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,
  )
  
  
  __snake_case             =	logging.getLogger(__name__)
  torch.set_grad_enabled(False)
  __snake_case             =	'''cuda''' if torch.cuda.is_available() else '''cpu'''
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase=1_00				,     _lowercase=" "   )				->   List[str]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = text.split(_lowercase   )
       return [character.join(text[i : i + n]   ).strip() for i in range(0				,     len(_lowercase   )				,     _lowercase   )]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   dict:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = [], []
       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 		_A		(		_lowercase				,     _lowercase				,     _lowercase   )				->   dict:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = ctx_tokenizer(
           documents['title']				,     documents['text']				,     truncation=_lowercase				,     padding='longest'				,     return_tensors='pt'   )['input_ids']
       __UpperCamelCase   = ctx_encoder(input_ids.to(device=_lowercase   )				,     return_dict=_lowercase   ).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     )				->   Any:
  
  
       """simple docstring"""
  
  
       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   = 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   = dataset.map(_lowercase				,     batched=_lowercase				,     num_proc=processing_args.num_proc   )
  
       # And compute the embeddings
       __UpperCamelCase   = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=_lowercase   )
       __UpperCamelCase   = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
       __UpperCamelCase   = Features(
           {'text': Value('string'   ), 'title': Value('string'   ), 'embeddings': Sequence(Value('float32'   )   )}   )  # optional, save as float32 instead of float64 to save space
       __UpperCamelCase   = dataset.map(
           partial(_lowercase				,     ctx_encoder=_lowercase				,     ctx_tokenizer=_lowercase   )				,     batched=_lowercase				,     batch_size=processing_args.batch_size				,     features=_lowercase				,     )
  
       # And finally save your dataset
       __UpperCamelCase   = os.path.join(rag_example_args.output_dir				,     'my_knowledge_dataset'   )
       dataset.save_to_disk(_lowercase   )
       # 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   = faiss.IndexHNSWFlat(index_hnsw_args.d				,     index_hnsw_args.m				,     faiss.METRIC_INNER_PRODUCT   )
       dataset.add_faiss_index('embeddings'				,     custom_index=_lowercase   )
  
       # And save the index
       __UpperCamelCase   = os.path.join(rag_example_args.output_dir				,     'my_knowledge_dataset_hnsw_index.faiss'   )
       dataset.get_index('embeddings'   ).save(_lowercase   )
       # dataset.load_faiss_index("embeddings", index_path)  # to reload the index
  
       ######################################
       logger.info('Step 3 - Load RAG'   )
       ######################################
  
       # Easy way to load the model
       __UpperCamelCase   = RagRetriever.from_pretrained(
           rag_example_args.rag_model_name				,     index_name='custom'				,     indexed_dataset=_lowercase   )
       __UpperCamelCase   = RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name				,     retriever=_lowercase   )
       __UpperCamelCase   = 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   = rag_example_args.question or 'What does Moses\' rod turn into ?'
       __UpperCamelCase   = tokenizer.question_encoder(_lowercase				,     return_tensors='pt'   )['input_ids']
       __UpperCamelCase   = model.generate(_lowercase   )
       __UpperCamelCase   = tokenizer.batch_decode(_lowercase				,     skip_special_tokens=_lowercase   )[0]
       logger.info('Q: ' + question   )
       logger.info('A: ' + generated_string   )
  
  
  
  @dataclass
  class   __lowerCamelCase :
         _lowercase       							=    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'"""}							,			)
         _lowercase       							=    field(
             default=_a							,			metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}							,			)
         _lowercase       							=    field(
             default="""facebook/rag-sequence-nq"""							,			metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}							,			)
         _lowercase       							=    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       							=    field(
             default=_a							,			metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}							,			)
  
  
  
  @dataclass
  class   __lowerCamelCase :
         _lowercase       							=    field(
             default=_a							,			metadata={
                 """help""": """The number of processes to use to split the documents into passages. Default is single process."""
             }							,			)
         _lowercase       							=    field(
             default=1_6							,			metadata={
                 """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
             }							,			)
  
  
  
  @dataclass
  class   __lowerCamelCase :
         _lowercase       							=    field(
             default=7_6_8							,			metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}							,			)
         _lowercase       							=    field(
             default=1_2_8							,			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)
  
      __snake_case             =	HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      __snake_case       ,  __snake_case       ,  __snake_case             =	parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
          __snake_case             =	rag_example_args.output_dir or tmp_dir
          main(rag_example_args, processing_args, index_hnsw_args)
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   int | float:
  
  
       """simple docstring"""
  
  
       if len(_lowercase   ) == 0:
            raise ValueError('find_max() arg is an empty sequence'   )
       __UpperCamelCase   = nums[0]
       for x in nums:
            if x > max_num:
                 __UpperCamelCase   = x
       return max_num
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod(verbose=True)
  
  
  