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      = logging.getLogger(__name__)
  torch.set_grad_enabled(False)
  __UpperCAmelCase      = '''cuda''' if torch.cuda.is_available() else '''cpu'''
  def 	UpperCamelCase			(     snake_case__ :					str ,  snake_case__ :					int=100 ,  snake_case__ :					Dict=" "			)			->  List[str]:
     UpperCamelCase					:						str					=			text.split(snake_case__			)
     return [character.join(text[i : i + n]			).strip() for i in range(0 ,  len(snake_case__			) ,  snake_case__			)]
  def 	UpperCamelCase			(     snake_case__ :					dict			)			->  dict:
     UpperCamelCase			,  UpperCamelCase					:						Tuple					=			[], []
     for title, text in zip(documents['title'] ,  documents['text']			):
        if text is not None:
           for passage in split_text(snake_case__			):
              titles.append(title if title is not None else ''			)
              texts.append(snake_case__			)
     return {"title": titles, "text": texts}
  def 	UpperCamelCase			(     snake_case__ :					dict ,  snake_case__ :					DPRContextEncoder ,  snake_case__ :					DPRContextEncoderTokenizerFast			)			->  dict:
     UpperCamelCase					:						int					=			ctx_tokenizer(
         documents['title'] ,  documents['text'] ,  truncation=snake_case__ ,  padding='longest' ,  return_tensors='pt'			)['input_ids']
     UpperCamelCase					:						int					=			ctx_encoder(input_ids.to(device=snake_case__			) ,  return_dict=snake_case__			).pooler_output
     return {"embeddings": embeddings.detach().cpu().numpy()}
  def 	UpperCamelCase			(     snake_case__ :					"RagExampleArguments" ,  snake_case__ :					"ProcessingArguments" ,  snake_case__ :					"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
     UpperCamelCase					:						List[str]					=			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
     UpperCamelCase					:						int					=			dataset.map(snake_case__ ,  batched=snake_case__ ,  num_proc=processing_args.num_proc			)
  
     # And compute the embeddings
     UpperCamelCase					:						int					=			DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name			).to(device=snake_case__			)
     UpperCamelCase					:						List[str]					=			DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name			)
     UpperCamelCase					:						Any					=			Features(
         {'text': Value('string'			), 'title': Value('string'			), 'embeddings': Sequence(Value('float32'			)			)}			)  # optional, save as float32 instead of float64 to save space
     UpperCamelCase					:						Any					=			dataset.map(
         partial(snake_case__ ,  ctx_encoder=snake_case__ ,  ctx_tokenizer=snake_case__			) ,  batched=snake_case__ ,  batch_size=processing_args.batch_size ,  features=snake_case__ ,  )
  
     # And finally save your dataset
     UpperCamelCase					:						Tuple					=			os.path.join(rag_example_args.output_dir ,  'my_knowledge_dataset'			)
     dataset.save_to_disk(snake_case__			)
     # 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
     UpperCamelCase					:						int					=			faiss.IndexHNSWFlat(index_hnsw_args.d ,  index_hnsw_args.m ,  faiss.METRIC_INNER_PRODUCT			)
     dataset.add_faiss_index('embeddings' ,  custom_index=snake_case__			)
  
     # And save the index
     UpperCamelCase					:						Dict					=			os.path.join(rag_example_args.output_dir ,  'my_knowledge_dataset_hnsw_index.faiss'			)
     dataset.get_index('embeddings'			).save(snake_case__			)
     # dataset.load_faiss_index("embeddings", index_path)  # to reload the index
  
     ######################################
     logger.info('Step 3 - Load RAG'			)
     ######################################
  
     # Easy way to load the model
     UpperCamelCase					:						Optional[int]					=			RagRetriever.from_pretrained(
         rag_example_args.rag_model_name ,  index_name='custom' ,  indexed_dataset=snake_case__			)
     UpperCamelCase					:						str					=			RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name ,  retriever=snake_case__			)
     UpperCamelCase					:						Union[str, Any]					=			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'			)
     ######################################
  
     UpperCamelCase					:						int					=			rag_example_args.question or 'What does Moses\' rod turn into ?'
     UpperCamelCase					:						Tuple					=			tokenizer.question_encoder(snake_case__ ,  return_tensors='pt'			)['input_ids']
     UpperCamelCase					:						str					=			model.generate(snake_case__			)
     UpperCamelCase					:						Tuple					=			tokenizer.batch_decode(snake_case__ ,  skip_special_tokens=snake_case__			)[0]
     logger.info('Q: ' + question			)
     logger.info('A: ' + generated_string			)
  
  
  
  @dataclass
  class        lowerCAmelCase_	:
         UpperCAmelCase__       :      str       =						field(
             default=str(Path(a__							).parent / "test_data" / "my_knowledge_dataset.csv"							) ,					metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"} ,					)
         UpperCAmelCase__       :      Optional[str]       =						field(
             default=a__ ,					metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."} ,					)
         UpperCAmelCase__       :      str       =						field(
             default="facebook/rag-sequence-nq" ,					metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"} ,					)
         UpperCAmelCase__       :      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'"
                 )
             } ,					)
         UpperCAmelCase__       :      Optional[str]       =						field(
             default=a__ ,					metadata={"help": "Path to a directory where the dataset passages and the index will be saved"} ,					)
  
  
  
  
  
  
  @dataclass
  class        lowerCAmelCase_	:
         UpperCAmelCase__       :      Optional[int]       =						field(
             default=a__ ,					metadata={
                 "help": "The number of processes to use to split the documents into passages. Default is single process."
             } ,					)
         UpperCAmelCase__       :      int       =						field(
             default=16 ,					metadata={
                 "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
             } ,					)
  
  
  
  
  
  
  @dataclass
  class        lowerCAmelCase_	:
         UpperCAmelCase__       :      int       =						field(
             default=768 ,					metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."} ,					)
         UpperCAmelCase__       :      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      = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      __UpperCAmelCase     ,						__UpperCAmelCase     ,						__UpperCAmelCase      = parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
          __UpperCAmelCase      = rag_example_args.output_dir or tmp_dir
          main(rag_example_args, processing_args, index_hnsw_args)
  
  
  
  
style_context: 
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __UpperCAmelCase      = logging.get_logger(__name__)
  
  __UpperCAmelCase      = {
      '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json''',
  }
  
  
  
  class        lowerCAmelCase_	(						a__							):
         UpperCAmelCase__       :      Union[str, Any]       =						"mpnet"
         def __init__(    self,    SCREAMING_SNAKE_CASE_=3_0527,    SCREAMING_SNAKE_CASE_=768,    SCREAMING_SNAKE_CASE_=12,    SCREAMING_SNAKE_CASE_=12,    SCREAMING_SNAKE_CASE_=3072,    SCREAMING_SNAKE_CASE_="gelu",    SCREAMING_SNAKE_CASE_=0.1,    SCREAMING_SNAKE_CASE_=0.1,    SCREAMING_SNAKE_CASE_=512,    SCREAMING_SNAKE_CASE_=0.02,    SCREAMING_SNAKE_CASE_=1e-12,    SCREAMING_SNAKE_CASE_=32,    SCREAMING_SNAKE_CASE_=1,    SCREAMING_SNAKE_CASE_=0,    SCREAMING_SNAKE_CASE_=2,    **SCREAMING_SNAKE_CASE_,    )  ->	Optional[int]:
            super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,    bos_token_id=SCREAMING_SNAKE_CASE_,    eos_token_id=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						Any					=			vocab_size
            UpperCamelCase					:						Optional[Any]					=			hidden_size
            UpperCamelCase					:						Union[str, Any]					=			num_hidden_layers
            UpperCamelCase					:						Optional[int]					=			num_attention_heads
            UpperCamelCase					:						Optional[Any]					=			hidden_act
            UpperCamelCase					:						str					=			intermediate_size
            UpperCamelCase					:						Optional[Any]					=			hidden_dropout_prob
            UpperCamelCase					:						Union[str, Any]					=			attention_probs_dropout_prob
            UpperCamelCase					:						List[Any]					=			max_position_embeddings
            UpperCamelCase					:						Union[str, Any]					=			initializer_range
            UpperCamelCase					:						List[str]					=			layer_norm_eps
            UpperCamelCase					:						str					=			relative_attention_num_buckets
  
  
  
  