




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_						=			logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCAmelCase_						=			'cuda' if torch.cuda.is_available() else 'cpu'






def  snake_case(    __magic_name__			,		__magic_name__=1_00			,		__magic_name__=" "   )			->							List[str]:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			text.split(__magic_name__   )
     return [character.join(text[i : i + n]   ).strip() for i in range(0			,		len(__magic_name__   )			,		__magic_name__   )]






def  snake_case(    __magic_name__   )			->							dict:





     '''simple docstring'''


     lowercase      ,	lowercase :       str       =			[], []
     for title, text in zip(documents['''title''']			,		documents['''text''']   ):
          if text is not None:
               for passage in split_text(__magic_name__   ):
                    titles.append(title if title is not None else ''''''   )
                    texts.append(__magic_name__   )
     return {"title": titles, "text": texts}






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							dict:





     '''simple docstring'''


     lowercase :       Tuple       =			ctx_tokenizer(
         documents['''title''']			,		documents['''text''']			,		truncation=__magic_name__			,		padding='''longest'''			,		return_tensors='''pt'''   )['''input_ids''']
     lowercase :       List[Any]       =			ctx_encoder(input_ids.to(device=__magic_name__   )			,		return_dict=__magic_name__   ).pooler_output
     return {"embeddings": embeddings.detach().cpu().numpy()}






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		)			->							Optional[int]:





     '''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
     lowercase :       Optional[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
     lowercase :       Union[str, Any]       =			dataset.map(__magic_name__			,		batched=__magic_name__			,		num_proc=processing_args.num_proc   )

     # And compute the embeddings
     lowercase :       List[Any]       =			DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=__magic_name__   )
     lowercase :       int       =			DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
     lowercase :       int       =			Features(
         {'''text''': Value('''string'''   ), '''title''': Value('''string'''   ), '''embeddings''': Sequence(Value('''float32'''   )   )}   )  # optional, save as float32 instead of float64 to save space
     lowercase :       Union[str, Any]       =			dataset.map(
         partial(__magic_name__			,		ctx_encoder=__magic_name__			,		ctx_tokenizer=__magic_name__   )			,		batched=__magic_name__			,		batch_size=processing_args.batch_size			,		features=__magic_name__			,		)

     # And finally save your dataset
     lowercase :       List[str]       =			os.path.join(rag_example_args.output_dir			,		'''my_knowledge_dataset'''   )
     dataset.save_to_disk(__magic_name__   )
     # 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
     lowercase :       int       =			faiss.IndexHNSWFlat(index_hnsw_args.d			,		index_hnsw_args.m			,		faiss.METRIC_INNER_PRODUCT   )
     dataset.add_faiss_index('''embeddings'''			,		custom_index=__magic_name__   )

     # And save the index
     lowercase :       List[Any]       =			os.path.join(rag_example_args.output_dir			,		'''my_knowledge_dataset_hnsw_index.faiss'''   )
     dataset.get_index('''embeddings'''   ).save(__magic_name__   )
     # dataset.load_faiss_index("embeddings", index_path)  # to reload the index
@dataclass
class 						_A :
  _UpperCamelCase       :      str			=      field(
      default=str(Path(_lowerCamelCase				).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=_lowerCamelCase      ,       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(_lowerCamelCase				).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=_lowerCamelCase      ,       metadata={
          '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
      }      ,       )
  _UpperCamelCase       :      int			=      field(
      default=1_6      ,       metadata={
          '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
      }      ,       )


@dataclass
class 						_A :
  _UpperCamelCase       :      int			=      field(
      default=7_6_8      ,       metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}      ,       )
  _UpperCamelCase       :      int			=      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)

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





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_						=			logging.get_logger(__name__)

lowerCAmelCase_						=			{'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_						=			{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

lowerCAmelCase_						=			{
    'moussaKam/mbarthez': 10_24,
    'moussaKam/barthez': 10_24,
    'moussaKam/barthez-orangesum-title': 10_24,
}

lowerCAmelCase_						=			'▁'
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Optional[Any]			=      VOCAB_FILES_NAMES
  _UpperCamelCase       :      Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
  _UpperCamelCase       :      Optional[Any]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCamelCase       :      List[Any]			=      ['''input_ids''', '''attention_mask''']






  def __init__(  self		:     Any				,						_A		:     Optional[int]				,						_A		:     List[str]="<s>"				,						_A		:     Tuple="</s>"				,						_A		:     Dict="</s>"				,						_A		:     Dict="<s>"				,						_A		:     List[str]="<unk>"				,						_A		:     str="<pad>"				,						_A		:     Any="<mask>"				,						_A		:     Optional[Dict[str, Any]] = None				,						**_A		:     Union[str, Any]				,						)		->   None:






       """simple docstring"""







       lowercase :       List[str]       =			AddedToken(_A				,						lstrip=_A				,						rstrip=_A				) if isinstance(_A				,						_A				) else mask_token

       lowercase :       List[Any]       =			{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=_A				,						eos_token=_A				,						unk_token=_A				,						sep_token=_A				,						cls_token=_A				,						pad_token=_A				,						mask_token=_A				,						sp_model_kwargs=self.sp_model_kwargs				,						**_A				,						)

       lowercase :       Any       =			vocab_file
       lowercase :       Any       =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(str(_A				)				)

       lowercase :       Optional[Any]       =			{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

       lowercase :       Tuple       =			len(self.sp_model				) - 1
       lowercase :       Any       =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}






  def  __a      (  self		:     Tuple				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				)		->   List[int]:






       """simple docstring"""







       if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       lowercase :       Any       =			[self.cls_token_id]
       lowercase :       int       =			[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






  def  __a      (  self		:     Optional[int]				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				,						_A		:     bool = False				)		->   List[int]:






       """simple docstring"""







       if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=_A				,						token_ids_a=_A				,						already_has_special_tokens=_A				)

       if token_ids_a is None:
            return [1] + ([0] * len(_A				)) + [1]
       return [1] + ([0] * len(_A				)) + [1, 1] + ([0] * len(_A				)) + [1]






  def  __a      (  self		:     Optional[int]				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				)		->   List[int]:






       """simple docstring"""







       lowercase :       List[str]       =			[self.sep_token_id]
       lowercase :       List[str]       =			[self.cls_token_id]

       if token_ids_a is None:
            return len(cls + token_ids_a + sep				) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]






  @property
  def  __a      (  self		:     List[Any]				)		->   Optional[Any]:






       """simple docstring"""







       return len(self.sp_model				)






  def  __a      (  self		:     List[Any]				)		->   int:






       """simple docstring"""







       lowercase :       int       =			{self.convert_ids_to_tokens(_A				): i for i in range(self.vocab_size				)}
       vocab.update(self.added_tokens_encoder				)
       return vocab






  def  __a      (  self		:     Union[str, Any]				,						_A		:     str				)		->   List[str]:






       """simple docstring"""







       return self.sp_model.encode(_A				,						out_type=_A				)






  def  __a      (  self		:     Optional[int]				,						_A		:     str				)		->   Dict:






       """simple docstring"""







       if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
       lowercase :       Optional[int]       =			self.sp_model.PieceToId(_A				)

       return spm_id if spm_id else self.unk_token_id






  def  __a      (  self		:     Any				,						_A		:     List[str]				)		->   List[str]:






       """simple docstring"""







       if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(_A				)






  def  __a      (  self		:     Any				,						_A		:     Tuple				)		->   Tuple:






       """simple docstring"""







       lowercase :       Dict       =			[]
       lowercase :       Any       =			''''''
       lowercase :       str       =			False
       for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                 if not prev_is_special:
                      out_string += " "
                 out_string += self.sp_model.decode(_A				) + token
                 lowercase :       int       =			True
                 lowercase :       Optional[Any]       =			[]
            else:
                 current_sub_tokens.append(_A				)
                 lowercase :       List[str]       =			False
       out_string += self.sp_model.decode(_A				)
       return out_string.strip()






  def __getstate__(  self		:     int				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       str       =			self.__dict__.copy()
       lowercase :       Any       =			None
       return state






  def __setstate__(  self		:     Optional[int]				,						_A		:     Tuple				)		->   str:






       """simple docstring"""







       lowercase :       List[str]       =			d

       # for backward compatibility
       if not hasattr(self				,						'''sp_model_kwargs'''				):
            lowercase :       Union[str, Any]       =			{}

       lowercase :       List[Any]       =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(self.vocab_file				)






  def  __a      (  self		:     str				,						_A		:     str				,						_A		:     Optional[str] = None				)		->   Tuple[str]:






       """simple docstring"""







       if not os.path.isdir(_A				):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
            return
       lowercase :       Any       =			os.path.join(
           _A				,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

       if os.path.abspath(self.vocab_file				) != os.path.abspath(_A				) and os.path.isfile(self.vocab_file				):
            copyfile(self.vocab_file				,						_A				)
       elif not os.path.isfile(self.vocab_file				):
            with open(_A				,						'''wb'''				) as fi:
                 lowercase :       Optional[Any]       =			self.sp_model.serialized_model_proto()
                 fi.write(_A				)

       return (out_vocab_file,)