
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   =			logging.getLogger(__name__)







class 					lowerCamelCase		(  A_  ):





     def __init__(self				:   List[Any]    ,   _A				:   Optional[int]    ,   _A				:   Dict    ,   _A				:   int    ,   _A				:   Any=None       )						->						Dict:
         super().__init__(
             _A    ,   question_encoder_tokenizer=_A    ,   generator_tokenizer=_A    ,   index=_A    ,   init_retrieval=_A    ,   )
         snake_case								= None





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   int       )						->						Tuple:
         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
             snake_case								= self._infer_socket_ifname()
             # avoid clash with the NCCL port
             snake_case								= str(distributed_port + 1       )
             snake_case								= 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 		UpperCAmelCase(self				:   Dict       )						->						Any:
         return dist.get_rank(group=self.process_group       ) == 0





     def 		UpperCAmelCase(self				:   Dict    ,   _A				:   Optional[Any]    ,   _A				:   Tuple    ,   _A				:   Optional[Any]=torch.floataa       )						->						Optional[Any]:
         snake_case								= torch.empty(_A    ,   dtype=_A       )
         dist.scatter(_A    ,   src=0    ,   scatter_list=_A    ,   group=self.process_group       )
         return target_tensor





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





     def 		UpperCAmelCase(self				:   str    ,   _A				:   np.ndarray    ,   _A				:   int       )						->						Tuple[np.ndarray, List[dict]]:

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

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

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



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


_A   =			logging.get_logger(__name__)

_A   =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_A   =			{
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

_A   =			{"mobilebert-uncased": 5_12}


_A   =			{}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Tuple									=		VOCAB_FILES_NAMES
     UpperCAmelCase__    :							Any									=		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__    :							Optional[Any]									=		MobileBertTokenizer





     def __init__(self				:   Any    ,   _A				:   str=None    ,   _A				:   str=None    ,   _A				:   Union[str, Any]=True    ,   _A				:   Optional[Any]="[UNK]"    ,   _A				:   int="[SEP]"    ,   _A				:   Dict="[PAD]"    ,   _A				:   int="[CLS]"    ,   _A				:   Union[str, Any]="[MASK]"    ,   _A				:   Any=True    ,   _A				:   Dict=None    ,   **_A				:   List[str]    ,   )						->						List[str]:
         super().__init__(
             _A    ,   tokenizer_file=_A    ,   do_lower_case=_A    ,   unk_token=_A    ,   sep_token=_A    ,   pad_token=_A    ,   cls_token=_A    ,   mask_token=_A    ,   tokenize_chinese_chars=_A    ,   strip_accents=_A    ,   **_A    ,   )

         snake_case								= json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
         if (
             normalizer_state.get("lowercase"    ,   _A       ) != do_lower_case
             or normalizer_state.get("strip_accents"    ,   _A       ) != strip_accents
             or normalizer_state.get("handle_chinese_chars"    ,   _A       ) != tokenize_chinese_chars
         ):
             snake_case								= getattr(_A    ,   normalizer_state.pop("type"       )       )
             snake_case								= do_lower_case
             snake_case								= strip_accents
             snake_case								= tokenize_chinese_chars
             snake_case								= normalizer_class(**_A       )

         snake_case								= do_lower_case





     def 		UpperCAmelCase(self				:   List[str]    ,   _A				:   Union[str, Any]    ,   _A				:   Dict=None       )						->						Optional[Any]:
         snake_case								= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

         return output





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None       )						->						List[int]:
         snake_case								= [self.sep_token_id]
         snake_case								= [self.cls_token_id]
         if token_ids_a is None:
             return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]





     def 		UpperCAmelCase(self				:   int    ,   _A				:   str    ,   _A				:   Optional[str] = None       )						->						Tuple[str]:
         snake_case								= self._tokenizer.model.save(_A    ,   name=_A       )
         return tuple(_A       )

