






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    :   Union[str, Any]        =			logging.getLogger(__name__)
torch.set_grad_enabled(False)
_a    :   Union[str, Any]        =			"""cuda""" if torch.cuda.is_available() else """cpu"""


def 				snake_case__							(							UpperCAmelCase							:  str    ,			UpperCAmelCase							:  Optional[Any]=1_0_0    ,			UpperCAmelCase							:  List[Any]=" "       ):
    lowerCAmelCase__   :List[str]      							=				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__   :Any      							=				[], []
    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__   :Optional[int]      							=				ctx_tokenizer(
        documents["title"]    ,			documents["text"]    ,			truncation=UpperCAmelCase    ,			padding="longest"    ,			return_tensors="pt"       )["input_ids"]
    lowerCAmelCase__   :Tuple      							=				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__   :int      							=				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__   :Optional[Any]      							=				dataset.map(UpperCAmelCase    ,			batched=UpperCAmelCase    ,			num_proc=processing_args.num_proc       )

    # And compute the embeddings
    lowerCAmelCase__   :List[str]      							=				DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       ).to(device=UpperCAmelCase       )
    lowerCAmelCase__   :Optional[int]      							=				DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       )
    lowerCAmelCase__   :List[str]      							=				Features(
        {"text": Value("string"       ), "title": Value("string"       ), "embeddings": Sequence(Value("float32"       )       )}       )  # optional, save as float32 instead of float64 to save space
    lowerCAmelCase__   :Optional[Any]      							=				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__   :int      							=				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__   :str      							=				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__   :Optional[Any]      							=				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







@dataclass
class 	_UpperCAmelCase  :




      """simple docstring"""



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







@dataclass
class 	_UpperCAmelCase  :




      """simple docstring"""



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







@dataclass
class 	_UpperCAmelCase  :




      """simple docstring"""



      A          =    field(
          default=7_68				,							metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}				,							)
      A          =    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    :   Optional[Any]        =			HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 _a  ,				_a  ,				_a    :   Optional[Any]        =			parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  _a    :   Optional[Any]        =			rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)











from __future__ import annotations

import numpy as np


def 				snake_case__							(							UpperCAmelCase							:  np.ndarray       ):
    lowerCAmelCase__							,lowerCAmelCase__   :List[str]      							=				np.shape(UpperCAmelCase       )
    if rows != columns:
        lowerCAmelCase__   :Tuple      							=				(
            "'table' has to be of square shaped array but got a "
            F'''{rows}x{columns} array:\n{table}'''
        )
        raise ValueError(UpperCAmelCase       )

    lowerCAmelCase__   :Optional[Any]      							=				np.zeros((rows, columns)       )
    lowerCAmelCase__   :Tuple      							=				np.zeros((rows, columns)       )
    for i in range(UpperCAmelCase       ):
        for j in range(UpperCAmelCase       ):
            lowerCAmelCase__   :Any      							=				sum(lower[i][k] * upper[k][j] for k in range(UpperCAmelCase       )       )
            if upper[j][j] == 0:
                raise ArithmeticError("No LU decomposition exists"       )
            lowerCAmelCase__   :Any      							=				(table[i][j] - total) / upper[j][j]
        lowerCAmelCase__   :Tuple      							=				1
        for j in range(UpperCAmelCase    ,			UpperCAmelCase       ):
            lowerCAmelCase__   :Optional[Any]      							=				sum(lower[i][k] * upper[k][j] for k in range(UpperCAmelCase       )       )
            lowerCAmelCase__   :Dict      							=				table[i][j] - total
    return lower, upper


if __name__ == "__main__":
 import doctest

 doctest.testmod()



