code: 
  
  
  
  """simple docstring"""
  import os
  from functools import partial
  from glob import glob
  
  import faiss
  from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk
  
  from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	Any=100  , UpperCamelCase__	:	int=" "							):
      _UpperCAmelCase							:						Tuple       							=					text.split(UpperCamelCase__							)
      return [character.join(text[i : i + n]							).strip() for i in range(0  , len(UpperCamelCase__							)  , UpperCamelCase__							)]
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[str]							):
      _UpperCAmelCase							,       _UpperCAmelCase							:						Optional[int]       							=					[], []
      for title, text in zip(documents['''title''']  , documents['''text''']							):
          if text is not None:
              for passage in split_text(UpperCamelCase__							):
                  titles.append(title if title is not None else ''''''							)
                  texts.append(UpperCamelCase__							)
      return {"title": titles, "text": texts}
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	Dict  , UpperCamelCase__	:	int							):
      _UpperCAmelCase							:						str       							=					load_dataset(
          '''csv'''  , data_files=[csv_path]  , split='''train'''  , delimiter='''\t'''  , column_names=['''title''', '''text''']							)
      _UpperCAmelCase							:						Tuple       							=					kb_dataset.map(
          UpperCamelCase__  , batched=UpperCamelCase__  , num_proc=1							)  # if you want you can load already splitted csv.
      _UpperCAmelCase							:						Dict       							=					[kb_dataset.shard(UpperCamelCase__  , UpperCamelCase__  , contiguous=UpperCamelCase__							) for i in range(UpperCamelCase__							)]
      _UpperCAmelCase							:						Dict       							=					kb_list[process_num]
  
      _UpperCAmelCase							:						str       							=					'''data_''' + str(UpperCamelCase__							)
      _UpperCAmelCase							:						int       							=					os.path.join(UpperCamelCase__  , UpperCamelCase__							)
  
      _UpperCAmelCase							:						Tuple       							=					DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''							)
      _UpperCAmelCase							:						List[Any]       							=					ctx_encoder.to(device=UpperCamelCase__							)
  
      def embed(
          UpperCamelCase__	:	dict  , UpperCamelCase__	:	DPRContextEncoder  , UpperCamelCase__	:	DPRContextEncoderTokenizerFast  , UpperCamelCase__	:	Optional[Any]							) -> dict:
          _UpperCAmelCase							:						Any       							=					ctx_tokenizer(
              documents['''title''']  , documents['''text''']  , truncation=UpperCamelCase__  , padding='''longest'''  , return_tensors='''pt'''							)['''input_ids''']
          _UpperCAmelCase							:						Dict       							=					ctx_encoder(input_ids.to(device=UpperCamelCase__							)  , return_dict=UpperCamelCase__							).pooler_output
          return {"embeddings": embeddings.detach().cpu().numpy()}
  
      _UpperCAmelCase							:						List[Any]       							=					Features(
          {'''text''': Value('''string'''							), '''title''': Value('''string'''							), '''embeddings''': Sequence(Value('''float32'''							)							)}							)  # optional, save as float32 instead of float64 to save space
  
      _UpperCAmelCase							:						Tuple       							=					data_shrad.map(
          partial(UpperCamelCase__  , ctx_encoder=UpperCamelCase__  , ctx_tokenizer=UpperCamelCase__  , device=UpperCamelCase__							)  , batched=UpperCamelCase__  , batch_size=16  , features=UpperCamelCase__  , )
      dataset.save_to_disk(UpperCamelCase__							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Union[str, Any]  , UpperCamelCase__	:	Optional[int]							):
      _UpperCAmelCase							:						Dict       							=					[]
  
      for shard_address in glob(str(UpperCamelCase__							) + '''/*/'''							):
          data_shard_list.append(load_from_disk(UpperCamelCase__							)							)
  
      _UpperCAmelCase							:						Dict       							=					concatenate_datasets(UpperCamelCase__							)
      faiss.omp_set_num_threads(96							)
  
      _UpperCAmelCase							:						Optional[int]       							=					faiss.IndexHNSWFlat(768  , 128  , faiss.METRIC_INNER_PRODUCT							)
      concat.add_faiss_index('''embeddings'''  , custom_index=UpperCamelCase__							)
      concat.get_index('''embeddings'''							).save(
          UpperCamelCase__							)  # since we load the index in to memory,we can directly update the index in the disk
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import json
  from typing import List, Optional, Tuple
  
  from tokenizers import normalizers
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_mpnet import MPNetTokenizer
  
  
  _lowerCAmelCase					:Dict  =      logging.get_logger(__name__)
  
  _lowerCAmelCase					:Tuple  =      {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
  
  _lowerCAmelCase					:Any  =      {
      'vocab_file': {
          'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
      },
      'tokenizer_file': {
          'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
      },
  }
  
  _lowerCAmelCase					:str  =      {
      'microsoft/mpnet-base': 512,
  }
  
  _lowerCAmelCase					:Union[str, Any]  =      {
      'microsoft/mpnet-base': {'do_lower_case': True},
  }
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =VOCAB_FILES_NAMES
       a__             =PRETRAINED_VOCAB_FILES_MAP
       a__             =PRETRAINED_INIT_CONFIGURATION
       a__             =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a__             =MPNetTokenizer
       a__             =['''input_ids''', '''attention_mask''']
  
  
  
  
  
       def __init__(      self ,			A=None ,			A=None ,			A=True ,			A="<s>" ,			A="</s>" ,			A="</s>" ,			A="<s>" ,			A="[UNK]" ,			A="<pad>" ,			A="<mask>" ,			A=True ,			A=None ,			**A ,			)							->      Dict:
           super().__init__(
               A ,			tokenizer_file=A ,			do_lower_case=A ,			bos_token=A ,			eos_token=A ,			sep_token=A ,			cls_token=A ,			unk_token=A ,			pad_token=A ,			mask_token=A ,			tokenize_chinese_chars=A ,			strip_accents=A ,			**A ,			)
  
           _UpperCAmelCase							:						List[str]       							=					json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
           if (
               pre_tok_state.get('''lowercase''' ,			A	) != do_lower_case
               or pre_tok_state.get('''strip_accents''' ,			A	) != strip_accents
           ):
               _UpperCAmelCase							:						Optional[int]       							=					getattr(A ,			pre_tok_state.pop('''type'''	)	)
               _UpperCAmelCase							:						List[Any]       							=					do_lower_case
               _UpperCAmelCase							:						List[Any]       							=					strip_accents
               _UpperCAmelCase							:						Tuple       							=					pre_tok_class(**A	)
  
           _UpperCAmelCase							:						Union[str, Any]       							=					do_lower_case
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      str:
           if self._mask_token is None:
               if self.verbose:
                   logger.error('''Using mask_token, but it is not set yet.'''	)
               return None
           return str(self._mask_token	)
  
  
  
  
  
       @mask_token.setter
       def 				__lowerCAmelCase			(      self ,			A	)							->      str:
           _UpperCAmelCase							:						Optional[int]       							=					AddedToken(A ,			lstrip=A ,			rstrip=A	) if isinstance(A ,			A	) else value
           _UpperCAmelCase							:						List[Any]       							=					value
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A=None	)							->      str:
           _UpperCAmelCase							:						List[str]       							=					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
               return output
  
           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      List[int]:
           _UpperCAmelCase							:						int       							=					[self.sep_token_id]
           _UpperCAmelCase							:						int       							=					[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 + sep + token_ids_a + sep	) * [0]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = None	)							->      Tuple[str]:
           _UpperCAmelCase							:						str       							=					self._tokenizer.model.save(A ,			name=A	)
           return tuple(A	)
  
  
  