





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


__A									=				logging.getLogger(__name__)



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __init__(self				:   List[Any]       , UpperCAmelCase_				:   List[Any]       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   List[str]=None)       ->List[str]:



   '''simple docstring'''



   super().__init__(
       __UpperCAmelCase       , question_encoder_tokenizer=__UpperCAmelCase       , generator_tokenizer=__UpperCAmelCase       , index=__UpperCAmelCase       , init_retrieval=__UpperCAmelCase       , )
   lowerCamelCase__:      str			  =None

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any]       , UpperCAmelCase_				:   List[str])       ->Union[str, Any]:



   '''simple docstring'''



   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
     lowerCamelCase__:      Optional[Any]			  =self._infer_socket_ifname()
     # avoid clash with the NCCL port
     lowerCamelCase__:      Dict			  =str(distributed_port + 1)
     lowerCamelCase__:      List[Any]			  =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 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Optional[int]:



   '''simple docstring'''



   return dist.get_rank(group=self.process_group) == 0

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any]       , UpperCAmelCase_				:   Optional[int]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Tuple=torch.floataa)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =torch.empty(__UpperCAmelCase       , dtype=__UpperCAmelCase)
   dist.scatter(__UpperCAmelCase       , src=0       , scatter_list=__UpperCAmelCase       , group=self.process_group)
   return target_tensor

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->str:



   '''simple docstring'''



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







 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any]       , UpperCAmelCase_				:   Optional[Any]       , UpperCAmelCase_				:   Union[str, Any])       ->Dict:



   '''simple docstring'''



   if not dist.is_initialized():
     lowerCamelCase__:      Union[str, Any]			  =self._main_retrieve(__UpperCAmelCase       , __UpperCAmelCase)
     return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__UpperCAmelCase)

   # distributed training
   lowerCamelCase__:      List[str]			  =dist.get_world_size(group=self.process_group)

   # gather logic
   lowerCamelCase__:      Any			  =None
   if self._is_main():
     lowerCamelCase__:      Dict			  =[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
   lowerCamelCase__:      Optional[int]			  =question_hidden_states.shape[0]
   lowerCamelCase__:      Tuple			  =[]
   lowerCamelCase__:      str			  =[]
   if self._is_main():
     assert len(__UpperCAmelCase) == world_size
     lowerCamelCase__:      Any			  =self._main_retrieve(torch.cat(__UpperCAmelCase).numpy()       , __UpperCAmelCase)
     lowerCamelCase__:      str			  =torch.tensor(__UpperCAmelCase), torch.tensor(__UpperCAmelCase)
     lowerCamelCase__:      Dict			  =self._chunk_tensor(__UpperCAmelCase       , __UpperCAmelCase)
     lowerCamelCase__:      Tuple			  =self._chunk_tensor(__UpperCAmelCase       , __UpperCAmelCase)
   lowerCamelCase__:      List[str]			  =self._scattered(__UpperCAmelCase       , [n_queries, n_docs]       , target_type=torch.intaa)
   lowerCamelCase__:      Optional[int]			  =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"""





from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
__A											=		Lock()
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->List[Any]:





  """simple docstring"""


  global process_lock

  # we perform n swaps since after n swaps we know we are sorted
  # we *could* stop early if we are sorted already, but it takes as long to
  # find out we are sorted as it does to sort the list with this algorithm
  for i in range(0     ,   10			):
    if (i + position) % 2 == 0 and r_send is not None:
      # send your value to your right neighbor
      process_lock.acquire()
      r_send[1].send(_SCREAMING_SNAKE_CASE			)
      process_lock.release()

      # receive your right neighbor's value
      process_lock.acquire()
      lowerCAmelCase__			:Any											=							rr_cv[0].recv()
      process_lock.release()

      # take the lower value since you are on the left
      lowerCAmelCase__			:Tuple											=							min(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
    elif (i + position) % 2 != 0 and l_send is not None:
      # send your value to your left neighbor
      process_lock.acquire()
      l_send[1].send(_SCREAMING_SNAKE_CASE			)
      process_lock.release()

      # receive your left neighbor's value
      process_lock.acquire()
      lowerCAmelCase__			:Optional[int]											=							lr_cv[0].recv()
      process_lock.release()

      # take the higher value since you are on the right
      lowerCAmelCase__			:Optional[int]											=							max(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
    # after all swaps are performed, send the values back to main
  result_pipe[1].send(_SCREAMING_SNAKE_CASE			)
def  __A   (_SCREAMING_SNAKE_CASE			)					->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__			:str											=							[]
  lowerCAmelCase__			:Optional[Any]											=							[]
  # initialize the list of pipes where the values will be retrieved
  for _ in arr:
    result_pipe.append(Pipe()			)
  # creates the processes
  # the first and last process only have one neighbor so they are made outside
  # of the loop
  lowerCAmelCase__			:List[str]											=							Pipe()
  lowerCAmelCase__			:List[Any]											=							Pipe()
  process_array_.append(
      Process(
          target=_SCREAMING_SNAKE_CASE     ,   args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])     ,   )			)
  lowerCAmelCase__			:Dict											=							temp_rs
  lowerCAmelCase__			:Optional[Any]											=							temp_rr

  for i in range(1     ,   len(_SCREAMING_SNAKE_CASE			) - 1			):
    lowerCAmelCase__			:Union[str, Any]											=							Pipe()
    lowerCAmelCase__			:List[str]											=							Pipe()
    process_array_.append(
        Process(
            target=_SCREAMING_SNAKE_CASE     ,   args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])     ,   )			)
    lowerCAmelCase__			:Union[str, Any]											=							temp_rs
    lowerCAmelCase__			:Any											=							temp_rr

  process_array_.append(
      Process(
          target=_SCREAMING_SNAKE_CASE     ,   args=(
              len(_SCREAMING_SNAKE_CASE			) - 1,
              arr[len(_SCREAMING_SNAKE_CASE			) - 1],
              temp_ls,
              None,
              temp_lr,
              None,
              result_pipe[len(_SCREAMING_SNAKE_CASE			) - 1],
          )     ,   )			)

  # start the processes
  for p in process_array_:
    p.start()

  # wait for the processes to end and write their values to the list
  for p in range(0     ,   len(_SCREAMING_SNAKE_CASE			)			):
    lowerCAmelCase__			:str											=							result_pipe[p][0].recv()
    process_array_[p].join()
  return arr
def  __A   ()					->List[Any]:





  """simple docstring"""


  lowerCAmelCase__			:Union[str, Any]											=							list(range(10     ,   0     ,   -1			)			)
  print('Initial List'			)
  print(*_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:List[str]											=							odd_even_transposition(_SCREAMING_SNAKE_CASE			)
  print('Sorted List\n'			)
  print(*_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
  main()
