code: 
  
  import logging
  import os
  from dataclasses import dataclass, field
  from functools import partial
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from typing import List, Optional
  
  import faiss
  import torch
  from datasets import Features, Sequence, Value, load_dataset
  
  from transformers import (
      DPRContextEncoder,
      DPRContextEncoderTokenizerFast,
      HfArgumentParser,
      RagRetriever,
      RagSequenceForGeneration,
      RagTokenizer,
  )
  
  
  UpperCAmelCase_       :				Optional[int]		   =      logging.getLogger(__name__)
  torch.set_grad_enabled(False)
  UpperCAmelCase_       :				Union[str, Any]		   =      '''cuda''' if torch.cuda.is_available() else '''cpu'''
  def        __SCREAMING_SNAKE_CASE    (      a__						:      str ,a__						:      Tuple=100 ,a__						:      str=" "					)				->							List[str]:
  	__A   : Dict								= text.split(a__					)
  	return [character.join(text[i : i + n]					).strip() for i in range(0 ,len(a__					) ,a__					)]
  def        __SCREAMING_SNAKE_CASE    (      a__						:      dict					)				->							dict:
  	__A						,							__A   : Tuple								= [], []
  	for title, text in zip(documents["""title"""] ,documents["""text"""]					):
  		if text is not None:
  			for passage in split_text(a__					):
  				titles.append(title if title is not None else """"""					)
  				texts.append(a__					)
  	return {"title": titles, "text": texts}
  def        __SCREAMING_SNAKE_CASE    (      a__						:      dict ,a__						:      DPRContextEncoder ,a__						:      DPRContextEncoderTokenizerFast					)				->							dict:
  	__A   : List[Any]								= ctx_tokenizer(
  	    documents["""title"""] ,documents["""text"""] ,truncation=a__ ,padding="""longest""" ,return_tensors="""pt"""					)["""input_ids"""]
  	__A   : Dict								= ctx_encoder(input_ids.to(device=a__					) ,return_dict=a__					).pooler_output
  	return {"embeddings": embeddings.detach().cpu().numpy()}
  def        __SCREAMING_SNAKE_CASE    (      a__						:      "RagExampleArguments" ,a__						:      "ProcessingArguments" ,a__						:      "IndexHnswArguments" ,)				->							List[Any]:
  	######################################
  	logger.info("""Step 1 - Create the dataset"""					)
  	######################################
  
  	# The dataset needed for RAG must have three columns:
  	# - title (string): title of the document
  	# - text (string): text of a passage of the document
  	# - embeddings (array of dimension d): DPR representation of the passage
  
  	# Let's say you have documents in tab-separated csv files with columns "title" and "text"
  	assert os.path.isfile(rag_example_args.csv_path					), "Please provide a valid path to a csv file"
  
  	# You can load a Dataset object this way
  	__A   : Dict								= load_dataset(
  	    """csv""" ,data_files=[rag_example_args.csv_path] ,split="""train""" ,delimiter="""\t""" ,column_names=["""title""", """text"""]					)
  
  	# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
  
  	# Then split the documents into passages of 100 words
  	__A   : Tuple								= dataset.map(a__ ,batched=a__ ,num_proc=processing_args.num_proc					)
  
  	# And compute the embeddings
  	__A   : Optional[Any]								= DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=a__					)
  	__A   : Tuple								= DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
  	__A   : Dict								= Features(
  	    {"""text""": Value("""string"""					), """title""": Value("""string"""					), """embeddings""": Sequence(Value("""float32"""					)					)}					)  # optional, save as float32 instead of float64 to save space
  	__A   : Tuple								= dataset.map(
  	    partial(a__ ,ctx_encoder=a__ ,ctx_tokenizer=a__					) ,batched=a__ ,batch_size=processing_args.batch_size ,features=a__ ,)
  
  	# And finally save your dataset
  	__A   : List[str]								= os.path.join(rag_example_args.output_dir ,"""my_knowledge_dataset"""					)
  	dataset.save_to_disk(a__					)
  	# from datasets import load_from_disk
  	# dataset = load_from_disk(passages_path)  # to reload the dataset
  
  	######################################
  	logger.info("""Step 2 - Index the dataset"""					)
  	######################################
  
  	# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
  	__A   : Tuple								= faiss.IndexHNSWFlat(index_hnsw_args.d ,index_hnsw_args.m ,faiss.METRIC_INNER_PRODUCT					)
  	dataset.add_faiss_index("""embeddings""" ,custom_index=a__					)
  
  	# And save the index
  	__A   : Any								= os.path.join(rag_example_args.output_dir ,"""my_knowledge_dataset_hnsw_index.faiss"""					)
  	dataset.get_index("""embeddings"""					).save(a__					)
  	# dataset.load_faiss_index("embeddings", index_path)  # to reload the index
  
  	######################################
  	logger.info("""Step 3 - Load RAG"""					)
  	######################################
  
  	# Easy way to load the model
  	__A   : Union[str, Any]								= RagRetriever.from_pretrained(
  	    rag_example_args.rag_model_name ,index_name="""custom""" ,indexed_dataset=a__					)
  	__A   : Any								= RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name ,retriever=a__					)
  	__A   : Tuple								= RagTokenizer.from_pretrained(rag_example_args.rag_model_name					)
  
  	# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
  	# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)
  
  	######################################
  	logger.info("""Step 4 - Have fun"""					)
  	######################################
  
  	__A   : Optional[int]								= rag_example_args.question or """What does Moses' rod turn into ?"""
  	__A   : List[str]								= tokenizer.question_encoder(a__ ,return_tensors="""pt"""					)["""input_ids"""]
  	__A   : Optional[int]								= model.generate(a__					)
  	__A   : List[Any]								= tokenizer.batch_decode(a__ ,skip_special_tokens=a__					)[0]
  	logger.info("""Q: """ + question					)
  	logger.info("""A: """ + generated_string					)
  
  
  
  
  
  
  @dataclass
  class     lowerCamelCase_			:
  	_lowercase		:      str    				=							field(
  	    default=str(Path(_lowercase  ).parent / '''test_data''' / '''my_knowledge_dataset.csv'''  )	,			metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}	,			)
  	_lowercase		:      Optional[str]    				=							field(
  	    default=_lowercase	,			metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}	,			)
  	_lowercase		:      str    				=							field(
  	    default='''facebook/rag-sequence-nq'''	,			metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}	,			)
  	_lowercase		:      str    				=							field(
  	    default='''facebook/dpr-ctx_encoder-multiset-base'''	,			metadata={
  	        '''help''': (
  	            '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
  	            ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
  	        )
  	    }	,			)
  	_lowercase		:      Optional[str]    				=							field(
  	    default=_lowercase	,			metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}	,			)
  
  
  
  
  
  
  @dataclass
  class     lowerCamelCase_			:
  	_lowercase		:      Optional[int]    				=							field(
  	    default=_lowercase	,			metadata={
  	        '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
  	    }	,			)
  	_lowercase		:      int    				=							field(
  	    default=16	,			metadata={
  	        '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
  	    }	,			)
  
  
  
  
  
  
  @dataclass
  class     lowerCamelCase_			:
  	_lowercase		:      int    				=							field(
  	    default=768	,			metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}	,			)
  	_lowercase		:      int    				=							field(
  	    default=128	,			metadata={
  	        '''help''': (
  	            '''The number of bi-directional links created for every new element during the HNSW index construction.'''
  	        )
  	    }	,			)
  
  
  if __name__ == "__main__":
  			logging.basicConfig(level=logging.WARNING)
  			logger.setLevel(logging.INFO)
  
  			UpperCAmelCase_       :				Tuple		   =      HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
  			UpperCAmelCase_   ,		UpperCAmelCase_   ,		UpperCAmelCase_       :				Any		   =      parser.parse_args_into_dataclasses()
  			with TemporaryDirectory() as tmp_dir:
  						UpperCAmelCase_       :				Union[str, Any]		   =      rag_example_args.output_dir or tmp_dir
  						main(rag_example_args, processing_args, index_hnsw_args)
  
  
  
  
  
  
style_context: 
  
  import os
  from typing import List, Union
  
  import tensorflow as tf
  from tensorflow_text import BertTokenizer as BertTokenizerLayer
  from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs
  
  from .tokenization_bert import BertTokenizer
  
  
  
  
  
  
  class     lowerCamelCase_			(  tf.keras.layers.Layer  ):
  
  
  
  
  	def __init__(    self	:    int				,			__A	:    List				,			__A	:    bool				,			__A	:    int = None				,			__A	:    int = None				,			__A	:    int = None				,			__A	:    str = "longest"				,			__A	:    bool = True				,			__A	:    int = 512				,			__A	:    int = None				,			__A	:    bool = True				,			__A	:    bool = True				,			__A	:    bool = True				,			**__A	:    Any				,			):
  		super().__init__()
  		if use_fast_bert_tokenizer:
  			__A   : Optional[int]								= FastBertTokenizer(
  			    __A				,			token_out_type=tf.intaa				,			lower_case_nfd_strip_accents=__A				,			**__A       )
  		else:
  			__A   : Optional[Any]								= tf.lookup.StaticVocabularyTable(
  			    tf.lookup.KeyValueTensorInitializer(
  			        keys=__A				,			key_dtype=tf.string				,			values=tf.range(tf.size(__A				,			out_type=tf.intaa       )				,			dtype=tf.intaa       )				,			value_dtype=tf.intaa				,			)				,			num_oov_buckets=1				,			)
  			__A   : Any								= BertTokenizerLayer(
  			    __A				,			token_out_type=tf.intaa				,			lower_case=__A				,			**__A       )
  
  		__A   : Union[str, Any]								= vocab_list
  		__A   : List[str]								= do_lower_case
  		__A   : List[Any]								= cls_token_id or vocab_list.index("""[CLS]"""       )
  		__A   : Tuple								= sep_token_id or vocab_list.index("""[SEP]"""       )
  		__A   : str								= pad_token_id or vocab_list.index("""[PAD]"""       )
  		__A   : Optional[int]								= ShrinkLongestTrimmer(max_length - 3				,			axis=1       )  # Allow room for special tokens
  		__A   : Union[str, Any]								= max_length
  		__A   : List[Any]								= padding
  		__A   : Optional[int]								= truncation
  		__A   : Tuple								= pad_to_multiple_of
  		__A   : Optional[Any]								= return_token_type_ids
  		__A   : int								= return_attention_mask
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    List[Any]				,			__A	:    "PreTrainedTokenizerBase"				,			**__A	:    List[str]       ):  # noqa: F821
  		__A   : Any								= kwargs.pop("""do_lower_case"""				,			__A       )
  		__A   : Dict								= tokenizer.do_lower_case if do_lower_case is None else do_lower_case
  		__A   : List[str]								= kwargs.pop("""cls_token_id"""				,			__A       )
  		__A   : str								= tokenizer.cls_token_id if cls_token_id is None else cls_token_id
  		__A   : str								= kwargs.pop("""sep_token_id"""				,			__A       )
  		__A   : Dict								= tokenizer.sep_token_id if sep_token_id is None else sep_token_id
  		__A   : Tuple								= kwargs.pop("""pad_token_id"""				,			__A       )
  		__A   : List[Any]								= tokenizer.pad_token_id if pad_token_id is None else pad_token_id
  
  		__A   : Union[str, Any]								= tokenizer.get_vocab()
  		__A   : Any								= sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]				,			key=lambda __A       : x[1]       )
  		__A   : int								= [entry[0] for entry in vocab]
  		return cls(
  		    vocab_list=__A				,			do_lower_case=__A				,			cls_token_id=__A				,			sep_token_id=__A				,			pad_token_id=__A				,			**__A				,			)
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    List[str]				,			__A	:    Union[str, os.PathLike]				,			*__A	:    Tuple				,			**__A	:    Optional[int]       ):
  		try:
  			__A   : int								= BertTokenizer.from_pretrained(__A				,			*__A				,			**__A       )
  		except:  # noqa: E722
  			from .tokenization_bert_fast import BertTokenizerFast
  
  			__A   : Optional[int]								= BertTokenizerFast.from_pretrained(__A				,			*__A				,			**__A       )
  		return cls.from_tokenizer(__A				,			**__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    Optional[Any]       ):
  		if self.do_lower_case:
  			__A   : Tuple								= case_fold_utfa(__A       )
  		__A   : Optional[int]								= self.tf_tokenizer.tokenize(__A       )
  		return tokens.merge_dims(1				,			-1       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    Optional[Any]				,			__A	:    Dict=None				,			__A	:    List[Any]=None				,			__A	:    str=None				,			__A	:    Union[str, Any]=None				,			__A	:    Union[str, Any]=None				,			__A	:    int=None				,			__A	:    Any=None				,			):
  		if padding is None:
  			__A   : List[Any]								= self.padding
  		if padding not in ("longest", "max_length"):
  			raise ValueError("""Padding must be either 'longest' or 'max_length'!"""       )
  		if max_length is not None and text_pair is not None:
  			# Because we have to instantiate a Trimmer to do it properly
  			raise ValueError("""max_length cannot be overridden at call time when truncating paired texts!"""       )
  		if max_length is None:
  			__A   : Any								= self.max_length
  		if truncation is None:
  			__A   : int								= self.truncation
  		if pad_to_multiple_of is None:
  			__A   : List[str]								= self.pad_to_multiple_of
  		if return_token_type_ids is None:
  			__A   : int								= self.return_token_type_ids
  		if return_attention_mask is None:
  			__A   : Any								= self.return_attention_mask
  		if not isinstance(__A				,			tf.Tensor       ):
  			__A   : str								= tf.convert_to_tensor(__A       )
  		if text_pair is not None and not isinstance(__A				,			tf.Tensor       ):
  			__A   : Optional[Any]								= tf.convert_to_tensor(__A       )
  		if text_pair is not None:
  			if text.shape.rank > 1:
  				raise ValueError("""text argument should not be multidimensional when a text pair is supplied!"""       )
  			if text_pair.shape.rank > 1:
  				raise ValueError("""text_pair should not be multidimensional!"""       )
  		if text.shape.rank == 2:
  			__A						,							__A   : Optional[int]								= text[:, 0], text[:, 1]
  		__A   : List[Any]								= self.unpaired_tokenize(__A       )
  		if text_pair is None:  # Unpaired text
  			if truncation:
  				__A   : Union[str, Any]								= text[:, : max_length - 2]  # Allow room for special tokens
  			__A						,							__A   : List[Any]								= combine_segments(
  			    (text,)				,			start_of_sequence_id=self.cls_token_id				,			end_of_segment_id=self.sep_token_id       )
  		else:  # Paired text
  			__A   : Tuple								= self.unpaired_tokenize(__A       )
  			if truncation:
  				__A						,							__A   : Optional[int]								= self.paired_trimmer.trim([text, text_pair]       )
  			__A						,							__A   : int								= combine_segments(
  			    (text, text_pair)				,			start_of_sequence_id=self.cls_token_id				,			end_of_segment_id=self.sep_token_id       )
  		if padding == "longest":
  			__A   : str								= input_ids.bounding_shape(axis=1       )
  			if pad_to_multiple_of is not None:
  				# No ceiling division in tensorflow, so we negate floordiv instead
  				__A   : Optional[Any]								= pad_to_multiple_of * (-tf.math.floordiv(-pad_length				,			__A       ))
  		else:
  			__A   : Dict								= max_length
  
  		__A						,							__A   : str								= pad_model_inputs(__A				,			max_seq_length=__A				,			pad_value=self.pad_token_id       )
  		__A   : List[str]								= {"""input_ids""": input_ids}
  		if return_attention_mask:
  			__A   : Any								= attention_mask
  		if return_token_type_ids:
  			__A						,							__A   : Tuple								= pad_model_inputs(
  			    __A				,			max_seq_length=__A				,			pad_value=self.pad_token_id       )
  			__A   : List[str]								= token_type_ids
  		return output
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
  		return {
  		    "vocab_list": self.vocab_list,
  		    "do_lower_case": self.do_lower_case,
  		    "cls_token_id": self.cls_token_id,
  		    "sep_token_id": self.sep_token_id,
  		    "pad_token_id": self.pad_token_id,
  		}
  
  
  
  
  
  