

'''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					 =	logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCAmelCase					 =	'''cuda''' if torch.cuda.is_available() else '''cpu'''

def 					__UpperCamelCase				(						lowercase__    :	str,       lowercase__    :	int=1_00,       lowercase__    :	Optional[int]=" "			):






     '''simple docstring'''





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

def 					__UpperCamelCase				(						lowercase__    :	dict			):






     '''simple docstring'''





     __lowercase		,  __lowercase      		=[], []
     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 					__UpperCamelCase				(						lowercase__    :	dict,       lowercase__    :	DPRContextEncoder,       lowercase__    :	DPRContextEncoderTokenizerFast			):






     '''simple docstring'''





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

def 					__UpperCamelCase				(						lowercase__    :	"RagExampleArguments",       lowercase__    :	"ProcessingArguments",       lowercase__    :	"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
     __lowercase      		=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
     __lowercase      		=dataset.map(lowercase__,       batched=lowercase__,       num_proc=processing_args.num_proc			)

     # And compute the embeddings
     __lowercase      		=DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name			).to(device=lowercase__			)
     __lowercase      		=DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name			)
     __lowercase      		=Features(
         {'text': Value('string'			), 'title': Value('string'			), 'embeddings': Sequence(Value('float32'			)			)}			)  # optional, save as float32 instead of float64 to save space
     __lowercase      		=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
     __lowercase      		=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
     __lowercase      		=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
     __lowercase      		=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


@dataclass
class 	lowerCAmelCase  :
       lowerCAmelCase_												=			field(
           default=str(Path(A ).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv" ) ,      metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"} ,      )
       lowerCAmelCase_												=			field(
           default=A ,      metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."} ,      )
       lowerCAmelCase_												=			field(
           default="facebook/rag-sequence-nq" ,      metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"} ,      )
       lowerCAmelCase_												=			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_												=			field(
           default=str(Path(A ).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_												=			field(
           default=A ,      metadata={
               "help": "The number of processes to use to split the documents into passages. Default is single process."
           } ,      )
       lowerCAmelCase_												=			field(
           default=1_6 ,      metadata={
               "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
           } ,      )






@dataclass
class 	lowerCAmelCase  :
       lowerCAmelCase_												=			field(
           default=7_6_8 ,      metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."} ,      )
       lowerCAmelCase_												=			field(
           default=1_2_8 ,      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					 =	HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
       UpperCAmelCase ,      UpperCAmelCase ,      UpperCAmelCase					 =	parser.parse_args_into_dataclasses()
       with TemporaryDirectory() as tmp_dir:
              UpperCAmelCase					 =	rag_example_args.output_dir or tmp_dir
              main(rag_example_args, processing_args, index_hnsw_args)





'''simple docstring'''

import os
from collections.abc import Iterator

def 					__UpperCamelCase				(						lowercase__    :	str = "."			):






     '''simple docstring'''





     for dir_path, dir_names, filenames in os.walk(lowercase__			):
          __lowercase      		=[d for d in dir_names if d != 'scripts' and d[0] not in '._']
          for filename in filenames:
               if filename == "__init__.py":
                    continue
               if os.path.splitext(lowercase__			)[1] in (".py", ".ipynb"):
                    yield os.path.join(lowercase__,       lowercase__			).lstrip('./'			)

def 					__UpperCamelCase				(						lowercase__    :	Optional[Any]			):






     '''simple docstring'''





     return F'''{i * "  "}*''' if i else "\n##"

def 					__UpperCamelCase				(						lowercase__    :	str,       lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=old_path.split(os.sep			)
     for i, new_part in enumerate(new_path.split(os.sep			)			):
          if (i + 1 > len(lowercase__			) or old_parts[i] != new_part) and new_part:
               print(F'''{md_prefix(lowercase__			)} {new_part.replace("_",       " "			).title()}'''			)
     return new_path

def 					__UpperCamelCase				(						lowercase__    :	str = "."			):






     '''simple docstring'''





     __lowercase      		=''
     for filepath in sorted(good_file_paths(lowercase__			)			):
          __lowercase		,  __lowercase      		=os.path.split(lowercase__			)
          if filepath != old_path:
               __lowercase      		=print_path(lowercase__,       lowercase__			)
          __lowercase      		=(filepath.count(os.sep			) + 1) if filepath else 0
          __lowercase      		=F'''{filepath}/{filename}'''.replace(' ',       '%20'			)
          __lowercase      		=os.path.splitext(filename.replace('_',       ' '			).title()			)[0]
          print(F'''{md_prefix(lowercase__			)} [{filename}]({url})'''			)


if __name__ == "__main__":
       print_directory_md('''.''')


