagRetriever(
											    snake_case_				,	question_encoder_tokenizer=snake_case_				,	generator_tokenizer=snake_case_				,	index=snake_case_				,	init_retrieval=snake_case_				,	)
											__lowerCAmelCase	   =True



	def 		UpperCamelCase							(						self							:    int)->							List[str]:
						self.retriever.index.init_index()



	def 		UpperCamelCase							(						self							:    Dict				,	snake_case_							:    Tuple				,	snake_case_							:    Optional[int])->							Optional[Any]:
						__lowerCAmelCase   ,	__lowerCAmelCase	   =self.retriever._main_retrieve(snake_case_				,	snake_case_)
						return doc_ids, retrieved_doc_embeds






class 						__a (     SCREAMING_SNAKE_CASE				):



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



	def 		UpperCamelCase							(						self							:    int)->							Union[str, Any]:
						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							:    Dict				,	snake_case_							:    List[str]				,	snake_case_							:    Union[str, Any])->							Any:
						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	   =ray.get(random_worker.retrieve.remote(snake_case_				,	snake_case_))
						else:
											__lowerCAmelCase   ,	__lowerCAmelCase	   =self._main_retrieve(snake_case_				,	snake_case_)
						return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(snake_case_)



	@classmethod
	def 		UpperCamelCase							(						cls							:    Any				,	snake_case_							:    Dict				,	snake_case_							:    Dict=None				,	**snake_case_							:    str)->							Dict:
						return super(snake_case_				,	cls).get_tokenizers(snake_case_				,	snake_case_				,	**snake_case_)



	@classmethod
	def 		UpperCamelCase							(						cls							:    Union[str, Any]				,	snake_case_							:    List[str]				,	snake_case_							:    Dict				,	snake_case_							:    Optional[int]=None				,	**snake_case_							:    int)->							List[str]:
						__lowerCAmelCase	   =kwargs.pop("""config"""				,	snake_case_) or RagConfig.from_pretrained(snake_case_				,	**snake_case_)
						__lowerCAmelCase	   =RagTokenizer.from_pretrained(snake_case_				,	config=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				,	snake_case_)
						else:
											__lowerCAmelCase	   =cls._build_index(snake_case_)
						return cls(
						    snake_case_				,	question_encoder_tokenizer=snake_case_				,	generator_tokenizer=snake_case_				,	retrieval_workers=snake_case_				,	index=snake_case_				,	)









def 					__lowerCAmelCase		(  __lowerCamelCase	:    list	)  ->						list:
					if len(__lowerCamelCase	) <= 1:
										return lst

					__lowerCAmelCase	   =1

					while i < len(__lowerCamelCase	):
										if lst[i - 1] <= lst[i]:
															i += 1
										else:
															__lowerCAmelCase   ,	__lowerCAmelCase	   =lst[i], lst[i - 1]
															i -= 1
															if i == 0:
																				__lowerCAmelCase	   =1

					return lst


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




