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  __a  (					lowerCAmelCase_     :      List[str]    ,lowerCAmelCase_     :      Union[str, Any]=1_00    ,lowerCAmelCase_     :      Union[str, Any]=" "       )   ->				Tuple:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     text.split(lowerCAmelCase_       )
  						return [character.join(text[i : i + n]       ).strip() for i in range(0    ,len(lowerCAmelCase_       )    ,lowerCAmelCase_       )]
  
  
  
  def  __a  (					lowerCAmelCase_     :      str       )   ->				Union[str, Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_,     UpperCAmelCase_=     [], []
  						for title, text in zip(documents["""title"""]    ,documents["""text"""]       ):
  												if text is not None:
  																		for passage in split_text(lowerCAmelCase_       ):
  																								titles.append(title if title is not None else """"""       )
  																								texts.append(lowerCAmelCase_       )
  						return {"title": titles, "text": texts}
  
  
  
  def  __a  (					lowerCAmelCase_     :      Optional[int]    ,lowerCAmelCase_     :      List[Any]    ,lowerCAmelCase_     :      Dict    ,lowerCAmelCase_     :      Dict    ,lowerCAmelCase_     :      Optional[Any]    ,lowerCAmelCase_     :      Union[str, Any]       )   ->				Dict:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     load_dataset(
  						    """csv"""    ,data_files=[csv_path]    ,split="""train"""    ,delimiter="""\t"""    ,column_names=["""title""", """text"""]       )
  						UpperCAmelCase_=     kb_dataset.map(
  						    lowerCAmelCase_    ,batched=lowerCAmelCase_    ,num_proc=1       )  # if you want you can load already splitted csv.
  						UpperCAmelCase_=     [kb_dataset.shard(lowerCAmelCase_    ,lowerCAmelCase_    ,contiguous=lowerCAmelCase_       ) for i in range(lowerCAmelCase_       )]
  						UpperCAmelCase_=     kb_list[process_num]
  
  						UpperCAmelCase_=     """data_""" + str(lowerCAmelCase_       )
  						UpperCAmelCase_=     os.path.join(lowerCAmelCase_    ,lowerCAmelCase_       )
  
  						UpperCAmelCase_=     DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""       )
  						UpperCAmelCase_=     ctx_encoder.to(device=lowerCAmelCase_       )
  
  						def embed(
  						    lowerCAmelCase_     :      dict    ,lowerCAmelCase_     :      DPRContextEncoder    ,lowerCAmelCase_     :      DPRContextEncoderTokenizerFast    ,lowerCAmelCase_     :      Any       ) -> dict:
  												UpperCAmelCase_=     ctx_tokenizer(
  												    documents["""title"""]    ,documents["""text"""]    ,truncation=lowerCAmelCase_    ,padding="""longest"""    ,return_tensors="""pt"""       )["""input_ids"""]
  												UpperCAmelCase_=     ctx_encoder(input_ids.to(device=lowerCAmelCase_       )    ,return_dict=lowerCAmelCase_       ).pooler_output
  												return {"embeddings": embeddings.detach().cpu().numpy()}
  
  						UpperCAmelCase_=     Features(
  						    {"""text""": Value("""string"""       ), """title""": Value("""string"""       ), """embeddings""": Sequence(Value("""float32"""       )       )}       )  # optional, save as float32 instead of float64 to save space
  
  						UpperCAmelCase_=     data_shrad.map(
  						    partial(lowerCAmelCase_    ,ctx_encoder=lowerCAmelCase_    ,ctx_tokenizer=lowerCAmelCase_    ,device=lowerCAmelCase_       )    ,batched=lowerCAmelCase_    ,batch_size=16    ,features=lowerCAmelCase_    ,)
  						dataset.save_to_disk(lowerCAmelCase_       )
  
  
  
  def  __a  (					lowerCAmelCase_     :      int    ,lowerCAmelCase_     :      List[Any]       )   ->				Optional[int]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     []
  
  						for shard_address in glob(str(lowerCAmelCase_       ) + """/*/"""       ):
  												data_shard_list.append(load_from_disk(lowerCAmelCase_       )       )
  
  						UpperCAmelCase_=     concatenate_datasets(lowerCAmelCase_       )
  						faiss.omp_set_num_threads(96       )
  
  						UpperCAmelCase_=     faiss.IndexHNSWFlat(7_68    ,1_28    ,faiss.METRIC_INNER_PRODUCT       )
  						concat.add_faiss_index("""embeddings"""    ,custom_index=lowerCAmelCase_       )
  						concat.get_index("""embeddings"""       ).save(
  						    lowerCAmelCase_       )  # since we load the index in to memory,we can directly update the index in the disk
  
  
  
  
  
style_context: 
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __A       					=		logging.get_logger(__name__)
  
  __A       					=		{
      '''ZinengTang/tvlt-base''': '''https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json''',
  }
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Tuple	     =							"tvlt"
  
  
  
  
  	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		List[str]=224		,						__UpperCAmelCase			:		List[str]=2_048		,						__UpperCAmelCase			:		Tuple=128		,						__UpperCAmelCase			:		Union[str, Any]=[16, 16]		,						__UpperCAmelCase			:		str=[16, 16]		,						__UpperCAmelCase			:		str=3		,						__UpperCAmelCase			:		List[str]=1		,						__UpperCAmelCase			:		Tuple=8		,						__UpperCAmelCase			:		Optional[Any]=768		,						__UpperCAmelCase			:		List[Any]=12		,						__UpperCAmelCase			:		Tuple=12		,						__UpperCAmelCase			:		List[str]=3_072		,						__UpperCAmelCase			:		List[Any]="gelu"		,						__UpperCAmelCase			:		str=0.0		,						__UpperCAmelCase			:		str=0.0		,						__UpperCAmelCase			:		List[Any]=0.02		,						__UpperCAmelCase			:		Optional[int]=1E-6		,						__UpperCAmelCase			:		Tuple=True		,						__UpperCAmelCase			:		List[Any]=False		,						__UpperCAmelCase			:		Union[str, Any]=16		,						__UpperCAmelCase			:		Any=512		,						__UpperCAmelCase			:		Optional[Any]=8		,						__UpperCAmelCase			:		Dict=2_048		,						__UpperCAmelCase			:		Dict=0.75		,						__UpperCAmelCase			:		Union[str, Any]=0.15		,						__UpperCAmelCase			:		Any="frame-level"		,						__UpperCAmelCase			:		int=True		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		Union[str, Any]="classification"		,						**__UpperCAmelCase			:		Any		,						)    ->				Tuple:
  							super().__init__(**__UpperCAmelCase  )
  
  							if audio_mask_type not in ("frame-level", "patch_level"):
  													raise ValueError(
  													    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
  													    F"""got {audio_mask_type}"""  )
  
  							UpperCAmelCase_=     image_size
  							UpperCAmelCase_=     spectrogram_length
  							UpperCAmelCase_=     frequency_length
  							UpperCAmelCase_=     image_patch_size
  							UpperCAmelCase_=     audio_patch_size
  							UpperCAmelCase_=     num_image_channels
  							UpperCAmelCase_=     num_audio_channels
  							UpperCAmelCase_=     num_frames
  
  							UpperCAmelCase_=     hidden_size
  							UpperCAmelCase_=     num_hidden_layers
  							UpperCAmelCase_=     num_attention_heads
  							UpperCAmelCase_=     intermediate_size
  							UpperCAmelCase_=     hidden_act
  							UpperCAmelCase_=     hidden_dropout_prob
  							UpperCAmelCase_=     attention_probs_dropout_prob
  							UpperCAmelCase_=     initializer_range
  							UpperCAmelCase_=     layer_norm_eps
  							UpperCAmelCase_=     qkv_bias
  							UpperCAmelCase_=     use_mean_pooling
  
  							UpperCAmelCase_=     decoder_num_attention_heads
  							UpperCAmelCase_=     decoder_hidden_size
  							UpperCAmelCase_=     decoder_num_hidden_layers
  							UpperCAmelCase_=     decoder_intermediate_size
  							UpperCAmelCase_=     pixel_mask_ratio
  							UpperCAmelCase_=     audio_mask_ratio
  							UpperCAmelCase_=     audio_mask_type
  
  							UpperCAmelCase_=     task_matching
  							UpperCAmelCase_=     task_mae
  							UpperCAmelCase_=     loss_type
  
  
  
  
  