





'''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							,	)						->					List[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)












'''simple docstring'''

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

a        =						[
    "python",
    "tqdm",
    "regex",
    "requests",
    "packaging",
    "filelock",
    "numpy",
    "tokenizers",
    "huggingface-hub",
    "safetensors",
    "accelerate",
    "pyyaml",
]

for pkg in pkgs_to_check_at_runtime:
    if pkg in deps:
        if pkg == "tokenizers":
            # must be loaded here, or else tqdm check may fail
            from .utils import is_tokenizers_available

            if not is_tokenizers_available():
                continue  # not required, check version only if installed
        elif pkg == "accelerate":
            # must be loaded here, or else tqdm check may fail
            from .utils import is_accelerate_available

            # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
            # Transformers with PyTorch
            if not is_accelerate_available():
                continue  # not required, check version only if installed

        require_version_core(deps[pkg])
    else:
        raise ValueError(F'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=None      )						->					Optional[Any]:

   '''simple docstring'''







   require_version(deps[pkg]							,	__UpperCAmelCase      )





