'''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_:     Union[str, Any]  =logging.getLogger(__name__)




class        __A      ( lowerCAmelCase__	):



    def __init__(self    :    Optional[Any]   ,			__a    :    Dict   ,			__a    :    List[str]   ,			__a    :    Any   ,			__a    :    Optional[Any]=None ):
         super().__init__(
             UpperCamelCase__   ,			question_encoder_tokenizer=UpperCamelCase__   ,			generator_tokenizer=UpperCamelCase__   ,			index=UpperCamelCase__   ,			init_retrieval=UpperCamelCase__   ,			)
         UpperCAmelCase_           =   None



    def 					_lowercase						(self    :    str   ,			__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
              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 					_lowercase						(self    :    List[str] ):
         return dist.get_rank(group=self.process_group ) == 0



    def 					_lowercase						(self    :    Any   ,			__a    :    int   ,			__a    :    Tuple   ,			__a    :    Optional[Any]=torch.floataa ):
         UpperCAmelCase_           =   torch.empty(UpperCamelCase__   ,			dtype=UpperCamelCase__ )
         dist.scatter(UpperCamelCase__   ,			src=0   ,			scatter_list=UpperCamelCase__   ,			group=self.process_group )
         return target_tensor



    def 					_lowercase						(self    :    Union[str, Any] ):
         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 					_lowercase						(self    :    Dict   ,			__a    :    int   ,			__a    :    Any ):

         # single GPU training
         if not dist.is_initialized():
              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_           =   self._main_retrieve(torch.cat(UpperCamelCase__ ).numpy()   ,			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__ )










from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   {'configuration_mmbt': ['MMBTConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__       :      List[Any]  =   ['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
       from .configuration_mmbt import MMBTConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
       import sys

       SCREAMING_SNAKE_CASE__       :      int  =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






