





'''simple docstring'''


import logging
import random

import ray

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


lowerCAmelCase      :  Union[str, Any]      							=logging.getLogger(__name__)
class    a_     :





     def __init__(				self	:      Optional[Any]		):





        """simple docstring"""




        lowercase_    :int					    =				False





     def 							lowercase__   (				self	:      int			,   lowercase	:      Optional[int]			,   lowercase	:      Optional[Any]			,   lowercase	:      Any			,   lowercase	:      List[str]		):





        """simple docstring"""




        if not self.initialized:
           lowercase_    :str					    =				RagRetriever(
               lowercase			,   question_encoder_tokenizer=lowercase			,   generator_tokenizer=lowercase			,   index=lowercase			,   init_retrieval=lowercase			,   )
           lowercase_    :Optional[Any]					    =				True





     def 							lowercase__   (				self	:      Tuple		):





        """simple docstring"""




        self.retriever.index.init_index()





     def 							lowercase__   (				self	:      Optional[Any]		):





        """simple docstring"""




        del self.retriever
        lowercase_    :Any					    =				False







     def 							lowercase__   (				self	:      str			,   lowercase	:      Tuple			,   lowercase	:      Tuple		):





        """simple docstring"""




        lowercase_    :Any					    =				self.retriever._main_retrieve(lowercase			,   lowercase		)
        lowercase_    :List[Any]					    =				self.retriever.index.get_doc_dicts(lowercase		)
        return doc_ids, retrieved_doc_embeds, doc_dicts







class    a_     (       _lowerCAmelCase							):





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





        """simple docstring"""




        if index is not None and index.is_initialized() and len(lowercase		) > 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__(
            lowercase			,   question_encoder_tokenizer=lowercase			,   generator_tokenizer=lowercase			,   index=lowercase			,   init_retrieval=lowercase			,   )

        lowercase_    :int					    =				retrieval_workers
        lowercase_    :Any					    =				question_encoder_tokenizer
        lowercase_    :List[Any]					    =				generator_tokenizer
        if len(self.retrieval_workers		) > 0:
           ray.get(
               [
                   worker.create_rag_retriever.remote(lowercase			,   lowercase			,   lowercase			,   lowercase		)
                   for worker in self.retrieval_workers
               ]		)





     def 							lowercase__   (				self	:      int		):





        """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 							lowercase__   (				self	:      Optional[Any]			,   lowercase	:      Any			,   lowercase	:      Union[str, Any]		):





        """simple docstring"""




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





     @classmethod
     def 							lowercase__   (				cls	:      Union[str, Any]			,   lowercase	:      List[Any]			,   lowercase	:      Union[str, Any]=None			,   **lowercase	:      Tuple		):





        """simple docstring"""




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





     @classmethod
     def 							lowercase__   (				cls	:      Optional[Any]			,   lowercase	:      int			,   lowercase	:      Union[str, Any]			,   lowercase	:      Optional[Any]=None			,   **lowercase	:      List[str]		):





        """simple docstring"""




        lowercase_    :Union[str, Any]					    =				kwargs.pop("config"			,   lowercase		) or RagConfig.from_pretrained(lowercase			,   **lowercase		)
        lowercase_    :Tuple					    =				RagTokenizer.from_pretrained(lowercase			,   config=lowercase		)
        lowercase_    :Tuple					    =				rag_tokenizer.question_encoder
        lowercase_    :Tuple					    =				rag_tokenizer.generator

        if indexed_dataset is not None:
           lowercase_    :int					    =				"custom"
           lowercase_    :Any					    =				CustomHFIndex(config.retrieval_vector_size			,   lowercase		)
        else:
           lowercase_    :Optional[int]					    =				cls._build_index(lowercase		)

        return cls(
            lowercase			,   question_encoder_tokenizer=lowercase			,   generator_tokenizer=lowercase			,   retrieval_workers=lowercase			,   index=lowercase			,   )







     def 							lowercase__   (				self	:      Tuple		):





        """simple docstring"""




        logger.info("re-loading the new dataset with embeddings"		)
        # access from the training loop

        ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]		)

        # build the index object again
        lowercase_    :Tuple					    =				self._build_index(self.config		)

        ray.get(
            [
                worker.create_rag_retriever.remote(
                    self.config			,   self.question_encoder_tokenizer			,   self.generator_tokenizer			,   lowercase		)
                for worker in self.retrieval_workers
            ]		)









'''simple docstring'''


import math
def 							UpperCAmelCase_						(		__lowerCamelCase      :      int				):

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5			,int(math.sqrt(__lowerCamelCase				) + 1				)			,6				):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True
def 							UpperCAmelCase_						(		__lowerCamelCase      :      int = 2_00_00_00				):
   return sum(num for num in range(3			,__lowerCamelCase			,2				) if is_prime(__lowerCamelCase				)				) + 2 if n > 2 else 0


if __name__ == "__main__":
 print(F'''{solution() = }''')


