


'''simple docstring'''






def lowerCamelCase__      (						__lowerCamelCase	:							list	,   __lowerCamelCase	:							int = 0							):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =length or len(__lowerCamelCase							)
   _UpperCAmelCase       :       List[Any]							   =False
   for i in range(length - 1							):
      if list_data[i] > list_data[i + 1]:
         _UpperCAmelCase   ,	_UpperCAmelCase       :       str							   =list_data[i + 1], list_data[i]
         _UpperCAmelCase       :       Union[str, Any]							   =True

   return list_data if not swapped else bubble_sort(__lowerCamelCase	,   length - 1							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()





'''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


lowercase							=logging.getLogger(__name__)






class  __magic_name__							(		lowerCAmelCase    ):






       def __init__(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case=None)						->  List[Any]:




          '''simple docstring'''
          super().__init__(
              snake_case  ,							question_encoder_tokenizer=snake_case  ,							generator_tokenizer=snake_case  ,							index=snake_case  ,							init_retrieval=snake_case  ,							)
          _UpperCAmelCase       :       Union[str, Any]							   =None






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Optional[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
             _UpperCAmelCase       :       Optional[Any]							   =self._infer_socket_ifname()
             # avoid clash with the NCCL port
             _UpperCAmelCase       :       Optional[int]							   =str(distributed_port + 1)
             _UpperCAmelCase       :       Any							   =dist.new_group(ranks=snake_case  ,							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       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          return dist.get_rank(group=self.process_group) == 0






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case=torch.floataa)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =torch.empty(snake_case  ,							dtype=snake_case)
          dist.scatter(snake_case  ,							src=0  ,							scatter_list=snake_case  ,							group=self.process_group)
          return target_tensor






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




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =psutil.net_if_addrs()
          # a hacky way to deal with varying network interface names
          _UpperCAmelCase       :       str							   =next((addr for addr in addrs if addr.startswith('e'))  ,							snake_case)
          return ifname






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




          '''simple docstring'''

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

          # distributed training
          _UpperCAmelCase       :       Optional[int]							   =dist.get_world_size(group=self.process_group)

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

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

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

