code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  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__(							_SCREAMING_SNAKE_CASE				:   str,_SCREAMING_SNAKE_CASE				:   List[str]=100,_SCREAMING_SNAKE_CASE				:   int=" "   ):
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     __A=  text.split(_SCREAMING_SNAKE_CASE   )
     return [character.join(text[i : i + n]   ).strip() for i in range(0,len(_SCREAMING_SNAKE_CASE   ),_SCREAMING_SNAKE_CASE   )]
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   dict   ):
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     __A,  __A=  [], []
     for title, text in zip(documents['title'],documents['text']   ):
        if text is not None:
           for passage in split_text(_SCREAMING_SNAKE_CASE   ):
              titles.append(title if title is not None else ''   )
              texts.append(_SCREAMING_SNAKE_CASE   )
     return {"title": titles, "text": texts}
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   dict,_SCREAMING_SNAKE_CASE				:   DPRContextEncoder,_SCREAMING_SNAKE_CASE				:   DPRContextEncoderTokenizerFast   ):
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     __A=  ctx_tokenizer(
         documents['title'],documents['text'],truncation=_SCREAMING_SNAKE_CASE,padding='longest',return_tensors='pt'   )['input_ids']
     __A=  ctx_encoder(input_ids.to(device=_SCREAMING_SNAKE_CASE   ),return_dict=_SCREAMING_SNAKE_CASE   ).pooler_output
     return {"embeddings": embeddings.detach().cpu().numpy()}
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   "RagExampleArguments",_SCREAMING_SNAKE_CASE				:   "ProcessingArguments",_SCREAMING_SNAKE_CASE				:   "IndexHnswArguments",):
  
  
  
  
  
  
     """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
     __A=  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=  dataset.map(_SCREAMING_SNAKE_CASE,batched=_SCREAMING_SNAKE_CASE,num_proc=processing_args.num_proc   )
  
     # And compute the embeddings
     __A=  DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=_SCREAMING_SNAKE_CASE   )
     __A=  DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
     __A=  Features(
         {'text': Value('string'   ), 'title': Value('string'   ), 'embeddings': Sequence(Value('float32'   )   )}   )  # optional, save as float32 instead of float64 to save space
     __A=  dataset.map(
         partial(_SCREAMING_SNAKE_CASE,ctx_encoder=_SCREAMING_SNAKE_CASE,ctx_tokenizer=_SCREAMING_SNAKE_CASE   ),batched=_SCREAMING_SNAKE_CASE,batch_size=processing_args.batch_size,features=_SCREAMING_SNAKE_CASE,)
  
     # And finally save your dataset
     __A=  os.path.join(rag_example_args.output_dir,'my_knowledge_dataset'   )
     dataset.save_to_disk(_SCREAMING_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=  faiss.IndexHNSWFlat(index_hnsw_args.d,index_hnsw_args.m,faiss.METRIC_INNER_PRODUCT   )
     dataset.add_faiss_index('embeddings',custom_index=_SCREAMING_SNAKE_CASE   )
  
     # And save the index
     __A=  os.path.join(rag_example_args.output_dir,'my_knowledge_dataset_hnsw_index.faiss'   )
     dataset.get_index('embeddings'   ).save(_SCREAMING_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=  RagRetriever.from_pretrained(
         rag_example_args.rag_model_name,index_name='custom',indexed_dataset=_SCREAMING_SNAKE_CASE   )
     __A=  RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name,retriever=_SCREAMING_SNAKE_CASE   )
     __A=  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=  rag_example_args.question or 'What does Moses\' rod turn into ?'
     __A=  tokenizer.question_encoder(_SCREAMING_SNAKE_CASE,return_tensors='pt'   )['input_ids']
     __A=  model.generate(_SCREAMING_SNAKE_CASE   )
     __A=  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE,skip_special_tokens=_SCREAMING_SNAKE_CASE   )[0]
     logger.info('Q: ' + question   )
     logger.info('A: ' + generated_string   )
  @dataclass
  class 	a__				:
  
  
  
  
  
     '''simple docstring'''
  
     A	:							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\''''}			,     )
     A	:							Optional[str]							= field(
         default=a_			,     metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}			,     )
     A	:							str							= field(
         default='''facebook/rag-sequence-nq'''			,     metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}			,     )
     A	:							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\''''
             )
         }			,     )
     A	:							Optional[str]							= field(
         default=a_			,     metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}			,     )
  
  
  @dataclass
  class 	a__				:
  
  
  
  
  
     '''simple docstring'''
  
     A	:							Optional[int]							= field(
         default=a_			,     metadata={
             '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
         }			,     )
     A	:							int							= field(
         default=16			,     metadata={
             '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
         }			,     )
  
  
  @dataclass
  class 	a__				:
  
  
  
  
  
     '''simple docstring'''
  
     A	:							int							= field(
         default=768			,     metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}			,     )
     A	:							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: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  UpperCAmelCase__      =							logging.get_logger(__name__)
  
  UpperCAmelCase__      =							{
      '''dandelin/vilt-b32-mlm''': '''https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'''
  }
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Dict							= '''vilt'''
  			def __init__(     self       : str ,						lowerCAmelCase_       : Tuple=30_522 ,						lowerCAmelCase_       : List[Any]=2 ,						lowerCAmelCase_       : Optional[Any]=2 ,						lowerCAmelCase_       : Union[str, Any]=40 ,						lowerCAmelCase_       : str=768 ,						lowerCAmelCase_       : Tuple=12 ,						lowerCAmelCase_       : List[Any]=12 ,						lowerCAmelCase_       : int=3_072 ,						lowerCAmelCase_       : List[Any]="gelu" ,						lowerCAmelCase_       : int=0.0 ,						lowerCAmelCase_       : str=0.0 ,						lowerCAmelCase_       : Optional[Any]=0.02 ,						lowerCAmelCase_       : Union[str, Any]=1E-12 ,						lowerCAmelCase_       : Dict=384 ,						lowerCAmelCase_       : Optional[int]=32 ,						lowerCAmelCase_       : Optional[Any]=3 ,						lowerCAmelCase_       : str=True ,						lowerCAmelCase_       : List[Any]=-1 ,						lowerCAmelCase_       : Union[str, Any]=False ,						lowerCAmelCase_       : str=-1 ,						**lowerCAmelCase_       : Any ,						)			->		Tuple:
  						super().__init__(tie_word_embeddings=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  						__A=  vocab_size
  						__A=  type_vocab_size
  						__A=  modality_type_vocab_size
  						__A=  max_position_embeddings
  
  						__A=  hidden_size
  						__A=  num_hidden_layers
  						__A=  num_attention_heads
  						__A=  intermediate_size
  						__A=  hidden_act
  						__A=  hidden_dropout_prob
  						__A=  attention_probs_dropout_prob
  						__A=  initializer_range
  						__A=  layer_norm_eps
  
  						__A=  image_size
  						__A=  patch_size
  						__A=  num_channels
  						__A=  qkv_bias
  						__A=  max_image_length
  						__A=  num_images
  
  
  
  