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_			    =							logging.getLogger(__name__)




class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  List[Any]		,  _lowercase					:  Dict		,  _lowercase					:  Optional[Any]		,  _lowercase					:  Optional[int]		,  _lowercase					:  Tuple=None							):



    """simple docstring"""



    super().__init__(
        _lowercase		,  question_encoder_tokenizer=_lowercase		,  generator_tokenizer=_lowercase		,  index=_lowercase		,  init_retrieval=_lowercase		,  )
    _UpperCamelCase:     Dict							= None



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  int							):



    """simple docstring"""



    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:     Any							= self._infer_socket_ifname()
     # avoid clash with the NCCL port
     _UpperCamelCase:     str							= str(distributed_port + 1							)
     _UpperCamelCase:     str							= dist.new_group(ranks=_lowercase		,  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   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    return dist.get_rank(group=self.process_group							) == 0



   def   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  int		,  _lowercase					:  int		,  _lowercase					:  Optional[Any]=torch.floataa							):



    """simple docstring"""



    _UpperCamelCase:     str							= torch.empty(_lowercase		,  dtype=_lowercase							)
    dist.scatter(_lowercase		,  src=0		,  scatter_list=_lowercase		,  group=self.process_group							)
    return target_tensor



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Any							= psutil.net_if_addrs()
    # a hacky way to deal with varying network interface names
    _UpperCamelCase:     str							= next((addr for addr in addrs if addr.startswith('''e'''							))		,  _lowercase							)
    return ifname



   def   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  np.ndarray		,  _lowercase					:  int							):



    """simple docstring"""



    if not dist.is_initialized():
     _UpperCamelCase	,							_UpperCamelCase:     Any							= self._main_retrieve(_lowercase		,  _lowercase							)
     return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_lowercase							)

    # distributed training
    _UpperCamelCase:     str							= dist.get_world_size(group=self.process_group							)

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

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

    return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(_lowercase							)
import warnings

from ..trainer import Trainer
from ..utils import logging


UpperCAmelCase_			    =							logging.get_logger(__name__)




class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  List[Any]		,  _lowercase					:  int=None		,  **_lowercase					:  Optional[Any]							):



    """simple docstring"""



    warnings.warn(
        '''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
        '''instead.'''		,  _lowercase		,  )
    super().__init__(args=_lowercase		,  **_lowercase							)