



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


a_			=					logging.getLogger(__name__)
torch.set_grad_enabled(False)
a_			=					"cuda" if torch.cuda.is_available() else "cpu"





def 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: Optional[Any]=1_00 ,_UpperCamelCase			: Dict=" "							):
   __lowerCamelCase													=    text.split(a_							)
   return [character.join(text[i : i + n]							).strip() for i in range(0 ,len(a_							) ,a_							)]





def 		a__			(		_UpperCamelCase			: dict							):
   __lowerCamelCase													=    [], []
   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 		a__			(		_UpperCamelCase			: dict ,_UpperCamelCase			: DPRContextEncoder ,_UpperCamelCase			: DPRContextEncoderTokenizerFast							):
   __lowerCamelCase													=    ctx_tokenizer(
       documents['''title'''] ,documents['''text'''] ,truncation=a_ ,padding='''longest''' ,return_tensors='''pt'''							)['''input_ids''']
   __lowerCamelCase													=    ctx_encoder(input_ids.to(device=a_							) ,return_dict=a_							).pooler_output
   return {"embeddings": embeddings.detach().cpu().numpy()}





def 		a__			(		_UpperCamelCase			: "RagExampleArguments" ,_UpperCamelCase			: "ProcessingArguments" ,_UpperCamelCase			: "IndexHnswArguments" ,):
   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
   __lowerCamelCase													=    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
   __lowerCamelCase													=    dataset.map(a_ ,batched=a_ ,num_proc=processing_args.num_proc							)

   # And compute the embeddings
   __lowerCamelCase													=    DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name							).to(device=a_							)
   __lowerCamelCase													=    DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name							)
   __lowerCamelCase													=    Features(
       {'''text''': Value('''string'''							), '''title''': Value('''string'''							), '''embeddings''': Sequence(Value('''float32'''							)							)}							)  # optional, save as float32 instead of float64 to save space
   __lowerCamelCase													=    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
   __lowerCamelCase													=    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
   __lowerCamelCase													=    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
   __lowerCamelCase													=    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



@dataclass
class  __lowerCAmelCase     :
 lowerCAmelCase__       					=  field(
     default=str(Path(_UpperCAmelCase   ).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv"""   )  ,	metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""}  ,	)
 lowerCAmelCase__       					=  field(
     default=_UpperCAmelCase  ,	metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}  ,	)
 lowerCAmelCase__       					=  field(
     default="""facebook/rag-sequence-nq"""  ,	metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}  ,	)
 lowerCAmelCase__       					=  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'"""
         )
     }  ,	)
 lowerCAmelCase__       					=  field(
     default=str(Path(_UpperCAmelCase   ).parent / """test_run""" / """dummy-kb"""   )  ,	metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}  ,	)



@dataclass
class  __lowerCAmelCase     :
 lowerCAmelCase__       					=  field(
     default=_UpperCAmelCase  ,	metadata={
         """help""": """The number of processes to use to split the documents into passages. Default is single process."""
     }  ,	)
 lowerCAmelCase__       					=  field(
     default=1_6  ,	metadata={
         """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
     }  ,	)



@dataclass
class  __lowerCAmelCase     :
 lowerCAmelCase__       					=  field(
     default=7_6_8  ,	metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}  ,	)
 lowerCAmelCase__       					=  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)

  a_			=					HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
  a_			=					parser.parse_args_into_dataclasses()
  with TemporaryDirectory() as tmp_dir:
    a_			=					rag_example_args.output_dir or tmp_dir
    main(rag_example_args, processing_args, index_hnsw_args)









def 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: str = " "							):
   __lowerCamelCase													=    []

   __lowerCamelCase													=    0
   for index, char in enumerate(_UpperCamelCase							):
      if char == separator:
         split_words.append(string[last_index:index]							)
         __lowerCamelCase													=    index + 1
      elif index + 1 == len(_UpperCamelCase							):
         split_words.append(string[last_index : index + 1]							)
   return split_words


if __name__ == "__main__":
  from doctest import testmod

  testmod()






