code: import logging
  import random
  
  import ray
  
  from transformers import RagConfig, RagRetriever, RagTokenizer
  from transformers.models.rag.retrieval_rag import CustomHFIndex
  
  
  UpperCAmelCase					=				logging.getLogger(__name__)
  
  
  
  
  class    __lowerCAmelCase  :
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		Optional[Any]  ) ->							Union[str, Any]:
          _UpperCamelCase     :       str  						=  False
  
   def 					__snake_case					(			self       :		Optional[Any]       ,			lowerCamelCase       :		List[str]       ,			lowerCamelCase       :		Optional[int]       ,			lowerCamelCase       :		Union[str, Any]       ,			lowerCamelCase       :		Union[str, Any]  ) ->							Optional[int]:
          if not self.initialized:
                 _UpperCamelCase     :       Dict  						=  RagRetriever(
                     lowerCamelCase       ,			question_encoder_tokenizer=lowerCamelCase       ,			generator_tokenizer=lowerCamelCase       ,			index=lowerCamelCase       ,			init_retrieval=lowerCamelCase       ,			)
                 _UpperCamelCase     :       Optional[int]  						=  True
  
   def 					__snake_case					(			self       :		Union[str, Any]  ) ->							str:
          self.retriever.index.init_index()
  
   def 					__snake_case					(			self       :		Optional[int]  ) ->							str:
          # delete the old self.retriever object before assigning the new index
          del self.retriever
          _UpperCamelCase     :       Tuple  						=  False
  
   def 					__snake_case					(			self       :		Optional[int]       ,			lowerCamelCase       :		int       ,			lowerCamelCase       :		Dict  ) ->							Optional[Any]:
          _UpperCamelCase						,      _UpperCamelCase     :       Optional[int]  						=  self.retriever._main_retrieve(lowerCamelCase       ,			lowerCamelCase  )
          _UpperCamelCase     :       Union[str, Any]  						=  self.retriever.index.get_doc_dicts(lowerCamelCase  )
          return doc_ids, retrieved_doc_embeds, doc_dicts
  
  
  
  
  
  
  
  class    __lowerCAmelCase  (       a_						):
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		Any       ,			lowerCamelCase       :		Optional[Any]       ,			lowerCamelCase       :		List[Any]       ,			lowerCamelCase       :		int       ,			lowerCamelCase       :		Tuple       ,			lowerCamelCase       :		Tuple=None  ) ->							Optional[int]:
          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       ,			)
  
          _UpperCamelCase     :       List[str]  						=  retrieval_workers
          _UpperCamelCase     :       int  						=  question_encoder_tokenizer
          _UpperCamelCase     :       int  						=  generator_tokenizer
          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[Any]  ) ->							Dict:
          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       :		Optional[Any]       ,			lowerCamelCase       :		Dict       ,			lowerCamelCase       :		str  ) ->							List[Any]:
          if len(self.retrieval_workers  ) > 0:
                 # Select a random retrieval actor.
                 _UpperCamelCase     :       Optional[Any]  						=  self.retrieval_workers[random.randint(0       ,			len(self.retrieval_workers  ) - 1  )]
                 _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       List[Any]  						=  ray.get(
                     random_worker.retrieve.remote(lowerCamelCase       ,			lowerCamelCase  )  )
          else:
                 _UpperCamelCase						,      _UpperCamelCase     :       List[str]  						=  self._main_retrieve(lowerCamelCase       ,			lowerCamelCase  )
                 _UpperCamelCase     :       Dict  						=  self.index.get_doc_dicts(lowerCamelCase  )
          return retrieved_doc_embeds, doc_ids, doc_dicts
  
   @classmethod
   def 					__snake_case					(			cls       :		Optional[int]       ,			lowerCamelCase       :		Optional[Any]       ,			lowerCamelCase       :		Union[str, Any]=None       ,			**lowerCamelCase       :		Tuple  ) ->							List[Any]:
          return super(lowerCamelCase       ,			cls  ).get_tokenizers(lowerCamelCase       ,			lowerCamelCase       ,			**lowerCamelCase  )
  
   @classmethod
   def 					__snake_case					(			cls       :		List[str]       ,			lowerCamelCase       :		Optional[Any]       ,			lowerCamelCase       :		Optional[Any]       ,			lowerCamelCase       :		List[Any]=None       ,			**lowerCamelCase       :		List[str]  ) ->							List[str]:
          _UpperCamelCase     :       Any  						=  kwargs.pop('config'       ,			lowerCamelCase  ) or RagConfig.from_pretrained(lowerCamelCase       ,			**lowerCamelCase  )
          _UpperCamelCase     :       Union[str, Any]  						=  RagTokenizer.from_pretrained(lowerCamelCase       ,			config=lowerCamelCase  )
          _UpperCamelCase     :       str  						=  rag_tokenizer.question_encoder
          _UpperCamelCase     :       Tuple  						=  rag_tokenizer.generator
  
          if indexed_dataset is not None:
                 _UpperCamelCase     :       Any  						=  'custom'
                 _UpperCamelCase     :       Optional[int]  						=  CustomHFIndex(config.retrieval_vector_size       ,			lowerCamelCase  )
          else:
                 _UpperCamelCase     :       Optional[Any]  						=  cls._build_index(lowerCamelCase  )
  
          return cls(
              lowerCamelCase       ,			question_encoder_tokenizer=lowerCamelCase       ,			generator_tokenizer=lowerCamelCase       ,			retrieval_workers=lowerCamelCase       ,			index=lowerCamelCase       ,			)
  
   def 					__snake_case					(			self       :		Optional[Any]  ) ->							Tuple:
          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
          _UpperCamelCase     :       Dict  						=  self._build_index(self.config  )
  
          ray.get(
              [
                  worker.create_rag_retriever.remote(
                      self.config       ,			self.question_encoder_tokenizer       ,			self.generator_tokenizer       ,			lowerCamelCase  )
                  for worker in self.retrieval_workers
              ]  )
  
  
  
  
style_context: import math
  from collections.abc import Generator
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
         _UpperCamelCase     :       Generator  						=  (i for i in range(1		,						(max_n + 1)				))
         for i in (n for n in numbers if n > 1):
                for j in range(2		,						lowercase_				):
                       if (i % j) == 0:
                              break
                else:
                       yield i
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
         _UpperCamelCase     :       Generator  						=  (i for i in range(1		,						(max_n + 1)				))
         for i in (n for n in numbers if n > 1):
                # only need to check for factors up to sqrt(i)
                _UpperCamelCase     :       Any  						=  int(math.sqrt(lowercase_				)				) + 1
                for j in range(2		,						lowercase_				):
                       if (i % j) == 0:
                              break
                else:
                       yield i
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
         _UpperCamelCase     :       Generator  						=  (i for i in range(1		,						(max_n + 1)		,						2				))
         # It's useless to test even numbers as they will not be prime
         if max_n > 2:
                yield 2  # Because 2 will not be tested, it's necessary to yield it now
         for i in (n for n in numbers if n > 1):
                _UpperCamelCase     :       Tuple  						=  int(math.sqrt(lowercase_				)				) + 1
                for j in range(3		,						lowercase_		,						2				):
                       # As we removed the even numbers, we don't need them now
                       if (i % j) == 0:
                              break
                else:
                       yield i
  
  
  def SCREAMING_SNAKE_CASE				(     ):
         from timeit import timeit
  
         _UpperCamelCase     :       Optional[Any]  						=  'from __main__ import slow_primes, primes, fast_primes'
         print(timeit('slow_primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
         print(timeit('primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
         print(timeit('fast_primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
  
  
  if __name__ == "__main__":
   UpperCAmelCase					=				int(input('''Calculate primes up to:\n>> ''').strip())
   for ret in primes(number):
    print(ret)
   benchmark()
  
  
  
  