





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


snake_case 				=       logging.getLogger(__name__)





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	str						,__A	:	Tuple						,__A	:	Optional[Any]						,__A	:	int						,__A	:	Tuple=None  )						->					Optional[Any]:
         super().__init__(
             __A						,question_encoder_tokenizer=__A						,generator_tokenizer=__A						,index=__A						,init_retrieval=__A						,)
         _lowercase							= None







       def    __UpperCAmelCase (    self	:	Dict						,__A	:	int  )						->					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
           _lowercase							= self._infer_socket_ifname()
           # avoid clash with the NCCL port
           _lowercase							= str(distributed_port + 1  )
           _lowercase							= 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	:	Union[str, Any]  )						->					List[Any]:
         return dist.get_rank(group=self.process_group  ) == 0







       def    __UpperCAmelCase (    self	:	Any						,__A	:	List[Any]						,__A	:	int						,__A	:	List[str]=torch.floataa  )						->					Union[str, Any]:
         _lowercase							= torch.empty(__A						,dtype=__A  )
         dist.scatter(__A						,src=0						,scatter_list=__A						,group=self.process_group  )
         return target_tensor







       def    __UpperCAmelCase (    self	:	Tuple  )						->					List[str]:
         _lowercase							= psutil.net_if_addrs()
         # a hacky way to deal with varying network interface names
         _lowercase							= next((addr for addr in addrs if addr.startswith('e'  ))						,__A  )
         return ifname







       def    __UpperCAmelCase (    self	:	str						,__A	:	np.ndarray						,__A	:	int  )						->					Tuple[np.ndarray, List[dict]]:

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

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

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










# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)