





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 			__UpperCamelCase    (							A__    ):





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase=None					):
        super().__init__(
            _UpperCamelCase				,							question_encoder_tokenizer=_UpperCamelCase				,							generator_tokenizer=_UpperCamelCase				,							index=_UpperCamelCase				,							init_retrieval=_UpperCamelCase				,							)
        _UpperCAmelCase           =   None





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        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
           _UpperCAmelCase           =   self._infer_socket_ifname()
           # avoid clash with the NCCL port
           _UpperCAmelCase           =   str(distributed_port + 1					)
           _UpperCAmelCase           =   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					):
        return dist.get_rank(group=self.process_group					) == 0





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase=torch.floataa					):
        _UpperCAmelCase           =   torch.empty(_UpperCamelCase				,							dtype=_UpperCamelCase					)
        dist.scatter(_UpperCamelCase				,							src=0				,							scatter_list=_UpperCamelCase				,							group=self.process_group					)
        return target_tensor





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





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase					):

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

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

        # gather logic
        _UpperCAmelCase           =   None
        if self._is_main():
           _UpperCAmelCase           =   [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
        _UpperCAmelCase           =   question_hidden_states.shape[0]
        _UpperCAmelCase           =   []
        _UpperCAmelCase           =   []
        if self._is_main():
           assert len(_UpperCamelCase					) == world_size
           _UpperCAmelCase       ,      _UpperCAmelCase           =   self._main_retrieve(torch.cat(_UpperCamelCase					).numpy()				,							_UpperCamelCase					)
           _UpperCAmelCase       ,      _UpperCAmelCase           =   torch.tensor(_UpperCamelCase					), torch.tensor(_UpperCamelCase					)
           _UpperCAmelCase           =   self._chunk_tensor(_UpperCamelCase				,							_UpperCamelCase					)
           _UpperCAmelCase           =   self._chunk_tensor(_UpperCamelCase				,							_UpperCamelCase					)
        _UpperCAmelCase           =   self._scattered(_UpperCamelCase				,							[n_queries, n_docs]				,							target_type=torch.intaa					)
        _UpperCAmelCase           =   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					)







def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[Any]     )    ->   Any:





    """simple docstring"""

    stooge(__lowercase      ,					0      ,					len(__lowercase     ) - 1     )
    return arr



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      Dict      ,					__lowercase :      Dict     )    ->   int:





    """simple docstring"""

    if i >= h:
        return

    # If first element is smaller than the last then swap them
    if arr[i] > arr[h]:
        __A     ,						__A       =						arr[h], arr[i]

    # If there are more than 2 elements in the array
    if h - i + 1 > 2:
        __A       =						(int)((h - i + 1) / 3     )

        # Recursively sort first 2/3 elements
        stooge(__lowercase      ,					__lowercase      ,					(h - t)     )

        # Recursively sort last 2/3 elements
        stooge(__lowercase      ,					i + t      ,					(__lowercase)     )

        # Recursively sort first 2/3 elements
        stooge(__lowercase      ,					__lowercase      ,					(h - t)     )


if __name__ == "__main__":
  __a   :							List[Any]						= input("Enter numbers separated by a comma:\n").strip()
  __a   :							List[str]						= [int(item) for item in user_input.split(",")]
  print(stooge_sort(unsorted))




