

import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


snake_case_			  = logging.getLogger(__name__)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   a						,   a						,   a						,   a=None):
         super().__init__(
             a						,   question_encoder_tokenizer=a						,   generator_tokenizer=a						,   index=a						,   init_retrieval=a						,   )
         lowercase__    :   int      =       None





    def       snake_case_				(      self						,   a):
         logger.info('initializing retrieval')

         # initializing a separate process group for retrieval as the default
         # nccl backend doesn't support gather/scatter operations while gloo
         # is too slow to replace nccl for the core gpu communication
         if dist.is_initialized():
              logger.info('dist initialized')
              # needs to be set manually
              lowercase__    :   List[Any]      =       self._infer_socket_ifname()
              # avoid clash with the NCCL port
              lowercase__    :   str      =       str(distributed_port + 1)
              lowercase__    :   List[str]      =       dist.new_group(ranks=a						,   backend='gloo')

         # initialize retriever only on the main worker
         if not dist.is_initialized() or self._is_main():
              logger.info('dist not initialized / main')
              self.index.init_index()

         # all processes wait untill the retriever is initialized by the main process
         if dist.is_initialized():
              torch.distributed.barrier(group=self.process_group)





    def       snake_case_				(      self):
         return dist.get_rank(group=self.process_group) == 0





    def       snake_case_				(      self						,   a						,   a						,   a=torch.floataa):
         lowercase__    :   int      =       torch.empty(a						,   dtype=a)
         dist.scatter(a						,   src=0						,   scatter_list=a						,   group=self.process_group)
         return target_tensor





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       psutil.net_if_addrs()
         # a hacky way to deal with varying network interface names
         lowercase__    :   Optional[Any]      =       next((addr for addr in addrs if addr.startswith('e'))						,   a)
         return ifname





    def       snake_case_				(      self						,   a						,   a):

         # single GPU training
         if not dist.is_initialized():
              lowercase__      , lowercase__    :   Optional[int]      =       self._main_retrieve(a						,   a)
              return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(a)

         # distributed training
         lowercase__    :   str      =       dist.get_world_size(group=self.process_group)

         # gather logic
         lowercase__    :   Optional[Any]      =       None
         if self._is_main():
              lowercase__    :   Any      =       [torch.empty(question_hidden_states.shape						,   dtype=torch.floataa) for _ in range(a)]
         dist.gather(torch.tensor(a)						,   dst=0						,   gather_list=a						,   group=self.process_group)

         # scatter logic
         lowercase__    :   int      =       question_hidden_states.shape[0]
         lowercase__    :   Any      =       []
         lowercase__    :   List[Any]      =       []
         if self._is_main():
              assert len(a) == world_size
              lowercase__      , lowercase__    :   Union[str, Any]      =       self._main_retrieve(torch.cat(a).numpy()						,   a)
              lowercase__      , lowercase__    :   Any      =       torch.tensor(a), torch.tensor(a)
              lowercase__    :   Dict      =       self._chunk_tensor(a						,   a)
              lowercase__    :   Optional[Any]      =       self._chunk_tensor(a						,   a)
         lowercase__    :   Any      =       self._scattered(a						,   [n_queries, n_docs]						,   target_type=torch.intaa)
         lowercase__    :   Dict      =       self._scattered(a						,   [n_queries, n_docs, question_hidden_states.shape[1]])

         return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(a)




import math

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    int			):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       []
     lowercase__    :   str      =       2
     lowercase__    :   Optional[Any]      =       int(math.sqrt(SCREAMING_SNAKE_CASE_			)			)  # Size of every segment
     lowercase__    :   Dict      =       [True] * (end + 1)
     lowercase__    :   Union[str, Any]      =       []

     while start <= end:
          if temp[start] is True:
               in_prime.append(SCREAMING_SNAKE_CASE_			)
               for i in range(start * start	,     end + 1	,     SCREAMING_SNAKE_CASE_			):
                    lowercase__    :   int      =       False
          start += 1
     prime += in_prime

     lowercase__    :   Optional[int]      =       end + 1
     lowercase__    :   List[str]      =       min(2 * end	,     SCREAMING_SNAKE_CASE_			)

     while low <= n:
          lowercase__    :   str      =       [True] * (high - low + 1)
          for each in in_prime:
               lowercase__    :   str      =       math.floor(low / each			) * each
               if t < low:
                    t += each

               for j in range(SCREAMING_SNAKE_CASE_	,     high + 1	,     SCREAMING_SNAKE_CASE_			):
                    lowercase__    :   Optional[Any]      =       False

          for j in range(len(SCREAMING_SNAKE_CASE_			)			):
               if temp[j] is True:
                    prime.append(j + low			)

          lowercase__    :   Optional[Any]      =       high + 1
          lowercase__    :   Optional[int]      =       min(high + end	,     SCREAMING_SNAKE_CASE_			)

     return prime

print(sieve(10**6))

