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,
  )
  
  
  __lowerCamelCase       :       Tuple				   =			logging.getLogger(__name__)
  torch.set_grad_enabled(False)
  __lowerCamelCase       :       List[str]				   =			"cuda" if torch.cuda.is_available() else "cpu"
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_=1_0_0,	snake_case_=" "	)			->							List[str]:
  				A__     :							str   				=text.split(snake_case_	)
  				return [character.join(text[i : i + n]	).strip() for i in range(0,	len(snake_case_	),	snake_case_	)]
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							dict:
  				A__     :							str   				=[], []
  				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   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							dict:
  				A__     :							Dict   				=ctx_tokenizer(
  				    documents['''title'''],	documents['''text'''],	truncation=snake_case_,	padding='''longest''',	return_tensors='''pt'''	)['''input_ids''']
  				A__     :							str   				=ctx_encoder(input_ids.to(device=snake_case_	),	return_dict=snake_case_	).pooler_output
  				return {"embeddings": embeddings.detach().cpu().numpy()}
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_,	)			->							List[str]:
  				######################################
  				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__     :							Optional[Any]   				=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__     :							int   				=dataset.map(snake_case_,	batched=snake_case_,	num_proc=processing_args.num_proc	)
  
  				# And compute the embeddings
  				A__     :							List[Any]   				=DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	).to(device=snake_case_	)
  				A__     :							str   				=DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	)
  				A__     :							Optional[Any]   				=Features(
  				    {'''text''': Value('''string'''	), '''title''': Value('''string'''	), '''embeddings''': Sequence(Value('''float32'''	)	)}	)  # optional, save as float32 instead of float64 to save space
  				A__     :							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
  				A__     :							List[str]   				=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
  				A__     :							Union[str, Any]   				=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
  				A__     :							List[str]   				=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
  				A__     :							Union[str, Any]   				=RagRetriever.from_pretrained(
  				    rag_example_args.rag_model_name,	index_name='''custom''',	indexed_dataset=snake_case_	)
  				A__     :							Union[str, Any]   				=RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name,	retriever=snake_case_	)
  				A__     :							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'''	)
  				######################################
  
  				A__     :							Union[str, Any]   				=rag_example_args.question or '''What does Moses\' rod turn into ?'''
  				A__     :							Optional[int]   				=tokenizer.question_encoder(snake_case_,	return_tensors='''pt'''	)['''input_ids''']
  				A__     :							Optional[Any]   				=model.generate(snake_case_	)
  				A__     :							Tuple   				=tokenizer.batch_decode(snake_case_,	skip_special_tokens=snake_case_	)[0]
  				logger.info('''Q: ''' + question	)
  				logger.info('''A: ''' + generated_string	)
  
  
  
  @dataclass
  class 			a      :
  		__lowercase													=		field(
  		    default=str(Path(UpperCamelCase_   ).parent / """test_data""" / """my_knowledge_dataset.csv"""   )      ,metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""}      ,)
  		__lowercase													=		field(
  		    default=UpperCamelCase_      ,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=UpperCamelCase_      ,metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}      ,)
  
  
  
  
  
  
  
  @dataclass
  class 			a      :
  		__lowercase													=		field(
  		    default=UpperCamelCase_      ,metadata={
  		        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
  		    }      ,)
  		__lowercase													=		field(
  		    default=16      ,metadata={
  		        """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
  		    }      ,)
  
  
  
  
  
  
  
  @dataclass
  class 			a      :
  		__lowercase													=		field(
  		    default=768      ,metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}      ,)
  		__lowercase													=		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)
  
  						__lowerCamelCase       :       List[str]				   =			HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
  						__lowerCamelCase       :       Optional[int]				   =			parser.parse_args_into_dataclasses()
  						with TemporaryDirectory() as tmp_dir:
  												__lowerCamelCase       :       Optional[int]				   =			rag_example_args.output_dir or tmp_dir
  												main(rag_example_args, processing_args, index_hnsw_args)
  
  
  
  
style_context: 
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				barcode //= 1_0  # exclude the last digit
  				A__     :							List[str]   				=False
  				A__     :							Dict   				=0
  
  				# extract and check each digit
  				while barcode != 0:
  								A__     :							Union[str, Any]   				=1 if checker else 3
  								s += mult * (barcode % 1_0)
  								barcode //= 1_0
  								A__     :							Union[str, Any]   				=not checker
  
  				return (1_0 - (s % 1_0)) % 1_0
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							bool:
  				return len(str(snake_case_	)	) == 1_3 and get_check_digit(snake_case_	) == barcode % 1_0
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				if str(snake_case_	).isalpha():
  								A__     :							Tuple   				=f'Barcode \'{barcode}\' has alphabetic characters.'
  								raise ValueError(snake_case_	)
  				elif int(snake_case_	) < 0:
  								raise ValueError('''The entered barcode has a negative value. Try again.'''	)
  				else:
  								return int(snake_case_	)
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  						__lowerCamelCase       :       List[Any]				   =			get_barcode(input("Barcode: ").strip())
  
  						if is_valid(barcode):
  												print(F"'{barcode}' is a valid barcode.")
  						else:
  												print(F"'{barcode}' is NOT a valid barcode.")
  
  
  
  