code: 
  
  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 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					Any=100			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=" "    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Optional[int]   = text.split(SCREAMING_SNAKE_CASE_    )
       return [character.join(text[i : i + n]    ).strip() for i in range(0			,  len(SCREAMING_SNAKE_CASE_    )			,  SCREAMING_SNAKE_CASE_    )]
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Optional[Any]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__   ,				lowercase__					:     Optional[int]   = [], []
       for title, text in zip(documents['title']			,  documents['text']    ):
            if text is not None:
                 for passage in split_text(SCREAMING_SNAKE_CASE_    ):
                      titles.append(title if title is not None else ''    )
                      texts.append(SCREAMING_SNAKE_CASE_    )
       return {"title": titles, "text": texts}
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Optional[int]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Optional[Any]   = load_dataset(
           'csv'			,  data_files=[csv_path]			,  split='train'			,  delimiter='\t'			,  column_names=['title', 'text']    )
       lowercase__					:     int   = kb_dataset.map(
           SCREAMING_SNAKE_CASE_			,  batched=SCREAMING_SNAKE_CASE_			,  num_proc=1    )  # if you want you can load already splitted csv.
       lowercase__					:     str   = [kb_dataset.shard(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  contiguous=SCREAMING_SNAKE_CASE_    ) for i in range(SCREAMING_SNAKE_CASE_    )]
       lowercase__					:     str   = kb_list[process_num]
  
       lowercase__					:     int   = 'data_' + str(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Optional[Any]   = os.path.join(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
       lowercase__					:     int   = DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'    )
       lowercase__					:     Union[str, Any]   = ctx_encoder.to(device=SCREAMING_SNAKE_CASE_    )
  
       def embed(
           SCREAMING_SNAKE_CASE_	:					dict			,  SCREAMING_SNAKE_CASE_	:					DPRContextEncoder			,  SCREAMING_SNAKE_CASE_	:					DPRContextEncoderTokenizerFast			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]    ) -> dict:
            lowercase__					:     Dict   = ctx_tokenizer(
                documents['title']			,  documents['text']			,  truncation=SCREAMING_SNAKE_CASE_			,  padding='longest'			,  return_tensors='pt'    )['input_ids']
            lowercase__					:     Union[str, Any]   = ctx_encoder(input_ids.to(device=SCREAMING_SNAKE_CASE_    )			,  return_dict=SCREAMING_SNAKE_CASE_    ).pooler_output
            return {"embeddings": embeddings.detach().cpu().numpy()}
  
       lowercase__					:     List[Any]   = Features(
           {'text': Value('string'    ), 'title': Value('string'    ), 'embeddings': Sequence(Value('float32'    )    )}    )  # optional, save as float32 instead of float64 to save space
  
       lowercase__					:     Tuple   = data_shrad.map(
           partial(SCREAMING_SNAKE_CASE_			,  ctx_encoder=SCREAMING_SNAKE_CASE_			,  ctx_tokenizer=SCREAMING_SNAKE_CASE_			,  device=SCREAMING_SNAKE_CASE_    )			,  batched=SCREAMING_SNAKE_CASE_			,  batch_size=16			,  features=SCREAMING_SNAKE_CASE_			,  )
       dataset.save_to_disk(SCREAMING_SNAKE_CASE_    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Union[str, Any]   = []
  
       for shard_address in glob(str(SCREAMING_SNAKE_CASE_    ) + '/*/'    ):
            data_shard_list.append(load_from_disk(SCREAMING_SNAKE_CASE_    )    )
  
       lowercase__					:     Optional[Any]   = concatenate_datasets(SCREAMING_SNAKE_CASE_    )
       faiss.omp_set_num_threads(96    )
  
       lowercase__					:     List[str]   = faiss.IndexHNSWFlat(768			,  128			,  faiss.METRIC_INNER_PRODUCT    )
       concat.add_faiss_index('embeddings'			,  custom_index=SCREAMING_SNAKE_CASE_    )
       concat.get_index('embeddings'    ).save(
           SCREAMING_SNAKE_CASE_    )  # since we load the index in to memory,we can directly update the index in the disk
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  snake_case_    						=				{
      '''configuration_donut_swin''': ['''DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DonutSwinConfig'''],
      '''processing_donut''': ['''DonutProcessor'''],
  }
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''DonutSwinModel''',
             '''DonutSwinPreTrainedModel''',
         ]
  
  try:
         if not is_vision_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				['''DonutFeatureExtractor''']
         snake_case_    						=				['''DonutImageProcessor''']
  
  
  if TYPE_CHECKING:
         from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
         from .processing_donut import DonutProcessor
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_donut_swin import (
                    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DonutSwinModel,
                    DonutSwinPreTrainedModel,
                )
  
         try:
                if not is_vision_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .feature_extraction_donut import DonutFeatureExtractor
                from .image_processing_donut import DonutImageProcessor
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  