





'''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 						__a(SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       int=100		,							SCREAMING_SNAKE_CASE_	:       Optional[int]=" "     ):



    '''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 						__a(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 						__a(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 						__a(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  lowerCAmelCase_		:
 __lowerCamelCase    :				str								=       field(
     default=str(Path(__magic_name__		).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=__magic_name__    ,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(__magic_name__		).parent / "test_run" / "dummy-kb"		)    ,metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}    ,)







@dataclass
class  lowerCAmelCase_		:
 __lowerCamelCase    :				Optional[int]								=       field(
     default=__magic_name__    ,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  lowerCAmelCase_		:
 __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)

       _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
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       Optional[int]									=    LayoutLMTokenizer
   _a   :       Dict									=    LayoutLMTokenizerFast
   _a   :       Dict									=    True
   _a   :       Any									=    True


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    super().setUp()

    lowercase__       :       int				=						[
        """[UNK]""",
        """[CLS]""",
        """[SEP]""",
        """want""",
        """##want""",
        """##ed""",
        """wa""",
        """un""",
        """runn""",
        """##ing""",
        """,""",
        """low""",
        """lowest""",
    ]
    lowercase__       :       Dict				=						os.path.join(self.tmpdirname ,     VOCAB_FILES_NAMES["""vocab_file"""]						)
    with open(self.vocab_file ,     """w""" ,     encoding="""utf-8"""						) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							Optional[int]:
    return LayoutLMTokenizer.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[Any]:
    lowercase__       :       List[str]				=						"""UNwant\u00E9d,running"""
    lowercase__       :       Optional[int]				=						"""unwanted, running"""
    return input_text, output_text


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       str				=						self.tokenizer_class(self.vocab_file						)

    lowercase__       :       Optional[Any]				=						tokenizer.tokenize("""UNwant\u00E9d,running"""						)
    self.assertListEqual(lowerCamelCase__ ,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]						)
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__						) ,     [7, 4, 5, 10, 8, 9]						)







   def 					UpperCAmelCase__(						self						)      ->							Any:
    pass