





import math
import sys



def        __lowercase       (				_SCREAMING_SNAKE_CASE     ):


      '''simple docstring'''


      if number != int(__SCREAMING_SNAKE_CASE     ):
            raise ValueError("""the value of input must be a natural number"""     )
      if number < 0:
            raise ValueError("""the value of input must not be a negative number"""     )
      if number == 0:
            return 1
      SCREAMING_SNAKE_CASE		=						[-1] * (number + 1)
      SCREAMING_SNAKE_CASE		=						0
      for i in range(1			,						number + 1     ):
            SCREAMING_SNAKE_CASE		=						sys.maxsize
            SCREAMING_SNAKE_CASE		=						int(math.sqrt(__SCREAMING_SNAKE_CASE     )     )
            for j in range(1			,						root + 1     ):
                  SCREAMING_SNAKE_CASE		=						1 + answers[i - (j**2)]
                  SCREAMING_SNAKE_CASE		=						min(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE     )
            SCREAMING_SNAKE_CASE		=						answer
      return answers[number]


if __name__ == "__main__":
      import doctest

      doctest.testmod()









import logging
import random

import ray

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


SCREAMING_SNAKE_CASE_						       =	logging.getLogger(__name__)

class 		UpperCamelCase__			:



     '''simple docstring'''

     def __init__(							self						:   str	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						False






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Tuple	)					->		Optional[Any]:




           '''simple docstring'''


           if not self.initialized:
                 SCREAMING_SNAKE_CASE		=						RagRetriever(
                     lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,index=lowerCamelCase__						,init_retrieval=lowerCamelCase__						,)
                 SCREAMING_SNAKE_CASE		=						True






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Any:




           '''simple docstring'''


           self.retriever.index.init_index()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[str]	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.retriever._main_retrieve(lowerCamelCase__						,lowerCamelCase__	)
           return doc_ids, retrieved_doc_embeds

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     def __init__(							self						:   int						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   Dict=None	)					->		Any:




           '''simple docstring'''


           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__						,)
           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Union[str, Any]:




           '''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     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   int	)					->		Dict:




           '''simple docstring'''


           if len(self.retrieval_workers	) > 0:
                 # Select a random retrieval actor.
                 SCREAMING_SNAKE_CASE		=						self.retrieval_workers[random.randint(0						,len(self.retrieval_workers	) - 1	)]
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						ray.get(random_worker.retrieve.remote(lowerCamelCase__						,lowerCamelCase__	)	)
           else:
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self._main_retrieve(lowerCamelCase__						,lowerCamelCase__	)
           return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCamelCase__	)






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Optional[Any]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Union[str, Any]=None						,**lowerCamelCase__						:   Optional[Any]	)					->		Any:




           '''simple docstring'''


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






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Any						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[Any]=None						,**lowerCamelCase__						:   Any	)					->		Tuple:




           '''simple docstring'''


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


