

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




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):

    def __init__(			self :      Tuple							,    snake_case__ :      int							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Tuple=None    ):

     """simple docstring"""






     super().__init__(
         snake_case__							,    question_encoder_tokenizer=snake_case__							,    generator_tokenizer=snake_case__							,    index=snake_case__							,    init_retrieval=snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			None

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      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
      SCREAMING_SNAKE_CASE								=			self._infer_socket_ifname()
      # avoid clash with the NCCL port
      SCREAMING_SNAKE_CASE								=			str(distributed_port + 1    )
      SCREAMING_SNAKE_CASE								=			dist.new_group(ranks=snake_case__							,    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 :      Any    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Any							,    snake_case__ :      Tuple							,    snake_case__ :      int							,    snake_case__ :      List[Any]=torch.floataa    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			torch.empty(snake_case__							,    dtype=snake_case__    )
     dist.scatter(snake_case__							,    src=0							,    scatter_list=snake_case__							,    group=self.process_group    )
     return target_tensor

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






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




    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      np.ndarray							,    snake_case__ :      int    ):

     """simple docstring"""






     if not dist.is_initialized():
      SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self._main_retrieve(snake_case__							,    snake_case__    )
      return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(snake_case__    )

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

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

     # scatter logic
     SCREAMING_SNAKE_CASE								=			question_hidden_states.shape[0]
     SCREAMING_SNAKE_CASE								=			[]
     SCREAMING_SNAKE_CASE								=			[]
     if self._is_main():
      assert len(snake_case__    ) == world_size
      SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			self._main_retrieve(torch.cat(snake_case__    ).numpy()							,    snake_case__    )
      SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			torch.tensor(snake_case__    ), torch.tensor(snake_case__    )
      SCREAMING_SNAKE_CASE								=			self._chunk_tensor(snake_case__							,    snake_case__    )
      SCREAMING_SNAKE_CASE								=			self._chunk_tensor(snake_case__							,    snake_case__    )
     SCREAMING_SNAKE_CASE								=			self._scattered(snake_case__							,    [n_queries, n_docs]							,    target_type=torch.intaa    )
     SCREAMING_SNAKE_CASE								=			self._scattered(snake_case__							,    [n_queries, n_docs, question_hidden_states.shape[1]]    )

     return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(snake_case__    )




from ....configuration_utils import PretrainedConfig
from ....utils import logging


a_    :						Any    		=      logging.get_logger(__name__)

a_    :						Dict    		=      {
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="van"

    def __init__(			self :      Optional[Any]							,    snake_case__ :      Tuple=2_2_4							,    snake_case__ :      Dict=3							,    snake_case__ :      Union[str, Any]=[7, 3, 3, 3]							,    snake_case__ :      str=[4, 2, 2, 2]							,    snake_case__ :      Optional[Any]=[6_4, 1_2_8, 3_2_0, 5_1_2]							,    snake_case__ :      Optional[Any]=[3, 3, 1_2, 3]							,    snake_case__ :      Tuple=[8, 8, 4, 4]							,    snake_case__ :      Any="gelu"							,    snake_case__ :      Dict=0.02							,    snake_case__ :      List[str]=1E-6							,    snake_case__ :      int=1E-2							,    snake_case__ :      Any=0.0							,    snake_case__ :      Tuple=0.0							,    **snake_case__ :      Any							,    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			image_size
     SCREAMING_SNAKE_CASE								=			num_channels
     SCREAMING_SNAKE_CASE								=			patch_sizes
     SCREAMING_SNAKE_CASE								=			strides
     SCREAMING_SNAKE_CASE								=			hidden_sizes
     SCREAMING_SNAKE_CASE								=			depths
     SCREAMING_SNAKE_CASE								=			mlp_ratios
     SCREAMING_SNAKE_CASE								=			hidden_act
     SCREAMING_SNAKE_CASE								=			initializer_range
     SCREAMING_SNAKE_CASE								=			layer_norm_eps
     SCREAMING_SNAKE_CASE								=			layer_scale_init_value
     SCREAMING_SNAKE_CASE								=			drop_path_rate
     SCREAMING_SNAKE_CASE								=			dropout_rate

