





'''simple docstring'''

from __future__ import annotations







class __a			:





   def __init__(       self					:      Optional[int]					,lowerCamelCase					:      list[list[int]]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TypeError(
          """Matrices must be formed from a list of zero or more lists containing at """
          """least one and the same number of values, each of which must be of type """
          """int or float."""     )
      if len(lowerCamelCase     ) != 0:
         __SCREAMING_SNAKE_CASE   				=							len(rows[0]     )
         if cols == 0:
            raise error
         for row in rows:
            if len(lowerCamelCase     ) != cols:
               raise error
            for value in row:
               if not isinstance(lowerCamelCase					,(int, float)     ):
                  raise error
         __SCREAMING_SNAKE_CASE   				=							rows
      else:
         __SCREAMING_SNAKE_CASE   				=							[]





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      return [[row[i] for row in self.rows] for i in range(len(self.rows[0]     )     )]





   @property
   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return len(self.rows     )





   @property
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      return len(self.rows[0]     )





   @property
   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      return (self.num_rows, self.num_columns)





   @property
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      return self.order[0] == self.order[1]





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          [0 if column_num != row_num else 1 for column_num in range(self.num_rows     )]
          for row_num in range(self.num_rows     )
      ]
      return Matrix(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      if not self.is_square:
         return 0
      if self.order == (0, 0):
         return 1
      if self.order == (1, 1):
         return int(self.rows[0][0]     )
      if self.order == (2, 2):
         return int(
             (self.rows[0][0] * self.rows[1][1])
             - (self.rows[0][1] * self.rows[1][0])     )
      else:
         return sum(
             self.rows[0][column] * self.cofactors().rows[0][column]
             for column in range(self.num_columns     )     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return bool(self.determinant()     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          [
              self.rows[other_row][other_column]
              for other_column in range(self.num_columns     )
              if other_column != column
          ]
          for other_row in range(self.num_rows     )
          if other_row != row
      ]
      return Matrix(lowerCamelCase     ).determinant()





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      int					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      if (row + column) % 2 == 0:
         return self.get_minor(lowerCamelCase					,lowerCamelCase     )
      return -1 * self.get_minor(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      return Matrix(
          [
              [self.get_minor(lowerCamelCase					,lowerCamelCase     ) for column in range(self.num_columns     )]
              for row in range(self.num_rows     )
          ]     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      return Matrix(
          [
              [
                  self.minors().rows[row][column]
                  if (row + column) % 2 == 0
                  else self.minors().rows[row][column] * -1
                  for column in range(self.minors().num_columns     )
              ]
              for row in range(self.minors().num_rows     )
          ]     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[
          [self.cofactors().rows[column][row] for column in range(self.num_columns     )]
          for row in range(self.num_rows     )
      ]
      return Matrix(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.determinant()
      if not determinant:
         raise TypeError("""Only matrices with a non-zero determinant have an inverse"""     )
      return self.adjugate() * (1 / determinant)





   def __repr__(       self					:      List[Any]     ):

      '''simple docstring'''

      return str(self.rows     )





   def __str__(       self					:      List[str]     ):

      '''simple docstring'''

      if self.num_rows == 0:
         return "[]"
      if self.num_rows == 1:
         return "[[" + ". ".join(str(self.rows[0]     )     ) + "]]"
      return (
          "["
          + "\n ".join(
              [
                  """[""" + """. """.join([str(lowerCamelCase     ) for value in row]     ) + """.]"""
                  for row in self.rows
              ]     )
          + "]"
      )





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      list[int]					,lowerCamelCase					:      int | None = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TypeError("""Row must be a list containing all ints and/or floats"""     )
      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         raise type_error
      for value in row:
         if not isinstance(lowerCamelCase					,(int, float)     ):
            raise type_error
      if len(lowerCamelCase     ) != self.num_columns:
         raise ValueError(
             """Row must be equal in length to the other rows in the matrix"""     )
      if position is None:
         self.rows.append(lowerCamelCase     )
      else:
         __SCREAMING_SNAKE_CASE   				=							self.rows[0:position] + [row] + self.rows[position:]





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      list[int]					,lowerCamelCase					:      int | None = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TypeError(
          """Column must be a list containing all ints and/or floats"""     )
      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         raise type_error
      for value in column:
         if not isinstance(lowerCamelCase					,(int, float)     ):
            raise type_error
      if len(lowerCamelCase     ) != self.num_rows:
         raise ValueError(
             """Column must be equal in length to the other columns in the matrix"""     )
      if position is None:
         __SCREAMING_SNAKE_CASE   				=							[self.rows[i] + [column[i]] for i in range(self.num_rows     )]
      else:
         __SCREAMING_SNAKE_CASE   				=							[
             self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
             for i in range(self.num_rows     )
         ]





   def __eq__(       self					:      int					,lowerCamelCase					:      object     ):

      '''simple docstring'''

      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         return NotImplemented
      return self.rows == other.rows





   def __ne__(       self					:      Any					,lowerCamelCase					:      object     ):

      '''simple docstring'''

      return not self == other





   def __neg__(       self					:      Any     ):

      '''simple docstring'''

      return self * -1





   def __add__(       self					:      List[Any]					,lowerCamelCase					:      Matrix     ):

      '''simple docstring'''

      if self.order != other.order:
         raise ValueError("""Addition requires matrices of the same order"""     )
      return Matrix(
          [
              [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns     )]
              for i in range(self.num_rows     )
          ]     )





   def __sub__(       self					:      Any					,lowerCamelCase					:      Matrix     ):

      '''simple docstring'''

      if self.order != other.order:
         raise ValueError("""Subtraction requires matrices of the same order"""     )
      return Matrix(
          [
              [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns     )]
              for i in range(self.num_rows     )
          ]     )





   def __mul__(       self					:      Any					,lowerCamelCase					:      Matrix | int | float     ):

      '''simple docstring'''

      if isinstance(lowerCamelCase					,(int, float)     ):
         return Matrix(
             [[int(element * other     ) for element in row] for row in self.rows]     )
      elif isinstance(lowerCamelCase					,lowerCamelCase     ):
         if self.num_columns != other.num_rows:
            raise ValueError(
                """The number of columns in the first matrix must """
                """be equal to the number of rows in the second"""     )
         return Matrix(
             [
                 [Matrix.dot_product(lowerCamelCase					,lowerCamelCase     ) for column in other.columns()]
                 for row in self.rows
             ]     )
      else:
         raise TypeError(
             """A Matrix can only be multiplied by an int, float, or another matrix"""     )





   def __pow__(       self					:      Optional[int]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      if not isinstance(lowerCamelCase					,lowerCamelCase     ):
         raise TypeError("""A Matrix can only be raised to the power of an int"""     )
      if not self.is_square:
         raise ValueError("""Only square matrices can be raised to a power"""     )
      if other == 0:
         return self.identity()
      if other < 0:
         if self.is_invertable():
            return self.inverse() ** (-other)
         raise ValueError(
             """Only invertable matrices can be raised to a negative power"""     )
      __SCREAMING_SNAKE_CASE   				=							self
      for _ in range(other - 1     ):
         result *= self
      return result





   @classmethod
   def   UpperCAmelCase__  (       cls					:      str					,lowerCamelCase					:      list[int]					,lowerCamelCase					:      list[int]     ):

      '''simple docstring'''

      return sum(row[i] * column[i] for i in range(len(lowerCamelCase     )     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()












'''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


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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=100				,						__UpperCAmelCase=" "   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							text.split(__UpperCAmelCase   )
   return [character.join(text[i : i + n]   ).strip() for i in range(0				,						len(__UpperCAmelCase   )				,						__UpperCAmelCase   )]

def     __magic_name__							(				__UpperCAmelCase   )    ->       dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							[], []
   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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							ctx_tokenizer(
       documents["""title"""]				,						documents["""text"""]				,						truncation=__UpperCAmelCase				,						padding="""longest"""				,						return_tensors="""pt"""   )["""input_ids"""]
   __SCREAMING_SNAKE_CASE   				=							ctx_encoder(input_ids.to(device=__UpperCAmelCase   )				,						return_dict=__UpperCAmelCase   ).pooler_output
   return {"embeddings": embeddings.detach().cpu().numpy()}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						)    ->       Any:

   '''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
   __SCREAMING_SNAKE_CASE   				=							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
   __SCREAMING_SNAKE_CASE   				=							dataset.map(__UpperCAmelCase				,						batched=__UpperCAmelCase				,						num_proc=processing_args.num_proc   )

   # And compute the embeddings
   __SCREAMING_SNAKE_CASE   				=							DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
   __SCREAMING_SNAKE_CASE   				=							Features(
       {"""text""": Value("""string"""   ), """title""": Value("""string"""   ), """embeddings""": Sequence(Value("""float32"""   )   )}   )  # optional, save as float32 instead of float64 to save space
   __SCREAMING_SNAKE_CASE   				=							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
   __SCREAMING_SNAKE_CASE   				=							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
   __SCREAMING_SNAKE_CASE   				=							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
   __SCREAMING_SNAKE_CASE   				=							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 __a			:
   __UpperCamelCase :			str						   =						field(
       default=str(Path(_snake_case      ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'      ), metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}, )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=_snake_case, metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}, )
   __UpperCamelCase :			str						   =						field(
       default='facebook/rag-sequence-nq', metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}, )
   __UpperCamelCase :			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\''
           )
       }, )
   __UpperCamelCase :			Optional[str]						   =						field(
       default=str(Path(_snake_case      ).parent / 'test_run' / 'dummy-kb'      ), metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}, )







@dataclass
class __a			:
   __UpperCamelCase :			Optional[int]						   =						field(
       default=_snake_case, metadata={
           'help': 'The number of processes to use to split the documents into passages. Default is single process.'
       }, )
   __UpperCamelCase :			int						   =						field(
       default=16, metadata={
           'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
       }, )







@dataclass
class __a			:
   __UpperCamelCase :			int						   =						field(
       default=768, metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}, )
   __UpperCamelCase :			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)

    a   	=    HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
    a	, a	, 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)





