





"""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


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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=1_00							,						SCREAMING_SNAKE_CASE     :						str=" "			):







      '''simple docstring'''




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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						dict			):







      '''simple docstring'''




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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						dict							,						SCREAMING_SNAKE_CASE     :						DPRContextEncoder							,						SCREAMING_SNAKE_CASE     :						DPRContextEncoderTokenizerFast			):







      '''simple docstring'''




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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						"RagExampleArguments"							,						SCREAMING_SNAKE_CASE     :						"ProcessingArguments"							,						SCREAMING_SNAKE_CASE     :						"IndexHnswArguments"							,						):







      '''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
      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(SCREAMING_SNAKE_CASE							,						batched=SCREAMING_SNAKE_CASE							,						num_proc=processing_args.num_proc			)

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

      # And finally save your dataset
      lowerCAmelCase  						=       os.path.join(rag_example_args.output_dir							,						"""my_knowledge_dataset"""			)
      dataset.save_to_disk(SCREAMING_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
      lowerCAmelCase  						=       faiss.IndexHNSWFlat(index_hnsw_args.d							,						index_hnsw_args.m							,						faiss.METRIC_INNER_PRODUCT			)
      dataset.add_faiss_index("""embeddings"""							,						custom_index=SCREAMING_SNAKE_CASE			)

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

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







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







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

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













"""simple docstring"""





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__					=   "▁"

SCREAMING_SNAKE_CASE__					=   {"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE__					=   {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

SCREAMING_SNAKE_CASE__					=   {
    "google/pegasus-xsum": 512,
}


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      VOCAB_FILES_NAMES

      _SCREAMING_SNAKE_CASE					=      VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE					=      ['input_ids', 'attention_mask']





      def __init__(       self      ,						lowercase      ,						lowercase="<pad>"      ,						lowercase="</s>"      ,						lowercase="<unk>"      ,						lowercase="<mask_2>"      ,						lowercase="<mask_1>"      ,						lowercase=None      ,						lowercase=103      ,						lowercase = None      ,						**lowercase      ,						)		->     None:
            lowerCAmelCase  						=       offset
            if additional_special_tokens is not None:
                  if not isinstance(lowercase      ,						lowercase ):
                        raise TypeError(
                            f'additional_special_tokens should be of type {type(lowercase )}, but is'
                            f' {type(lowercase )}' )

                  lowerCAmelCase  						=       (
                      ([mask_token_sent] + additional_special_tokens)
                      if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                      else additional_special_tokens
                  )
                  # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                  additional_special_tokens_extended += [
                      f'<unk_{i}>' for i in range(len(lowercase )      ,						self.offset - 1 )
                  ]

                  if len(set(lowercase ) ) != len(lowercase ):
                        raise ValueError(
                            """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                            f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
                  lowerCAmelCase  						=       additional_special_tokens_extended
            else:
                  lowerCAmelCase  						=       [mask_token_sent] if mask_token_sent is not None else []
                  additional_special_tokens += [f'<unk_{i}>' for i in range(2      ,						self.offset )]

            lowerCAmelCase  						=       {} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                eos_token=lowercase      ,						unk_token=lowercase      ,						mask_token=lowercase      ,						pad_token=lowercase      ,						mask_token_sent=lowercase      ,						offset=lowercase      ,						additional_special_tokens=lowercase      ,						sp_model_kwargs=self.sp_model_kwargs      ,						**lowercase      ,						)
            lowerCAmelCase  						=       mask_token_sent
            lowerCAmelCase  						=       vocab_file
            lowerCAmelCase  						=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
            self.sp_model.Load(lowercase )

            # add special tokens to encoder dict
            lowerCAmelCase  						=       {
                0: self.pad_token,
                1: self.eos_token,
            }

            if self.mask_token_sent is not None:
                  self.encoder.update(
                      {
                          2: self.mask_token_sent,
                          3: self.mask_token,
                      } )

            if self.offset > 0:
                  # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
                  # mask_token_sent is already added to list -> so start at 1
                  self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1      ,						self.offset - 1 )} )

            lowerCAmelCase  						=       {v: k for k, v in self.encoder.items()}





      @property
      def     _snake_case			(       self )		->     int:
            return len(self.sp_model ) + self.offset





      def     _snake_case			(       self )		->     Dict[str, int]:
            lowerCAmelCase  						=       {self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
            vocab.update(self.added_tokens_encoder )
            return vocab





      def __getstate__(       self )		->     Optional[int]:
            lowerCAmelCase  						=       self.__dict__.copy()
            lowerCAmelCase  						=       None
            return state





      def __setstate__(       self      ,						lowercase )		->     List[Any]:
            lowerCAmelCase  						=       d

            # for backward compatibility
            if not hasattr(self      ,						"""sp_model_kwargs""" ):
                  lowerCAmelCase  						=       {}

            lowerCAmelCase  						=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
            self.sp_model.Load(self.vocab_file )





      def     _snake_case			(       self      ,						lowercase )		->     List[str]:
            return self.sp_model.encode(lowercase      ,						out_type=lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     int:
            if token in self.decoder:
                  return self.decoder[token]
            elif token in self.added_tokens_decoder:
                  return self.added_tokens_decoder[token]
            lowerCAmelCase  						=       self.sp_model.piece_to_id(lowercase )
            return sp_id + self.offset





      def     _snake_case			(       self      ,						lowercase )		->     str:
            if index in self.encoder:
                  return self.encoder[index]
            elif index in self.added_tokens_encoder:
                  return self.added_tokens_encoder[index]
            else:
                  lowerCAmelCase  						=       self.sp_model.IdToPiece(index - self.offset )
            return token





      def     _snake_case			(       self      ,						lowercase )		->     Optional[int]:
            lowerCAmelCase  						=       []
            lowerCAmelCase  						=       """"""
            for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                        out_string += self.sp_model.decode(lowercase ) + token
                        lowerCAmelCase  						=       []
                  else:
                        current_sub_tokens.append(lowercase )
            out_string += self.sp_model.decode(lowercase )
            return out_string.strip()





      def     _snake_case			(       self      ,						lowercase=False )		->     Tuple:
            return 1





      def     _snake_case			(       self      ,						lowercase )		->     Tuple:
            lowerCAmelCase  						=       set(self.all_special_ids )  # call it once instead of inside list comp
            all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special

            return [1 if x in all_special_ids else 0 for x in seq]





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None      ,						lowercase = False )		->     List[int]:
            if already_has_special_tokens:
                  return self._special_token_mask(lowercase )
            elif token_ids_a is None:
                  return self._special_token_mask(lowercase ) + [1]
            else:
                  return self._special_token_mask(token_ids_a + token_ids_a ) + [1]





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=None )		->     List[int]:
            if token_ids_a is None:
                  return token_ids_a + [self.eos_token_id]
            # We don't expect to process pairs, but leave the pair logic for API consistency
            return token_ids_a + token_ids_a + [self.eos_token_id]





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     Tuple[str]:
            if not os.path.isdir(lowercase ):
                  logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
                  return
            lowerCAmelCase  						=       os.path.join(
                lowercase      ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

            if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ) and os.path.isfile(self.vocab_file ):
                  copyfile(self.vocab_file      ,						lowercase )
            elif not os.path.isfile(self.vocab_file ):
                  with open(lowercase      ,						"""wb""" ) as fi:
                        lowerCAmelCase  						=       self.sp_model.serialized_model_proto()
                        fi.write(lowercase )

            return (out_vocab_file,)






