


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

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase=None    ):

  """simple docstring"""







  super().__init__(
      __lowercase   ,   question_encoder_tokenizer=__lowercase   ,   generator_tokenizer=__lowercase   ,   index=__lowercase   ,   init_retrieval=__lowercase   ,   )
  __A	:		Dict	  =      None


 def 			snake_case__ (   self   ,   __lowercase    ):

  """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
   __A	:		Optional[Any]	  =      self._infer_socket_ifname()
   # avoid clash with the NCCL port
   __A	:		Tuple	  =      str(distributed_port + 1    )
   __A	:		Dict	  =      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 			snake_case__ (   self    ):

  """simple docstring"""







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


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase=torch.floataa    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      torch.empty(__lowercase   ,   dtype=__lowercase    )
  dist.scatter(__lowercase   ,   src=0   ,   scatter_list=__lowercase   ,   group=self.process_group    )
  return target_tensor


 def 			snake_case__ (   self    ):

  """simple docstring"""







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







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  if not dist.is_initialized():
   __A  ,__A	:		Dict	  =      self._main_retrieve(__lowercase   ,   __lowercase    )
   return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowercase    )

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

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










'''simple docstring'''
from typing import TYPE_CHECKING

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


UpperCAmelCase_			:  Any										=   {
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Tuple										=   ['MaskFormerFeatureExtractor']
 UpperCAmelCase_			:  List[str]										=   ['MaskFormerImageProcessor']


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[Any]										=   [
     'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MaskFormerForInstanceSegmentation',
     'MaskFormerModel',
     'MaskFormerPreTrainedModel',
 ]
 UpperCAmelCase_			:  Optional[int]										=   [
     'MaskFormerSwinBackbone',
     'MaskFormerSwinModel',
     'MaskFormerSwinPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
 from .configuration_maskformer_swin import MaskFormerSwinConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_maskformer import MaskFormerFeatureExtractor
  from .image_processing_maskformer import MaskFormerImageProcessor
 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_maskformer import (
      MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      MaskFormerForInstanceSegmentation,
      MaskFormerModel,
      MaskFormerPreTrainedModel,
  )
  from .modeling_maskformer_swin import (
      MaskFormerSwinBackbone,
      MaskFormerSwinModel,
      MaskFormerSwinPreTrainedModel,
  )


else:
 import sys

 UpperCAmelCase_			:  Union[str, Any]										=   _LazyModule(__name__, globals()['__file__'], _import_structure)






