





'''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


__SCREAMING_SNAKE_CASE     :List[str]       =				logging.getLogger(__name__)







class   A_	(  lowerCAmelCase_			):




 def __init__(					self       :    List[Any]	,			snake_case_       :    Tuple	,			snake_case_       :    int	,			snake_case_       :    int	,			snake_case_       :    Optional[int]=None    ):
      super().__init__(
          __lowerCamelCase	,			question_encoder_tokenizer=__lowerCamelCase	,			generator_tokenizer=__lowerCamelCase	,			index=__lowerCamelCase	,			init_retrieval=__lowerCamelCase	,			)
      _UpperCAmelCase     							=   None




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    Optional[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
           _UpperCAmelCase     							=   self._infer_socket_ifname()
           # avoid clash with the NCCL port
           _UpperCAmelCase     							=   str(distributed_port + 1    )
           _UpperCAmelCase     							=   dist.new_group(ranks=__lowerCamelCase	,			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      lowercase	(					self       :    Optional[int]    ):
      return dist.get_rank(group=self.process_group    ) == 0




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Any	,			snake_case_       :    Any	,			snake_case_       :    Tuple=torch.floataa    ):
      _UpperCAmelCase     							=   torch.empty(__lowerCamelCase	,			dtype=__lowerCamelCase    )
      dist.scatter(__lowerCamelCase	,			src=0	,			scatter_list=__lowerCamelCase	,			group=self.process_group    )
      return target_tensor




 def      lowercase	(					self       :    int    ):
      _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"    ))	,			__lowerCamelCase    )
      return ifname




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    Optional[Any]    ):

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

      # 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(__lowerCamelCase    )]
      dist.gather(torch.tensor(__lowerCamelCase    )	,			dst=0	,			gather_list=__lowerCamelCase	,			group=self.process_group    )

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

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








from __future__ import annotations
def 							_UpperCAmelCase				(UpperCamelCase__     :  list[int]				,							UpperCamelCase__     :  list[int]				,							UpperCamelCase__     :  int						):
       _A      :       Dict				    = list(range(len(UpperCamelCase__						)						)						)
       _A      :       Any				    = [v / w for v, w in zip(UpperCamelCase__				,							UpperCamelCase__						)]
       index.sort(key=lambda UpperCamelCase__						: ratio[i]				,							reverse=UpperCamelCase__						)

       _A      :       float				    = 0
       _A      :       list[float]				    = [0] * len(UpperCamelCase__						)
       for i in index:
              if weight[i] <= capacity:
                     _A      :       Union[str, Any]				    = 1
                     max_value += value[i]
                     capacity -= weight[i]
              else:
                     _A      :       Optional[Any]				    = capacity / weight[i]
                     max_value += value[i] * capacity / weight[i]
                     break

       return max_value, fractions


if __name__ == "__main__":
    import doctest

    doctest.testmod()
