






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 	UpperCamelCase	(							snake_case__  : str						,snake_case__  : Tuple=100						,snake_case__  : int=" "	):




     '''simple docstring'''






     __snake_case      :List[str] 							=    text.split(snake_case__	)
     return [character.join(text[i : i + n]	).strip() for i in range(0						,len(snake_case__	)						,snake_case__	)]






def 	UpperCamelCase	(							snake_case__  : dict	):




     '''simple docstring'''






     __snake_case							,			__snake_case      :int 							=    [], []
     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 	UpperCamelCase	(							snake_case__  : dict						,snake_case__  : DPRContextEncoder						,snake_case__  : DPRContextEncoderTokenizerFast	):




     '''simple docstring'''






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






def 	UpperCamelCase	(							snake_case__  : "RagExampleArguments"						,snake_case__  : "ProcessingArguments"						,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
     __snake_case      :Dict 							=    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
     __snake_case      :int 							=    dataset.map(snake_case__						,batched=snake_case__						,num_proc=processing_args.num_proc	)

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

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




@dataclass
class       snake_case__ :

       '''simple docstring'''
       lowerCamelCase				:					Optional[int]								=			field(
           default=lowercase_						,     metadata={
               "help": "The number of processes to use to split the documents into passages. Default is single process."
           }						,     )
       lowerCamelCase				:					int								=			field(
           default=16						,     metadata={
               "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
           }						,     )




@dataclass
class       snake_case__ :

       '''simple docstring'''
       lowerCamelCase				:					int								=			field(
           default=768						,     metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}						,     )
       lowerCamelCase				:					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)

     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 List, Optional, Tuple

import sentencepiece as spm

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


lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {"""vocab_file""": """sentencepiece.model"""}

lowerCamelCase__       		=  {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

lowerCamelCase__       		=  {
    """google/rembert""": 256,
}




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					str								=			VOCAB_FILES_NAMES
       lowerCamelCase				:					Tuple								=			PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase				:					Optional[int]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



       def __init__(				self      ,    a__      ,    a__=False      ,    a__=True      ,    a__=True      ,    a__="[CLS]"      ,    a__="[SEP]"      ,    a__="[UNK]"      ,    a__="[SEP]"      ,    a__="[PAD]"      ,    a__="[CLS]"      ,    a__="[MASK]"      ,    **a__      ,    )			->   List[str]:






            '''simple docstring'''





            super().__init__(
                do_lower_case=a__      ,    remove_space=a__      ,    keep_accents=a__      ,    bos_token=a__      ,    eos_token=a__      ,    unk_token=a__      ,    sep_token=a__      ,    pad_token=a__      ,    cls_token=a__      ,    mask_token=a__      ,    **a__      ,    )

            __snake_case      :List[Any] 							=    do_lower_case
            __snake_case      :Dict 							=    remove_space
            __snake_case      :int 							=    keep_accents
            __snake_case      :Optional[int] 							=    vocab_file

            __snake_case      :int 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(a__     )



       @property
       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            return len(self.sp_model     )



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





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



       def __getstate__(				self     )			->   str:






            '''simple docstring'''





            __snake_case      :str 							=    self.__dict__.copy()
            __snake_case      :Dict 							=    None
            return state



       def __setstate__(				self      ,    a__     )			->   str:






            '''simple docstring'''





            __snake_case      :Dict 							=    d
            __snake_case      :Dict 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(self.vocab_file     )



       def        __lowercase    (				self      ,    a__      ,    a__=False     )			->   Tuple:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    self.sp_model.EncodeAsPieces(a__     )
            return pieces



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            return self.sp_model.PieceToId(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Any:






            '''simple docstring'''





            return self.sp_model.IdToPiece(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    self.sp_model.decode_pieces(a__     )
            return out_string



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





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



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = False     )			->   List[int]:






            '''simple docstring'''





            if already_has_special_tokens:
                 if token_ids_a is not None:
                      raise ValueError(
                          """You should not supply a second sequence if the provided sequence of """
                          """ids is already formatted with special tokens for the model."""     )
                 return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

            if token_ids_a is not None:
                 return [1] + ([0] * len(a__     )) + [1] + ([0] * len(a__     )) + [1]
            return [1] + ([0] * len(a__     )) + [1]



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Any 							=    [self.sep_token_id]
            __snake_case      :Any 							=    [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     ) * [0] + len(token_ids_a + sep     ) * [1]



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   Tuple[str]:






            '''simple docstring'''





            if not os.path.isdir(a__     ):
                 logger.error("""Vocabulary path ({}) should be a directory""".format(a__     )     )
                 return
            __snake_case      :Optional[int] 							=    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__     ):
                 copyfile(self.vocab_file      ,    a__     )

            return (out_vocab_file,)


