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
            ]  )




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()



