


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,
)


a_  :    Optional[Any]      =			logging.getLogger(__name__)
torch.set_grad_enabled(False)
a_  :    List[Any]      =			"""cuda""" if torch.cuda.is_available() else """cpu"""





def __snake_case (				UpperCAmelCase_						:						str ,    UpperCAmelCase_						:						Optional[int]=100 ,    UpperCAmelCase_						:						List[str]=" "		):
 lowerCamelCase_								=  text.split(UpperCAmelCase_		)
 return [character.join(text[i : i + n]		).strip() for i in range(0 ,    len(UpperCAmelCase_		) ,    UpperCAmelCase_		)]





def __snake_case (				UpperCAmelCase_						:						dict		):
 lowerCamelCase_			,lowerCamelCase_								=  [], []
 for title, text in zip(documents["title"] ,    documents["text"]		):
  if text is not None:
   for passage in split_text(UpperCAmelCase_		):
    titles.append(title if title is not None else ""		)
    texts.append(UpperCAmelCase_		)
 return {"title": titles, "text": texts}





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





def __snake_case (				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(UpperCAmelCase_ ,    batched=UpperCAmelCase_ ,    num_proc=processing_args.num_proc		)

 # And compute the embeddings
 lowerCamelCase_								=  DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name		).to(device=UpperCAmelCase_		)
 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(UpperCAmelCase_ ,    ctx_encoder=UpperCAmelCase_ ,    ctx_tokenizer=UpperCAmelCase_		) ,    batched=UpperCAmelCase_ ,    batch_size=processing_args.batch_size ,    features=UpperCAmelCase_ ,    )

 # And finally save your dataset
 lowerCamelCase_								=  os.path.join(rag_example_args.output_dir ,    "my_knowledge_dataset"		)
 dataset.save_to_disk(UpperCAmelCase_		)
 # 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=UpperCAmelCase_		)

 # And save the index
 lowerCamelCase_								=  os.path.join(rag_example_args.output_dir ,    "my_knowledge_dataset_hnsw_index.faiss"		)
 dataset.get_index("embeddings"		).save(UpperCAmelCase_		)
 # dataset.load_faiss_index("embeddings", index_path)  # to reload the index

 ######################################
 logger.info("Step 3 - Load RAG"		)
 ######################################

 # Easy way to load the model
 lowerCamelCase_								=  RagRetriever.from_pretrained(
     rag_example_args.rag_model_name ,    index_name="custom" ,    indexed_dataset=UpperCAmelCase_		)
 lowerCamelCase_								=  RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name ,    retriever=UpperCAmelCase_		)
 lowerCamelCase_								=  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"		)
 ######################################

 lowerCamelCase_								=  rag_example_args.question or "What does Moses' rod turn into ?"
 lowerCamelCase_								=  tokenizer.question_encoder(UpperCAmelCase_ ,    return_tensors="pt"		)["input_ids"]
 lowerCamelCase_								=  model.generate(UpperCAmelCase_		)
 lowerCamelCase_								=  tokenizer.batch_decode(UpperCAmelCase_ ,    skip_special_tokens=UpperCAmelCase_		)[0]
 logger.info("Q: " + question		)
 logger.info("A: " + generated_string		)





@dataclass
class 					snake_case   :





  """simple docstring"""




  _lowerCamelCase		   =  field(
      default=str(Path(lowercase      ).parent / "test_data" / "my_knowledge_dataset.csv"      )      ,						metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}      ,						)
  _lowerCamelCase		   =  field(
      default=lowercase      ,						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=lowercase      ,						metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}      ,						)





@dataclass
class 					snake_case   :





  """simple docstring"""




  _lowerCamelCase		   =  field(
      default=lowercase      ,						metadata={
          "help": "The number of processes to use to split the documents into passages. Default is single process."
      }      ,						)
  _lowerCamelCase		   =  field(
      default=16      ,						metadata={
          "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
      }      ,						)





@dataclass
class 					snake_case   :





  """simple docstring"""




  _lowerCamelCase		   =  field(
      default=7_68      ,						metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}      ,						)
  _lowerCamelCase		   =  field(
      default=1_28      ,						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_  :    str      =			HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
     a_  :    str      =			parser.parse_args_into_dataclasses()
     with TemporaryDirectory() as tmp_dir:
          a_  :    List[Any]      =			rag_example_args.output_dir or tmp_dir
          main(rag_example_args, processing_args, index_hnsw_args)







'''simple docstring'''

import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
a_  :    Any      =			"""\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
a_  :    Optional[Any]      =			"""\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
a_  :    List[str]      =			""""""

# TODO: Add the licence for the dataset here if you can find it
a_  :    Dict      =			""""""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
a_  :    Optional[int]      =			{
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}





class 					snake_case   (   datasets.GeneratorBasedBuilder      ):





  """simple docstring"""




  _lowerCamelCase		   =  datasets.Version("1.1.0"      )

  # This is an example of a dataset with multiple configurations.
  # If you don't want/need to define several sub-sets in your dataset,
  # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

  # If you need to make complex sub-parts in the datasets with configurable options
  # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
  # BUILDER_CONFIG_CLASS = MyBuilderConfig

  # You will be able to load one or the other configurations in the following list with
  # data = datasets.load_dataset('my_dataset', 'first_domain')
  # data = datasets.load_dataset('my_dataset', 'second_domain')
  _lowerCamelCase		   =  [
      datasets.BuilderConfig(name="first_domain"      ,						version=lowercase      ,						description="This part of my dataset covers a first domain"      ),
      datasets.BuilderConfig(name="second_domain"      ,						version=lowercase      ,						description="This part of my dataset covers a second domain"      ),
  ]

  _lowerCamelCase		   =  "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.






  def   snake_case		(   self					):







   """simple docstring"""
   # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
   if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
    lowerCamelCase_								=  datasets.Features(
        {
            "sentence": datasets.Value("string"					),
            "option1": datasets.Value("string"					),
            "answer": datasets.Value("string"					)
            # These are the features of your dataset like images, labels ...
        }					)
   else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
    lowerCamelCase_								=  datasets.Features(
        {
            "sentence": datasets.Value("string"					),
            "option2": datasets.Value("string"					),
            "second_domain_answer": datasets.Value("string"					)
            # These are the features of your dataset like images, labels ...
        }					)
   return datasets.DatasetInfo(
       # This is the description that will appear on the datasets page.
       description=_DESCRIPTION      ,  features=UpperCamelCase      ,  homepage=_HOMEPAGE      ,  license=_LICENSE      ,  citation=_CITATION      ,  )






  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
   # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

   # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
   # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
   # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
   lowerCamelCase_								=  _URLS[self.config.name]
   lowerCamelCase_								=  dl_manager.download_and_extract(UpperCamelCase					)
   return [
       datasets.SplitGenerator(
           name=datasets.Split.TRAIN      ,  gen_kwargs={
               "filepath": os.path.join(UpperCamelCase      ,  "train.jsonl"					),
               "split": "train",
           }      ,  ),
       datasets.SplitGenerator(
           name=datasets.Split.VALIDATION      ,  gen_kwargs={
               "filepath": os.path.join(UpperCamelCase      ,  "dev.jsonl"					),
               "split": "dev",
           }      ,  ),
       datasets.SplitGenerator(
           name=datasets.Split.TEST      ,  gen_kwargs={
               "filepath": os.path.join(UpperCamelCase      ,  "test.jsonl"					),
               "split": "test"
           }      ,  ),
   ]







  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
   # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
   with open(UpperCamelCase      ,  encoding="utf-8"					) as f:
    for key, row in enumerate(UpperCamelCase					):
     lowerCamelCase_								=  json.loads(UpperCamelCase					)
     if self.config.name == "first_domain":
      # Yields examples as (key, example) tuples
      yield key, {
          "sentence": data["sentence"],
          "option1": data["option1"],
          "answer": "" if split == "test" else data["answer"],
      }
     else:
      yield key, {
          "sentence": data["sentence"],
          "option2": data["option2"],
          "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
      }



