




'''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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


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



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase=100    ,      __lowercase=" "							):
     snake_case :			List[Any]					   = text.split(__lowercase							)
     return [character.join(text[i : i + n]							).strip() for i in range(0    ,      len(__lowercase							)    ,      __lowercase							)]



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



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



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase    ,      ):
     ######################################
     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 :			str					   = 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 :			str					   = dataset.map(__lowercase    ,      batched=__lowercase    ,      num_proc=processing_args.num_proc							)

     # And compute the embeddings
     snake_case :			Optional[Any]					   = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name							).to(device=__lowercase							)
     snake_case :			List[str]					   = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name							)
     snake_case :			Tuple					   = Features(
         {"""text""": Value("""string"""							), """title""": Value("""string"""							), """embeddings""": Sequence(Value("""float32"""							)							)}							)  # optional, save as float32 instead of float64 to save space
     snake_case :			Any					   = dataset.map(
         partial(__lowercase    ,      ctx_encoder=__lowercase    ,      ctx_tokenizer=__lowercase							)    ,      batched=__lowercase    ,      batch_size=processing_args.batch_size    ,      features=__lowercase    ,      )

     # And finally save your dataset
     snake_case :			str					   = os.path.join(rag_example_args.output_dir    ,      """my_knowledge_dataset"""							)
     dataset.save_to_disk(__lowercase							)
     # 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 :			Union[str, Any]					   = faiss.IndexHNSWFlat(index_hnsw_args.d    ,      index_hnsw_args.m    ,      faiss.METRIC_INNER_PRODUCT							)
     dataset.add_faiss_index("""embeddings"""    ,      custom_index=__lowercase							)

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

     ######################################
     logger.info("""Step 3 - Load RAG"""							)
     ######################################

     # Easy way to load the model
     snake_case :			List[Any]					   = RagRetriever.from_pretrained(
         rag_example_args.rag_model_name    ,      index_name="""custom"""    ,      indexed_dataset=__lowercase							)
     snake_case :			Optional[int]					   = RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name    ,      retriever=__lowercase							)
     snake_case :			List[str]					   = RagTokenizer.from_pretrained(rag_example_args.rag_model_name							)

     # For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
     # retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

     ######################################
     logger.info("""Step 4 - Have fun"""							)
     ######################################

     snake_case :			Dict					   = rag_example_args.question or """What does Moses' rod turn into ?"""
     snake_case :			List[Any]					   = tokenizer.question_encoder(__lowercase    ,      return_tensors="""pt"""							)["""input_ids"""]
     snake_case :			List[str]					   = model.generate(__lowercase							)
     snake_case :			Union[str, Any]					   = tokenizer.batch_decode(__lowercase    ,      skip_special_tokens=__lowercase							)[0]
     logger.info("""Q: """ + question							)
     logger.info("""A: """ + generated_string							)







@dataclass
class 				_a   :





     '''simple docstring'''



     lowerCAmelCase_     :    str									=				field(
         default=str(Path(a__  ).parent / """test_data""" / """my_knowledge_dataset.csv"""  ),		metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""},		)
     lowerCAmelCase_     :    Optional[str]									=				field(
         default=a__,		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=a__,		metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""},		)







@dataclass
class 				_a   :





     '''simple docstring'''



     lowerCAmelCase_     :    Optional[int]									=				field(
         default=a__,		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 				_a   :





     '''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)

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









'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

lowercase			:      Union[str, Any]			=					TypeVar("""T""", bound=bool)







class 				_a   (Generic[T]  ):





     '''simple docstring'''



     def __init__(							self       ,__a       )			->							None:
          snake_case :			T					   = value
          snake_case :			SkewNode[T] | None					   = None
          snake_case :			SkewNode[T] | None					   = None







     @property
     def  snake_case_			(							self       )			->							T:
          return self._value







     @staticmethod
     def  snake_case_			(							__a       ,__a       )			->							SkewNode[T] | None:
          if not roota:
               return roota

          if not roota:
               return roota

          if roota.value > roota.value:
               snake_case   ,		snake_case :			Tuple					   = roota, roota

          snake_case :			Tuple					   = roota
          snake_case :			Union[str, Any]					   = roota.right
          snake_case :			Any					   = roota.left
          snake_case :			Union[str, Any]					   = SkewNode.merge(__a       ,__a       )

          return result







class 				_a   (Generic[T]  ):





     '''simple docstring'''



     def __init__(							self       ,__a = ()       )			->							None:
          snake_case :			SkewNode[T] | None					   = None
          if data:
               for item in data:
                    self.insert(__a       )







     def __bool__(							self       )			->							bool:
          return self._root is not None







     def __iter__(							self       )			->							Iterator[T]:
          snake_case :			list[Any]					   = []
          while self:
               result.append(self.pop()       )

          # Pushing items back to the heap not to clear it.
          for item in result:
               self.insert(__a       )

          return iter(__a       )







     def  snake_case_			(							self       ,__a       )			->							None:
          snake_case :			List[Any]					   = SkewNode.merge(self._root       ,SkewNode(__a       )       )







     def  snake_case_			(							self       )			->							T | None:
          snake_case :			Tuple					   = self.top()
          snake_case :			int					   = (
              SkewNode.merge(self._root.left       ,self._root.right       ) if self._root else None
          )

          return result







     def  snake_case_			(							self       )			->							T:
          if not self._root:
               raise IndexError("""Can't get top element for the empty heap."""       )
          return self._root.value







     def  snake_case_			(							self       )			->							None:
          snake_case :			Any					   = None


if __name__ == "__main__":
      import doctest

      doctest.testmod()



