"""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     :		Dict											=					logging.getLogger(__name__)


class   UpperCAmelCase_			(					_a):


      def __init__(     self	,    a	,    a	,    a	,    a=None							) ->      Optional[int]:
             super().__init__(
                 a	,    question_encoder_tokenizer=a	,    generator_tokenizer=a	,    index=a	,    init_retrieval=a	,    )
             lowercase__    :	Any     					=			None


      def 	_UpperCAmelCase (     self	,    a							) ->      int:
             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
                    lowercase__    :	int     					=			self._infer_socket_ifname()
                    # avoid clash with the NCCL port
                    lowercase__    :	int     					=			str(distributed_port + 1							)
                    lowercase__    :	str     					=			dist.new_group(ranks=a	,    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							) ->      Optional[Any]:
             return dist.get_rank(group=self.process_group							) == 0


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a=torch.floataa							) ->      str:
             lowercase__    :	str     					=			torch.empty(a	,    dtype=a							)
             dist.scatter(a	,    src=0	,    scatter_list=a	,    group=self.process_group							)
             return target_tensor


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Optional[int]     					=			psutil.net_if_addrs()
             # a hacky way to deal with varying network interface names
             lowercase__    :	List[str]     					=			next((addr for addr in addrs if addr.startswith('e'							))	,    a							)
             return ifname


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

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

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

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

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

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


"""simple docstring"""





from __future__ import annotations





def        a_  (   _lowerCAmelCase						:				float     ,				_lowerCAmelCase						:				float     ,				_lowerCAmelCase						:				float     ,				):





       '''simple docstring'''

       if (stress, tangential_force, area).count(0  ) != 1:
              raise ValueError('You cannot supply more or less than 2 values'  )
       elif stress < 0:
              raise ValueError('Stress cannot be negative'  )
       elif tangential_force < 0:
              raise ValueError('Tangential Force cannot be negative'  )
       elif area < 0:
              raise ValueError('Area cannot be negative'  )
       elif stress == 0:
              return (
                  "stress",
                  tangential_force / area,
              )
       elif tangential_force == 0:
              return (
                  "tangential_force",
                  stress * area,
              )
       else:
              return (
                  "area",
                  tangential_force / stress,
              )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

