





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


_lowerCAmelCase							:					List[str]		    =		logging.getLogger(__name__)
torch.set_grad_enabled(False)
_lowerCAmelCase							:					Any		    =		'''cuda''' if torch.cuda.is_available() else '''cpu'''

def    __snake_case  (    _lowerCAmelCase       :       str , _lowerCAmelCase       :       Union[str, Any]=100 , _lowerCAmelCase       :       List[str]=" "		)       ->  Union[str, Any]:
   A_    : Union[str, Any]								=	text.split(_snake_case		)
   return [character.join(text[i : i + n]		).strip() for i in range(0 , len(_snake_case		) , _snake_case		)]

def    __snake_case  (    _lowerCAmelCase       :       dict		)       ->  Optional[int]:
   A_    : List[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    __snake_case  (    _lowerCAmelCase       :       dict , _lowerCAmelCase       :       DPRContextEncoder , _lowerCAmelCase       :       DPRContextEncoderTokenizerFast		)       ->  int:
   A_    : Optional[int]								=	ctx_tokenizer(
       documents["title"] , documents["text"] , truncation=_snake_case , padding="longest" , return_tensors="pt"		)['''input_ids''']
   A_    : List[str]								=	ctx_encoder(input_ids.to(device=_snake_case		) , return_dict=_snake_case		).pooler_output
   return {"embeddings": embeddings.detach().cpu().numpy()}

def    __snake_case  (    _lowerCAmelCase       :       "RagExampleArguments" , _lowerCAmelCase       :       "ProcessingArguments" , _lowerCAmelCase       :       "IndexHnswArguments" , )       ->  Tuple:
   ######################################
   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_    : 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_    : Optional[int]								=	dataset.map(_snake_case , batched=_snake_case , num_proc=processing_args.num_proc		)

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


@dataclass
class     __magic_name__     :





     """simple docstring"""



     __UpperCamelCase    			=   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\''''}			, )
     __UpperCamelCase    			=   field(
         default=a__			, metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}			, )
     __UpperCamelCase    			=   field(
         default='''facebook/rag-sequence-nq'''			, metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}			, )
     __UpperCamelCase    			=   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\''''
             )
         }			, )
     __UpperCamelCase    			=   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     __magic_name__     :





     """simple docstring"""



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




@dataclass
class     __magic_name__     :





     """simple docstring"""



     __UpperCamelCase    			=   field(
         default=7_68			, metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}			, )
     __UpperCamelCase    			=   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)

 _lowerCAmelCase							:					Any		    =		HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 _lowerCAmelCase							:					Dict		    =		parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  _lowerCAmelCase							:					List[Any]		    =		rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)




"""simple docstring"""





from __future__ import annotations

from decimal import Decimal

from numpy import array


def    _snake_case  (  _snake_case :	list[list[float]]	):
      lowerCAmelCase    :		str				=      Decimal

      # Check if the provided matrix has 2 rows and 2 columns
      # since this implementation only works for 2x2 matrices
      if len(_snake_case	) == 2 and len(matrix[0]	) == 2 and len(matrix[1]	) == 2:
            # Calculate the determinant of the matrix
            lowerCAmelCase    :		int				=      float(
                d(matrix[0][0]	) * d(matrix[1][1]	) - d(matrix[1][0]	) * d(matrix[0][1]	)	)
            if determinant == 0:
                  raise ValueError('''This matrix has no inverse.'''	)

            # Creates a copy of the matrix with swapped positions of the elements
            lowerCAmelCase    :		Optional[int]				=      [[0.0, 0.0], [0.0, 0.0]]
            lowerCAmelCase,					lowerCAmelCase    :		List[Any]				=      matrix[1][1], matrix[0][0]
            lowerCAmelCase,					lowerCAmelCase    :		Union[str, Any]				=      -matrix[1][0], -matrix[0][1]

            # Calculate the inverse of the matrix
            return [
                [(float(d(_snake_case	)	) / determinant) or 0.0 for n in row] for row in swapped_matrix
            ]
      elif (
          len(_snake_case	) == 3
          and len(matrix[0]	) == 3
          and len(matrix[1]	) == 3
          and len(matrix[2]	) == 3
      ):
            # Calculate the determinant of the matrix using Sarrus rule
            lowerCAmelCase    :		int				=      float(
                (
                    (d(matrix[0][0]	) * d(matrix[1][1]	) * d(matrix[2][2]	))
                    + (d(matrix[0][1]	) * d(matrix[1][2]	) * d(matrix[2][0]	))
                    + (d(matrix[0][2]	) * d(matrix[1][0]	) * d(matrix[2][1]	))
                )
                - (
                    (d(matrix[0][2]	) * d(matrix[1][1]	) * d(matrix[2][0]	))
                    + (d(matrix[0][1]	) * d(matrix[1][0]	) * d(matrix[2][2]	))
                    + (d(matrix[0][0]	) * d(matrix[1][2]	) * d(matrix[2][1]	))
                )	)
            if determinant == 0:
                  raise ValueError('''This matrix has no inverse.'''	)

            # Creating cofactor matrix
            lowerCAmelCase    :		Dict				=      [
                [d(0.0	), d(0.0	), d(0.0	)],
                [d(0.0	), d(0.0	), d(0.0	)],
                [d(0.0	), d(0.0	), d(0.0	)],
            ]
            lowerCAmelCase    :		List[str]				=      (d(matrix[1][1]	) * d(matrix[2][2]	)) - (
                d(matrix[1][2]	) * d(matrix[2][1]	)
            )
            lowerCAmelCase    :		Dict				=      -(
                (d(matrix[1][0]	) * d(matrix[2][2]	)) - (d(matrix[1][2]	) * d(matrix[2][0]	))
            )
            lowerCAmelCase    :		str				=      (d(matrix[1][0]	) * d(matrix[2][1]	)) - (
                d(matrix[1][1]	) * d(matrix[2][0]	)
            )
            lowerCAmelCase    :		Any				=      -(
                (d(matrix[0][1]	) * d(matrix[2][2]	)) - (d(matrix[0][2]	) * d(matrix[2][1]	))
            )
            lowerCAmelCase    :		Any				=      (d(matrix[0][0]	) * d(matrix[2][2]	)) - (
                d(matrix[0][2]	) * d(matrix[2][0]	)
            )
            lowerCAmelCase    :		Optional[int]				=      -(
                (d(matrix[0][0]	) * d(matrix[2][1]	)) - (d(matrix[0][1]	) * d(matrix[2][0]	))
            )
            lowerCAmelCase    :		Optional[int]				=      (d(matrix[0][1]	) * d(matrix[1][2]	)) - (
                d(matrix[0][2]	) * d(matrix[1][1]	)
            )
            lowerCAmelCase    :		Dict				=      -(
                (d(matrix[0][0]	) * d(matrix[1][2]	)) - (d(matrix[0][2]	) * d(matrix[1][0]	))
            )
            lowerCAmelCase    :		List[Any]				=      (d(matrix[0][0]	) * d(matrix[1][1]	)) - (
                d(matrix[0][1]	) * d(matrix[1][0]	)
            )

            # Transpose the cofactor matrix (Adjoint matrix)
            lowerCAmelCase    :		str				=      array(_snake_case	)
            for i in range(3	):
                  for j in range(3	):
                        lowerCAmelCase    :		Optional[Any]				=      cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
            lowerCAmelCase    :		Tuple				=      array(_snake_case	)
            for i in range(3	):
                  for j in range(3	):
                        inverse_matrix[i][j] /= d(_snake_case	)

        # Calculate the inverse of the matrix
            return [[float(d(_snake_case	)	) or 0.0 for n in row] for row in inverse_matrix]
      raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''	)


