




'''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


lowerCamelCase_			=						logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase_			=						'cuda' if torch.cuda.is_available() else 'cpu'
def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			int=1_00	,					__A    :			Tuple=" "  )			->			List[str]:
   _SCREAMING_SNAKE_CASE	=				text.split(__A  )
   return [character.join(text[i : i + n]  ).strip() for i in range(0	,					len(__A  )	,					__A  )]
def 			SCREAMING_SNAKE_CASE_			(							__A    :			dict  )			->			dict:
   _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(__A  ):
            titles.append(title if title is not None else ""  )
            texts.append(__A  )
   return {"title": titles, "text": texts}
def 			SCREAMING_SNAKE_CASE_			(							__A    :			dict	,					__A    :			DPRContextEncoder	,					__A    :			DPRContextEncoderTokenizerFast  )			->			dict:
   _SCREAMING_SNAKE_CASE	=				ctx_tokenizer(
       documents["title"]	,					documents["text"]	,					truncation=__A	,					padding="longest"	,					return_tensors="pt"  )["input_ids"]
   _SCREAMING_SNAKE_CASE	=				ctx_encoder(input_ids.to(device=__A  )	,					return_dict=__A  ).pooler_output
   return {"embeddings": embeddings.detach().cpu().numpy()}
def 			SCREAMING_SNAKE_CASE_			(							__A    :			"RagExampleArguments"	,					__A    :			"ProcessingArguments"	,					__A    :			"IndexHnswArguments"	,					)			->			int:
   ######################################
   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(__A	,					batched=__A	,					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=__A  )
   _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(__A	,					ctx_encoder=__A	,					ctx_tokenizer=__A  )	,					batched=__A	,					batch_size=processing_args.batch_size	,					features=__A	,					)

   # And finally save your dataset
   _SCREAMING_SNAKE_CASE	=				os.path.join(rag_example_args.output_dir	,					"my_knowledge_dataset"  )
   dataset.save_to_disk(__A  )
   # 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=__A  )

   # 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(__A  )
   # dataset.load_faiss_index("embeddings", index_path)  # to reload the index







@dataclass
class 		lowercase_   :


   """simple docstring"""

   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 		lowercase_   :


   """simple docstring"""

   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=16					,  metadata={
           '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
       }					,  )







@dataclass
class 		lowercase_   :


   """simple docstring"""

   lowerCamelCase_      =						field(
       default=768					,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}					,  )
   lowerCamelCase_      =						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)

 lowerCamelCase_			=						HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_			=						parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  lowerCamelCase_			=						rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)







'''simple docstring'''



from ...configuration_utils import PretrainedConfig







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''bert-generation'''



   def __init__(   self :     int  ,				__lowerCamelCase :     int=5_0_3_5_8  ,				__lowerCamelCase :     Union[str, Any]=1_0_2_4  ,				__lowerCamelCase :     str=2_4  ,				__lowerCamelCase :     int=1_6  ,				__lowerCamelCase :     Union[str, Any]=4_0_9_6  ,				__lowerCamelCase :     Optional[Any]="gelu"  ,				__lowerCamelCase :     Tuple=0.1  ,				__lowerCamelCase :     str=0.1  ,				__lowerCamelCase :     Optional[Any]=5_1_2  ,				__lowerCamelCase :     List[Any]=0.0_2  ,				__lowerCamelCase :     Union[str, Any]=1e-12  ,				__lowerCamelCase :     List[Any]=0  ,				__lowerCamelCase :     List[Any]=2  ,				__lowerCamelCase :     Dict=1  ,				__lowerCamelCase :     Tuple="absolute"  ,				__lowerCamelCase :     Union[str, Any]=True  ,				**__lowerCamelCase :     Optional[Any]  ,				):

      """simple docstring"""
      super().__init__(pad_token_id=__lowerCamelCase  ,				bos_token_id=__lowerCamelCase  ,				eos_token_id=__lowerCamelCase  ,				**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				vocab_size
      _SCREAMING_SNAKE_CASE	=				hidden_size
      _SCREAMING_SNAKE_CASE	=				num_hidden_layers
      _SCREAMING_SNAKE_CASE	=				num_attention_heads
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				intermediate_size
      _SCREAMING_SNAKE_CASE	=				hidden_dropout_prob
      _SCREAMING_SNAKE_CASE	=				attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE	=				max_position_embeddings
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				layer_norm_eps
      _SCREAMING_SNAKE_CASE	=				position_embedding_type
      _SCREAMING_SNAKE_CASE	=				use_cache

