
'''simple docstring'''


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     __snake_case			:




		def __init__(  self					:Tuple)					->			List[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Union[str, Any]					     =     False




		def 	_snake_case	(  self					:List[Any]			,    _SCREAMING_SNAKE_CASE					:List[str]			,    _SCREAMING_SNAKE_CASE					:Any			,    _SCREAMING_SNAKE_CASE					:str			,    _SCREAMING_SNAKE_CASE					:Optional[Any])					->			Tuple:






								'''simple docstring'''






								if not self.initialized:
														UpperCamelCase_     :	int					     =     RagRetriever(
														    _SCREAMING_SNAKE_CASE			,    question_encoder_tokenizer=_SCREAMING_SNAKE_CASE			,    generator_tokenizer=_SCREAMING_SNAKE_CASE			,    index=_SCREAMING_SNAKE_CASE			,    init_retrieval=_SCREAMING_SNAKE_CASE			,    )
														UpperCamelCase_     :	Optional[Any]					     =     True




		def 	_snake_case	(  self					:Optional[Any])					->			Any:






								'''simple docstring'''






								self.retriever.index.init_index()




		def 	_snake_case	(  self					:Any)					->			Union[str, Any]:






								'''simple docstring'''






								del self.retriever
								UpperCamelCase_     :	int					     =     False





		def 	_snake_case	(  self					:List[str]			,    _SCREAMING_SNAKE_CASE					:int			,    _SCREAMING_SNAKE_CASE					:Tuple)					->			int:






								'''simple docstring'''






								UpperCamelCase_     ,UpperCamelCase_     :	Any					     =     self.retriever._main_retrieve(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Dict					     =     self.retriever.index.get_doc_dicts(_SCREAMING_SNAKE_CASE)
								return doc_ids, retrieved_doc_embeds, doc_dicts







class     __snake_case			(    _a	):




		def __init__(  self					:Tuple			,    _SCREAMING_SNAKE_CASE					:Dict			,    _SCREAMING_SNAKE_CASE					:Optional[int]			,    _SCREAMING_SNAKE_CASE					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:int			,    _SCREAMING_SNAKE_CASE					:Optional[Any]=None)					->			Union[str, Any]:






								'''simple docstring'''






								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			,    )

								UpperCamelCase_     :	int					     =     retrieval_workers
								UpperCamelCase_     :	Union[str, Any]					     =     question_encoder_tokenizer
								UpperCamelCase_     :	List[str]					     =     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 	_snake_case	(  self					:Optional[int])					->			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 	_snake_case	(  self					:int			,    _SCREAMING_SNAKE_CASE					:Tuple			,    _SCREAMING_SNAKE_CASE					:str)					->			Any:






								'''simple docstring'''






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




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






								'''simple docstring'''






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




		@classmethod
		def 	_snake_case	(  cls					:int			,    _SCREAMING_SNAKE_CASE					:str			,    _SCREAMING_SNAKE_CASE					:Dict			,    _SCREAMING_SNAKE_CASE					:Optional[int]=None			,    **_SCREAMING_SNAKE_CASE					:str)					->			int:






								'''simple docstring'''






								UpperCamelCase_     :	Optional[Any]					     =     kwargs.pop("""config"""			,    _SCREAMING_SNAKE_CASE) or RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	int					     =     RagTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE			,    config=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	int					     =     rag_tokenizer.question_encoder
								UpperCamelCase_     :	List[str]					     =     rag_tokenizer.generator

								if indexed_dataset is not None:
														UpperCamelCase_     :	Optional[int]					     =     """custom"""
														UpperCamelCase_     :	Dict					     =     CustomHFIndex(config.retrieval_vector_size			,    _SCREAMING_SNAKE_CASE)
								else:
														UpperCamelCase_     :	Optional[Any]					     =     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 	_snake_case	(  self					:Tuple)					->			List[str]:






								'''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
								UpperCamelCase_     :	Dict					     =     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()




