




'''simple docstring'''


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


UpperCAmelCase_ 		=      logging.getLogger(__name__)





class    __lowercase						(  __magic_name__ ):







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    Tuple:
     super().__init__(
         UpperCamelCase      ,							question_encoder_tokenizer=UpperCamelCase      ,							generator_tokenizer=UpperCamelCase      ,							index=UpperCamelCase      ,							init_retrieval=UpperCamelCase      ,							)
     __a			=					None







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Any:
     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
       __a			=					self._infer_socket_ifname()
       # avoid clash with the NCCL port
       __a			=					str(distributed_port + 1      )
       __a			=					dist.new_group(ranks=UpperCamelCase      ,							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 		UpperCamelCase__			(	self      )				->    Tuple:
     return dist.get_rank(group=self.process_group      ) == 0







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase=torch.floataa      )				->    List[str]:
     __a			=					torch.empty(UpperCamelCase      ,							dtype=UpperCamelCase      )
     dist.scatter(UpperCamelCase      ,							src=0      ,							scatter_list=UpperCamelCase      ,							group=self.process_group      )
     return target_tensor







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					psutil.net_if_addrs()
     # a hacky way to deal with varying network interface names
     __a			=					next((addr for addr in addrs if addr.startswith('e'      ))      ,							UpperCamelCase      )
     return ifname







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Tuple[np.ndarray, List[dict]]:

     # single GPU training
     if not dist.is_initialized():
       __a	,       __a			=					self._main_retrieve(UpperCamelCase      ,							UpperCamelCase      )
       return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase      )

     # distributed training
     __a			=					dist.get_world_size(group=self.process_group      )

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

     # scatter logic
     __a			=					question_hidden_states.shape[0]
     __a			=					[]
     __a			=					[]
     if self._is_main():
       assert len(UpperCamelCase      ) == world_size
       __a	,       __a			=					self._main_retrieve(torch.cat(UpperCamelCase      ).numpy()      ,							UpperCamelCase      )
       __a	,       __a			=					torch.tensor(UpperCamelCase      ), torch.tensor(UpperCamelCase      )
       __a			=					self._chunk_tensor(UpperCamelCase      ,							UpperCamelCase      )
       __a			=					self._chunk_tensor(UpperCamelCase      ,							UpperCamelCase      )
     __a			=					self._scattered(UpperCamelCase      ,							[n_queries, n_docs]      ,							target_type=torch.intaa      )
     __a			=					self._scattered(UpperCamelCase      ,							[n_queries, n_docs, question_hidden_states.shape[1]]      )

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









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							int = 10**12			):
  __a			=					1
  __a			=					0

  __a			=					1
  __a			=					1

  while numerator <= 2 * min_total - 1:
    prev_numerator += 2 * numerator
    numerator += 2 * prev_numerator

    prev_denominator += 2 * denominator
    denominator += 2 * prev_denominator

  return (denominator + 1) // 2


if __name__ == "__main__":
  print(f"""{solution() = }""")



