




'''simple docstring'''





import os
from collections import deque

import torch
from torch.utils.data import Dataset

class        _a    (						__lowerCAmelCase							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE="" ,_SCREAMING_SNAKE_CASE="train"   )							->      int:
          assert os.path.isdir(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				[]
          _snake_case							=				os.listdir(_SCREAMING_SNAKE_CASE   )
          for story_filename in story_filenames_list:
               if "summary" in story_filename:
                    continue
               _snake_case							=				os.path.join(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               if not os.path.isfile(_SCREAMING_SNAKE_CASE   ):
                    continue
               self.documents.append(_SCREAMING_SNAKE_CASE   )






     def __len__(			self   )							->      Dict:
          return len(self.documents   )






     def __getitem__(			self ,_SCREAMING_SNAKE_CASE   )							->      str:
          _snake_case							=				self.documents[idx]
          _snake_case							=				document_path.split("/"   )[-1]
          with open(_SCREAMING_SNAKE_CASE ,encoding="utf-8"   ) as source:
               _snake_case							=				source.read()
               _snake_case							, _snake_case							=				process_story(_SCREAMING_SNAKE_CASE   )
          return document_name, story_lines, summary_lines







def      __a     (       _UpperCamelCase:   Optional[int]   )						->			Any:

     """simple docstring"""




     _snake_case							=				list(filter(lambda _UpperCamelCase   : len(_UpperCamelCase   ) != 0 ,						[line.strip() for line in raw_story.split("\n"   )]   )   )

     # for some unknown reason some lines miss a period, add it
     _snake_case							=				[_add_missing_period(_UpperCamelCase   ) for line in nonempty_lines]

     # gather article lines
     _snake_case							=				[]
     _snake_case							=				deque(_UpperCamelCase   )
     while True:
          try:
               _snake_case							=				lines.popleft()
               if element.startswith("@highlight"   ):
                    break
               story_lines.append(_UpperCamelCase   )
          except IndexError:
               # if "@highlight" is absent from the file we pop
               # all elements until there is None, raising an exception.
               return story_lines, []

    # gather summary lines
     _snake_case							=				list(filter(lambda _UpperCamelCase   : not t.startswith("@highlight"   ) ,						_UpperCamelCase   )   )

     return story_lines, summary_lines





def      __a     (       _UpperCamelCase:   List[str]   )						->			Optional[int]:

     """simple docstring"""




     _snake_case							=				[".", "!", "?", "...", "'", "`", "\"", "\u2019", "\u2019", ")"]
     if line.startswith("@highlight"   ):
          return line
     if line[-1] in END_TOKENS:
          return line
     return line + "."





def      __a     (       _UpperCamelCase:   str ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   int   )						->			Optional[int]:

     """simple docstring"""




     if len(_UpperCamelCase   ) > block_size:
          return sequence[:block_size]
     else:
          sequence.extend([pad_token_id] * (block_size - len(_UpperCamelCase   ))   )
          return sequence





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

     """simple docstring"""




     _snake_case							=				torch.ones_like(_UpperCamelCase   )
     _snake_case							=				sequence == pad_token_id
     _snake_case							=				0
     return mask





def      __a     (       _UpperCamelCase:   Dict ,						_UpperCamelCase:   Tuple ,						_UpperCamelCase:   Optional[int]   )						->			Optional[Any]:

     """simple docstring"""




     _snake_case							=				[tokenizer.encode(_UpperCamelCase   ) for line in story_lines]
     _snake_case							=				[token for sentence in story_lines_token_ids for token in sentence]
     _snake_case							=				[tokenizer.encode(_UpperCamelCase   ) for line in summary_lines]
     _snake_case							=				[token for sentence in summary_lines_token_ids for token in sentence]

     return story_token_ids, summary_token_ids





def      __a     (       _UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   List[str]   )						->			Optional[int]:

     """simple docstring"""




     _snake_case							=				[]
     for sequence in batch:
          _snake_case							=				-1
          _snake_case							=				[]
          for s in sequence:
               if s == separator_token_id:
                    sentence_num += 1
               embeddings.append(sentence_num % 2   )
          batch_embeddings.append(_UpperCamelCase   )
     return torch.tensor(_UpperCamelCase   )








'''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


UpperCamelCase_						:      Any       =						logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase_						:      Optional[Any]       =						'''cuda''' if torch.cuda.is_available() else '''cpu'''





def      __a     (       _UpperCamelCase:   str ,						_UpperCamelCase:   Union[str, Any]=100 ,						_UpperCamelCase:   List[str]=" "   )						->			List[str]:

     """simple docstring"""




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





def      __a     (       _UpperCamelCase:   dict   )						->			dict:

     """simple docstring"""




     _snake_case							, _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      __a     (       _UpperCamelCase:   dict ,						_UpperCamelCase:   DPRContextEncoder ,						_UpperCamelCase:   DPRContextEncoderTokenizerFast   )						->			dict:

     """simple docstring"""




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





def      __a     (       _UpperCamelCase:   "RagExampleArguments" ,						_UpperCamelCase:   "ProcessingArguments" ,						_UpperCamelCase:   "IndexHnswArguments" ,						)						->			Dict:

     """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							=				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							=				dataset.map(_UpperCamelCase ,						batched=_UpperCamelCase ,						num_proc=processing_args.num_proc   )

     # And compute the embeddings
     _snake_case							=				DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=_UpperCamelCase   )
     _snake_case							=				DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
     _snake_case							=				Features(
         {"text": Value("string"   ), "title": Value("string"   ), "embeddings": Sequence(Value("float32"   )   )}   )  # optional, save as float32 instead of float64 to save space
     _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
     _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
     _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
     _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    :
     SCREAMING_SNAKE_CASE_							:   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'"""} , )
     SCREAMING_SNAKE_CASE_							:   Optional[str]       =						field(
         default=__lowerCAmelCase , metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""} , )
     SCREAMING_SNAKE_CASE_							:   str       =						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_							:   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'"""
             )
         } , )
     SCREAMING_SNAKE_CASE_							:   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    :
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						field(
         default=__lowerCAmelCase , metadata={
             """help""": """The number of processes to use to split the documents into passages. Default is single process."""
         } , )
     SCREAMING_SNAKE_CASE_							:   int       =						field(
         default=16 , metadata={
             """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
         } , )







@dataclass
class        _a    :
     SCREAMING_SNAKE_CASE_							:   int       =						field(
         default=7_68 , metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""} , )
     SCREAMING_SNAKE_CASE_							:   int       =						field(
         default=1_28 , 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)

   UpperCamelCase_						:      List[str]       =						HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
   UpperCamelCase_     ,     UpperCamelCase_     ,     UpperCamelCase_						:      List[Any]       =						parser.parse_args_into_dataclasses()
   with TemporaryDirectory() as tmp_dir:
      UpperCamelCase_						:      str       =						rag_example_args.output_dir or tmp_dir
      main(rag_example_args, processing_args, index_hnsw_args)


