






"""simple docstring"""



import logging
import random

import ray

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


_UpperCamelCase						:    Union[str, Any]		=						logging.getLogger(__name__)

class 			a	:







    def __init__(     self			):
      lowercase				       =			False







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):






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







    def      UpperCamelCase_ (     self			):
      self.retriever.index.init_index()







    def      UpperCamelCase_ (     self			):
      del self.retriever
      lowercase				       =			False







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase			):
      lowercase				       =			self.retriever._main_retrieve(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE			)
      lowercase				       =			self.retriever.index.get_doc_dicts(_SCREAMING_SNAKE_CASE			)
      return doc_ids, retrieved_doc_embeds, doc_dicts


class 			a	(  _a      ):







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase=None			):






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

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







    def      UpperCamelCase_ (     self			):
      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				,						_lowerCamelCase				,						_lowerCamelCase			):






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







    @classmethod
    def      UpperCamelCase_ (     cls				,						_lowerCamelCase				,						_lowerCamelCase=None				,						**_lowerCamelCase			):
      return super(_SCREAMING_SNAKE_CASE				,						cls			).get_tokenizers(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						**_SCREAMING_SNAKE_CASE			)







    @classmethod
    def      UpperCamelCase_ (     cls				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase=None				,						**_lowerCamelCase			):
      lowercase				       =			kwargs.pop('config'				,						_SCREAMING_SNAKE_CASE			) or RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE				,						**_SCREAMING_SNAKE_CASE			)
      lowercase				       =			RagTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE				,						config=_SCREAMING_SNAKE_CASE			)
      lowercase				       =			rag_tokenizer.question_encoder
      lowercase				       =			rag_tokenizer.generator

      if indexed_dataset is not None:
        lowercase				       =			"""custom"""
        lowercase				       =			CustomHFIndex(config.retrieval_vector_size				,						_SCREAMING_SNAKE_CASE			)
      else:
        lowercase				       =			cls._build_index(_SCREAMING_SNAKE_CASE			)

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







    def      UpperCamelCase_ (     self			):
      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				       =			self._build_index(self.config			)

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




'''simple docstring'''


import math
from collections.abc import Generator
def 					__magic_name__(       UpperCAmelCase__ :       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							,				UpperCAmelCase__	):
																		if (i % j) == 0:
																								break
												else:
																		yield i
def 					__magic_name__(       UpperCAmelCase__ :       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_     :	List[Any]					     =     int(math.sqrt(UpperCAmelCase__	)	) + 1
												for j in range(2							,				UpperCAmelCase__	):
																		if (i % j) == 0:
																								break
												else:
																		yield i
def 					__magic_name__(       UpperCAmelCase__ :       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_     :	Optional[int]					     =     int(math.sqrt(UpperCAmelCase__	)	) + 1
												for j in range(3							,				UpperCAmelCase__							,				2	):
																		# As we removed the even numbers, we don't need them now
																		if (i % j) == 0:
																								break
												else:
																		yield i
def 					__magic_name__(       ):
						from timeit import timeit

						UpperCamelCase_     :	int					     =     """from __main__ import slow_primes, primes, fast_primes"""
						print(timeit("""slow_primes(1_000_000_000_000)"""							,				setup=UpperCAmelCase__							,				number=1000000	)	)
						print(timeit("""primes(1_000_000_000_000)"""							,				setup=UpperCAmelCase__							,				number=1000000	)	)
						print(timeit("""fast_primes(1_000_000_000_000)"""							,				setup=UpperCAmelCase__							,				number=1000000	)	)


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE						=      int(input("Calculate primes up to:\n>> ").strip())
	for ret in primes(number):
		print(ret)
	benchmark()




