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 	_lowercase	(			lowercase_		,							lowercase_=100		,							lowercase_=" "						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = text.split(lowercase_						)
  		return [character.join(text[i : i + n]						).strip() for i in range(0		,							len(lowercase_						)		,							lowercase_						)]
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case ,					_snake_case			 = [], []
  		for title, text in zip(documents["title"]		,							documents["text"]						):
  				if text is not None:
  						for passage in split_text(lowercase_						):
  								titles.append(title if title is not None else ""						)
  								texts.append(lowercase_						)
  		return {"title": titles, "text": texts}
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = load_dataset(
  		    "csv"		,							data_files=[csv_path]		,							split="train"		,							delimiter="\t"		,							column_names=["title", "text"]						)
  		_snake_case			 = kb_dataset.map(
  		    lowercase_		,							batched=lowercase_		,							num_proc=1						)  # if you want you can load already splitted csv.
  		_snake_case			 = [kb_dataset.shard(lowercase_		,							lowercase_		,							contiguous=lowercase_						) for i in range(lowercase_						)]
  		_snake_case			 = kb_list[process_num]
  
  		_snake_case			 = "data_" + str(lowercase_						)
  		_snake_case			 = os.path.join(lowercase_		,							lowercase_						)
  
  		_snake_case			 = DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"						)
  		_snake_case			 = ctx_encoder.to(device=lowercase_						)
  
  		def embed(
  		    lowercase_		,							lowercase_		,							lowercase_		,							lowercase_						) -> dict:
  				_snake_case			 = ctx_tokenizer(
  				    documents["title"]		,							documents["text"]		,							truncation=lowercase_		,							padding="longest"		,							return_tensors="pt"						)["input_ids"]
  				_snake_case			 = ctx_encoder(input_ids.to(device=lowercase_						)		,							return_dict=lowercase_						).pooler_output
  				return {"embeddings": embeddings.detach().cpu().numpy()}
  
  		_snake_case			 = Features(
  		    {"text": Value("string"						), "title": Value("string"						), "embeddings": Sequence(Value("float32"						)						)}						)  # optional, save as float32 instead of float64 to save space
  
  		_snake_case			 = data_shrad.map(
  		    partial(lowercase_		,							ctx_encoder=lowercase_		,							ctx_tokenizer=lowercase_		,							device=lowercase_						)		,							batched=lowercase_		,							batch_size=16		,							features=lowercase_		,							)
  		dataset.save_to_disk(lowercase_						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = []
  
  		for shard_address in glob(str(lowercase_						) + "/*/"						):
  				data_shard_list.append(load_from_disk(lowercase_						)						)
  
  		_snake_case			 = concatenate_datasets(lowercase_						)
  		faiss.omp_set_num_threads(96						)
  
  		_snake_case			 = faiss.IndexHNSWFlat(768		,							128		,							faiss.METRIC_INNER_PRODUCT						)
  		concat.add_faiss_index("embeddings"		,							custom_index=lowercase_						)
  		concat.get_index("embeddings"						).save(
  		    lowercase_						)  # since we load the index in to memory,we can directly update the index in the disk
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  _lowerCamelCase							:		Optional[Any]										=						logging.get_logger(__name__)
  
  
  
  
  
  class   lowerCamelCase_							( UpperCAmelCase_							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						*__UpperCamelCase,						**__UpperCamelCase				):
  							warnings.warn(
  							    "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
  							    " use ViltImageProcessor instead.",						__UpperCamelCase,						)
  							super().__init__(*__UpperCamelCase,						**__UpperCamelCase				)
  
  
  
  
  
  