'''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


_A     : Optional[int]    =							logging.getLogger(__name__)







class 							_lowercase	(							UpperCAmelCase__     ):


 '''simple docstring'''






 def __init__( self  :      int ,			SCREAMING_SNAKE_CASE__  :      Optional[int] ,			SCREAMING_SNAKE_CASE__  :      Union[str, Any] ,			SCREAMING_SNAKE_CASE__  :      Any ,			SCREAMING_SNAKE_CASE__  :      List[str]=None				)    ->       int:
  super().__init__(
      A_ ,			question_encoder_tokenizer=A_ ,			generator_tokenizer=A_ ,			index=A_ ,			init_retrieval=A_ ,			)
  __lowerCAmelCase											=		None
 def a   ( self  :      Tuple ,			SCREAMING_SNAKE_CASE__  :      Tuple				)    ->       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
   __lowerCAmelCase											=		self._infer_socket_ifname()
   # avoid clash with the NCCL port
   __lowerCAmelCase											=		str(distributed_port + 1				)
   __lowerCAmelCase											=		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 a   ( self  :      Tuple				)    ->       Optional[Any]:
  return dist.get_rank(group=self.process_group				) == 0
 def a   ( self  :      int ,			SCREAMING_SNAKE_CASE__  :      str ,			SCREAMING_SNAKE_CASE__  :      str ,			SCREAMING_SNAKE_CASE__  :      Optional[Any]=torch.floataa				)    ->       Tuple:
  __lowerCAmelCase											=		torch.empty(A_ ,			dtype=A_				)
  dist.scatter(A_ ,			src=0 ,			scatter_list=A_ ,			group=self.process_group				)
  return target_tensor
 def a   ( self  :      str				)    ->       Optional[int]:
  __lowerCAmelCase											=		psutil.net_if_addrs()
  # a hacky way to deal with varying network interface names
  __lowerCAmelCase											=		next((addr for addr in addrs if addr.startswith("""e"""				)) ,			A_				)
  return ifname





 def a   ( self  :      Any ,			SCREAMING_SNAKE_CASE__  :      Optional[Any] ,			SCREAMING_SNAKE_CASE__  :      Optional[Any]				)    ->       Dict:

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

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

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__					:     List[str]       =     [
     'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
     'SEWForCTC',
     'SEWForSequenceClassification',
     'SEWModel',
     'SEWPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_sew import (
      SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
      SEWForCTC,
      SEWForSequenceClassification,
      SEWModel,
      SEWPreTrainedModel,
  )


else:
 import sys

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





