


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


lowerCAmelCase__      		= logging.getLogger(__name__)







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase=None)			->				int:
            super().__init__(
                __lowerCamelCase ,						question_encoder_tokenizer=__lowerCamelCase ,						generator_tokenizer=__lowerCamelCase ,						index=__lowerCamelCase ,						init_retrieval=__lowerCamelCase ,						)
            _A      :       int				    = None


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				List[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
                   _A      :       Any				    = self._infer_socket_ifname()
                   # avoid clash with the NCCL port
                   _A      :       int				    = str(distributed_port + 1)
                   _A      :       Tuple				    = dist.new_group(ranks=__lowerCamelCase ,						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)			->				Optional[Any]:
            return dist.get_rank(group=self.process_group) == 0


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase=torch.floataa)			->				Union[str, Any]:
            _A      :       Tuple				    = torch.empty(__lowerCamelCase ,						dtype=__lowerCamelCase)
            dist.scatter(__lowerCamelCase ,						src=0 ,						scatter_list=__lowerCamelCase ,						group=self.process_group)
            return target_tensor


     def  _lowerCamelCase (						self)			->				List[str]:
            _A      :       Any				    = psutil.net_if_addrs()
            # a hacky way to deal with varying network interface names
            _A      :       Optional[Any]				    = next((addr for addr in addrs if addr.startswith("e")) ,						__lowerCamelCase)
            return ifname


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				Tuple[np.ndarray, List[dict]]:

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

            # distributed training
            _A      :       Tuple				    = dist.get_world_size(group=self.process_group)

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

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

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




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


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {
    'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= "vit_mae"


     def __init__(						self ,						__lowerCamelCase=7_6_8 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=3_0_7_2 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-12 ,						__lowerCamelCase=2_2_4 ,						__lowerCamelCase=1_6 ,						__lowerCamelCase=3 ,						__lowerCamelCase=True ,						__lowerCamelCase=1_6 ,						__lowerCamelCase=5_1_2 ,						__lowerCamelCase=8 ,						__lowerCamelCase=2_0_4_8 ,						__lowerCamelCase=0.7_5 ,						__lowerCamelCase=False ,						**__lowerCamelCase ,						)			->				int:
            super().__init__(**__lowerCamelCase)

            _A      :       int				    = hidden_size
            _A      :       List[str]				    = num_hidden_layers
            _A      :       List[Any]				    = num_attention_heads
            _A      :       Optional[Any]				    = intermediate_size
            _A      :       Optional[int]				    = hidden_act
            _A      :       List[Any]				    = hidden_dropout_prob
            _A      :       List[Any]				    = attention_probs_dropout_prob
            _A      :       Union[str, Any]				    = initializer_range
            _A      :       str				    = layer_norm_eps
            _A      :       Any				    = image_size
            _A      :       int				    = patch_size
            _A      :       int				    = num_channels
            _A      :       Dict				    = qkv_bias
            _A      :       Tuple				    = decoder_num_attention_heads
            _A      :       Tuple				    = decoder_hidden_size
            _A      :       List[str]				    = decoder_num_hidden_layers
            _A      :       Optional[Any]				    = decoder_intermediate_size
            _A      :       List[str]				    = mask_ratio
            _A      :       Union[str, Any]				    = norm_pix_loss
